A new program to compute and evaluate continuously monitored stopping boundaries for clinical trials

We present code for the calculation and evaluation of continuously monitored stopping boundaries for use in one-arm and two-arm clinical trials. These designs were first developed for one-arm trials by Thall, Simon and Estey (TSE) (P.F. Thall, R. Simon, E.H. Estey, Bayesian sequential monitoring designs for single-arm clinical trials with multiple outcomes, Stat. Med. 14 (1995) 357-379). Our code corrects some problems in the original TSE algorithms and extends these algorithms for use in a two-arm trial setting. It is written in S-Plus to improve interactivity for the statistically adept user, and employs external routines, dynamically loaded into S-Plus, to improve calculation efficiency. Efficient versions of our code require both a C compiler and the S-Plus program. Our code has been tested in UNIX and Microsoft Windows environments, and compiled code is available from our website. A numerical integration routine for the convolution of beta distributions is included.     

Designing group sequential randomized clinical trials with time to event end points using a R function

A major and difficult task is the design of clinical trials with a time to event endpoint. In fact, it is necessary to compute the number of events and in a second step the required number of patients. Several commercial software packages are available for computing sample size in clinical trials with sequential designs and time to event endpoints, but there are a few R functions implemented. The purpose of this paper is to describe features and use of the R function. plansurvct.func, which is an add-on function to the package gsDesign which permits in one run of the program to calculate the number of events, and required sample size but also boundaries and corresponding p-values for a group sequential design. The use of the function plansurvct.func is illustrated by several examples and validated using East software.     

Mining Pareto-optimal rules with respect to support and confirmation or support and anti-support

In knowledge discovery and data mining many measures of interestingness have been proposed in order to measure the relevance and utility of the discovered patterns. Among these measures, an important role is played by Bayesian confirmation measures, which express in what degree a premise confirms a conclusion. In this paper, we are considering knowledge patterns in a form of “if…, then…” rules with a fixed conclusion. We investigate a monotone link between Bayesian confirmation measures, and classic dimensions being rule support and confidence. In particular, we formulate and prove conditions for monotone dependence of two confirmation measures enjoying some desirable properties on rule support and confidence. As the confidence measure is unable to identify and eliminate non-interesting rules, for which a premise does not confirm a conclusion, we propose to substitute the confidence for one of the considered confirmation measures in mining the Pareto-optimal rules. We also provide general conclusions for the monotone link between any confirmation measure enjoying the desirable properties and rule support and confidence. Finally, we propose to mine rules maximizing rule support and minimizing rule anti-support, which is the number of examples, which satisfy the premise of the rule but not its conclusion (called counter-examples of the considered rule). We prove that in this way we are able to mine all the rules maximizing any confirmation measure enjoying the desirable properties. We also prove that this Pareto-optimal set includes all the rules from the previously considered Pareto-optimal borders.     

MSurvPow: a FORTRAN program to calculate the sample size and power for cluster-randomized clinical trials with survival outcomes

Manatunga and Chen [A.K. Manatunga, S. Chen, Sample size estimation for survival outcomes in cluster-randomized studies with small cluster sizes, Biometrics 56 (2000) 616-621] proposed a method to estimate sample size and power for cluster-randomized studies where the primary outcome variable was survival time. The sample size formula was constructed by considering a bivariate marginal distribution (Clayton-Oakes model) with univariate exponential marginal distributions. In this paper, a user-friendly FORTRAN 90 program was provided to implement this method and a simple example was used to illustrate the features of the program.