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Joint Modeling of Covariates and Censoring Process Assuming Non-Constant Dropout Hazard.

ABSTRACT: In this manuscript we propose a novel approach for the analysis of longitudinal data that have informative dropout. We jointly model the slopes of covariates of interest and the censoring process for which we assume a survival model with logistic non-constant dropout hazard in a likelihood function that is integrated over the random effects. Maximization of the marginal likelihood function results in acquiring maximum likelihood estimates for the population slopes and empirical Bayes estimates for the individual slopes that are predicted using Gaussian quadrature. Our simulation study results indicated that the performance of this model is superior in terms of accuracy and validity of the estimates compared to other models such as logistic non-constant hazard censoring model that does not include covariates, logistic constant censoring model with covariates, bootstrapping approach as well as mixed models. Sensitivity analyses for the dropout hazard and non-Gaussian errors were also undertaken to assess robustness of the proposed approach to such violations. Our model was illustrated using a cohort of renal transplant patients with estimated glomerular filtration rate as the outcome of interest.

SUBMITTER: Jaffa MA

PROVIDER: S-EPMC5010875 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Joint Modeling of Covariates and Censoring Process Assuming Non-Constant Dropout Hazard.

Jaffa Miran A MA Jaffa Ayad A AA

Statistical methods & applications 20150401 2

In this manuscript we propose a novel approach for the analysis of longitudinal data that have informative dropout. We jointly model the slopes of covariates of interest and the censoring process for which we assume a survival model with logistic non-constant dropout hazard in a likelihood function that is integrated over the random effects. Maximization of the marginal likelihood function results in acquiring maximum likelihood estimates for the population slopes and empirical Bayes estimates f ...[more]

PMID: 27605892

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Project description:BackgroundNon-proportional hazards are common with time-to-event data but the majority of randomised clinical trials (RCTs) are designed and analysed using approaches which assume the treatment effect follows proportional hazards (PH). Recent advances in oncology treatments have identified two forms of non-PH of particular importance - a time lag until treatment becomes effective, and an early effect of treatment that ceases after a period of time. In sample size calculations for treatment effects on time-to-event outcomes where information is based on the number of events rather than the number of participants, there is crucial importance in correct specification of the baseline hazard rate amongst other considerations. Under PH, the shape of the baseline hazard has no effect on the resultant power and magnitude of treatment effects using standard analytical approaches. However, in a non-PH context the appropriateness of analytical approaches can depend on the shape of the underlying hazard.MethodsA simulation study was undertaken to assess the impact of clinically plausible non-constant baseline hazard rates on the power, magnitude and coverage of commonly utilized regression-based measures of treatment effect and tests of survival curve difference for these two forms of non-PH used in RCTs with time-to-event outcomes.ResultsIn the presence of even mild departures from PH, the power, average treatment effect size and coverage were adversely affected. Depending on the nature of the non-proportionality, non-constant event rates could further exacerbate or somewhat ameliorate the losses in power, treatment effect magnitude and coverage observed. No single summary measure of treatment effect was able to adequately describe the full extent of a potentially time-limited treatment benefit whilst maintaining power at nominal levels.ConclusionsOur results show the increased importance of considering plausible potentially non-constant event rates when non-proportionality of treatment effects could be anticipated. In planning clinical trials with the potential for non-PH, even modest departures from an assumed constant baseline hazard could appreciably impact the power to detect treatment effects depending on the nature of the non-PH. Comprehensive analysis plans may be required to accommodate the description of time-dependent treatment effects.

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Joint Modeling of Covariates and Censoring Process Assuming Non-Constant Dropout Hazard.

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Joint Modeling of Covariates and Censoring Process Assuming Non-Constant Dropout Hazard.

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