Incorporating baseline measurements into the analysis of crossover trials with time-to-event endpoints

A New Method for Imputing Censored Values in Crossover Designs with Time-to-Event Outcomes Using Median Residual Life

Crossover designs are commonly applied in research due to efficiency and subject parsimony compared to parallel studies. Baseline measurements would improve the power of comparison. For time to event outcomes, the sample size is reduced due to censorship, if they are ignored; thus, applying traditional regression models will be limited. A logical solution is to impute the censored observation and apply common analytical models for analyzing the data. Nevertheless, techniques to impute censored data in time-to-event outcomes in crossover designs are not practiced as much. Accordingly, we propose a method to impute the censored observation using median residual life regression and then analyze the data using analyses of covariance (ANCOVA), considering the difference of period-specific baselines as covariate. We used simulation to show the favorable performance of our method relative to a recently proposed method, multiple imputation with model averaging and ANCOVA (MI^MI). Specifically, the censored observations were multiply-imputed using prespecified parametric event time models, and then, the methods were applied to a real data example.

Studentized bootstrap model-averaged tail area intervals

In many scientific studies, the underlying data-generating process is unknown and multiple statistical models are considered to describe it. For example, in a factorial experiment we might consider models involving just main effects, as well as those that include interactions. Model-averaging is a commonly-used statistical technique to allow for model uncertainty in parameter estimation. In the frequentist setting, the model-averaged estimate of a parameter is a weighted mean of the estimates from the individual models, with the weights typically being based on an information criterion, cross-validation, or bootstrapping. One approach to building a model-averaged confidence interval is to use a Wald interval, based on the model-averaged estimate and its standard error. This has been the default method in many application areas, particularly those in the life sciences. The MA-Wald interval, however, assumes that the studentized model-averaged estimate has a normal distribution, which can be far from true in practice due to the random, data-driven model weights. Recently, the model-averaged tail area Wald interval (MATA-Wald) has been proposed as an alternative to the MA-Wald interval, which only assumes that the studentized estimate from each model has a N(0, 1) or t-distribution, when that model is true. This alternative to the MA-Wald interval has been shown to have better coverage in simulation studies. However, when we have a response variable that is skewed, even these relaxed assumptions may not be valid, and use of these intervals might therefore result in poor coverage. We propose a new interval (MATA-SBoot) which uses a parametric bootstrap approach to estimate the distribution of the studentized estimate for each model, when that model is true. This method only requires that the studentized estimate from each model is approximately pivotal, an assumption that will often be true in practice, even for skewed data. We illustrate use of this new interval in the analysis of a three-factor marine global change experiment in which the response variable is assumed to have a lognormal distribution. We also perform a simulation study, based on the example, to compare the lower and upper error rates of this interval with those for existing methods. The results suggest that the MATA-SBoot interval can provide better error rates than existing intervals when we have skewed data, particularly for the upper error rate when the sample size is small.