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Qutenza® is a capsaicin patch used to treat peripheral neuropathic pain, including postherpetic neuralgia (PHN) and human immunodeficiency virus-associated neuropathy (HIV-AN). The Qutenza Clinical Trials Database has been assembled to more fully characterize the effects of Qutenza. We conducted a within-subject meta-analysis of Qutenza studies to further define the medication's efficacy profile across studies. The meta-analysis combined individual patient data from randomized, controlled studies of Qutenza in peripheral neuropathic pain (1458 subjects treated with approved doses of Qutenza or control patches; 1120 with PHN and 338 with HIV-AN). These 7 studies had similar designs and were performed with the high-dose 8% capsaicin Qutenza patch and a 0.04% low-dose control patch. The difference between treatment groups for the primary efficacy end point of percentage change from baseline to weeks 2 to 12 on pain intensity score was calculated. Response was defined as a ≥ 30% decrease in mean pain intensity score during weeks 2 to 12. The overall between-group difference in percentage change from baseline in pain intensity was 8.0% (95% confidence interval 4.6, 11.5; P<.001), which statistically significantly favored Qutenza over low-dose control. Qutenza superiority was demonstrated for both PHN and HIV-AN patients for the primary end point and the end point proportion of 30% pain reduction response, and for PHN patients for the end point of proportion of 50% pain reduction response. These results confirm that Qutenza is effective for the treatment of both PHN and HIV-AN compared to low-dose control patch., (Copyright © 2013 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.)

This paper presents a computer program for use in the design of long-term clinical trials with multiple treatment arms in which the primary outcome variables are censored survival times. The treatment arms may be structured as a one-way or multi-way factorial design. It is assumed that patients are entered and randomized to a treatment arm during an accrual period. The patients are then followed for a fixed period during which there may be dropouts. Various distributional assumptions can be used to model the survival times. These include an option in which there is an effect of treatment duly after a lag or delay time. The program then computes the power of various statistical tests of hypotheses concerning treatment differences, interactions and trends. The power computations are "exact" in that they use the Monte Carlo method to obtain Type I and II error probabilities. However the program also outputs the normal approximations for comparison, although they are typically not accurate in these situations. Fisher's LSD method is used to adjust for the multiple comparisons. By comparing the power for various sets of design parameters, such as sample size, numbers of factor levels, patient accrual rate, and length of follow-up, an appropriate design can be constructed. Two examples are provided. The first is a simple one-way layout with multiple treatment arms; the second a two-way factorial design for a proposed large scale cancer chemoprevention trial.

We describe group sequential tests for a bivariate response. The tests are defined in terms of the two response components jointly, rather than through a single summary statistic. Such methods are appropriate when the two responses concern different aspects of a treatment; for example, one might wish to show that a new treatment is both as effective and as safe as the current standard. We present a formulation of the bivariate testing problem, introduce group sequential tests that satisfy Type I error conditions, and show how to find the sample size guaranteeing a specified power. We describe how properties of group sequential tests for bivariate normal observations can be computed by numerical integration.

We describe methods for the construction of valid confidence intervals for parameters of interest at repeated times during the course of a clinical trial with two treatments. This approach gives the investigator a way to monitor the trial and allows greater flexibility than methods that have rigid statistical stopping rules. Two situations are considered. In the first, responses are available immediately and normally distributed, the parameter of interest being the difference in means. In the second, observations are survival times and a proportional hazards model is assumed. Here the parameter of interest is the hazard ratio. Group sequential tests can be based on repeated confidence interval methods and these are compared to other multiple testing procedures that have been proposed.