We present menu- and command-driven Stata programs for the calculation of sample size, number of events, and trial duration for a novel type of clinical trial design with a time-to-event outcome and two or more experimental arms. The approach is based on terminating accrual of patients to...

In 2005, Barthel, Royston, and Babiker presented a menu-driven Stata program under the generic name of ART (assessment of resources for trials) to calculate sample size and power for complex clinical trial designs with a time-to- event or binary outcome. In this article, we describe a Stata tool...

We present a menu-driven Stata program for the calculation of sample size or power for complex clinical trials with a survival time or a binary outcome. The features supported include up to six treatment arms, an arbitrary time-to- event distribution, fixed or time-varying hazard ratios, unequal...

In the presence of dependent competing risks in survival analysis, the Cox proportional hazard model can be utilised to examine the covariate effects on the cause-specific hazard function for each type of failure. The use of the Cox model was proposed by Lunn and McNeil (Biometrics, 1995). Their...

We consider a two-group clinical trial with a survival outcome, in which some subjects may 'cross over' to receive the treatment of the other arm. Our command strbee adjusts for treatment cross-over in one or both arms. This is done by a randomization-respecting method which preserves the...

strbee analyzes a two-group clinical trial with a survival outcome, in which some subjects may "crossover" to receive the treatment of the other arm. Adjustment for treatment crossover is done by a randomization-respecting method that preserves the intention-to-treat p-value. Copyright 2002 by...

Since its introduction to a wondering public in 1972, the Cox proportional hazards regression model has become an overwhelmingly popular tool in the analysis of censored survival data. However, some features of the Cox model may cause problems for the analyst or an interpreter of the data. They...

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to...

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to...