E B Bortey | PubMed Health

PubMed1991

Applying survival methodology to adverse experience occurrences in controlled clinical trials.

On the basis of calculated cumulative hazard rates for initial occurrence of adverse experiences of patients following treatment for rheumatoid arthritis and osteoarthritis, a simple function is evolved that fits such cumulative hazard rate data very well. From this simple function, we obtain the estimated hazard rate in terms of two physically meaningful parameters. These two parameters can be used to describe the rate of occurrence of adverse experiences, and to convey the concept of risk of adverse experience associated with duration of exposure to a drug. The parameters are rho 1, which represents the risk of adverse experience at baseline, and rho 2, which defines the rate of occurrence of adverse experience immediately following drug administration. A method of estimating rho 1 and rho 2 is the maximum likelihood approach, and the estimated parameters are given for series of data referring to patients treated for arthritis.

Clinical Trials as Topic↗

PubMed1991

Testing for consistency in a single multicenter trial.

One of the criteria for demonstrating efficacy in a single multicenter trial is that the centers are consistent with respect to the direction and significance of results. The purpose of this research is to discuss the use of the noncentrality parameter delta of an F distribution as a means of testing for the consistency of treatment effects across centers. We state the testing problem as H0: (delta > delta 0) versus H1: (delta < or = delta 0), where delta 0 is prespecified, so that H0 represents inconsistency and H1 consistency. Thus, strong evidence from the sample data is required in order to conclude that the treatment effects are consistent across centers. We discuss reasonable choices for delta 0 and develop the alpha-level, uniformly most powerful and unbiased test, which is equivalent to rejecting H0 if the 100(1 - alpha)% uniformly most accurate and unbiased upper confidence limit for delta is less than or equal to delta 0. We examine other tests based on upper confidence limits, such as those calculated from linear estimators of delta and those calculated from a likelihood approach. We investigate the performance of the tests in a small simulation study and present an example from a four-center clinical trial.

Humans↗