The waiting game: how long is long enough?
The time it takes to recruit patients into a clinical trial has a major impact on whether a drug development programme completes on time. Here Byron Jones explains how simple statistical models can be very useful in predicting the time to complete recruitment.
t-Test Variants
The t-test can be modified to allow the same subjects to be used to test two different treatments (one of which may be a placebo) while considering any effects that the order of administration of the treatments might have—the crossover test. In addition the t-test can be modified to demonstrate that a new treatment is equivalent to or not inferior to an existing treatment.
A likelihood approach to meta-analysis with random effects.
In a meta-analysis of a set of clinical trials, a crucial but problematic component is providing an estimate and confidence interval for the overall treatment effect theta. Since in the presence of heterogeneity a fixed effect approach yields an artificially narrow confidence interval for theta, the random effects method of DerSimonian and Laird, which incorporates a moment estimator of the between-trial components of variance sigma B2, has been advocated. With the additional distributional assumptions of normality, a confidence interval for theta may be obtained. However, this method does not provide a confidence interval for sigma B2, nor a confidence interval for theta which takes account of the fact that sigma B2 has to be estimated from the data. We show how a likelihood based method can be used to overcome these problems, and use profile likelihoods to construct likelihood based confidence intervals. This approach yields an appropriately widened confidence interval compared with the standard random effects method. Examples of application to a published meta-analysis and a multicentre clinical trial are discussed. It is concluded that likelihood based methods are preferred to the standard method in undertaking random effects meta-analysis when the value of sigma B2 has an important effect on the overall estimated treatment effect.