Res Synth Methods - Random-effects meta-analysis of time-to-event data using the expectation-maximisation algorithm and shrinkage estimators.

Tópicos

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Resumo

Meta-analysis of time-to-event data has proved difficult in the past because consistent summary statistics often cannot be extracted from published results. The use of individual patient data allows for the re-analysis of each study in a consistent fashion and thus makes meta-analysis of time-to-event data feasible. Time-to-event data can be analysed using proportional hazards models, but incorporating random effects into these models is not straightforward in standard software. This paper fits random-effects proportional hazards models by treating the random effects as missing data and applying the expectation-maximisation algorithm. This approach has been used before by using Markov chain Monte Carlo methods to perform the expectation step of the algorithm. In this paper, the expectation step is simplified, without sacrificing accuracy, by approximating the expected values of the random effects using simple shrinkage estimators. This provides a robust method for fitting random-effects models that can be implemented in standard statistical packages. Copyright ? 2012 John Wiley & Sons, Ltd.

Resumo Limpo

metaanalysi timetoev data prove difficult past consist summari statist often extract publish result use individu patient data allow reanalysi studi consist fashion thus make metaanalysi timetoev data feasibl timetoev data can analys use proport hazard model incorpor random effect model straightforward standard softwar paper fit randomeffect proport hazard model treat random effect miss data appli expectationmaximis algorithm approach use use markov chain mont carlo method perform expect step algorithm paper expect step simplifi without sacrif accuraci approxim expect valu random effect use simpl shrinkag estim provid robust method fit randomeffect model can implement standard statist packag copyright john wiley son ltd

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