Evaluating Treatment Effect in Multicenter Trials with Small Centers Using Survival Modeling

Clinical trials of rare diseases commonly enlist several centers to achieve recruitment goals. The aim of this study is to examine the estimation of treatment effects for survival outcomes in multicenter clinical trials with varying numbers of centers and few patients per center for rarer disease outcomes (i.e. rare cancers). We modeled the heterogeneity between centers using Cox frailty models to account for the variability in patients and patient care between centers and examined measures of model fit via smoothed functions of a prognostic factor. Through a simulation study, we were able to examine the consequence of having only a few centers or a few patients per center on the treatment and prognostic factor effects and model performance indices. Overall, we found it is preferable to have more patients per site and more sites in a multicenter trial as expected. However, having a few patients per site is feasible if there are many sites in a trial

Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance

Funding Information: This work was funded by a grant from the National Heart, Lung, and Blood Institute (NHLBI; grant number 1R01HL149948). The funding agency was not involved in the design of the study, collection and analysis of data, interpretation of results, or writing of the manuscript. Publisher Copyright: © 2023, The Author(s).Peer reviewedPublisher PD

The Comparison of Alternative Smoothing Methods for Fitting Non-Linear Exposure-Response Relationships with Cox Models in a Simulation Study*

We examined the behavior of alternative smoothing methods for modeling environmental epidemiology data. Model fit can only be examined when the true exposure-response curve is known and so we used simulation studies to examine the performance of penalized splines (P-splines), restricted cubic splines (RCS), natural splines (NS), and fractional polynomials (FP). Survival data were generated under six plausible exposure-response scenarios with a right skewed exposure distribution, typical of environmental exposures. Cox models with each spline or FP were fit to simulated datasets. The best models, e.g. degrees of freedom, were selected using default criteria for each method. The root mean-square error (rMSE) and area difference were computed to assess model fit and bias (difference between the observed and true curves). The test for linearity was a measure of sensitivity and the test of the null was an assessment of statistical power. No one method performed best according to all four measures of performance, however, all methods performed reasonably well. The model fit was best for P-splines for almost all true positive scenarios, although fractional polynomials and RCS were least biased, on average