Session 7: Advanced Survival Analysis
Organizers: Marlina Nasution (Parexel) and Changbin Guo (SAS)
Chair: Marlina Nasution (Parexel)
Peter Jakobs (Parexel) “Analysis of Recurrent Adverse Events of Special Interest: an Application for Hazard-Based Models”
For decades, safety risks on study or product level have been summarized by incidence estimates: typically, with $N_j$ denoting the number of subjects who received at least one dose of study treatment $j$ and $n_{j,x}$ denoting the number of subjects in treatment group $j$ who experienced at least once an adverse event $x$ (e.g., categorized as MedDRA Preferred Term), such an incidence is estimated by $n_{j,x} /N_j$ times 100%. Treatment groups have been compared by related estimates like risk difference, risk ratio -and odds ratio. Timing, duration, recurrence as well as duration of adverse events has been ignored frequently. A trend for utilizing time-to-first-event methodology (like cumulative incidence estimates and Cox proportional hazard regression models) in safety assessments has been observed over the last years, but this approach is still limited. My presentation will outline some statistical methodology for evaluating risks for recurrent (or otherwise complex) safety events of special interest, focusing on hazard-based models for counting processes and multi-state models. For example, states in a multi-state model for adverse events of special interest may be defined by administration of certain concomitant medication(s) over the course of the study (that either change the risk for such adverse events or are used to treat such adverse events). If time allows, a fictitious case study analysis will be presented as well.
Audrey Boruvka (University of Michigan) “Understanding the effect of treatment on progression-free survival and overall survival”
Cancer clinical trials are routinely designed on the basis of event-free survival time where the event of interest may represent a complication, metastasis, relapse, or progression. This talk is concerned with a number of statistical issues arising with use of such endpoints including interpretation and dual censoring schemes. We consider methods to evaluate this endpoint based on the Cox model. However, even when treatment is randomized, the resulting hazard ratios have limited interpretation as causal effects. We point to some ways in which one can draw causal inferences in this particular setting. This talk is based on joint work with Richard J. Cook and Leilei Zeng at the University of Waterloo.
Changbin Guo (SAS) “Current Methods in Survival Analysis Using SAS/STAT® Software”
Interval censoring occurs in clinical trials and medical studies when patients are assessed only periodically. As a result, an event is known to have occurred only within two assessment times. Traditional survival analysis methods for right-censored data are not applicable, and so specialized methods are needed for interval-censored data. The goal of this presentation is to give an overview of these techniques and their recent implementation in SAS software, both for estimation and comparison of survival functions as well as for proportional hazards regression. Competing risks arise in studies when individuals are subject to a number of potential failure events and the occurrence of one event may impede the occurrence of other events. A useful quantity in competing-risks analysis is the cumulative incidence function, which is the probability sub-distribution function of failure from a specific cause. This presentation describes how to use the LIFETEST procedure to compute the nonparametric estimate of the cumulative incidence function and test for group differences. In addition, this presentation will describe two approaches that are available with the PHREG procedure for evaluating the relationship of covariates to the cause-specific failure. The first approach models the cause-specific hazard, and the second approach models the cumulative incidence (Fine and Gray 1999).
