Dissertations / Theses on the topic 'Misclassification'
To see the other types of publications on this topic, follow the link: Misclassification.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Misclassification.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Liyanage, Nilani. "Misclassification bias in epidemiologic studies." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=23406.
Full textAbstract:
Misclassification of disease and/or exposure is a common and potentially serious problem in epidemiologic studies. The impact of misclassification may be profound and may invalidate results. Despite the fact that there have been a number of articles published on the significance of misclassification bias, many epidemiologic studies are carried out with little attention paid to this issue either in the design or the analysis. The goal of this thesis is to provide clarifications on issues surrounding misclassification of exposure in case-control studies. Specifically, the conditions under which misclassification is likely to occur, the potential impact on effect measures and how misclassification can be prevented through design and corrected for in the analysis are discussed in detail.
2
Rosychuk, Rhonda Jean. "Accounting for misclassification in binary longitudinal data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0004/NQ44779.pdf.
Full text
3
Rice, Kenneth Martin. "Models for misclassification of covariates in epidemiology." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620230.
Full text
4
Umar, Abdulkarim Mallam. "Stochastic SIR household epidemic model with misclassification." Thesis, University of Kent, 2016. https://kar.kent.ac.uk/62476/.
Full text
5
Pole, Jason. "Quantifying misclassification in water disinfection by-product analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0021/MQ53021.pdf.
Full text
6
Zhong, Mingyu. "AN ANALYSIS OF MISCLASSIFICATION RATES FOR DECISION TREES." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2496.
Full textAbstract:
The decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class probabilities. In contrast to existing research, we not only compute the expected values of the risks but also calculate the corresponding reliability of the risk (measured by standard deviations). We also provide an explicit expression of the k-norm estimation for the tree's misclassification rate that combines both the expected value and the reliability. Furthermore, our proposed and proven theorem on k-norm estimation suggests an efficient pruning algorithm that has a clear theoretical interpretation, is easily implemented, and does not require a validation set. Our experiments show that our proposed pruning algorithm produces accurate trees quickly that compares very favorably with two other well-known pruning algorithms, CCP of CART and EBP of C4.5. Finally, our work provides a deeper understanding of decision trees.Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD
7
Hilliam, Rachel M. "Statistical discrimination with disease categories subject to misclassification." Thesis, De Montfort University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391859.
Full text
8
Mayer, Cory A. "Improving Ultra-Wideband Localization by Detecting Radio Misclassification." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1957.
Full textAbstract:
The Global Positioning System (GPS) and other satellite-based positioning systems are often a key component in applications requiring localization. However, accurate positioning in areas with poor GPS coverage, such as inside buildings and in dense cities, is in increasing demand for many modern applications. Fortunately, recent developments in ultra-wideband (UWB) radio technology have enabled precise positioning in places where it was not previously possible by utilizing multipath-resistant wide band pulses.
Although ultra-wideband signals are less prone to multipath interference, it is still a bottleneck as increasingly ambitious projects continue to demand higher precision. Some UWB radios include on-board detection of multipath conditions, however the implementations are usually limited to basic condition checks. In order to address these shortcomings, We propose an application of machine learning to reliably detect non-line-of-sight conditions when the on-board radio classifier fails to recognize these conditions. Our solution includes a neural network classifier that is 99.98% accurate in a variety of environments.
9
Pole, Jason. "Quantifying misclassification in water disinfection by-product analysis." Ottawa : National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.nlc-bnc.ca/obj/s4/f2/dsk1/tape4/PQDD%5F0021/MQ53021.pdf.
Full text
10
Chu, Rong. "Bayesian adjustment for exposure misclassification in case-control studies." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/32108.
Full textAbstract:
Measurement error occurs frequently in observational studies investigating the relationship
between exposure variables and the clinical outcome. Error-prone observations on
the explanatory variable may lead to biased estimation and loss of power in detecting the
impact of an exposure variable. The mechanism of measurement error, such as whether
or in what way the quality of data is affected by the disease status, is seldom completely
revealed to the investigators. This increases uncertainty in assessing the consequences
of ignoring measurement error associated with observed data, and brings difficulties to
adjustment for mismeasurement.
In this study, we consider situations with a correctly specified binary response, and
a misclassified binary exposure. We propose a solution to conduct Bayesian adjustment
to correct for measurement error subject to varying differentiality, including the
nondifferential misclassification, differential misclassification and nearly nondifferential
misclassification. Our Bayesian model incorporates the randomness of exposure prevalences
and misclassification parameters as prior distributions. The posterior model is
constructed upon simulations generated by Gibbs sampler and Metropolis-Hastings algorithm.
Internal validation data is utilized to insure the resulting model is identifiable.
Meanwhile, we compare the Bayesian model with maximum likelihood estimation
(MLE) and simulation extrapolation (MC-SIMEX) methods, using simulated datasets.
The Bayesian and MLE models produce accurate and similar estimates for odds ratio
in describing the association between the disease and exposure, when appropriate assumptions regarding the differentially of misclassification are made. The 90% credible
or confidence intervals capture the truth approximately 90% of the time. A Bayesian
method corresponding to nearly nondifferential prior belief compromises between the
loss of efficiency and loss of accuracy associated with other prior assumptions. At the
end, we look at two case-control studies with misclassified exposure variables, and aim
to make valid inference about the effect parameter.Science, Faculty of
Statistics, Department of
Graduate
11
Gu, Yuanyuan Economics Australian School of Business UNSW. "Misclassification of the dependent variable in binary choice models." Awarded by:University of New South Wales. Economics, 2006. http://handle.unsw.edu.au/1959.4/26218.
Full textAbstract:
Survey data are often subject to a number of measurement errors. The measurement error associated with a multinomial variable is called a misclassification error. In this dissertation we study such errors when the outcome is binary. It is known that ignoring such misclassification errors may affect the parameter estimates, see for example Hausman, Abrevaya and Scott-Morton (1998). However, previous studies showed that robust estimation of the parameters is achievable if we take misclassification into account. There are many attempts to do so in the literature and the major problem in implementing them is to avoid poor or fragile identifiability of the misclassification probabilities. Generally we restrict these parameters by imposing prior information on them. Such prior constraints on the parameters are simple to impose within a Bayesian framework. Hence we consider a Bayesian logistic regression model that takes into account the misclassification of the dependent variable. A very convenient way to implement such a Bayesian analysis is to estimate the hierarchical model using the WinBUGS software package developed by the MRC biostatistics group, Institute of Public Health, at Cambridge University. WinGUGS allows us to estimate the posterior distributions of all the parameters using relatively little programming and once the program is written it is trivial to change the link function, for example from logit to probit. If we wish to have more control over the sampling scheme or to deal with more complex models, then we propose a data augmentation approach using the Metropolis-Hastings algorithm within a Gibbs sampling framework. The sampling scheme can be made more efficient by using a one-step Newton-Raphson algorithm to form the Metropolis-Hastings proposal. Results from empirically analyzing real data and from the simulation studies suggest that if suitable priors are specified for the misclassification parameters and the regression parameters, then logistic regression allowing for misclassification results in better estimators than the estimators that do not take misclassification into account.
12
Wang, Dongxu. "Topics on the effect of non-differential exposure misclassification." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42776.
Full textAbstract:
There is quite an extensive literature on the deleterious impact of exposure misclassification when inferring exposure-disease associations, and on statistical methods to mitigate this impact. When the exposure is a continuous variable or a binary variable, a general mismeasurement phenomenon is attenuation in the strength of the relationship between exposure and outcome. However, few have investigated the effect of misclassification on a polychotomous variable. Using Bayesian methods, we investigate how misclassification affects the exposure-disease associations under different settings of classification matrix. Also, we apply a trend test and understand the effect of misclassification according to the power of the test. In addition, since virtually all of work on the impact of exposure misclassification presumes the simplest situation where both the true status and the classified status are binary, my work diverges from the norm, in considering classification into three categories when the actual exposure status is simply binary. Intuitively, the classification states might be labeled as `unlikely exposed', `maybe exposed', and `likely exposed'. While this situation has been discussed informally in the literature, we provide some theory concerning what can be learned about the exposure-disease relationship, under various assumptions about the classification scheme. We focus on the challenging situation whereby no validation data is available from which to infer classification probabilities, but some prior assertions about these probabilities might be justified.
13
Kahiri, James Mwangi K. "Impact of measurement errors on categorical data." Thesis, University of Southampton, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318197.
Full text
14
Cormier, Eric. "Time-varying exposure subject to misclassification : bias characterization and adjustment." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27839.
Full textAbstract:
Measurement error occurs frequently in observational studies investigating the relationship between exposure variables and a clinical outcome. Error-prone observations on the explanatory variable may lead to biased estimation and loss of power in detecting the impact of an exposure variable. When the exposure variable is time-varying, the impact of misclassification is complicated and significant. This increases uncertainty in assessing the consequences of ignoring measurement error associated with observed data, and brings difficulties to adjustment for misclassification.
In this study we considered situations in which the exposure is time-varying and nondifferential misclassification occurs independently over time. We determined how misclassification biases the exposure outcome relationship through probabilistic arguments and then characterized the effect of misclassification as the model parameters vary. We show that misclassification of time-varying exposure measurements has a complicated effect when estimating the exposure-disease relationship. In particular the bias toward the null seen in the static case is not observed.
After misclassification had been characterized we developed a means to adjust for misclassification by recreating, with greatest likelihood, the exposure path of each subject. Our adjustment uses hidden Markov chain theory to quickly and efficiently reduce the number of misclassified states and reduce the effect of misclassification on estimating the disease-exposure relationship.
The method we propose makes use of only the observed misclassified exposure data and no validation data needs to be obtained. This is achieved by estimated switching probabilities and misclassification probabilities from the observed data. When these estimates are obtained the effect of misclassification can be determined through the characterization of the effect of misclassification presented previously. We can also directly adjust for misclassification by recreating the most likely exposure path using the Viterbi algorithm.
The methods developed in this dissertation allow the effect of misclassification, on estimating the exposure-disease relationship, to be determined. It accounts for misclassification by reducing the number of misclassified states and allows the exposure-disease relationship to be estimated significantly more accurately. It does this without the use of validation data and is easy to implement in existing statistical software.
15
Gordon, John C., and L. Lee Glenn. "Body Mass Index Misclassification of Obesity Among Community Police Officers." Digital Commons @ East Tennessee State University, 2012. https://dc.etsu.edu/etsu-works/7512.
Full text
16
Grunow, Nathan Daniel, and Nathan Daniel Grunow. "Analysis of Recurrent Polyp Data in the Presence of Misclassification." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/622835.
Full textAbstract:
Several standard methods are available to analyze and estimate parameters of count data. None of these methods are designed to account for potential misclassification of the data, where counts are observed or recorded as higher or lower than their actual value. These false counts can result in erroneous conclusions and biased estimates. For this paper, a standard estimation model was modified in several ways in order to incorporate each misclassification mechanism. The probability distribution of the observed data was derived and combined with informative distributions for the misclassification parameters. Once this additional information was taken into account, a distribution of observed data conditional on only the parameter of interest was obtained. By incorporating information about the misclassification mechanisms, the resulting estimation will be more accurate than the standard methods. To demonstrate the flexibility of this approach, data from a count distribution affected by various misclassification mechanisms were simulated. Each dataset was analyzed by several standard estimation methods and an appropriate new method. The results from all simulated data were compared, and the impact of each mechanism in regards to each estimation method was discussed. Data from a colorectal polyp prevention study were also analyzed with all available methods to showcase the incorporation of additional covariates.
17
Lamina, Claudia. "Misclassification in genetic variants and its impact on genetic association studies." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-100284.
Full text
18
Hui, Qin. "Testing an Assumption of Non-Differential Misclassification in Case-Control Studies." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_theses/103.
Full textAbstract:
One of the issues regarding the misclassification in case-control studies is whether the misclassification error rates are the same for both cases and controls. Currently, a common practice is to assume that the rates are the same (“non-differential” assumption). However, it is suspicious that this assumption is valid in many case-control studies. Unfortunately, no test is available so far to test the validity of the assumption of non-differential misclassification when the validation data are not available. We propose the first such method to test the validity of non-differential assumption in a case-control study with 2 × 2 contingency table. First, the Exposure Operating Characteristic curve is defined. Next, two non-parametric methods are applied to test the assumption of non-differential misclassification. Three examples from practical applications are used to illustrate the methods and a comparison is made.
19
Brown, Janet. "Misclassification of exposure, coffee as a surrogate for caffeine and methylxanthine intake." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29204.pdf.
Full text
20
Nordholm, Elin, and Anette Björkstrand. "To issue or not to issue a going concern opinion : A study of factors and incentives influencing auditors’ ability and decision to issue going concern opinions." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-226641.
Full textAbstract:
If auditors question a company’s ability to continue existing, they should issue a going concern opinion in the audit report. Whether or not auditors will issue a going concern opinion depends on auditors’ ability to identify going concern problems, as well as their decision whether or not to issue going concern opinions. In Sweden, the going concern accuracy rate has been low compared to other countries. The aim of this study is therefore to analyse whether it is auditors’ lack of ability to identify going concern problems or their decision not to issue a going concern opinion, or perhaps both, that could explain the relatively low accuracy rate. Interviews with four auditors from the Big Four audit firms and four CFOs from middle sized companies were conducted. The results show that there are factors speaking both for and against auditors’ ability to identify going concern problems, why we cannot say for sure whether auditors’ lack of ability to identify going concern problems could be an explanation to the relatively low accuracy rate. The results do however reveal that auditors actively make decisions not to issue going concern opinions to their clients as much as possible, which could explain the relatively low accuracy rate.
21
Brooks, J. Paul. "Solving a mixed-integer programming formulation of a classification model with misclassification limits." Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-08232005-133023/.
Full textAbstract:
Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2006.Prausnitz, Mark, Committee Member ; Vidakovic, Brani, Committee Member ; Lee, Eva, Committee Chair ; Nemhauser, George, Committee Member ; Johnson, Ellis, Committee Member. Includes bibliographical references.
22
Prescott, Gordon J. "A Bayesian approach to epidemiological studies with misclassification in a binary risk factor." Thesis, University of Aberdeen, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.424970.
Full textAbstract:
Bayesian statistical methods permit greater flexibility than most frequentist method by allowing misclassification rates to differ between the validation study and the remainder of the study. Bayesian approaches were developed using the freeware package WinBUGS. Simple methods may be applied to tables of summary data from unmatched or individually matched case-control studies to correct for misclassification in a single risk factor. A Bayesian model for prospective case-control studies has been developed which permits greater flexibility in the types of relationships between covariates, and also between the probability of misclassification and other covariates, than is allowed by other methods. The literature has been concerned about the application of prospective Bayesian methods to retrospectively collected data, but typically assumes that this is unimportant for maximum likelihood estimates without misclassification. Empirical work shoed very little difference between estimates obtained from prospective and retrospective approaches in the frequentist and Bayesian frameworks. Therefore it is justified to use prospective fixed effects Bayesian methods for retrospective data with or without misclassification. The methods developed quantify and correct for misclassification in the most common study designs encountered in epidemiology. The only additional cost would be the data collection for the internal validation study, but without this there is no way to evaluate or correct for potential bias.
23
Li, Yuansha. "Corporate governance and earnings management by misclassification : a study of eight East Asian economies." HKBU Institutional Repository, 2008. http://repository.hkbu.edu.hk/etd_ra/946.
Full text
24
Zhang, Yanwei. "Impacts of multidimensionality and content misclassification on ability estimation in computerized adaptive sequential testing (CAST)." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 156 p, 2006. http://proquest.umi.com/pqdweb?did=1179954311&sid=8&Fmt=2&clientId=8331&RQT=309&VName=PQD.
Full text
25
Miller, Scott N. "Scale effects of geometric complexity, misclassification error and land cover change in distributed hydrologic modeling." Diss., The University of Arizona, 2002. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_e9791_2002_216_sip1_w.pdf&type=application/pdf.
Full text
26
Caillat, Marjolaine. "Assessing and correcting for the effects of species misclassification during passive acoustic surveys of cetaceans." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/4209.
Full textAbstract:
In conservation ecology, abundance estimates are an important factor from which management decisions are based. Methods to estimate abundance of cetaceans from visual detections are largely developed, whereas parallel methods based on passive acoustic detections are still in their infancy. To estimate the abundance of cetacean species using acoustic detection data, it is first necessary to correctly identify the species that are detected. The current automatic PAMGUARD Whistle Classifier used to automatically identify whistle detection of cetacean species is modified with the objective to facilitate the use of these detections to estimate cetacean abundance. Given the variability of cetacean sounds within and between species, developing an automated species classifier with a 100% correct classification probability for any species is unfeasible. However, through the examples of two case studies it is shown that large and high quality datasets with which to develop these automatic classifiers increase the probability of creating reliable classifiers with low and precise misclassification probability. Given that misclassification is unavoidable, it is necessary to consider the effect of misclassified detections on the number of observed acoustic calls detected and thus on abundance estimates, and to develop robust methods to cope with these misclassifications. Through both heuristic and Bayesian approaches it is demonstrated that if misclassification probabilities are known or estimated precisely, it is possible to estimate the true number of detected calls accurately and precisely. However, misclassification and uncertainty increase the variance of the estimates. If the true numbers of detections from different species are similar, then a small amount of misclassification between species and a small amount of uncertainty in the probabilities of misclassification does not have a detrimental effect on the overall variance and bias of the estimate. However, if there is a difference in the encounter rate between species calls associated with a large amount of uncertainty in the probabilities of misclassification, then the variance of the estimates becomes larger and the bias increases; this in return increases the variance and the bias of the final abundance estimate. This study despite not bringing perfect results highlights for the first time the importance of dealing with the problem of species misclassification for cetacean if acoustic detections are to be used to estimate abundance of cetaceans.
27
Lu, Juan. "SENSITIVITY ANALYSIS – THE EFFECTS OF GLASGOW OUTCOME SCALE MISCLASSIFICATION ON TRAUMATIC BRAIN INJURY CLINICAL TRIALS." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/52.
Full textAbstract:
I. EFFECTS OF GLASGOW OUTCOME SCALE MISCLASSIFICATION ON TRAUMATIC BRAIN INJURY CLINICAL TRIALS The Glasgow Outcome Scale (GOS) is the primary endpoint for efficacy analysis of clinical trials in traumatic brain injury (TBI). Accurate and consistent assessment of outcome after TBI is essential to the evaluation of treatment results, particularly in the context of multicenter studies and trials. The inconsistent measurement or interobserver variation on GOS outcome, or for that matter, on any outcome scales, may adversely affect the sensitivity to detect treatment effects in clinical trial. The objective of this study is to examine effects of nondifferential misclassification of the widely used five-category GOS outcome scale and in particular to assess the impact of this misclassification on detecting a treatment effect and statistical power. We followed two approaches. First, outcome differences were analyzed before and after correction for misclassification using a dataset of 860 patients with severe brain injury randomly sampled from two TBI trials with known differences in outcome. Second, the effects of misclassification on outcome distribution and statistical power were analyzed in simulation studies on a hypothetical 800-patient dataset. Three potential patterns of nondifferential misclassification (random, upward and downward) on the dichotomous GOS outcome were analyzed, and the power of finding treatments differences was investigated in detail. All three patterns of misclassification reduce the power of detecting the true treatment effect and therefore lead to a reduced estimation of the true efficacy. The magnitude of such influence not only depends on the size of the misclassification, but also on the magnitude of the treatment effect. In conclusion, nondifferential misclassification directly reduces the power of finding the true treatment effect. An awareness of this procedural error and methods to reduce misclassification should be incorporated in TBI clinical trials. II. IMPACT OF MISCLASSIFICATION ON THE ORDINAL GLASGOW OUTCOME SCALE IN TRAUMATIC BRIAN INJURY CLINICAL TRIALS The methods of ordinal GOS analysis are recommended to increase efficiency and optimize future TBI trials. To further explore the utility of the ordinal GOS in TBI trials, this study extends our previous investigation regarding the effect of misclassification on the dichotomous GOS to examine the impact of misclassification on the 5-point ordinal scales. The impact of nondifferential misclassification on the ordinal GOS was explored via probabilistic sensitivity analyses using TBI patient datasets contained in the IMPACT database (N=9,205). Three patterns of misclassification including random, upward and downward patterns were extrapolated, with the pre-specified outcome classification error distributions. The conventional 95% confidence intervals and the simulation intervals, which account for the misclassification only and the misclassification and random errors together, were reported. Our simulation results showed that given a specification of a minimum of 80%, modes of 85% and 95% and a maximum of 100% for both sensitivity and specificity (random pattern), or given the same trapezoidal distributed sensitivity but a perfect specificity (upward pattern), the misclassification would have caused an underestimated ordinal GOS in the observed data. In another scenario, given the same trapezoidal distributed specificity but a perfect sensitivity (downward pattern), the misclassification would have resulted in an inflated GOS estimation. Thus, the probabilistic sensitivity analysis suggests that the effect of nondifferential misclassification on the ordinal GOS is likely to be small, compared with the impact on the binary GOS situation. The results indicate that the ordinal GOS analysis may not only gain the efficiency from the nature of the ordinal outcome, but also from the relative smaller impact of the potential misclassification, compared with the conventional binary GOS analysis. Nevertheless, the outcome assessment following TBI is a complex problem. The assessment quality could be influenced by many factors. All possible aspects must be considered to ensure the consistency and reliability of the assessment and optimize the success of the trial. III. A METHOD FOR REDUCING MISCLASSIFICATION IN THE EXTENDED GLASGOW OUTCOME SCORE The eight-point extended Glasgow Outcome Scale (GOSE) is commonly used as the primary outcome measure in traumatic brain injury (TBI) clinical trials. The outcome is conventionally collected through a structured interview with the patient alone or together with a caretaker. Despite the fact that using the structured interview questionnaires helps reach agreement in GOSE assessment between raters, significant variation remains among different raters. We introduce an alternate GOSE rating system as an aid in determining GOSE scores, with the objective of reducing inter-rater variation in the primary outcome assessment in TBI trials. Forty-five trauma centers were randomly assigned to three groups to assess GOSE scores on sample cases, using the alternative GOSE rating system coupled with central quality control (Group 1), the alternative system alone (Group 2), or conventional structured interviews (Group 3). The inter-rater variation between an expert and untrained raters was assessed for each group and reported through raw agreement and with weighted kappa (k) statistics. Groups 2 and 3 without central review yielded inter-rater agreements of 83% (weighted k¼0.81; 95% CI 0.69, 0.92) and 83% (weighted k¼0.76, 95% CI 0.63, 0.89), respectively, in GOS scores. In GOSE, the groups had an agreement of 76% (weighted k¼0.79; 95% CI 0.69, 0.89), and 63% (weighted k¼0.70; 95% CI 0.60, 0.81), respectively. The group using the alternative rating system coupled with central monitoring yielded the highest inter-rater agreement among the three groups in rating GOS (97%; weighted k¼0.95; 95% CI 0.89, 1.00), and GOSE (97%; weighted k¼0.97; 95% CI 0.91, 1.00). The alternate system is an improved GOSE rating method that reduces inter-rater variations and provides for the first time, source documentation and structured narratives that allow a thorough central review of information. The data suggest that a collective effort can be made to minimize inter-rater variation.
28
He, Jun. "Evaluating and Reducing the Effects of Misclassification in a Sequential Multiple Assignment Randomized Trial (SMART)." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5678.
Full textAbstract:
SMART designs tailor individual treatment by re-randomizing patients to subsequent therapies based on their response to initial treatment. However, the classification of patients being responders/non-responders could be inaccurate and thus lead to inappropriate treatment assignment. In a two-step SMART design, by assuming equal randomization, and equal variances of misclassified patients and correctly classified patients, we evaluated misclassification effects on mean, variance, and type I error/ power of single sequential treatment outcome (SST), dynamic treatment outcome (DTRs), and overall outcome. The results showed that misclassification could introduce bias to estimates of treatment effect in all types of outcome. Though the magnitude of bias could vary according to different templates, there were a few constant conclusions: 1) for any fixed sensitivity the bias of mean of SSTs responders always approached to 0 as specificity increased to 1, and for any fixed specificity the bias of mean of SSTs non-responders always approached to 0 as sensitivity increased to 1; 2) for any fixed specificity there was monotonic nonlinear relationship between the bias of mean of SSTs responders and sensitivity, and for any fixed sensitivity there was also monotonic nonlinear relationship between the bias of mean of SSTs non-responders and specificity; 3) the bias of variance of SSTs was always non-monotone nonlinear equation; 4) the variance of SSTs under misclassification was always over-estimated; 5) the maximized absolute relative bias of variance of SSTs was always ¼ of the squared mean difference between misclassified patients and correctly classified patients divided by true variance, but it might not be observed in the range of sensitivity and specificity (0,1); 6) regarding to sensitivity and specificity, the bias of mean of DTRs or overall outcomes was always linear equation and their bias of variance was always non-monotone nonlinear equation; 7) the relative bias of mean/ variance of DTRs or overall outcomes could approach to 0 where sensitivity or specificity wasn’t necessarily to be 1. Furthermore, the results showed that the misclassification could affect statistical inference. Power could be less or bigger than planned 80% under misclassification and showed either monotonic or non-monotonic pattern as sensitivity or specificity decreased.
To mitigate these adverse effects, patient observations could be weighted by the likelihood that their response was correctly classified. We investigated both normal-mixture-model (NM) and k-nearest-neighbor (KNN) strategies to attempt to reduce bias of mean and variance and improve inference at final stage outcome. The NM estimated the early stage probabilities of being a responder for each patient through optimizing the likelihood function by EM algorithm, while KNN estimated these probabilities based upon classifications for the k nearest observations. Simulations were used to compare the performance of these approaches. The results showed that 1) KNN and NM produced modest reductions of bias of point estimates of SSTs; 2) both strategies reduced bias on point estimates of DTRs when the misclassified patients and correctly classified patients from same initial treatment had unequal means; 3) NM reduced the bias of point estimates of overall outcome more than KNN; 4) in general, there were little effect on power adjustment; 5) type I error should always be preserved at 0.05 regardless of misclassification when same response rate and same treatment effects among responders or among non-responders were assumed, but the observed type I error tended to be less than 0.05; 6) KNN preserved type I error at 0.05, but NM might increase type I error rate. Even though most of time both KNN and NM strategies improved point estimates in SMART designs while we knew misclassification might be involved, the tradeoff were increased type I error rate and little effect on power.
Our work showed that misclassification should be considered in SMART design because it introduced bias, but KNN or NM strategies at the final stage couldn’t completely reduce bias of point estimates or improve power. However, in future by adjusting with covariates, these two strategies might be used to improve the classification accuracy in the early stage outcomes.
29
Polisetti, Haritha. "Hidden Markov Chain Analysis: Impact of Misclassification on Effect of Covariates in Disease Progression and Regression." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6568.
Full textAbstract:
Most of the chronic diseases have a well-known natural staging system through which the disease progression is interpreted. It is well established that the transition rates from one stage of disease to other stage can be modeled by multi state Markov models. But, it is also well known that the screening systems used to diagnose disease states may subject to error some times. In this study, a simulation study is conducted to illustrate the importance of addressing for misclassification in multi-state Markov models by evaluating and comparing the estimates for the disease progression Markov model with misclassification opposed to disease progression Markov model. Results of simulation study support that models not accounting for possible misclassification leads to bias. In order to illustrate method of accounting for misclassification is illustrated using dementia data which was staged as no cognitive impairment, mild cognitive impairment and dementia and diagnosis of dementia stage is prone to error sometimes. Subjects entered the study irrespective of their state of disease and were followed for one year and their disease state at follow up visit was recorded. This data is used to illustrate that application of multi state Markov model which is an example of Hidden Markov model in accounting for misclassification which is based on an assumption that the observed (misclassified) states conditionally depend on the underlying true disease states which follow the Markov process. The misclassification probabilities for all the allowed disease transitions were also estimated. The impact of misclassification on the effect of covariates is estimated by comparing the hazard ratios estimated by fitting data with progression multi state model and by fitting data with multi state model with misclassification which revealed that if misclassification has not been addressed the results are biased. Results suggest that the gene apoe ε4 is significantly associated with disease progression from mild cognitive impairment to dementia but, this effect was masked when general multi state Markov model was used. While there is no significant relation is found for other transitions.
30
Sbihi, Hind. "Adjusting retrospective noise exposure assessment for use of hearing protection devices." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1499.
Full textAbstract:
Earlier retrospective noise exposure assessments for use in epidemiological research were not adequately characterized because they did not properly account for use of hearing protection devices (HPD) which would result in potential misclassification. Exposure misclassification has been shown to attenuate exposure-outcomes relations. In the case of already subtle relationships such as noise and cardiovascular diseases, this would potentially annihilate any association.
We investigated two approaches using Workers’ Compensation Board (WorkSafe BC) audiometric surveillance data to (i) re-assess the noise exposure in a cohort of lumber mill workers in British Columbia using data on the use of HPD and the determinants of their use available through WorkSafe BC, and (ii) test the validity of the new exposure measures by testing their predictions of noise-induced hearing loss, a well-established association.
Work history, noise exposure measurements, and audiometric surveillance data were merged together, forming job-exposure-audiometric information for each of 13,147 lumber mill workers. Correction factors specific to each type and class of HPD were determined based on research and standards. HPD-relevant correction factors were created using 1) deterministic methods and self-reported HPD use after filling gaps in the exposure history, or 2) a model of the determinants of use of HPD, then adjusting noise estimates according to the methods’ predictions and attenuation factors. For both methods, the HPD-adjusted and unadjusted noise exposure estimates were cumulated across all jobs each worker held in a cohort-participating lumber mill.
Finally, these noise metrics were compared by examining how well each predicted hearing loss. Analyses controlled for gender, age, race as well as medical and non-occupational risk factors.
Both methods led to a strengthening of the noise-hearing loss relationships compared to methods using HPD-unadjusted noise estimates. The method based on the modeling of HPD use had the best performance with a four-fold increase in the slope compared to the unadjusted noise-hearing loss slope.
Accounting for HPD use in noise exposure assessment is necessary since we have shown that misclassification attenuated the exposure-response relationships. Exposure-response analyses subsequent to exposure reassessment provide predictive validity and gives confidence in the exposure adjustment methods.
31
Ahmed, Mohammad Faruque. "Simulating and assessing salinisation in the lower Namoi Valley." Thesis, The University of Sydney, 2001. http://hdl.handle.net/2123/811.
Full textAbstract:
Dryland salinity is increasing in the upper catchments of central and northern New South Wales, Australia. Consequently, salts may be exported downstream, which could adversely affect cotton irrigated-farming systems. In order to assess the potential threat of salinity a simple salt balance model based on progressively saline water (i.e., ECiw 0.4, 1.5, 4.0 and 9.0 dS/m) was used to simulate the potential impact of salinisation due to the farming systems. The study was carried out in the lower Namoi valley of northern New South Wales, Australia. A comparison has been made of the various non-linear techniques (indicator kriging, multiple indicator kriging and disjunctive kriging) to determine an optimal simulation method for the risk assessment. The simulation results indicate that potential salinisation due to application of the water currently used for irrigation (ECiw) is minimal and may not pose any problems to sustainability of irrigated agriculture. The same results were obtained by simulation based on irrigation using slightly more saline water (ECiw 1.4 dS/m). However, simulations based on irrigation using water of even lower quality (ECiw of 4 and 9.0 dS/m), shows potential high salinisation, which will require management inputs for sustainable cropping systems, especially legumes and wheat, which are used extensively in rotation with cotton. Disjunctive kriging was the best simulation method, as it produced fewer misclassifications in comparison with multiple-indicator kriging and indicator kriging. This study thus demonstrates that we can predict the salinity risk due to application of irrigation water of lower quality than that of the current water used. In addition, the results suggest here problems of excessive deep drainage and inefficient use of water might be a problem. The second part of this thesis deals with soil information required at the field scale for management practices particularly in areas where deep drainage is large. Unfortunately, traditional methods of soil inventory at the field level involve the design and adoption of sampling regimes and laboratory analysis that are time-consuming and costly. Because of this more often than not only limited data are collected. In areas where soil salinity is prevalent, detailed quantitative information for determining its cause is required to prescribe management solutions. This part deals with the description of a Mobile Electromagnetic Sensing System (MESS) and its application in an irrigated-cotton field suspected of exhibiting soil salinity. The field is within the study area of part one of this thesis-located about 2 km south west of Wee Waa. The EM38 and EM31 (ECa) data provide information, which was used in deciding where soil sample sites could be located in the field. The ECa data measured by the EM38 instrument was highly correlated with the effective cation exchange capacity. This relationship can be explained by soil mineralogy. Using different soil chemical properties (i.e. ESP and Ca/Mg ratio) a detailed transect study was undertaken to measure soil salinity adjoining the water storage. It is concluded that the most appropriate management option to remediation of the problem would be to excavate the soil directly beneath the storage floor where leakage is suspected. It is recommended that the dam not be enlarged from its current size owing to the unfavourable soil mineralogy (i.e. kaolin/illite) located in the area where it is located.
32
Ahmed, Mohammad Faruque. "Simulating and assessing salinisation in the lower Namoi Valley." University of Sydney. Land Water and Crop Sciences, 2001. http://hdl.handle.net/2123/811.
Full textAbstract:
Dryland salinity is increasing in the upper catchments of central and northern New South Wales, Australia. Consequently, salts may be exported downstream, which could adversely affect cotton irrigated-farming systems. In order to assess the potential threat of salinity a simple salt balance model based on progressively saline water (i.e., ECiw 0.4, 1.5, 4.0 and 9.0 dS/m) was used to simulate the potential impact of salinisation due to the farming systems. The study was carried out in the lower Namoi valley of northern New South Wales, Australia. A comparison has been made of the various non-linear techniques (indicator kriging, multiple indicator kriging and disjunctive kriging) to determine an optimal simulation method for the risk assessment. The simulation results indicate that potential salinisation due to application of the water currently used for irrigation (ECiw) is minimal and may not pose any problems to sustainability of irrigated agriculture. The same results were obtained by simulation based on irrigation using slightly more saline water (ECiw 1.4 dS/m). However, simulations based on irrigation using water of even lower quality (ECiw of 4 and 9.0 dS/m), shows potential high salinisation, which will require management inputs for sustainable cropping systems, especially legumes and wheat, which are used extensively in rotation with cotton. Disjunctive kriging was the best simulation method, as it produced fewer misclassifications in comparison with multiple-indicator kriging and indicator kriging. This study thus demonstrates that we can predict the salinity risk due to application of irrigation water of lower quality than that of the current water used. In addition, the results suggest here problems of excessive deep drainage and inefficient use of water might be a problem. The second part of this thesis deals with soil information required at the field scale for management practices particularly in areas where deep drainage is large. Unfortunately, traditional methods of soil inventory at the field level involve the design and adoption of sampling regimes and laboratory analysis that are time-consuming and costly. Because of this more often than not only limited data are collected. In areas where soil salinity is prevalent, detailed quantitative information for determining its cause is required to prescribe management solutions. This part deals with the description of a Mobile Electromagnetic Sensing System (MESS) and its application in an irrigated-cotton field suspected of exhibiting soil salinity. The field is within the study area of part one of this thesis-located about 2 km south west of Wee Waa. The EM38 and EM31 (ECa) data provide information, which was used in deciding where soil sample sites could be located in the field. The ECa data measured by the EM38 instrument was highly correlated with the effective cation exchange capacity. This relationship can be explained by soil mineralogy. Using different soil chemical properties (i.e. ESP and Ca/Mg ratio) a detailed transect study was undertaken to measure soil salinity adjoining the water storage. It is concluded that the most appropriate management option to remediation of the problem would be to excavate the soil directly beneath the storage floor where leakage is suspected. It is recommended that the dam not be enlarged from its current size owing to the unfavourable soil mineralogy (i.e. kaolin/illite) located in the area where it is located.
33
Umunoza, Gasana Emelyne. "Misclassification Probabilities through Edgeworth-type Expansion for the Distribution of the Maximum Likelihood based Discriminant Function." Licentiate thesis, Linköpings universitet, Tillämpad matematik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-175873.
Full textAbstract:
This thesis covers misclassification probabilities via an Edgeworth-type expansion of the maximum likelihood based discriminant function. When deriving misclassification errors, first the expectation and variance in the population are assumed to be known where the variance is the same across populations and thereafter we consider the case where those parameters are unknown. Cumulants of the discriminant function for discriminating between two multivariate normal populations are derived. Approximate probabilities of the misclassification errors are established via an Edgeworth-type expansion using a standard normal distribution.
34
Shoucri, Marie-Rose. "Defining the role of Epstein-Barr virus infection in multiple sclerosis : issues in exposure measurement and misclassification." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82426.
Full textAbstract:
A leading hypothesis for the etiology of multiple sclerosis (MS) is that a delayed infection by Epstein-Barr Virus (EBV) generates an abnormal immune response, thereby inducing the disease. EBV is ubiquitous. While asymptomatic in children, the infection produces the clinical entity infectious mononucleosis (IM) when it is delayed until adolescence or young adulthood. Typically, case-control studies have measured exposure to EBV by serology assaying EBV antibodies or by IM self-report. The findings of these studies are not consistent.Part of the problem in establishing a definite causal relationship between EBV and MS in case-control studies is related to exposure measurement and misclassification. Since neither EBV serology or IM self-report represent a 'gold standard' for exposure, particularly of delayed exposure, it is difficult to estimate the exposure misclassification that would occur in a case-control study, and the subsequent bias in the odds ratio (OR).
These results support that EBV serology is not a good measure of delayed EBV infection due to its lack specificity, and that estimates of association between MS and EBV serology may overestimate or underestimate the OR between MS and IM. EBV is extremely prevalent, and likely to be differentially misclassified by serology. We therefore recommend the use of IM self-report for future case-control studies of MS and EBV.
35
Karim, Mohammad Ehsanul. "Evaluating the performance of hypothesis testing in case-control studies with exposure misclassification, using frequentist and Bayesian techniques." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/22472.
Full textAbstract:
In epidemiologic studies, measurement error in the exposure variable can have large effects on the power of hypothesis testing for detecting the impact of exposure in the development of a disease. As it distorts the structure of
data, more uncertainty is associated with the inferential procedure involving such exposure variables. The underlying theme of this thesis is the adjustment for misclassification in the hypothesis testing procedure. We consider
problems involving a correctly measured binary response and a misclassified binary exposure variable in a retrospective case-control scenario. We account for misclassification error via validation data under the assumption of non-differential misclassification. The objective here is to develop a test to check whether the exposure prevalence rates of cases and controls are the same or not, under the frequentist and Bayesian point of view. To evaluate the test developed under the Bayesian approach, we compare that with an equivalent test developed under the frequentist approach. Both these approaches were developed in two different settings: in the presence or absence of validation data, to evaluate whether there is any gain in hypothesis
testing for having such validation data. The frequentist approach involves
the likelihood ratio test, while the Bayesian test is developed from posterior distribution generated by a mixed MCMC algorithm and a normal prior
under realistic assumptions. The comparison between these two approaches is conducted using different simulated scenarios, as well as two real case-control studies having partial validation (internal) data. Different scenarios include settings with varying sensitivity and specificity, sample sizes, exposure prevalence and proportion of unvalidated and validated data. One
other scenario that was considered is to evaluate the performance under a fixed budgetary constraint. In the scenarios under consideration, we reach the same conclusion from the two hypothesis testing procedures. The simulation study suggests that the adjusted model (with validation data model) is always better than the unadjusted model (without validation data model).
However, exception is possible in the fixed budget scenario.
36
Thola, Forest D. "Minimizing Recommended Error Costs Under Noisy Inputs in Rule-Based Expert Systems." NSUWorks, 2012. http://nsuworks.nova.edu/gscis_etd/323.
Full textAbstract:
This dissertation develops methods to minimize recommendation error costs when inputs to a rule-based expert system are prone to errors. The problem often arises in web-based applications where data are inherently noisy or provided by users who perceive some benefit from falsifying inputs. Prior studies proposed methods that attempted to minimize the probability of recommendation error, but did not take into account the relative costs of different types of errors. In situations where these differences are significant, an approach that minimizes the expected misclassification error costs has advantages over extant methods that ignore these costs.
Building on the existing literature, two new techniques - Cost-Based Input Modification (CBIM) and Cost-Based Knowledge-Base Modification (CBKM) were developed and evaluated. Each method takes as inputs (1) the joint probability distribution of a set of rules, (2) the distortion matrix for input noise as characterized by the probability distribution of the observed input vectors conditioned on their true values, and (3) the misclassification cost for each type of recommendation error. Under CBIM, for any observed input vector v, the recommendation is based on a modified input vector v' such that the expected error costs are minimized. Under CBKM the rule base itself is modified to minimize the expected cost of error.
The proposed methods were investigated as follows: as a control, in the special case where the costs associated with different types of errors are identical, the recommendations under these methods were compared for consistency with those obtained under extant methods. Next, the relative advantages of CBIM and CBKM were compared as (1) the noise level changed, and (2) the structure of the cost matrix varied.
As expected, CBKM and CBIM outperformed the extant Knowledge Base Modification (KM) and Input Modification (IM) methods over a wide range of input distortion and cost matrices, with some restrictions. Under the control, with constant misclassification costs, the new methods performed equally with the extant methods. As misclassification costs increased, CBKM outperformed KM and CBIM outperformed IM. Using different cost matrices to increase misclassification cost asymmetry and order, CBKM and CBIM performance increased. At very low distortion levels, CBKM and CBIM underperformed as error probability became more significant in each method's estimation. Additionally, CBKM outperformed CBIM over a wide range of input distortion as its technique of modifying an original knowledge base outperformed the technique of modifying inputs to an unmodified decision tree.
37
Thompson, Jeffrey A. "Generic Drug Discount Programs, Cash-Only Drug Exposure Misclassification Bias, and the Implications for Claims-Based Adherence Measure Estimates." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1521191260356822.
Full text
38
Sarkar, Saurabh. "Feature Selection with Missing Data." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378194989.
Full text
39
Dhillon, Preet Kaur. "Bias due to exposure misclassification and rising screening levels : a case-control study of prostate-specific antigen (PSA) screening efficacy /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/10949.
Full text
40
LI, XUAN. "Response Adaptive Designs in the Presence of Mismeasurement." Elsevier, 2012. http://hdl.handle.net/1993/8095.
Full textAbstract:
Response adaptive randomization represents a major advance in clinical trial methodology that helps balance the benefits of the collective and the benefits of the individual and improves efficiency without undermining the validity and integrity of the clinical research. Response adaptive designs use information so far accumulated from the trial to modify the randomization procedure and deliberately bias treatment allocation in order to assign more patients to the potentially better treatment. No attention has been paid to incorporating the problem of errors-in-variables in adaptive clinical trials. In this work, some important issues and methods of response adaptive design of clinical trials in the presence of mismeasurement are examined. We formulate response adaptive designs when the dichotomous response may be misclassified. We consider the optimal allocations under various objectives, investigate the asymptotically best response adaptive randomization procedure, and discuss effects of misclassification on the optimal allocation. We derive explicit expressions for the variance-penalized criterion with misclassified binary responses and propose a new target proportion of treatment allocation under the criterion. A real-life clinical trial and some related simulation results are also presented.
41
Simo, Beatrice. "Epidemic of Lung Cancer or Artifact of Classification in the State of Kentucky?" Digital Commons @ East Tennessee State University, 2007. https://dc.etsu.edu/etd/2158.
Full textAbstract:
Lung cancer remains the leading cause of cancer deaths in the United States despite public health campaigns aimed at reducing its rate of mortality. Kentucky is the state with the highest lung cancer incidence and mortality. This study aims to assess the impact of misclassification of cause of death from Lung Cancer in Kentucky for the period 1979 to 2002. We will examine the potential competing classification of death for two other smoking-related diseases, Chronic Obstructive Pulmonary Disease (COPD) and Emphysema. Age-adjusted mortality rates for these diseases for white males were obtained from the National Center for Health Statistics. There was little evidence that any misclassification between COPD or Emphysema mortality rates was in agreement with the rising lung cancer rates in Kentucky. The long-term increase in lung cancer mortality in Kentucky is likely because of a combination of risk effects between smoking and other risk-factors for this disease.
42
Goldman, Gretchen Tanner. "Characterization and impact of ambient air pollution measurement error in time-series epidemiologic studies." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41158.
Full textAbstract:
Time-series studies of ambient air pollution and acute health outcomes utilize measurements from fixed outdoor monitoring sites to assess changes in pollution concentration relative to time-variable health outcome measures. These studies rely on measured concentrations as a surrogate for population exposure. The degree to which monitoring site measurements accurately represent true ambient concentrations is of interest from both an etiologic and regulatory perspective, since associations observed in time-series studies are used to inform health-based ambient air quality standards. Air pollutant measurement errors associated with instrument precision and lack of spatial correlation between monitors have been shown to attenuate associations observed in health studies. Characterization and adjustment for air pollution measurement error can improve effect estimates in time-series studies. Measurement error was characterized for 12 ambient air pollutants in Atlanta. Simulations of instrument and spatial error were generated for each pollutant, added to a reference pollutant time-series, and used in a Poisson generalized linear model of air pollution and cardiovascular emergency department visits. This method allows for pollutant-specific quantification of impacts of measurement error on health effect estimates, both the assessed strength of association and its significance. To inform on the amount and type of error present in Atlanta measurements, air pollutant concentrations were simulated over the 20-county metropolitan area for a 6-year period, incorporating several distribution characteristics observed in measurement data. The simulated concentration fields were then used to characterize the amount and type of error due to spatial variability in ambient concentrations, as well as the impact of use of different exposure metrics in a time-series epidemiologic study. Finally, methodologies developed for the Atlanta area were applied to air pollution measurements in Dallas, Texas with consideration for the impact of this error on a health study of the Dallas-Fort Worth region that is currently underway.
43
Zhang, Angang. "Some Advances in Classifying and Modeling Complex Data." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/77958.
Full textAbstract:
In statistical methodology of analyzing data, two of the most commonly used techniques are classification and regression modeling. As scientific technology progresses rapidly, complex data often occurs and requires novel classification and regression modeling methodologies according to the data structure. In this dissertation, I mainly focus on developing a few approaches for analyzing the data with complex structures.
Classification problems commonly occur in many areas such as biomedical, marketing, sociology and image recognition. Among various classification methods, linear classifiers have been widely used because of computational advantages, ease of implementation and interpretation compared with non-linear classifiers. Specifically, linear discriminant analysis (LDA) is one of the most important methods in the family of linear classifiers.
For high dimensional data with number of variables p larger than the number of observations n occurs more frequently, it calls for advanced classification techniques.
In Chapter 2, I proposed a novel sparse LDA method which generalizes LDA through a regularized approach for the two-class classification problem.
The proposed method can obtain an accurate classification accuracy with attractive computation, which is suitable for high dimensional data with p>n.
In Chapter 3, I deal with the classification when the data complexity lies in the non-random missing responses in the training data set. Appropriate classification method needs to be developed accordingly. Specifically, I considered the "reject inference problem'' for the application of fraud detection for online business. For online business, to prevent fraud transactions, suspicious transactions are rejected with unknown fraud status, yielding a training data with selective missing response. A two-stage modeling approach using logistic regression is proposed to enhance the efficiency and accuracy of fraud detection.
Besides the classification problem, data from designed experiments in scientific areas often have complex structures. Many experiments are conducted with multiple variance sources. To increase the accuracy of the statistical modeling, the model need to be able to accommodate more than one error terms. In Chapter 4, I propose a variance component mixed model for a nano material experiment data to address the between group, within group and within subject variance components into a single model. To adjust possible systematic error introduced during the experiment, adjustment terms can be added. Specifically a group adaptive forward and backward selection (GFoBa) procedure is designed to select the significant adjustment terms.Ph. D.
44
Detterfelt, Sebastian, and Isak Björkman. "Om att bedöma formler för att formulera bedömningar : En kvantitativ studie om precisionen i revisorers fortlevnadsbedömningar och konkursprediktionsmodeller." Thesis, Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176943.
Full textAbstract:
Revisorer i Sverige har i tidigare studier funnits restriktiva med att ge ut fortlevnadsvarningar till konkursbolag. Det saknas studier om huruvida konkursprediktionsmodeller gör bättre förutsägelser än revisorer i Sverige med hänsyn till kostnader för missklassificeringar (EMC). Syftet med studien är att jämföra precisionen i revisorers fortlevnadsbedömningar med precisionen i konkursprediktionsmodeller baserade på finansiella nyckeltal, samt att undersöka relationen mellan fortlevnadsvarningar, finansiella nyckeltal och konkurser. Studien är kvantitativ och har en deduktiv ansats med en komparativ forskningsdesign. Sekundärdata från svenska onoterade aktiebolags årsredovisningar har använts. Resultaten visar att revisorer i Sverige alltjämt är restriktiva med att ge ut fortlevnadsvarningar, men att deras bedömningar i huvudsak har högre precision än konkursprediktionsmodeller för K2-redovisande bolag. När redovisningen blir mer sofistikerad (K3), kan dock konkursprediktionsmodeller ge en högre precision. Studien belyser fortlevnadsbedömningar och konkursprediktionsmodellers utfall utifrån ett kostnadsperspektiv, samt bidrar med kunskap kring fördelar och begränsningar med konkursprediktionsmodeller inom revision.Auditors in Sweden has been found restrictive with issuing going concern opinions to subsequent bankrupt companies. There is a lack of studies examining if bankruptcy prediction models make better predictions than auditors in Sweden when estimated misclassification costs (EMC) are considered. The purpose with this study is to compare the precision in going concern opinions with the precision from accounting-based bankruptcy prediction models, and to examine the relation between going concern opinions, accounting measures and bankruptcies. The study is quantitative with a deductive approach and comparative design. Secondary data from Swedish private limited companies’ annual reports has been used. Our results show that auditors in Sweden are still restrictive with issuing going concern opinions, but that their evaluations to a large degree are more precise than the bankruptcy prediction models when used on companies that are reporting by the K2 framework. However, when the accounting numbers are more sophisticated by using the K3 framework, bankruptcy prediction models may be more precise. The study highlights going concern opinions and the outcome from bankruptcy prediction models through a perspective of estimated misclassification costs (EMC). It also contributes with knowledge regarding advantages and disadvantages with using bankruptcy prediction models in auditing.
45
Yu, Xue Qin. "Comparing survival from cancer using population-based cancer registry data - methods and applications." Thesis, The University of Sydney, 2007. http://hdl.handle.net/2123/1774.
Full textAbstract:
Over the past decade, population-based cancer registry data have been used increasingly worldwide to evaluate and improve the quality of cancer care. The utility of the conclusions from such studies relies heavily on the data quality and the methods used to analyse the data. Interpretation of comparative survival from such data, examining either temporal trends or geographical differences, is generally not easy. The observed differences could be due to methodological and statistical approaches or to real effects. For example, geographical differences in cancer survival could be due to a number of real factors, including access to primary health care, the availability of diagnostic and treatment facilities and the treatment actually given, or to artefact, such as lead-time bias, stage migration, sampling error or measurement error. Likewise, a temporal increase in survival could be the result of earlier diagnosis and improved treatment of cancer; it could also be due to artefact after the introduction of screening programs (adding lead time), changes in the definition of cancer, stage migration or several of these factors, producing both real and artefactual trends. In this thesis, I report methods that I modified and applied, some technical issues in the use of such data, and an analysis of data from the State of New South Wales (NSW), Australia, illustrating their use in evaluating and potentially improving the quality of cancer care, showing how data quality might affect the conclusions of such analyses. This thesis describes studies of comparative survival based on population-based cancer registry data, with three published papers and one accepted manuscript (subject to minor revision). In the first paper, I describe a modified method for estimating spatial variation in cancer survival using empirical Bayes methods (which was published in Cancer Causes and Control 2004). I demonstrate in this paper that the empirical Bayes method is preferable to standard approaches and show how it can be used to identify cancer types where a focus on reducing area differentials in survival might lead to important gains in survival. In the second paper (published in the European Journal of Cancer 2005), I apply this method to a more complete analysis of spatial variation in survival from colorectal cancer in NSW and show that estimates of spatial variation in colorectal cancer can help to identify subgroups of patients for whom better application of treatment guidelines could improve outcome. I also show how estimates of the numbers of lives that could be extended might assist in setting priorities for treatment improvement. In the third paper, I examine time trends in survival from 28 cancers in NSW between 1980 and 1996 (published in the International Journal of Cancer 2006) and conclude that for many cancers, falls in excess deaths in NSW from 1980 to 1996 are unlikely to be attributable to earlier diagnosis or stage migration; thus, advances in cancer treatment have probably contributed to them. In the accepted manuscript, I described an extension of the work reported in the second paper, investigating the accuracy of staging information recorded in the registry database and assessing the impact of error in its measurement on estimates of spatial variation in survival from colorectal cancer. The results indicate that misclassified registry stage can have an important impact on estimates of spatial variation in stage-specific survival from colorectal cancer. Thus, if cancer registry data are to be used effectively in evaluating and improving cancer care, the quality of stage data might have to be improved. Taken together, the four papers show that creative, informed use of population-based cancer registry data, with appropriate statistical methods and acknowledgement of the limitations of the data, can be a valuable tool for evaluating and possibly improving cancer care. Use of these findings to stimulate evaluation of the quality of cancer care should enhance the value of the investment in cancer registries. They should also stimulate improvement in the quality of cancer registry data, particularly that on stage at diagnosis. The methods developed in this thesis may also be used to improve estimation of geographical variation in other count-based health measures when the available data are sparse.
46
Yu, Xue Qin. "Comparing survival from cancer using population-based cancer registry data - methods and applications." University of Sydney, 2007. http://hdl.handle.net/2123/1774.
Full textAbstract:
Doctor of PhilosophyOver the past decade, population-based cancer registry data have been used increasingly worldwide to evaluate and improve the quality of cancer care. The utility of the conclusions from such studies relies heavily on the data quality and the methods used to analyse the data. Interpretation of comparative survival from such data, examining either temporal trends or geographical differences, is generally not easy. The observed differences could be due to methodological and statistical approaches or to real effects. For example, geographical differences in cancer survival could be due to a number of real factors, including access to primary health care, the availability of diagnostic and treatment facilities and the treatment actually given, or to artefact, such as lead-time bias, stage migration, sampling error or measurement error. Likewise, a temporal increase in survival could be the result of earlier diagnosis and improved treatment of cancer; it could also be due to artefact after the introduction of screening programs (adding lead time), changes in the definition of cancer, stage migration or several of these factors, producing both real and artefactual trends. In this thesis, I report methods that I modified and applied, some technical issues in the use of such data, and an analysis of data from the State of New South Wales (NSW), Australia, illustrating their use in evaluating and potentially improving the quality of cancer care, showing how data quality might affect the conclusions of such analyses. This thesis describes studies of comparative survival based on population-based cancer registry data, with three published papers and one accepted manuscript (subject to minor revision). In the first paper, I describe a modified method for estimating spatial variation in cancer survival using empirical Bayes methods (which was published in Cancer Causes and Control 2004). I demonstrate in this paper that the empirical Bayes method is preferable to standard approaches and show how it can be used to identify cancer types where a focus on reducing area differentials in survival might lead to important gains in survival. In the second paper (published in the European Journal of Cancer 2005), I apply this method to a more complete analysis of spatial variation in survival from colorectal cancer in NSW and show that estimates of spatial variation in colorectal cancer can help to identify subgroups of patients for whom better application of treatment guidelines could improve outcome. I also show how estimates of the numbers of lives that could be extended might assist in setting priorities for treatment improvement. In the third paper, I examine time trends in survival from 28 cancers in NSW between 1980 and 1996 (published in the International Journal of Cancer 2006) and conclude that for many cancers, falls in excess deaths in NSW from 1980 to 1996 are unlikely to be attributable to earlier diagnosis or stage migration; thus, advances in cancer treatment have probably contributed to them. In the accepted manuscript, I described an extension of the work reported in the second paper, investigating the accuracy of staging information recorded in the registry database and assessing the impact of error in its measurement on estimates of spatial variation in survival from colorectal cancer. The results indicate that misclassified registry stage can have an important impact on estimates of spatial variation in stage-specific survival from colorectal cancer. Thus, if cancer registry data are to be used effectively in evaluating and improving cancer care, the quality of stage data might have to be improved. Taken together, the four papers show that creative, informed use of population-based cancer registry data, with appropriate statistical methods and acknowledgement of the limitations of the data, can be a valuable tool for evaluating and possibly improving cancer care. Use of these findings to stimulate evaluation of the quality of cancer care should enhance the value of the investment in cancer registries. They should also stimulate improvement in the quality of cancer registry data, particularly that on stage at diagnosis. The methods developed in this thesis may also be used to improve estimation of geographical variation in other count-based health measures when the available data are sparse.
47
Obořilová, Veronika. "OUTSOURCING IT PRACOVNÍKŮ NA ČESKÉM TRHU PRÁCE V ROCE 2015." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-262355.
Full textAbstract:
This thesis focuses on current very important and widespread IT sector on the Czech labor market and discusses the frequent solutions to the problems of IT workers employment: outsourcing. The theoretical part of this work solves development and overall approach to outsourcing. The practical part includes the legal framework of outsourcing in the Czech Republic. Given is the definition of the ICT sector, including individual sub-branches of the IT sector with their individual specifications and performance of EI-PRAGUE, s.r.o. whose database of IT specialists, together with data of Czech Statistical Office it is also used for actual calculations. These calculations help to dismantle the advantages and disadvantages of outsourcing, both from the perspective of a worker employed on main job or self-employed, but also from the perspective of the IT specialists provider, state and health insurers. Carried comparisons have conflicting results. From the perspective of the worker is in almost all respects advantageous to work as self-employed, while in terms of state income and health insurers is more suitable if the workers are in an employment relationship at main job.
48
HwuxBiingxShyang and 胡炳祥. "Kernel Estimation for the Misclassification Data." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/44186791320463169371.
Full text
49
"Analysis of categorical data with misclassification errors." Chinese University of Hong Kong, 1988. http://library.cuhk.edu.hk/record=b5885918.
Full text
50
"Modelling and analysis of ranking data with misclassification." 2007. http://library.cuhk.edu.hk/record=b5893383.
Full textAbstract:
Chan, Ho Wai.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.
Includes bibliographical references (leaves 56).
Abstracts in English and Chinese.
Abstract --- p.ii
Acknowledgement --- p.iv
Chapter 1 --- Introduction --- p.1
Chapter 2 --- Model --- p.3
Chapter 3 --- Implementation by Mx --- p.10
Chapter 3.1 --- Example 1 --- p.10
Chapter 3.2 --- Example 2 --- p.22
Chapter 4 --- Covariance structure analysis --- p.26
Chapter 5 --- Simulation --- p.29
Chapter 5.1 --- Simulation 1 --- p.29
Chapter 5.2 --- Simulation 2 --- p.36
Chapter 6 --- Discussion --- p.41
Appendix A: Mx input script for ranking data data with p =4 --- p.43
Appendix B: Selection matrices for ranking data with p = 4 --- p.47
Appendix C: Mx input script for ranking data data with p = 3 --- p.50
Appendix D: Mx input script for p = 4 with covariance structure --- p.53
References --- p.56