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Journal articles on the topic 'Design of observational studies'

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Published: 10 December 2022
Last updated: 28 January 2023

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1

Ramji, Siddarth. "Study Design: Observational Studies." Indian Pediatrics 59, no. 6 (April 26, 2022): 493–98. http://dx.doi.org/10.1007/s13312-022-2541-2.

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2

Bedogni, Giorgio. "Design of Observational Studies." Journal of the Royal Statistical Society: Series A (Statistics in Society) 174, no. 1 (January 2011): 244–45. http://dx.doi.org/10.1111/j.1467-985x.2010.00676_8.x.

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3

Rosenbaum, P. R. "Design sensitivity in observational studies." Biometrika 91, no. 1 (March 1, 2004): 153–64. http://dx.doi.org/10.1093/biomet/91.1.153.

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4

Suresh, KP, MR Gajendragad, and H. Rahman. "Design and Analysis of Observational Studies." International Journal of Infertility & Fetal Medicine 5, no. 2 (2014): 33–39. http://dx.doi.org/10.5005/jp-journals-10016-1079.

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ABSTRACT Appropriate study design forms the basis of any successful organized research. Irrespective of the underlying rationale of the research question, inappropriate design of an experiment or execution of a study may not give a valid result. As defined, the research should have the following characteristics: it should be undertaken within the framework of a set of philosophies, should have an unbiased objective and a design comprising of methods and techniques that have been tested for their validity and reliability. These principles of research clearly emphasize the need for an appropriate study design. The current review focuses on the planning and analysis of an observational study to obtain significant and accurate findings. How to cite this article Suresh KP, Gajendragad MR, Rahman H. Design and Analysis of Observational Studies. Int J Infertil Fetal Med 2014;5(2):33-39.
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5

Zhang, J., M. Zhang, D. Zhang, and F. Yang. "Observational studies also need rigorous design." Focus on Alternative and Complementary Therapies 21, no. 3-4 (December 2016): 185–86. http://dx.doi.org/10.1111/fct.12276.

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Rosenbaum, Paul R. "Design Sensitivity and Efficiency in Observational Studies." Journal of the American Statistical Association 105, no. 490 (June 2010): 692–702. http://dx.doi.org/10.1198/jasa.2010.tm09570.

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7

Prashanth, GP. "Design and statistical analysis of observational studies." BJOG: An International Journal of Obstetrics & Gynaecology 120, no. 7 (May 10, 2013): 901. http://dx.doi.org/10.1111/1471-0528.12143.

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8

Usta, IM, J. Awwad, and AH Nassar. "Design and statistical analysis of observational studies." BJOG: An International Journal of Obstetrics & Gynaecology 120, no. 7 (May 10, 2013): 901–2. http://dx.doi.org/10.1111/1471-0528.12144.

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9

Stuart, Elizabeth A. "A Review of Design of Observational Studies." Journal of Educational and Behavioral Statistics 35, no. 6 (December 2010): 774–78. http://dx.doi.org/10.3102/1076998610385399.

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10

Miratrix, Luke W. "ROSENBAUM, P.R. (2010). Design of Observational Studies." Psychometrika 79, no. 3 (November 28, 2013): 540–42. http://dx.doi.org/10.1007/s11336-013-9389-2.

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11

Yoshida, Kazuki, Daniel H. Solomon, and Seoyoung C. Kim. "Active-comparator design and new-user design in observational studies." Nature Reviews Rheumatology 11, no. 7 (March 24, 2015): 437–41. http://dx.doi.org/10.1038/nrrheum.2015.30.

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12

Carlson, Melissa D. A., and R. Sean Morrison. "Study Design, Precision, and Validity in Observational Studies." Journal of Palliative Medicine 12, no. 1 (January 2009): 77–82. http://dx.doi.org/10.1089/jpm.2008.9690.

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13

Heller, Ruth, Paul R. Rosenbaum, and Dylan S. Small. "Split Samples and Design Sensitivity in Observational Studies." Journal of the American Statistical Association 104, no. 487 (September 2009): 1090–101. http://dx.doi.org/10.1198/jasa.2009.tm08338.

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14

Page, Lindsay C., Matthew A. Lenard, and Luke Keele. "The Design of Clustered Observational Studies in Education." AERA Open 6, no. 3 (July 2020): 233285842095440. http://dx.doi.org/10.1177/2332858420954401.

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Clustered observational studies (COSs) are a critical analytic tool for educational effectiveness research. We present a design framework for the development and critique of COSs. The framework is built on the counterfactual model for causal inference and promotes the concept of designing COSs that emulate the targeted randomized trial that would have been conducted were it feasible. We emphasize the key role of understanding the assignment mechanism to study design. We review methods for statistical adjustment and highlight a recently developed form of matching designed specifically for COSs. We review how regression models can be profitably combined with matching and note best practices for estimates of statistical uncertainty. Finally, we review how sensitivity analyses can determine whether conclusions are sensitive to bias from potential unobserved confounders. We demonstrate concepts with an evaluation of a summer school reading intervention in a large U.S. school district.
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15

Liski, Erkki P. "Design of Observational Studies by Paul R. Rosenbaum." International Statistical Review 78, no. 3 (December 2010): 477–78. http://dx.doi.org/10.1111/j.1751-5823.2010.00122_30.x.

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16

Hsu, Jesse Y., Dylan S. Small, and Paul R. Rosenbaum. "Effect Modification and Design Sensitivity in Observational Studies." Journal of the American Statistical Association 108, no. 501 (March 2013): 135–48. http://dx.doi.org/10.1080/01621459.2012.742018.

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17

Caswell, Jeff L., Laura L. Bassel, Jamie L. Rothenburger, Andrea Gröne, Jan M. Sargeant, Amanda P. Beck, Stina Ekman, et al. "Observational Study Design in Veterinary Pathology, Part 1: Study Design." Veterinary Pathology 55, no. 5 (August 2, 2018): 607–21. http://dx.doi.org/10.1177/0300985818785705.

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Observational studies are the basis for much of our knowledge of veterinary pathology and are highly relevant to the daily practice of pathology. However, recommendations for conducting pathology-based observational studies are not readily available. In part 1 of this series, we offer advice on planning and conducting an observational study with examples from the veterinary pathology literature. Investigators should recognize the importance of creativity, insight, and innovation in devising studies that solve problems and fill important gaps in knowledge. Studies should focus on specific and testable hypotheses, questions, or objectives. The methodology is developed to support these goals. We consider the merits and limitations of different types of analytic and descriptive studies, as well as of prospective vs retrospective enrollment. Investigators should define clear inclusion and exclusion criteria and select adequate numbers of study subjects, including careful selection of the most appropriate controls. Studies of causality must consider the temporal relationships between variables and the advantages of measuring incident cases rather than prevalent cases. Investigators must consider unique aspects of studies based on archived laboratory case material and take particular care to consider and mitigate the potential for selection bias and information bias. We close by discussing approaches to adding value and impact to observational studies. Part 2 of the series focuses on methodology and validation of methods.
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18

Ray, W. A. "Clinical studies of drug effects in humans." Clinical Chemistry 42, no. 8 (August 1, 1996): 1306–11. http://dx.doi.org/10.1093/clinchem/42.8.1306.

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Abstract Many important drug properties cannot be predicted from in vitro or animal studies but must be quantified from studies of clinically relevant endpoints in humans. Clinical study designs include the randomized controlled trial (RCT) and a variety of observational designs. In RCTs, randomization usually ensures treatment group comparability with respect to other factors, so outcome differences reflect treatment differences per se. Properly conducted RCTs thus are the strongest study design, the mainstay of mandatory premarketing studies, and essential for evaluation of therapeutic efficacy. RCT limitations include frequent use of surrogate endpoints, limited power, short-term follow-up, and cross-contamination of study groups. For ethical reasons, some questions cannot be studied with RCTs. The simplest observational design, the case series, has limited value because it lacks a denominator. Limitations of cohort and case-control studies include misclassification, selection bias, and confounding. Despite their limitations, properly conducted experimental and observational clinical studies provide essential data for clinical practice.
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19

Andrade, Chittaranjan. "Research Design: Cohort Studies." Indian Journal of Psychological Medicine 44, no. 2 (February 21, 2022): 189–91. http://dx.doi.org/10.1177/02537176211073764.

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In a cohort study, a group of subjects (the cohort) is followed for a period of time; assessments are conducted at baseline, during follow-up, and at the end of follow-up. Cohort studies are, therefore, empirical, longitudinal studies based on data obtained from a sample; they are also observational and (usually) naturalistic. Analyses can be conducted for the cohort as a whole or for subgroups amongst which comparisons can be drawn. Because there is no randomization to the subgroups of interest, cause and effect relationships cannot be determined, and relationships between variables must be stated as associations that may or may not be influenced by confounding. The cohort that is studied can be prospectively or retrospectively defined, and each method has its advantages and disadvantages. These and other issues are explained with the help of examples. A special note is made of cohort studies in Indian psychiatry.
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20

Gilbert, Peter B. "Design of Observational Studies edited by P. R. Rosenbaum." Biometrics 66, no. 3 (September 2010): 994–95. http://dx.doi.org/10.1111/j.1541-0420.2010.01477.x.

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21

van Stralen, Karlijn J., Friedo W. Dekker, Carmine Zoccali, and Kitty J. Jager. "Case-Control Studies – An Efficient Observational Study Design." Nephron Clinical Practice 114, no. 1 (2010): c1—c4. http://dx.doi.org/10.1159/000242442.

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22

Rosenman, Evan T. R., and Art B. Owen. "Designing experiments informed by observational studies." Journal of Causal Inference 9, no. 1 (January 1, 2021): 147–71. http://dx.doi.org/10.1515/jci-2021-0010.

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Abstract The increasing availability of passively observed data has yielded a growing interest in “data fusion” methods, which involve merging data from observational and experimental sources to draw causal conclusions. Such methods often require a precarious tradeoff between the unknown bias in the observational dataset and the often-large variance in the experimental dataset. We propose an alternative approach, which avoids this tradeoff: rather than using observational data for inference, we use it to design a more efficient experiment. We consider the case of a stratified experiment with a binary outcome and suppose pilot estimates for the stratum potential outcome variances can be obtained from the observational study. We extend existing results to generate confidence sets for these variances, while accounting for the possibility of unmeasured confounding. Then, we pose the experimental design problem as a regret minimization problem subject to the constraints imposed by our confidence sets. We show that this problem can be converted into a concave maximization and solved using conventional methods. Finally, we demonstrate the practical utility of our methods using data from the Women’s Health Initiative.
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23

LeBrun, Drake G., Mininder S. Kocher, Keith D. Baldwin, and Neeraj M. Patel. "HOW OFTEN ARE STUDY DESIGN AND LEVEL OF EVIDENCE MISREPORTED IN THE PEDIATRIC ORTHOPAEDIC LITERATURE?" Orthopaedic Journal of Sports Medicine 8, no. 4_suppl3 (April 1, 2020): 2325967120S0013. http://dx.doi.org/10.1177/2325967120s00137.

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Background: Observational studies are the most commonly employed study designs in the pediatric orthopaedic literature. The differences between observational study designs are important but not widely understood, leading to potential discrepancies between the reported and actual study design. Study design misclassification is associated with a potential for misreporting level of evidence (LOE). Purpose: To determine the degree of study design and LOE misclassification in the pediatric orthopaedic literature. Methods: The Institute for Scientific Information (ISI) Web of Science was queried to identify all pediatric orthopaedic observational studies published from 2014-2017. Reported study design and LOE were recorded for each study. The actual study design and LOE were determined based on established clinical epidemiological criteria by reviewers with advanced epidemiological training. Studies with a discrepancy between reported and actual study design and LOE were identified. The following covariates were recorded for each study: subspecialty, inclusion of a statistician coauthor, sample size, journal, and journal impact factor. Chi-square analysis was used to identify factors associated with study design and LOE misreporting. Results: One thousand articles were screened, yielding 647 observational studies. Three hundred thirty-five publications (52%) did not clearly report a study design in either the abstract or manuscript text. Of those that did, 59/312 (19%) reported the incorrect study design (Figure 1). The largest discrepancy was in the 109 studies that were reported to be case series, among which 30 (27.5%) were actually retrospective cohort studies. Three hundred thirteen publications (48%) did not report a LOE. Of those that did, 95/334 (28%) reported the incorrect LOE (Figure 2). Thirty-three studies (19%) reported a LOE that was higher than the actual LOE and 62 (35%) under-reported the LOE. Conclusion: The majority of observational pediatric orthopaedic studies either did not report a study design or reported the wrong study design. Similarly, the majority of studies did not report or misreported their LOE. Greater epidemiological rigor in classifying and evaluating observational studies is required on the part of investigators, reviewers, and journal editors. [Figure: see text][Figure: see text]
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24

Gail, Mitchell H., Douglas G. Altman, Suzanne M. Cadarette, Gary Collins, Stephen JW Evans, Peggy Sekula, Elizabeth Williamson, and Mark Woodward. "Design choices for observational studies of the effect of exposure on disease incidence." BMJ Open 9, no. 12 (December 2019): e031031. http://dx.doi.org/10.1136/bmjopen-2019-031031.

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The purpose of this paper is to help readers choose an appropriate observational study design for measuring an association between an exposure and disease incidence. We discuss cohort studies, sub-samples from cohorts (case-cohort and nested case-control designs), and population-based or hospital-based case-control studies. Appropriate study design is the foundation of a scientifically valid observational study. Mistakes in design are often irremediable. Key steps are understanding the scientific aims of the study and what is required to achieve them. Some designs will not yield the information required to realise the aims. The choice of design also depends on the availability of source populations and resources. Choosing an appropriate design requires balancing the pros and cons of various designs in view of study aims and practical constraints. We compare various cohort and case-control designs to estimate the effect of an exposure on disease incidence and mention how certain design features can reduce threats to study validity.
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25

Bulanov, N. M., O. B. Blyuss, D. B. Munblit, N. A. Nekliudov, D. V. Butnaru, K. B. Kodzoeva, M. Yu Nadinskaya, and A. A. Zaikin. "Studies and research design in medicine." Sechenov Medical Journal 12, no. 1 (September 15, 2021): 4–17. http://dx.doi.org/10.47093/2218-7332.2021.12.1.4-17.

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Adequate design is an essential condition for conducting a successful study. This review describes the most common types of research design in medicine. We discuss the differences between different types of observational and interventional studies, their advantages and limitations providing examples for each study design. The concept of bias and its potential sources in different studies are covered. We suggest the most suitable approaches to study design for different research objectives and outline approaches to data presentation. During the last decades, several guidelines for conducting and reporting different types of research were proposed and they are also covered in this manuscript.
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26

Scherrer, Jeffrey F., Sumitra Balasubramanian, Kenneth E. Freedland, and Patrick J. Lustman. "SMART Designs in Observational Studies." Journal of General Internal Medicine 29, no. 7 (April 23, 2014): 986. http://dx.doi.org/10.1007/s11606-014-2859-0.

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Kim, Kyoung-Nam. "Improving Causal Inference in Observational Studies: Interrupted Time Series Design." Cardiovascular Prevention and Pharmacotherapy 2, no. 1 (2020): 18. http://dx.doi.org/10.36011/cpp.2020.2.e2.

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28

LANGENSKIÖLD and RUBIN. "Outcome-free Design of Observational Studies: Peer Influence on Smoking." Annales d'Économie et de Statistique, no. 91/92 (2008): 107. http://dx.doi.org/10.2307/27917241.

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29

Longagnani, C., L. Simoni, and GG Fiori. "Do We Need to Strengthen Study Design in Observational Studies?" Value in Health 18, no. 7 (November 2015): A723. http://dx.doi.org/10.1016/j.jval.2015.09.2744.

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Aikens, Rachael C., Dylan Greaves, and Michael Baiocchi. "A pilot design for observational studies: Using abundant data thoughtfully." Statistics in Medicine 39, no. 30 (October 5, 2020): 4821–40. http://dx.doi.org/10.1002/sim.8754.

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31

Austin, Peter C., and Robert W. Platt. "Author's response: the design of observational studies—defining baseline time." Journal of Clinical Epidemiology 63, no. 2 (February 2010): 141. http://dx.doi.org/10.1016/j.jclinepi.2009.07.018.

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32

Caswell, Jeff L., Laura L. Bassel, Jamie L. Rothenburger, Andrea Gröne, Jan M. Sargeant, Amanda P. Beck, Stina Ekman, et al. "Observational Study Design in Veterinary Pathology, Part 2: Methodology." Veterinary Pathology 55, no. 6 (September 18, 2018): 774–85. http://dx.doi.org/10.1177/0300985818798121.

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Observational studies are a basis for much of our knowledge of veterinary pathology, yet considerations for conducting pathology-based observational studies are not readily available. In part 1 of this series, we offered advice on planning and carrying out an observational study. Part 2 of the series focuses on methodology. Our general recommendations are to consider using already-validated methods, published guidelines, data from primary sources, and quantitative analyses. We discuss 3 common methods in pathology research—histopathologic scoring, immunohistochemistry, and polymerase chain reaction—to illustrate principles of method validation. Some aspects of quality control include use of clear objective grading criteria, validation of key reagents, assessing sample quality, determining specificity and sensitivity, use of technical and biologic negative and positive controls, blinding of investigators, approaches to minimizing operator-dependent variation, measuring technical variation, and consistency in analysis of the different study groups. We close by discussing approaches to increasing the rigor of observational studies by corroborating results with complementary methods, using sufficiently large numbers of study subjects, consideration of the data in light of similar published studies, replicating the results in a second study population, and critical analysis of the study findings.
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Louie, Christopher E., Erin D’Agostino, Alexander Woods, and Timothy Ryken. "Study Design in Neurosurgical Research: Considerations for Observational and Experimental Cohort Studies." Neurosurgery 86, no. 1 (September 24, 2019): 14–18. http://dx.doi.org/10.1093/neuros/nyz386.

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Abstract There is inadequate neurosurgical literature discussing appropriate clinical study design. Here, we explore considerations for 2 fundamental study designs of epidemiology: experimental and observational cohort studies, through examples of theoretical yet realistic neurosurgical research questions. By examining 2 common neurosurgical procedures—namely, subdural drains for evacuation of chronic subdural hematoma, and the utility of navigation for placing external ventricular drains—we characterize the framework of cohort study models for clinical research applications.
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Kang, Hyun. "Appropriate design of research and statistical analyses: observational versus experimental studies." Korean Journal of Anesthesiology 65, no. 2 (2013): 105. http://dx.doi.org/10.4097/kjae.2013.65.2.105.

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Rosenbaum, Paul R. "Impact of Multiple Matched Controls on Design Sensitivity in Observational Studies." Biometrics 69, no. 1 (February 4, 2013): 118–27. http://dx.doi.org/10.1111/j.1541-0420.2012.01821.x.

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36

Suarez, David. "A Review of: “Design of Observational Studies, by P. R. Rosenbaum.”." Journal of Biopharmaceutical Statistics 21, no. 1 (December 20, 2010): 170–72. http://dx.doi.org/10.1080/10543406.2011.532763.

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37

Teut, Michael, Harald Walach, Roja Varanasi, Raj K. Manchanda, Praveen Oberai, Elizabeth Thompson, and Susanne Ulbrich-Zürni. "Recommendations for Designing, Conducting and Reporting Observational Studies in Homeopathy." Homeopathy 109, no. 03 (May 14, 2020): 114–25. http://dx.doi.org/10.1055/s-0040-1708045.

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Abstract Background Randomized placebo-controlled trials are considered to be the gold standard in clinical research and have the highest importance in the hierarchical system of evidence-based medicine. However, from the viewpoint of decision makers, due to lower external validity, practical results of efficacy research are often not in line with the huge investments made over decades. Method We conducted a narrative review. With a special focus on homeopathy, we give an overview on cohort, comparative cohort, case-control and cross-sectional study designs and explain guidelines and tools that help to improve the quality of observational studies, such as the STROBE Statement, RECORD, GRACE and ENCePP Guide. Results Within the conventional medical research field, two types of arguments have been employed in favor of observational studies. First, observational studies allow for a more generalizable and robust estimation of effects in clinical practice, and if cohorts are large enough, there is no over-estimation of effect sizes, as is often feared. We argue that observational research is needed to balance the current over-emphasis on internal validity at the expense of external validity. Thus, observational research can be considered an important research tool to describe “real-world” care settings and can assist with the design and inform the results of randomised controlled trails. Conclusions We present recommendations for designing, conducting and reporting observational studies in homeopathy and provide recommendations to complement the STROBE Statement for homeopathic observational studies.
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Snyder, Graham M., Heather Young, Meera Varman, Aaron M. Milstone, Anthony D. Harris, and Silvia Munoz-Price. "Research Methods in Healthcare Epidemiology and Antimicrobial Stewardship—Observational Studies." Infection Control & Hospital Epidemiology 37, no. 10 (June 20, 2016): 1141–46. http://dx.doi.org/10.1017/ice.2016.118.

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Observational studies compare outcomes among subjects with and without an exposure of interest, without intervention from study investigators. Observational studies can be designed as a prospective or retrospective cohort study or as a case-control study. In healthcare epidemiology, these observational studies often take advantage of existing healthcare databases, making them more cost-effective than clinical trials and allowing analyses of rare outcomes. This paper addresses the importance of selecting a well-defined study population, highlights key considerations for study design, and offers potential solutions including biostatistical tools that are applicable to observational study designs.Infect Control Hosp Epidemiol 2016;1–6
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Vestbo, Jørgen, Christer Janson, Javier Nuevo, and David Price. "Observational studies assessing the pharmacological treatment of obstructive lung disease: strengths, challenges and considerations for study design." ERJ Open Research 6, no. 4 (October 2020): 00044–2020. http://dx.doi.org/10.1183/23120541.00044-2020.

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Randomised controlled trials (RCTs) are the gold standard for evaluating treatment efficacy in patients with obstructive lung disease. However, due to strict inclusion criteria and the conditions required for ascertaining statistical significance, the patients included typically represent as little as 5% of the general obstructive lung disease population. Thus, studies in broader patient populations are becoming increasingly important. These can be randomised effectiveness trials or observational studies providing data on real-world treatment effectiveness and safety data that complement efficacy RCTs.In this review we describe the features associated with the diagnosis of asthma and chronic obstructive pulmonary disease (COPD) in the real-world clinical practice setting. We also discuss how RCTs and observational studies have reported opposing outcomes with several treatments and inhaler devices due to differences in study design and the variations in patients recruited by different study types. Whilst observational studies are not without weaknesses, we outline recently developed tools for defining markers of quality of observational studies. We also examine how observational studies are capable of providing valuable insights into disease mechanisms and management and how they are a vital component of research into obstructive lung disease.As we move into an era of personalised medicine, recent observational studies, such as the NOVEL observational longiTudinal studY (NOVELTY), have the capacity to provide a greater understanding of the value of a personalised healthcare approach in patients in clinical practice by focussing on standardised outcome measures of patient-reported outcomes, physician assessments, airway physiology, and blood and airway biomarkers across both primary and specialist care.
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Howard, George, and Virginia J. Howard. "Design and implementation of observational studies to measure disease burden with a focus on stroke." International Journal of Stroke 13, no. 2 (November 17, 2017): 157–65. http://dx.doi.org/10.1177/1747493017743800.

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Observational epidemiological studies have the dual goals of measuring disease burden and assessing the association between exposures and outcomes. This report focuses on the first of these goals and provides an overview of design considerations of commonly used approaches, specifically community surveillance studies, cross-sectional studies, and longitudinal cohort studies. Each of these designs has strengths and weaknesses, with no study design being superior in all cases. Rather, these designs are complementary to achieve a better understanding of the burden of stroke.
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Gong, Tong, Bronwyn Brew, Arvid Sjölander, and Catarina Almqvist. "Towards non-conventional methods of designing register-based epidemiological studies: An application to pediatric research." Scandinavian Journal of Public Health 45, no. 17_suppl (July 2017): 30–35. http://dx.doi.org/10.1177/1403494817702339.

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Aims: Various epidemiological designs have been applied to investigate the causes and consequences of fetal growth restriction in register-based observational studies. This review seeks to provide an overview of several conventional designs, including cohort, case-control and more recently applied non-conventional designs such as family-based designs. We also discuss some practical points regarding the application and interpretation of family-based designs. Methods: Definitions of each design, the study population, the exposure and the outcome measures are briefly summarised. Examples of study designs are taken from the field of low birth-weight research for illustrative purposes. Also examined are relative advantages and disadvantages of each design in terms of assumptions, potential selection and information bias, confounding and generalisability. Kinship data linkage, statistical models and result interpretation are discussed specific to family-based designs. Results: When all information is retrieved from registers, there is no evident preference of the case-control design over the cohort design to estimate odds ratios. All conventional designs included in the review are prone to bias, particularly due to residual confounding. Family-based designs are able to reduce such bias and strengthen causal inference. In the field of low birth-weight research, family-based designs have been able to confirm a negative association not confounded by genetic or shared environmental factors between low birth weight and the risk of asthma. Conclusions: We conclude that there is a broader need for family-based design in observational research as evidenced by the meaningful contributions to the understanding of the potential causal association between low birth weight and subsequent outcomes.
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Stoto, Michael, Michael Oakes, Elizabeth Stuart, Elisa L. Priest, and Lucy Savitz. "Analytical Methods for a Learning Health System: 2. Design of Observational Studies." eGEMs (Generating Evidence & Methods to improve patient outcomes) 5, no. 1 (December 7, 2017): 29. http://dx.doi.org/10.5334/egems.251.

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Lazcano, Gabriel, Cristian Papuzinski, Eva Madrid, and Marcelo Arancibia. "General concepts in biostatistics and clinical epidemiology: observational studies with cohort design." Medwave 19, no. 11 (December 30, 2019): e7748-e7748. http://dx.doi.org/10.5867/medwave.2019.11.7748.

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Niknam, Bijan A., and Jose R. Zubizarreta. "Using Cardinality Matching to Design Balanced and Representative Samples for Observational Studies." JAMA 327, no. 2 (January 11, 2022): 173. http://dx.doi.org/10.1001/jama.2021.20555.

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Bansal, Parveen, Supreet Kaur Gill, and Vikas Gupta. "INFORMED CONSENT STATUS IN OBSERVATIONAL STUDIES WITH RETROSPECTIVE DESIGN: A POOR SHOW." Asian Journal of Pharmaceutical and Clinical Research 10, no. 3 (March 1, 2017): 480. http://dx.doi.org/10.22159/ajpcr.2017.v10i3.16588.

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Abstract:
ABSTRACTObjective: Informed consent is very important to protect the rights of patients and is obtained as a vital component of any clinical study. Requirementof patient consent in retrospective research continues to stir controversy even today. Some of directions of regulatory authorities even waive off theconsent for retrospective studies, whereas few recommend that at least clearance from the Ethical Review Board may be taken or information topatient may be given or oral consent must be given by patient for usage of the data in any retrospective study. The aim of this study was to analyze thecurrent status of informed consent procedures in observational studies with retrospective design.Methods: This review was intended to find out the current status of procedures involved in informed consent in India and abroad. A total of 100retrospective studies were searched for this objective from the internet and other literature sources.Results: Data show that in 65% of studies neither informed consent/oral consent nor ethical clearance was taken. Only 1% of studies have beenconducted with informed consent as well as ethical clearance. Only 14% of studies were conducted with informed consent and 21% with ethicalapproval.Conclusion: The study reflects very poor status of informed consent in retrospective studies and noncompliance of ICH guidelines for clinical researchin relation to informed consent.Keywords: Clinical research, Informed consent, Retrospective studies.
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Rosenbaum, Paul R. "A New u-Statistic with Superior Design Sensitivity in Matched Observational Studies." Biometrics 67, no. 3 (December 22, 2010): 1017–27. http://dx.doi.org/10.1111/j.1541-0420.2010.01535.x.

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Yue, Lilly Q. "Regulatory Considerations in the Design of Comparative Observational Studies Using Propensity Scores." Journal of Biopharmaceutical Statistics 22, no. 6 (October 17, 2012): 1272–79. http://dx.doi.org/10.1080/10543406.2012.715111.

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Kim, Hyoun K., and Deborah M. Capaldi. "Generalizability Issues in Observational Studies of Couples: Sample Characteristics and Task Design." Journal of Marriage and Family 69, no. 1 (February 2007): 86–91. http://dx.doi.org/10.1111/j.1741-3737.2006.00346.x.

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Puliti, Donella, Guido Miccinesi, and Eugenio Paci. "Overdiagnosis in breast cancer: Design and methods of estimation in observational studies." Preventive Medicine 53, no. 3 (September 2011): 131–33. http://dx.doi.org/10.1016/j.ypmed.2011.05.012.

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Ranganathan, Priya, and Rakesh Aggarwal. "Study designs: Part 3 - Analytical observational studies." Perspectives in Clinical Research 10, no. 2 (2019): 91. http://dx.doi.org/10.4103/picr.picr_35_19.

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