In randomized clinical trials with baseline variables that are prognostic for the primary outcome, there is potential to improve precision and reduce sample size by appropriately adjusting for these variables.
The volume of high-throughput data makes it a daunting prospect to plot, but relying primarily on false discovery rate adjusted p-values is not enough. Making plots of the data is essential to diagnose the models and understand the results.
In the first part of this talk I will introduce one of the methods to estimate the causal effect of time-varying exposures: inverse probability of treatment weighting (IPTW) of marginal structural models (MSMs).
Multiple endpoints are increasingly used in clinical trials. The significance of some of these clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested.
Understanding treatment effect heterogeneity is an important aspect of randomised trials, and process variables describing the intervention content are crucial components of this. Frequently these variables can only be measured in intervention groups.
In clinical trials one traditionally models the effect of treatment on the mean response. The underlying assumption is that treatment affects the response distribution through a mean location shift on a suitable scale, with other aspects of the distribution (shape/dispersion/variance) remaining the same.