Design Considerations for Complex Survival Models

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  • March 20, 2019
Creator
  • Chen, Liddy Miaoli
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
Abstract
  • Various complex survival models, such as joint models of survival and longitudinal data and multivariate frailty models, have gained popularity in recent years because these models can maximize the utilization of information collected. It has been shown that these methods can reduce bias and/or improve efficiency, and thus can increase the power for statistical inference. Statistical design, such as sample size and power calculations, is a crucial first step in clinical trials. We derived a closed form sample size formula for estimating the effect of the longitudinal process in joint modeling, and extend Schoenfeld's (1983) sample size formula to the joint modeling setting for estimating the overall treatment effect. The sample size formula we developed is general, allowing for p-degree polynomial trajectories. The robustness of our model was demonstrated in simulation studies with linear and quadratic trajectories. We discussed the impact of the within subject variability on power, and data collection strategies, such as spacing and frequency of repeated measurements, in order to maximize power. When the within subject variability is large, different data collection strategies can influence the power of the study in a significant way. We also developed a sample size determination method for the shared frailty model to investigate the treatment effect on multivariate time to events, including recurrent events. We first assumed a common treatment effect on multiple event times, and the sample size determination was based on testing the common treatment effect. We then considered testing the treatment effect on one time-to-event while treating the other time-to-events as nuisance, and compared the power from a multivariate frailty model versus that from a univariate parametric and semi-parametric survival model. The multivariate frailty model has significant advantage over the univariate survival model when the time-to-event data is highly correlated. Group sequential methods had been developed to control the overall type I error rate in interim analysis of accumulating data in a clinical trial. These methods mainly apply to testing the same hypothesis at different interim analyses. Finally, we extended the methodology of the alpha spending function to group sequential stopping boundaries when the hypotheses can be different between analyses. We found that these stopping boundaries depend on the Fisher's Information matrix, and application to a bivariate frailty model and a joint model was considered.
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  • In Copyright
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  • "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biostatistics, Gillings School of Global Public Health."
Advisor
  • Ibrahim, Joseph
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  • Chapel Hill, NC
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  • Open access
Date uploaded
  • March 18, 2013

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