Projecting adverse event incidence rates using empirical Bayes methodology.

Although there is considerable interest in adverse events observed in clinical trials, projecting adverse event incidence rates in an extended period can be of interest when the trial duration is limited compared to clinical practice. A naïve method for making projections might involve modeling the observed rates into the future for each adverse event. However, such an approach overlooks the information that can be borrowed across all the adverse event data. We propose a method that weights each projection using a shrinkage factor; the adverse event-specific shrinkage is a probability, based on empirical Bayes methodology, estimated from all the adverse event data, reflecting evidence in support of the null or non-null hypotheses. Also proposed is a technique to estimate the proportion of true nulls, called the common area under the density curves, which is a critical step in arriving at the shrinkage factor. The performance of the method is evaluated by projecting from interim data and then comparing the projected results with observed results. The method is illustrated on two data sets.

Robust inference from multiple test statistics via permutations: a better alternative to the single test statistic approach for randomized trials.

Formal inference in randomized clinical trials is based on controlling the type I error rate associated with a single pre-specified statistic. The deficiency of using just one method of analysis is that it depends on assumptions that may not be met. For robust inference, we propose pre-specifying multiple test statistics and relying on the minimum p-value for testing the null hypothesis of no treatment effect. The null hypothesis associated with the various test statistics is that the treatment groups are indistinguishable. The critical value for hypothesis testing comes from permutation distributions. Rejection of the null hypothesis when the smallest p-value is less than the critical value controls the type I error rate at its designated value. Even if one of the candidate test statistics has low power, the adverse effect on the power of the minimum p-value statistic is not much. Its use is illustrated with examples. We conclude that it is better to rely on the minimum p-value rather than a single statistic particularly when that single statistic is the logrank test, because of the cost and complexity of many survival trials.

Assessing Similarity to Existing Drugs to Decide Whether to Continue Drug Development.

Developing a drug requires large investments, over many years, with dramatic increases in development costs at later stages. Thus, one wants to make a No Go decision on a compound early, unless evidence continues to suggest that the project will ultimately be successful, so that resources can be focused on the most promising compounds to benefit patients. Instead of predicting the probability of success of a Phase III study, our approach to this decision uses the Phase II study results to assess similarity of the novel compound to existing drugs that are classified by different decision categories, such as a clear Go decision (e.g., a clearly effective drug), a (unfortunately common) Not Sure decision (e.g., a potentially useful but not outstanding drug), and a clear No Go decision (e.g., a clearly not effective drug). We describe how this modeling can be done using both individual and binary endpoints and how results can be combined for several different endpoints. Potential extensions of the method are also discussed.

Atypical depression: enhanced right hemispheric dominance for perceiving emotional chimeric faces.

Two studies compared hemispatial bias for perceiving chimeric faces in patients having either atypical or typical depression and healthy controls. A total of 245 patients having major depressive disorder (MDD) or dysthymia (164 with atypical features) and 115 controls were tested on the Chimeric Faces Test. Atypical depression differed from typical depression and controls in showing abnormally large right hemisphere bias. This was present in patients having either MDD or dysthymia and was not related to anxiety, physical anhedonia, or vegetative symptoms. In contrast, patients having MDD with melancholia showed essentially no right hemisphere bias. This is further evidence that atypical depression is a biologically distinct subtype and underscores the importance of this diagnostic distinction for neurophysiologic studies.