A quantitative study of the bias in estimating the treatment effect caused by omitting a balanced covariate in survival models

Claude Chastang, David Byar, Steven Piantadosi

    Research output: Contribution to journalArticle

    Abstract

    This paper discusses the quantitative aspects of bias in estimates of treatment effect in survival models when there is failure to adjust on balanced prognostic variables. A simple numerical example of this bias is given along with approximate formulae for its calculation in the multiplicative exponential survival model. The accuracy of the formulae is checked by simulation. In addition, approximate calculations and simulations of power loss and the effects of omitting more than one prognostic covariate are presented. The Weibull and Cox models are also examined using simulation. Study of this bias is pertinent to much applied work, and shows that the effect of omitting balanced covariates can be modest unless the variables are strongly prognostic or many in number. This work emphasizes the need for thorough comparisons of adjusted and unadjusted analyses for sensible interpretation of treatment effects.

    Original languageEnglish (US)
    Pages (from-to)1243-1255
    Number of pages13
    JournalStatistics in Medicine
    Volume7
    Issue number12
    DOIs
    StatePublished - Dec 1988

    Keywords

    • Adjustment
    • Bias
    • Exponential survival model
    • Survival models
    • Weibull model

    ASJC Scopus subject areas

    • Epidemiology
    • Statistics and Probability

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