Statistics in Medicine

The standard analysis of variance (ANOVA) method is usually applied to analyse continuous data from cross-over studies. The method, however, has been known to be not robust for general variance-covariance structure. The simple empirical generalized least squares (EGLS) method, proposed in an attempt to improve the precision of the standard ANOVA method for general variance-covariance structure, is usually insufficient for small-sample cross-over trials. In this paper we compare the following commonly used or recent approaches: standard ANOVA; simple EGLS; modified ANOVA method derived from a modified approximate F-distribution; and a modified EGLS method adjusted by the Kenward and Roger procedure in terms of robustness and power while applying to small-sample cross-over studies (say, the sample size is less than 40) over a variety of variance-covariance structures by simulation. We find that the unconditional modified ANOVA method has robust performance for all of the simulated small-sample cross-over studies over the various variance-covariance structures, and has comparable power with the standard ANOVA method whenever they are comparable in type I error rate. The EGLS method (simple or modified) is not reliable when the sample size of a cross-over study is too small, say, less than 24 in the simulation, unless a simple covariance structure is correctly assumed. Given a relatively larger sample size, the modified EGLS method, assuming an unstructured covariance matrix, demonstrates robust performance over the various variance-covariance structures in the simulation and provides more powerful tests than those of the modified (or standard) ANOVA method.

The data with which Student illustrated the application of his famous distribution are examined from a number of aspects. Central to the discussion is the within-patient clinical trial at Kalamazoo whose results were published by Cushny and Peebles and misquoted by Student and Fisher. This trial is discussed from historical, pharmacological and statistical perspectives. Student's and Fisher's analyses and a more modern analysis by Preece are considered as is Cushny's and Peebles's interpretation. Brief biographies of the five physicians involved in running the trial are presented.

The assumption that comparative effectiveness research will provide timely, relevant evidence rests on changing the current framework for assembling evidence. In this commentary, we provide the background of how coverage decisions for new medical technologies are currently made in the United States. We focus on the statistical issues regarding how to use the ensemble of information for inferring comparative effectiveness. It is clear a paradigm shift in how clinical information is integrated in real-world settings to establish effectiveness is required.

The Cancer Research UK study CR0720-11 is a trial to determine the tolerability and effect on survival of using two agents in combination in patients with advanced pancreatic cancer. In particular, the trial is designed first to identify the most suitable combination of doses of the two agents in terms of the incidence of dose-limiting toxicities. Then, the survival of all patients who have received that dose combination in the study so far, together with additional patients assigned to that dose combination to ensure that the total number is sufficient, will be analysed. If the survival outcomes show promise, then a definitive randomised study of that dose combination will be recommended. The first two patients in the trial will be treated with the lowest doses of each agent in combination. An adaptive Bayesian procedure based only on monotonicity constraints concerning the risks of toxicity at different dose levels will then be used to suggest dose combinations for subsequent patients. The survival analysis will concern only patients who received the chosen dose combination, and will compare observed mortality with that expected from an exponential model based on the known survival rates associated with current treatment. In this paper, the Bayesian dose-finding procedure is described and illustrated, and its properties are evaluated through simulation. Computation of the appropriate sample size for the survival investigation is also discussed.

We propose a practical group sequential method, a conditional sequential sampling procedure, to test if a drug of interest (D) leads to an elevated risk for an adverse event E compared with a comparison drug C. The method is designed for prospective drug safety surveillance studies, in which, for each considered drug, a summary table with the exposed person-times and the associated numbers of adverse events summed by strata defined by several potential confounders, is collected and updated periodically using the health plans' administrative claims data. This new approach can be applied to test for elevated relative risk whenever the data are updated. Our approach adjusts for multiple testing to preserve the overall type I error with any specified alpha-spending function. Furthermore, it automatically adjusts for temporal trend and population heterogeneity across strata by conditioning on the numbers of adverse events within each stratum during each time period. Therefore, this approach is very flexible and applies to a wide class of settings. We conduct a simulation study to evaluate its performance under various scenarios. The approach is also applied to an example to examine if Rofecoxib leads to an increased relative risk for acute myocardial infraction (AMI) compared with its two counterparts Diclofenac and Naproxen, respectively. We end with discussions.

A method of analysis is presented for estimating the magnitude of a treatment effect among compliers in a clinical trial which is asymptotically unbiased and respects the randomization. The approach is valid even when compliers have a different baseline risk than non-compliers. Adjustments for contamination (use of the treatment by individuals in the control arm) are also developed. When the baseline failure rates in non-compliers and contaminators are the same as those who accept their allocated treatment, the method produces larger treatment effects than an 'intent-to-treat' analysis, but the confidence limits are also wider, and (even without this assumption) asymptotically the efficiencies are the same. In addition to providing a better estimate of the true effect of a treatment in compliers, the method also provides a more realistic confidence interval, which can be especially important for trials aimed at showing the equivalence of two treatments. In this case the intent-to-treat analysis can give unrealistically narrow confidence intervals if substantial numbers of patients elect to have the treatment they were not randomized to receive.