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Subject‐Specific and Population‐Averaged Continuation Ratio Logit Models for Multiple Discrete Time Survival Profiles

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  • Thomas R. Ten Have
  • David H. Uttal

Abstract

Subject‐specific and population‐averaged continuation ratio logit models are presented for multivariate discrete time survival data. The models characterize data from a psychological experiment by using a quadratic polynomial relationship across time that depends on a time‐independent condition. A multivariate normal random effects distribution is imposed on intercept, linear and quadratic terms in the subject‐specific model, which is fitted by using a combination of Gibbs sampling and buffered stochastic substitution. Variance components that tend towards 0 are addressed in this context. In addition, generalized estimating equations estimates of the parameters in the population‐averaged model are compared with analogous estimates for the mixed effects model.

Suggested Citation

  • Thomas R. Ten Have & David H. Uttal, 1994. "Subject‐Specific and Population‐Averaged Continuation Ratio Logit Models for Multiple Discrete Time Survival Profiles," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(2), pages 371-384, June.
    • Handle: RePEc:bla:jorssc:v:43:y:1994:i:2:p:371-384
    DOI: 10.2307/2986027
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    Cited by:

    1. Kendzor, Darla E. & Reitzel, Lorraine R. & Mazas, Carlos A. & Cofta-Woerpel, Ludmila M. & Cao, Yumei & Ji, Lingyun & Costello, Tracy J. & Vidrine, Jennifer Irvin & Businelle, Michael S. & Li, Yisheng , 2012. "Individual- and area-level unemployment influence smoking cessation among African Americans participating in a randomized clinical trial," Social Science & Medicine, Elsevier, vol. 74(9), pages 1394-1401.
    2. Patrick J. Heagerty & Scott L. Zeger, 2000. "Multivariate Continuation Ratio Models: Connections and Caveats," Biometrics, The International Biometric Society, vol. 56(3), pages 719-732, September.
    3. James H. Albert & Siddhartha Chib, 2001. "Sequential Ordinal Modeling with Applications to Survival Data," Biometrics, The International Biometric Society, vol. 57(3), pages 829-836, September.

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