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Maximum Likelihood Fitting of General Risk Models to Stratified Data

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  • Barry E. Storer
  • Sholom Wacholder
  • Norman E. Breslow

Abstract

A recursive algorithm (Howard, 1972; Gail et al., 1981) useful for maximum conditional likelihood fitting of logistic regression models with large strata can be generalized to arbitrary relative risk models. An example is presented which permits comparison between fitting methods vis a vis stratified vs. unstratified analysis, additive vs. multiplicative risk model, and use of expected vs. observed information. On the basis of results from this comparison we suggest that Wald's test and the score test computed with observed information be avoided in non‐standard models. An interactive computer program is available for fitting multiplicative, additive and general risk models to stratified data.

Suggested Citation

  • Barry E. Storer & Sholom Wacholder & Norman E. Breslow, 1983. "Maximum Likelihood Fitting of General Risk Models to Stratified Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 32(2), pages 172-181, June.
    • Handle: RePEc:bla:jorssc:v:32:y:1983:i:2:p:172-181
    DOI: 10.2307/2347296
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    Cited by:

    1. Li, Gang & Qin, Jing, 2006. "Analysis of two-sample truncated data using generalized logistic models," Journal of Multivariate Analysis, Elsevier, vol. 97(3), pages 675-697, March.
    2. Raymond Fang & Nhu Le & Pierre Band, 2011. "Identification of Occupational Cancer Risks in British Columbia, Canada: A Population-Based Case—Control Study of 1,155 Cases of Colon Cancer," IJERPH, MDPI, vol. 8(10), pages 1-23, September.
    3. Anders Skrondal & Sophia Rabe-Hesketh, 2022. "The Role of Conditional Likelihoods in Latent Variable Modeling," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 799-834, September.

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