Abstract
We studied a heteroscedastic hazards regression (HHR) model in order to evaluate bias when intrinsic heterogeneity is ignored. We used the proportional hazards model and the associated estimate to show that bias resulting from neglecting the heterogeneity over different ‘treatment’ groups or other covariates is an analytically calculated value. We then compared the value with those obtained from simulation results. Further, considering that a ‘stratified’ proportional hazards (SPH) model could possibly be used as an alternative, we made similar calculations to clarify when the SPH model will succeed or fail in order to account for heterogeneity when the underlying model is the HHR model.KeywordsHeteroscedastic hazards regressionheterogeneityproportional hazardsnonproportional hazardsbias
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