Res Synth Methods - Semiparametric hazard function estimation in meta-analysis for time to event data.

Tópicos

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Resumo

Meta-analyses have been widely used to combine information from survival data using estimated parameters in, for example, a Cox model. A number of approaches dealing with study level random effects have been developed. However, there are far fewer meta-analysis approaches for estimating survival or hazard functions. Typical approaches are based on the cumulative survival function using the generalized estimating equation. We propose an alternative approach following Efron's discrete logistic regression (Efron, 1988), but using generalized linear mixed models. We show that spline functions can be used in fitting the models to obtain smoothed estimates for hazard functions. The models also allow a semi-parametric structure to include factors such as random study effects and treatment groups. This approach models the hazard function based on which the survival function can be estimated too. We also propose a Bayesian bootstrap approach for statistical inference for both hazard and survival functions. This approach was applied to two meta-analysis data sets as examples to illustrate its use. Copyright ? 2012 John Wiley & Sons, Ltd.

Resumo Limpo

metaanalys wide use combin inform surviv data use estim paramet exampl cox model number approach deal studi level random effect develop howev far fewer metaanalysi approach estim surviv hazard function typic approach base cumul surviv function use general estim equat propos altern approach follow efron discret logist regress efron use general linear mix model show spline function can use fit model obtain smooth estim hazard function model also allow semiparametr structur includ factor random studi effect treatment group approach model hazard function base surviv function can estim also propos bayesian bootstrap approach statist infer hazard surviv function approach appli two metaanalysi data set exampl illustr use copyright john wiley son ltd

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