Statistics in medicine
-
Statistics in medicine · Jan 2004
ReviewThe evaluation of disease modifying therapies in Alzheimer's disease: a regulatory viewpoint.
Several drugs have received marketing approval in this country for the treatment of dementia of the Alzheimer's type. Their approval has been based on clinical trial designs that do not permit a distinction to be made between an effect of the drug on the symptoms of that disease, and an effect on the pathophysiological mechanisms that underlie that disorder. The latter effect has been referred to as 'disease-modifying.'In recent years there has been considerable interest in developing disease-modifying treatments for Alzheimer's disease (AD), using either specific clinical designs, or surrogate markers, such as brain imaging modalities. This paper outlines the regulatory framework governing how the Food and Drug Administration addresses new drug claims, the current basis for approving drugs for the treatment of AD, clinical trial designs that have been proposed as a means of demonstrating disease-modifying effects, a general and regulatory background to the use of surrogate markers in drug development, and, finally, views about the possible role of surrogate markers, especially brain imaging, as outcome measures in clinical trials intended to produce disease-modifying effects in Alzheimer's Disease.
-
Statistics in medicine · Dec 2003
Sample size re-estimation in group-sequential response-adaptive clinical trials.
In clinical trials where the variances of the response variables are unknown, in accurate estimates of these can affect the type II error rate considerably. More accurate estimates of the variances may be obtained by taking a look at the data available part way through the trial and re-calculating the required sample size based on these new estimates. The main impetus for sample size re-estimation came from a two-stage procedure developed by Stein in 1945 and the literature is now replete with variations on this approach. ⋯ The test is compared to modified versions of a simple test and a Stein-type group sequential t-test studied in the recent literature. These tests calculate the required sample sizes based on less accurate estimates of the variances. The type I error rate is close to the nominal value and the power is more accurately maintained in the new test.
-
Statistics in medicine · Sep 2003
Baseline adjustments for binary data in repeated cross-sectional cluster randomized trials.
Analysis of covariance models, which adjust for a baseline covariate, are often used to compare treatment groups in a controlled trial in which individuals are randomized. Such analysis adjusts for any baseline imbalance and usually increases the precision of the treatment effect estimate. We assess the value of such adjustments in the context of a cluster randomized trial with repeated cross-sectional design and a binary outcome. ⋯ We compare the estimated treatment effect and its precision in models that incorporate a covariate measuring the cluster level probabilities at baseline and those that do not. In two data sets, taken from a cluster randomized trial in the treatment of menorrhagia, the value of baseline adjustment is only evident when the number of subjects per cluster is large. We assess the generalizability of these findings by undertaking a simulation study, and find that increased precision of the treatment effect requires both large cluster sizes and substantial heterogeneity between clusters at baseline, but baseline imbalance arising by chance in a randomized study can always be effectively adjusted for.
-
First generation HIV vaccines are not likely to provide complete protection from HIV-1 infection. Therefore, it is important to assess a vaccine's effect on disease progression and infectiousness of infected vaccinees in an efficacy trial; however, direct assessment of such vaccine effects is not feasible within current trial designs. Viral load in HIV-infected individuals correlates with infectiousness and disease progression in a natural history setting, and thus is a reasonable candidate for a surrogate outcome in vaccine efficacy trials. ⋯ Thus, the usual statistical tests for no difference between groups do not test the biologically and clinically relevant hypothesis. We propose a model for the possible selective effects of a vaccine and develop several test statistics for assessing a direct effect of the vaccine on viral load given this selection model. Finite sample properties of these tests are evaluated using computer simulations.
-
Statistics in medicine · Apr 2003
Robustness and power of analysis of covariance applied to ordinal scaled data as arising in randomized controlled trials.
In clinical trials comparing two treatments, ordinal scales of three, four or five points are often used to assess severity, both prior to and after treatment. Analysis of covariance is an attractive technique, however, the data clearly violate the normality assumption and in the presence of small samples, and large sample theory may not apply. The robustness and power of various versions of parametric analysis of covariance applied to small samples of ordinal scaled data are investigated through computer simulation. ⋯ The hierarchical approach which first tests for homogeneity of regression slopes and then fits separate slopes if there is significant non-homogeneity produced significance levels that exceeded the nominal levels especially when the sample sizes were small. The model which assumes homogeneous regression slopes produced the highest power among competing tests for all of the configurations investigated. The t-test on difference scores also produced good power in the presence of small samples.