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Journal articles on the topic 'Mixture cure model'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Omer, Mohamed Elamin Abdallah Mohamed Elamin, Mohd Rizam Abu Bakar, Mohd Bakri Adam, and Mohd Shafie Mustafa. "Cure Models with Exponentiated Weibull Exponential Distribution for the Analysis of Melanoma Patients." Mathematics 8, no. 11 (November 2, 2020): 1926. http://dx.doi.org/10.3390/math8111926.

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In the survival data analysis, commonly, it is presumed that all study subjects will eventually have the event of concern. Nonetheless, it tends to be unequivocally expected that a fraction of these subjects will never expose to the event of interest. The cure rate models are usually used to model this type of data. In this paper, we introduced a maximum likelihood estimates analysis for the four-parameter exponentiated Weibull exponential (EWE) distribution in the existence of cured subjects, censored observations, and predictors. Aiming to include the fraction of unsusceptible (cured) individuals in the analysis, a mixture cure model, and two non-mixture cure models—bounded cumulative hazard model, and geometric non-mixture model with EWE distribution—are proposed. The mixture cure model provides a better fit to real data from a Melanoma clinical trial compared to the other two non-mixture cure models.

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2

TAWEAB, FAUZIA ALI, NOOR AKMA IBRAHIM, and BADER AHMAD I. ALJAWADI. "ESTIMATION OF CURE FRACTION FOR LOGNORMAL RIGHT CENSORED DATA WITH COVARIATES." International Journal of Modern Physics: Conference Series 09 (January 2012): 308–15. http://dx.doi.org/10.1142/s2010194512005363.

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In clinical studies, a proportion of patients might be unsusceptible to the event of interest and can be considered as cured. The survival models that incorporate the cured proportion are known as cure rate models where the most widely used model is the mixture cure model. However, in cancer clinical trials, mixture model is not the appropriate model and the viable alternative is the Bounded Cumulative Hazard (BCH) model. In this paper we consider the BCH model to estimate the cure fraction based on the lognormal distribution. The parametric estimation of the cure fraction for survival data with right censoring with covariates is obtained by using EM algorithm.

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3

Swain, Prafulla Kumar, Gurprit Grover, and Komal Goel. "Mixture and Non-Mixture Cure Fraction Models Based on Generalized Gompertz Distribution under Bayesian Approach." Tatra Mountains Mathematical Publications 66, no. 1 (June 1, 2016): 121–35. http://dx.doi.org/10.1515/tmmp-2016-0025.

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Abstract The cure fraction models are generally used to model lifetime data with long term survivors. In a cohort of cancer patients, it has been observed that due to the development of new drugs some patients are cured permanently, and some are not cured. The patients who are cured permanently are called cured or long term survivors while patients who experience the recurrence of the disease are termed as susceptibles or uncured. Thus, the population is divided into two groups: a group of cured individuals and a group of susceptible individuals. The proportion of cured individuals after the treatment is typically known as the cure fraction. In this paper, we have introduced a three parameter Gompertz (viz. scale, shape and acceleration) or generalized Gompertz distribution in the presence of cure fraction, censored data and covariates for estimating the proportion of cure fraction through Bayesian Approach. Inferences are obtained using the standard Markov Chain Monte Carlo technique in openBUGS software.

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4

Zhang, Jiajia, and Yingwei Peng. "Accelerated hazards mixture cure model." Lifetime Data Analysis 15, no. 4 (August 21, 2009): 455–67. http://dx.doi.org/10.1007/s10985-009-9126-4.

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5

Scolas, Sylvie, Catherine Legrand, Abderrahim Oulhaj, and Anouar El Ghouch. "Diagnostic checks in mixture cure models with interval-censoring." Statistical Methods in Medical Research 27, no. 7 (November 4, 2016): 2114–31. http://dx.doi.org/10.1177/0962280216676502.

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Models for interval-censored survival data presenting a fraction of “cure” or “immune” patients have recently been proposed in the literature, particularly extending the mixture cure model to interval-censored data. However, little is known about the goodness-of-fit of such models. In a mixture cure model, the survival distribution of the entire population is improper and expressed in terms of the survival distribution of uncured individuals, i.e. the latency part of the model, and the probability to experience the event of interest, i.e. the incidence part. To validate a mixture cure model, assumptions made on both parts need to be checked, i.e. the survival distribution of uncured individuals, the link function used in the latency and the linearity of the covariates used in the both parts of the model. In this work, we investigate the Cox-Snell and deviance residuals and show how they can be adapted and used to perform diagnostics checks when all subjects are right- or interval-censored and some subjects are cured with unknown cure status. A large simulation study investigates the ability of these residuals to detect a departure from the assumptions of the mixture model. Developed techniques are applied to a real data set about Alzheimer’s disease.

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6

Marinho, Anna R. S., and Rosangela H. Loschi. "Bayesian cure fraction models with measurement error in the scale mixture of normal distribution." Statistical Methods in Medical Research 29, no. 9 (January 12, 2020): 2411–44. http://dx.doi.org/10.1177/0962280219893034.

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Cure fraction models have been widely used to model time-to-event data when part of the individuals survives long-term after disease and are considered cured. Most cure fraction models neglect the measurement error that some covariates may experience which leads to poor estimates for the cure fraction. We introduce a Bayesian promotion time cure model that accounts for both mismeasured covariates and atypical measurement errors. This is attained by assuming a scale mixture of the normal distribution to describe the uncertainty about the measurement error. Extending previous works, we also assume that the measurement error variance is unknown and should be estimated. Three classes of prior distributions are assumed to model the uncertainty about the measurement error variance. Simulation studies are performed evaluating the proposed model in different scenarios and comparing it to the standard promotion time cure fraction model. Results show that the proposed models are competitive ones. The proposed model is fitted to analyze a dataset from a melanoma clinical trial assuming that the Breslow depth is mismeasured.

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7

Folorunso, Serifat A., Timothy A. O. Oluwasola, Angela U. Chukwu, and Akintunde A. Odukogbe. "Application of Modified Generalized–Gamma Mixture Cure Model in the Analysis of Ovarian Cancer Data." Journal of Physics: Conference Series 2123, no. 1 (November 1, 2021): 012041. http://dx.doi.org/10.1088/1742-6596/2123/1/012041.

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Abstract The modeling and analysis of lifetime for terminal diseases such as cancer is a significant aspect of statistical work. This study considered data from thirty-seven women diagnosed with Ovarian Cancer and hospitalized for care at theDepartment of Obstetrics and Gynecology, University of Ibadan, Nigeria. Focus was on the application of a parametric mixture cure model that can handle skewness associated with survival data – a modified generalized-gamma mixture cure model (MGGMCM). The effectiveness of MGGMCM was compared with existing parametric mixture cure models using Akaike Information Criterion, median time-to-cure and variance of the cure rate. It was observed that the MGGMCM is an improved parametric model for the mixture cure model.

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8

Peng, Yingwei, and Jeremy M. G. Taylor. "Residual-based model diagnosis methods for mixture cure models." Biometrics 73, no. 2 (September 6, 2016): 495–505. http://dx.doi.org/10.1111/biom.12582.

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9

Jahani, Sardar, Mina Hoseini, Rashed Pourhamidi, Mahshid Askari, and Azam Moslemi. "Determining the Factors Affecting Long-Term and Short-Term Survival of Breast Cancer Patients in Rafsanjan Using a Mixture Cure Model." Journal of Research in Health Sciences 21, no. 2 (May 26, 2021): e00516-e00516. http://dx.doi.org/10.34172/jrhs.2021.51.

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Background: Breast cancer is one of the most common causes of death among women worldwide and the second leading cause of death among Iranian women. The incidence of this malignancy in Iran is 22 per 100,000 women. These patients have long-term survival time with advances in medical sciences. The present study aimed to identify the risk factors of breast cancer using Cox proportional hazard and Cox mixture cure models. Study design: It is a retrospective cohort study. Methods: In this cohort study, we recorded the survival time of 140 breast cancer patients referred to Ali Ibn Abitaleb Hospital in Rafsanjan, Iran, from 2001 to 2015. The Kaplan-Meier curve was plotted; moreover, two Cox proportional hazards and the Cox mixture cure models were fitted for the patients. Data analysis was performed using SAS 9.4 M5 software. Results: The mean age of patients was reported as 47.12 ±12.48 years at the commencement of the study. Moreover, 83.57% of patients were censored. The stage of disease was a significant variable in Cox and the survival portion of Cox mixture cure models (P=0.001). The consumption of herbal tea, tumor size, duration of the last lactation, family history of cancer, and the type of treatment were significant variables in the cured proportion of the Cox mixture cure model (P=0.001). Conclusion: The Cox mixture cure model is a flexible model which is able to distinguish between the long-term and short-term survival of breast cancer patients. For breast cancer patients, cure effective factors were the stage of the disease, consumption of herbal tea, tumor size, duration of the last lactation, family history, and the type of treatment.

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10

Amico, Maïlis, Ingrid Van Keilegom, and Catherine Legrand. "The single‐index/Cox mixture cure model." Biometrics 75, no. 2 (March 29, 2019): 452–62. http://dx.doi.org/10.1111/biom.12999.

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11

Peng, Yingwei, and Jiajia Zhang. "Identifiability of a mixture cure frailty model." Statistics & Probability Letters 78, no. 16 (November 2008): 2604–8. http://dx.doi.org/10.1016/j.spl.2008.07.044.

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12

Chown, Justin, Cédric Heuchenne, and Ingrid Van Keilegom. "The nonparametric location-scale mixture cure model." TEST 29, no. 4 (December 17, 2019): 1008–28. http://dx.doi.org/10.1007/s11749-019-00698-8.

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13

Hamadani, Mehdi, Christopher N. Graham, Laura Liao, Katherine H. Zhang, Hannah Strat, David Ungar, Weiyun Z. Ai, Lei Chen, and Carmelo Carlo-Stella. "Long-term survival projections of loncastuximab tesirine-treated patients in relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL)." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e19551-e19551. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e19551.

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e19551 Background: Loncastuximab tesirine (loncastuximab tesirine-lpyl; Lonca) is an FDA approved CD19-directed antibody-drug conjugate for R/R DLBCL. From the LOTIS-2 trial primary data cut (April 6, 2020), overall response rate was 48.3% and median overall survival (OS) was 9.9 months. The OS Kaplan-Meier (KM) plot displayed a survival plateau suggesting the presence of long-term survivors (LTS). Survival analyses were conducted on a more mature data cut (March 1, 2021; median follow-up = 1.7 years, follow-up completeness at median = 81%) to estimate the percentage of LTS and expected lifetime survival (mean OS) for lonca-treated patients. Methods: Consistent with studies of other R/R DLBCL treatments, identified through a literature review, parametric and mixture cure models were fit utilizing multiple distributions. Flexible cubic spline (hazard scale 1-3 knots) and non-mixture cure analyses were also conducted. Age- and gender-matched United States life table hazards were used in projections for LTS and to ensure modeled hazards were not less than the general population. Best-fit models were determined through fit statistics, KM and fitted curve overlays, and clinical plausibility. The best-fit model from each method was a candidate for overall best fit. A hybrid model following the best-fit parametric/spline model to a defined time point and switching to general population mortality was also constructed. Results: Mixture and non-mixture cure models fit best (individual best fits gamma and Weibull, respectively). Parametric and spline models (individual best fits log-normal and 2 knot models, respectively) did not fit the observed data well nor fit the clinical expectation of long-term survival. Due to better fit, spline models were used in the hybrid model. LTS from the mixture cure and non-mixture cure models were 24-26%. Mixture cure, non-mixture cure, and hybrid model with a 2-year switch point were consistent in survival predictions (6.11-6.23 years). In a sensitivity analysis with 3-year switch point in the hybrid model, the estimated survival was shorter due to the switch point being below the observed survival plateau. Table presents full survival results and fit statistics. Conclusions: The observed survival plateau suggests lonca-treated patients may include LTS. Mixture cure, non-mixture cure, and hybrid models fit the trial data well and align on survival projections (6.11-6.23 years). Additional follow-up may help refine the switch point of the hybrid model and confirm presence of LTS.[Table: see text]

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14

Hamadani, Mehdi, Christopher N. Graham, Laura Liao, Katherine H. Zhang, Hannah Strat, David Ungar, Weiyun Z. Ai, Lei Chen, and Carmelo Carlo-Stella. "Long-term survival projections of loncastuximab tesirine-treated patients in relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL)." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e19551-e19551. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e19551.

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e19551 Background: Loncastuximab tesirine (loncastuximab tesirine-lpyl; Lonca) is an FDA approved CD19-directed antibody-drug conjugate for R/R DLBCL. From the LOTIS-2 trial primary data cut (April 6, 2020), overall response rate was 48.3% and median overall survival (OS) was 9.9 months. The OS Kaplan-Meier (KM) plot displayed a survival plateau suggesting the presence of long-term survivors (LTS). Survival analyses were conducted on a more mature data cut (March 1, 2021; median follow-up = 1.7 years, follow-up completeness at median = 81%) to estimate the percentage of LTS and expected lifetime survival (mean OS) for lonca-treated patients. Methods: Consistent with studies of other R/R DLBCL treatments, identified through a literature review, parametric and mixture cure models were fit utilizing multiple distributions. Flexible cubic spline (hazard scale 1-3 knots) and non-mixture cure analyses were also conducted. Age- and gender-matched United States life table hazards were used in projections for LTS and to ensure modeled hazards were not less than the general population. Best-fit models were determined through fit statistics, KM and fitted curve overlays, and clinical plausibility. The best-fit model from each method was a candidate for overall best fit. A hybrid model following the best-fit parametric/spline model to a defined time point and switching to general population mortality was also constructed. Results: Mixture and non-mixture cure models fit best (individual best fits gamma and Weibull, respectively). Parametric and spline models (individual best fits log-normal and 2 knot models, respectively) did not fit the observed data well nor fit the clinical expectation of long-term survival. Due to better fit, spline models were used in the hybrid model. LTS from the mixture cure and non-mixture cure models were 24-26%. Mixture cure, non-mixture cure, and hybrid model with a 2-year switch point were consistent in survival predictions (6.11-6.23 years). In a sensitivity analysis with 3-year switch point in the hybrid model, the estimated survival was shorter due to the switch point being below the observed survival plateau. Table presents full survival results and fit statistics. Conclusions: The observed survival plateau suggests lonca-treated patients may include LTS. Mixture cure, non-mixture cure, and hybrid models fit the trial data well and align on survival projections (6.11-6.23 years). Additional follow-up may help refine the switch point of the hybrid model and confirm presence of LTS.[Table: see text]

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15

Han, Dongxiao, Haijin He, Liuquan Sun, Xinyuan Song, and Wei Xu. "Inference in a mixture additive hazards cure model." Statistics and Its Interface 14, no. 3 (2021): 323–38. http://dx.doi.org/10.4310/20-sii642.

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16

Peng, Yingwei, and Keith B. G. Dear. "A Nonparametric Mixture Model for Cure Rate Estimation." Biometrics 56, no. 1 (March 2000): 237–43. http://dx.doi.org/10.1111/j.0006-341x.2000.00237.x.

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17

Li, Xuan, Yincai Tang, and Ancha Xu. "Objective Bayesian analysis of Weibull mixture cure model." Quality Engineering 32, no. 3 (May 27, 2020): 449–64. http://dx.doi.org/10.1080/08982112.2020.1757706.

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18

Peng, Yingwei, Keith B. G. Dear, and J. W. Denham. "A generalizedF mixture model for cure rate estimation." Statistics in Medicine 17, no. 8 (April 30, 1998): 813–30. http://dx.doi.org/10.1002/(sici)1097-0258(19980430)17:8<813::aid-sim775>3.0.co;2-#.

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19

de la Cruz, Rolando, Claudio Fuentes, and Oslando Padilla. "A Bayesian Mixture Cure Rate Model for Estimating Short-Term and Long-Term Recidivism." Entropy 25, no. 1 (December 28, 2022): 56. http://dx.doi.org/10.3390/e25010056.

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Mixture cure rate models have been developed to analyze failure time data where a proportion never fails. For such data, standard survival models are usually not appropriate because they do not account for the possibility of non-failure. In this context, mixture cure rate models assume that the studied population is a mixture of susceptible subjects who may experience the event of interest and non-susceptible subjects that will never experience it. More specifically, mixture cure rate models are a class of survival time models in which the probability of an eventual failure is less than one and both the probability of eventual failure and the timing of failure depend (separately) on certain individual characteristics. In this paper, we propose a Bayesian approach to estimate parametric mixture cure rate models with covariates. The probability of eventual failure is estimated using a binary regression model, and the timing of failure is determined using a Weibull distribution. Inference for these models is attained using Markov Chain Monte Carlo methods under the proposed Bayesian framework. Finally, we illustrate the method using data on the return-to-prison time for a sample of prison releases of men convicted of sexual crimes against women in England and Wales and we use mixture cure rate models to investigate the risk factors for long-term and short-term survival of recidivism.

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20

Barbieri, Antoine, and Catherine Legrand. "Joint longitudinal and time-to-event cure models for the assessment of being cured." Statistical Methods in Medical Research 29, no. 4 (June 19, 2019): 1256–70. http://dx.doi.org/10.1177/0962280219853599.

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Medical time-to-event studies frequently include two groups of patients: those who will not experience the event of interest and are said to be “cured” and those who will develop the event and are said to be “susceptible”. However, the cure status is unobserved in (right-)censored patients. While most of the work on cure models focuses on the time-to-event for the uncured patients (latency) or on the baseline probability of being cured or not (incidence), we focus in this research on the conditional probability of being cured after a medical intervention given survival until a certain time. Assuming the availability of longitudinal measurements collected over time and being informative on the risk to develop the event, we consider joint models for longitudinal and survival data given a cure fraction. These models include a linear mixed model to fit the trajectory of longitudinal measurements and a mixture cure model. In simulation studies, different shared latent structures linking both submodels are compared in order to assess their predictive performance. Finally, an illustration on HIV patient data completes the comparison.

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21

Müller, U. U., and I. Van Keilegom. "Goodness-of-fit tests for the cure rate in a mixture cure model." Biometrika 106, no. 1 (December 17, 2018): 211–27. http://dx.doi.org/10.1093/biomet/asy058.

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22

Asha, G., and C. S. Soorya. "On a Class of Transmuted Distributions to Model Survival Data with a Cure Fraction." Calcutta Statistical Association Bulletin 73, no. 2 (November 2021): 106–26. http://dx.doi.org/10.1177/00080683211052768.

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Modelling time to event data, when there is always a proportion of the individuals, commonly referred to as immunes who do not experience the event of interest, is of importance in many biomedical studies. Improper distributions are used to model these situations and they are generally referred to as cure rate models. In the literature, two main families of cure rate models have been proposed, namely the mixture cure models and promotion time cure models. Here we propose a new model by extending the mixture model via a generating function by considering a shifted Bernoulli distribution. This gives rise to a new class of popular distributions called the transmuted class of distributions to model survival data with a cure fraction. The properties of the proposed model are investigated and parameters estimated. The Bayesian approach to the estimation of parameters is also adopted. The complexity of the likelihood function is handled through the Metropolis-Hasting algorithm. The proposed method is illustrated with few examples using different baseline distributions. A real life data set is also analysed. AMS subject classifications: 62N02, 62F15

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Pham, H. A., B. Heeg, A. Garcia, M. Postma, and D. M. Ouwens. "PCN413 A BAYESIAN MIXTURE CURE MODEL USING INFORMATIVE PRIORS." Value in Health 22 (November 2019): S516. http://dx.doi.org/10.1016/j.jval.2019.09.607.

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Li, Peizhi, Yingwei Peng, Ping Jiang, and Qingli Dong. "A support vector machine based semiparametric mixture cure model." Computational Statistics 35, no. 3 (November 4, 2019): 931–45. http://dx.doi.org/10.1007/s00180-019-00931-w.

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Kutal, Durga, and Lianfen Qian. "A Non-Mixture Cure Model for Right-Censored Data with Fréchet Distribution." Stats 1, no. 1 (November 15, 2018): 176–88. http://dx.doi.org/10.3390/stats1010013.

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This paper considers a non-mixture cure model for right-censored data. It utilizes the maximum likelihood method to estimate model parameters in the non-mixture cure model. The simulation study is based on Fréchet susceptible distribution to evaluate the performance of the method. Compared with Weibull and exponentiated exponential distributions, the non-mixture Fréchet distribution is shown to be the best in modeling a real data on allogeneic marrow HLA-matched donors and ECOG phase III clinical trial e1684 data.

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Hubbell, Earl, and Christina Clarke. "Abstract 2239: Detecting cancer when it can be cured: The potential for cure across all stageable cancers." Cancer Research 82, no. 12_Supplement (June 15, 2022): 2239. http://dx.doi.org/10.1158/1538-7445.am2022-2239.

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Abstract Introduction: Early detection of cancer may reduce cancer mortality by providing access to treatments with the potential to cure cancer at early stages. A mixture cure model divides cancer cases into two populations: one where cancer is likely to severely impact mortality (not-cured) and one where long-term survival with low risk is possible (cured). Previous work on such models has concentrated on estimating cure for either many cancer types without regard to stage (public health arena), or single cancers by stage at diagnosis (screening arena). A gap in the current literature is an estimation of likelihood of cure in many cancer types at all stages. Methods: We estimate a mixture-cure model for all stages simultaneously for stageable cancers using the fact that cancer is a progressive disease (worse by stage) as a constraint on model parameters. We applied this estimation procedure to 21 cancer classes with standard American Joint Committee on Cancer (AJCC) staging using 12 years of cancer-specific survival data in 50-79 year old individuals using Surveillance, Epidemiology and End Results (SEER) program data from 2006-2015 followed to 2018. For each stage, we recover the fraction of those cured (“cure fraction”, i.e., long-term survivors with minimal excess hazard) and not-cured (severe acute mortality due to cancer, modeled as a Weibull distribution). Results: Cure fractions varied between cancer classes. Two important types of cancer behavior are illustrated by colorectal and gallbladder cancers. Colorectal cancer had a good potential for cure at any stage before metastasis, with a precipitous drop from 63% (95% CI: 62-64%) cure at stage III to 7% (6-7%) cure at stage IV. In contrast, gallbladder cancer exhibited a systematic decrease at each stage, with 47% (43-52%) cure fraction at stage I, 22% (20-24%) at stage II, 20% (17-22%) at stage III, and 2% (2-3%) at stage IV. Differences in 5-year survival between earlier stages and metastasis were highly correlated to differences in cure fraction (r^2=0.97), suggesting differences in 5-year survival are a proxy for differences in cure. Conclusions: Long-term survivors were evident at early stages for all 21 cancer types examined. These survival fractions were all greatly reduced by the time cancer reached metastasis. This indicates early-stage cancers do not differ from late-stage cancers simply by lead time and provides statistical evidence that detection of cancer in early stages may result in long-term survival for many stageable cancer types. Citation Format: Earl Hubbell, Christina Clarke. Detecting cancer when it can be cured: The potential for cure across all stageable cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2239.

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Sun, Liuquan, Shuwei Li, Lianming Wang, and Xinyuan Song. "A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup." Statistical Methods in Medical Research 30, no. 8 (July 1, 2021): 1890–903. http://dx.doi.org/10.1177/09622802211023985.

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Failure time data with a cured subgroup are frequently confronted in various scientific fields and many methods have been proposed for their analysis under right or interval censoring. However, a cure model approach does not seem to exist in the analysis of partly interval-censored data, which consist of both exactly observed and interval-censored observations on the failure time of interest. In this article, we propose a two-component mixture cure model approach for analyzing such type of data. We employ a logistic model to describe the cured probability and a proportional hazards model to model the latent failure time distribution for uncured subjects. We consider maximum likelihood estimation and develop a new expectation-maximization algorithm for its implementation. The asymptotic properties of the resulting estimators are established and the finite sample performance of the proposed method is examined through simulation studies. An application to a set of real data on childhood mortality in Nigeria is provided.

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Niu, Yi, and Yingwei Peng. "A semiparametric marginal mixture cure model for clustered survival data." Statistics in Medicine 32, no. 14 (December 3, 2012): 2364–73. http://dx.doi.org/10.1002/sim.5687.

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29

Zhang, Jenny J., and Molin Wang. "An Accelerated Failure Time Mixture Cure Model with Masked Event." Biometrical Journal 51, no. 6 (December 2009): 932–45. http://dx.doi.org/10.1002/bimj.200800244.

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30

Niu, Yi, Lixin Song, Yufeng Liu, and Yingwei Peng. "Modeling clustered long-term survivors using marginal mixture cure model." Biometrical Journal 60, no. 4 (May 7, 2018): 780–96. http://dx.doi.org/10.1002/bimj.201700114.

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Peng, Yingwei, and Jiajia Zhang. "Estimation method of the semiparametric mixture cure gamma frailty model." Statistics in Medicine 27, no. 25 (November 10, 2008): 5177–94. http://dx.doi.org/10.1002/sim.3358.

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KARA, Pınar, and Nihal ATA TUTKUN. "Cox Mixture Cure Model and its Application to Glioma Data Set." Turkiye Klinikleri Journal of Biostatistics 9, no. 3 (2017): 241–56. http://dx.doi.org/10.5336/biostatic.2017-57194.

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33

Zheng, Chenlu, Jianping Zhu, Xinyan Fan, Song Chen, and Zhiyuan Zhang. "Promoting Variable Effect Consistency in Mixture Cure Model for Credit Scoring." Discrete Dynamics in Nature and Society 2022 (February 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/3112987.

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Mixture cure models are widely adopted in credit scoring. Mixture cure models consist of two parts: an incident part which predicts the probability of default and a latency part which predicts when they are likely to default. The two model parts describe two quite relevant credit aspects. So, it is reasonable to expect that the two sets of the coefficients are somewhat related. Moreover, in practical cases, it is difficult to interpret the results when the two sets of the coefficients of the same variables have conflicting signs. Most existing works either ignore the interconnections of the two sets of coefficients or impose a strict constraint between them. We proposed a mixture cure model considering the variable effect consistency using a sign-based penalty. It is a more flexible model that allows the two sets of coefficients to be in different distributions and magnitudes. To accommodate high-dimensional credit data, a group lasso penalty is also imposed for variable selection. Simulation shows that the proposed method has competitive performance compared with alternative methods in terms of estimation and prediction. Furthermore, the empirical study illustrates that the proposed method outperforms the alternative method and can improve the interpretability of the results.

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Shi, Haolun, Da Ma, Mirza Faisal Beg, and Jiguo Cao. "A functional proportional hazard cure rate model for interval-censored data." Statistical Methods in Medical Research 31, no. 1 (November 22, 2021): 154–68. http://dx.doi.org/10.1177/09622802211052972.

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Existing survival models involving functional covariates typically rely on the Cox proportional hazards structure and the assumption of right censorship. Motivated by the aim of predicting the time of conversion to Alzheimer’s disease from sparse biomarker trajectories in patients with mild cognitive impairment, we propose a functional mixture cure rate model with both functional and scalar covariates for interval censoring and sparsely sampled functional data. To estimate the nonparametric coefficient function that depicts the effect of the shape of the trajectories on the survival outcome and cure probability, we utilize the functional principal component analysis to extract the functional features from the sparsely and irregularly sampled trajectories. To obtain parameter estimates from the mixture cure rate model with interval censoring, we apply the expectation-maximization algorithm based on Poisson data augmentation. The estimation accuracy of our method is assessed via a simulation study and we apply our model on Alzheimer’s disease Neuroimaging Initiative data set.

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Mazucheli, Josmar, Emílio A. Coelho-Barros, and Jorge Alberto Achcar. "The exponentiated exponential mixture and non-mixture cure rate model in the presence of covariates." Computer Methods and Programs in Biomedicine 112, no. 1 (October 2013): 114–24. http://dx.doi.org/10.1016/j.cmpb.2013.06.015.

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Wang, Lu, Pang Du, and Hua Liang. "Two-Component Mixture Cure Rate Model with Spline Estimated Nonparametric Components." Biometrics 68, no. 3 (December 14, 2011): 726–35. http://dx.doi.org/10.1111/j.1541-0420.2011.01715.x.

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Zhang, Jiajia, and Yingwei Peng. "Semiparametric estimation methods for the accelerated failure time mixture cure model." Journal of the Korean Statistical Society 41, no. 3 (September 2012): 415–22. http://dx.doi.org/10.1016/j.jkss.2012.01.003.

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Zhang, Jiajia, Yingwei Peng, and Haifen Li. "A new semiparametric estimation method for accelerated hazards mixture cure model." Computational Statistics & Data Analysis 59 (March 2013): 95–102. http://dx.doi.org/10.1016/j.csda.2012.09.017.

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Yu, Binbing. "A Frailty Mixture Cure Model with Application to Hospital Readmission Cata." Biometrical Journal 50, no. 3 (June 2008): 386–94. http://dx.doi.org/10.1002/bimj.200710399.

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Yu, Binbing. "A Frailty Mixture Cure Model with Application to Hospital Readmission Cata." Biometrical Journal 50, no. 3 (June 2008): 394. http://dx.doi.org/10.1002/bimj.200890005.

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Lambert, Paul C. "Modeling of the Cure Fraction in Survival Studies." Stata Journal: Promoting communications on statistics and Stata 7, no. 3 (September 2007): 351–75. http://dx.doi.org/10.1177/1536867x0700700304.

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Cure models are a special type of survival analysis model where it is assumed that there are a proportion of subjects who will never experience the event and thus the survival curve will eventually reach a plateau. In population-based cancer studies, cure is said to occur when the mortality (hazard) rate in the diseased group of individuals returns to the same level as that expected in the general population. The cure fraction is of interest to patients and a useful measure to monitor trends and differences in survival of curable disease. I will describe the strsmix and strsnmix commands, which fit the two main types of cure fraction model, namely, the mixture and nonmixture cure fraction models. These models allow incorporation of the expected background mortality rate and thus enable the modeling of relative survival when cure is a possibility. I give an example to illustrate the commands.

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Martinez, Edson Zangiacomi, Bruno Caparroz Lopes de Freitas, Jorge Alberto Achcar, Davi Casale Aragon, and Marcos Vinicius de Oliveira Peres. "Exponentiated Weibull Models Applied to Medical Data in Presence of Right-censoring, Cure Fraction and Covariates." Statistics, Optimization & Information Computing 10, no. 2 (November 29, 2021): 548–71. http://dx.doi.org/10.19139/soic-2310-5070-1266.

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Cure fraction models have been widely used to analyze survival data in which a proportion of the individuals isnot susceptible to the event of interest. This article considers frequentist and Bayesian methods to estimate the unknown model parameters of the exponentiated Weibull (EW) distribution considering right-censored survival data with a cure fraction and covariates. The EW distribution is as an extension to the Weibull distribution by considering an additional shape parameter to the model. We consider four types of cure fraction models: the mixture cure fraction (MCF), the nonmixture cure fraction (NMCF), the complementary promotion time cure (CPTC), and the cure rate proportional odds (CRPO) models. Bayesian inferences are obtained by using MCMC (Markov Chain Monte Carlo) methods. A simulation study was conducted to examine the performance of the maximum likelihood estimators for different sample sizes. Two real datasets were considered to illustrate the applicability of the proposed model. The EW distribution and its sub-models have the flexibility to accommodate different shapes for the hazard function and should be an attractive choice for survival data analysis when a cure fraction is present.

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Botta, Laura, Gemma Gatta, Annalisa Trama, and Riccardo Capocaccia. "Excess risk of dying of other causes of cured cancer patients." Tumori Journal 105, no. 3 (March 25, 2019): 199–204. http://dx.doi.org/10.1177/0300891619837896.

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Background: The proportion of patients cured of cancer is usually estimated with cure models assuming they have the same death risk as the general population. These patients, even when cured, often maintain an extra death risk compared to the overall population. Our aims were to estimate this extra risk, and to take it into account in estimating cure proportions and relative survival (RS). Methods: We used RS mixture model with an additional parameter expressing the extra noncancer death risk of patients, assumed constant with age. We applied the model to the SEER registries survival data (1990–1994 diagnosed patients) with colorectal, breast, and lung cancers, and followed up to 2013. Results: The estimated relative risk of death for cured patients versus the general population was 1.11 for colorectal, 1.16 for breast, and 2.17 and 2.12, respectively, for female and male lung cancers. Taking this extra risk into account leads, for all cancers, to a higher estimated proportion of cured and a lower RS of uncured patients. In addition, it leads to a higher estimated RS for all patients aged >70 years, and for lung cancer patients aged >50 years, at diagnosis. Conclusions: Mortality of survivors not directly due to the diagnosed cancer was significantly higher than in the general population. It affected the estimates of cure proportions for all age classes and RS in the elderly.

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Almeida, Frederico M., Enrico A. Colosimo, and Vinícius D. Mayrink. "Modified score function for monotone likelihood in the semiparametric mixture cure model." Biometrical Journal 64, no. 3 (November 30, 2021): 635–54. http://dx.doi.org/10.1002/bimj.202000254.

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Basu, Sanjib, and Ram C. Tiwari. "Breast cancer survival, competing risks and mixture cure model: a Bayesian analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 2 (April 2010): 307–29. http://dx.doi.org/10.1111/j.1467-985x.2009.00618.x.

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Wang, Xiaoguang, and Bo Han. "Efficient estimation for the non-mixture cure model with current status data." Statistics 54, no. 4 (June 22, 2020): 756–77. http://dx.doi.org/10.1080/02331888.2020.1783541.

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Jiang, Cuiqing, Zhao Wang, and Huimin Zhao. "A prediction-driven mixture cure model and its application in credit scoring." European Journal of Operational Research 277, no. 1 (August 2019): 20–31. http://dx.doi.org/10.1016/j.ejor.2019.01.072.

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Xu, Linzhi, and Jiajia Zhang. "Multiple imputation method for the semiparametric accelerated failure time mixture cure model." Computational Statistics & Data Analysis 54, no. 7 (July 2010): 1808–16. http://dx.doi.org/10.1016/j.csda.2010.01.034.

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Xiang, Liming, Xiangmei Ma, and Kelvin K. W. Yau. "Mixture cure model with random effects for clustered interval-censored survival data." Statistics in Medicine 30, no. 9 (January 13, 2011): 995–1006. http://dx.doi.org/10.1002/sim.4170.

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Muresan, Bogdan, Carla Mamolo, Joseph C. Cappelleri, Ruth Mokgokong, Athina Palaka, Fanni Soikkeli, and Bart Heeg. "Comparing cure rates for gemtuzumab ozogamicin plus standard chemotherapy vs standard chemotherapy alone in acute myeloid leukemia patients." Future Oncology 17, no. 22 (August 2021): 2883–92. http://dx.doi.org/10.2217/fon-2020-1287.

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Aim: Assess the suitability of standard parametric, piecewise and mixture cure models (MCMs) for modeling long-term survival of acute myeloid leukemia patients achieving remission following treatment with gemtuzumab ozogamicin (GO) + standard chemotherapy (SC) or SC alone. MCMs can model survival data comprising of statistically cured (patients in long-term remission) and uncured patients. Materials & methods: Models were fit to patient-level data corresponding to individual treatment arms. Results: Visual inspection showed that MCMs fit the clinical data best. Survival modeling with MCMs showed that treatment with GO + SC versus SC alone results in higher statistical cure rates for event-free survival (rates: 26–35% vs 21–23%) and overall survival (rates: 48–52% vs 38–44%). Conclusion: MCMs are well suited to modeling long-term survival in acute myeloid leukemia patients. Clinical trial registration: NCT00927498 ( ClinicalTrials.gov )

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