Informative dropout is a vexing problem for any biomedical study. Most existing statistical methods attempt to correct estimation bias related to this phenomenon by specifying unverifiable assumptions about the dropout mechanism. We consider a cohort study in Africa that uses an outreach programme to ascertain the vital status for dropout subjects. These data can be used to identify a number of relevant distributions. However, as only a subset of dropout subjects were followed, vital status ascertainment was incomplete. We use semi-competing risk methods as our analysis framework to address this specific case where the terminal event is incompletely ascertained and consider various procedures for estimating the marginal distribution of dropout and the marginal and conditional distributions of survival. We also consider model selection and estimation efficiency in our setting. Performance of the proposed methods is demonstrated via simulations, asymptotic study and analysis of the study data."> Informative dropout is a vexing problem for any biomedical study. Most existing statistical methods attempt to correct estimation bias related to this phenomenon by specifying unverifiable assumptions about the dropout mechanism. We consider a cohort study in Africa that uses an outreach programme to ascertain the vital status for dropout subjects. These data can be used to identify a number of relevant distributions. However, as only a subset of dropout subjects were followed, vital status ascertainment was incomplete. We use semi-competing risk methods as our analysis framework to address this specific case where the terminal event is incompletely ascertained and consider various procedures for estimating the marginal distribution of dropout and the marginal and conditional distributions of survival. We also consider model selection and estimation efficiency in our setting. Performance of the proposed methods is demonstrated via simulations, asymptotic study and analysis of the study data."> Informative dropout is a vexing problem for any biomedical study. Most existing statistical methods attempt to correct estimation bias rel">
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Marginal and Conditional Distribution Estimation from Double-sampled Semi-competing Risks Data

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  • Menggang Yu
  • Constantin T. Yiannoutsos
Abstract
type="main" xml:id="sjos12096-abs-0001"> Informative dropout is a vexing problem for any biomedical study. Most existing statistical methods attempt to correct estimation bias related to this phenomenon by specifying unverifiable assumptions about the dropout mechanism. We consider a cohort study in Africa that uses an outreach programme to ascertain the vital status for dropout subjects. These data can be used to identify a number of relevant distributions. However, as only a subset of dropout subjects were followed, vital status ascertainment was incomplete. We use semi-competing risk methods as our analysis framework to address this specific case where the terminal event is incompletely ascertained and consider various procedures for estimating the marginal distribution of dropout and the marginal and conditional distributions of survival. We also consider model selection and estimation efficiency in our setting. Performance of the proposed methods is demonstrated via simulations, asymptotic study and analysis of the study data.

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  • Menggang Yu & Constantin T. Yiannoutsos, 2015. "Marginal and Conditional Distribution Estimation from Double-sampled Semi-competing Risks Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 87-103, March.
    • Handle: RePEc:bla:scjsta:v:42:y:2015:i:1:p:87-103
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    References listed on IDEAS

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    1. Hongyu Jiang & Jason P. Fine & Michael R. Kosorok & Rick Chappell, 2005. "Pseudo Self‐Consistent Estimation of a Copula Model with Informative Censoring," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(1), pages 1-20, March.
    2. Weijing Wang, 2003. "Estimating the association parameter for copula models under dependent censoring," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 257-273, February.
    3. Debashis Ghosh, 2009. "On Assessing Surrogacy in a Single Trial Setting Using a Semicompeting Risks Paradigm," Biometrics, The International Biometric Society, vol. 65(2), pages 521-529, June.
    4. Lajmi Lakhal & Louis-Paul Rivest & Belkacem Abdous, 2008. "Estimating Survival and Association in a Semicompeting Risks Model," Biometrics, The International Biometric Society, vol. 64(1), pages 180-188, March.
    5. Constantine E. Frangakis & Donald B. Rubin, 2001. "Addressing an Idiosyncrasy in Estimating Survival Curves Using Double Sampling in the Presence of Self-Selected Right Censoring," Biometrics, The International Biometric Society, vol. 57(2), pages 333-342, June.
    6. Menggang Yu & Bin Nan, 2010. "Regression Calibration in Semiparametric Accelerated Failure Time Models," Biometrics, The International Biometric Society, vol. 66(2), pages 405-414, June.
    7. Jinfeng Xu & John D. Kalbfleisch & Beechoo Tai, 2010. "Statistical Analysis of Illness–Death Processes and Semicompeting Risks Data," Biometrics, The International Biometric Society, vol. 66(3), pages 716-725, September.
    8. Constantine E. Frangakis & Donald B. Rubin, 2001. "Rejoinder to Discussions on Addressing an Idiosyncrasy in Estimating Survival Curves Using Double Sampling in the Presence of Self-Selected Right Censoring," Biometrics, The International Biometric Society, vol. 57(2), pages 351-353, June.
    9. Jin‐Jian Hsieh & Weijing Wang & A. Adam Ding, 2008. "Regression analysis based on semicompeting risks data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 3-20, February.
    10. Ding, A. Adam, 2010. "Identifiability conditions for covariate effects model on survival times under informative censoring," Statistics & Probability Letters, Elsevier, vol. 80(11-12), pages 911-915, June.
    11. Rivest, Louis-Paul & Wells, Martin T., 2001. "A Martingale Approach to the Copula-Graphic Estimator for the Survival Function under Dependent Censoring," Journal of Multivariate Analysis, Elsevier, vol. 79(1), pages 138-155, October.
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