Biomedical subjects

J S Denne

Publications and source records attributed to J S Denne.

4 recordsLinked to original sources

PubMed2001

Monitoring a clinical trial with multiple hypotheses concerning the treatment effect on a single primary endpoint.

In a number of clinical trials there is interest in testing more than one hypothesis concerning the treatment effect on a single primary endpoint. For instance, a sponsor of a trial to demonstrate that a test treatment (T) is non-inferior to an active control (R) may also be interested in showing that T is superior to R, if this is the case. Using the closed testing method for constructing tests of multiple hypotheses which control the multiple level of significance, we provide a framework for testing these hypotheses sequentially during a trial at pre-planned interim analyses.

Clinical Trials as Topic↗

PubMed2000

Estimation following extension of a study on the basis of conditional power.

Proschan and Hunsberger (1) propose a method based on conditional power for designed extension of a study beyond its originally intended sample size. Their data-dependent sampling method can be viewed as a two-stage procedure in which the target total sample size is dependent upon the data observed at the first stage. We demonstrate that the maximum likelihood estimate of the parameter of interest upon completion may be biased, and that this bias is similar in direction and magnitude to that commonly associated with estimation following a group sequential test with predetermined target total sample size. Furthermore, we show how a bias adjusted estimate may be formed.

Algorithms↗

PubMed1999

Estimating the sample size for a t-test using an internal pilot.

If the sample size for a t-test is calculated on the basis of a prior estimate of the variance then the power of the test at the treatment difference of interest is not robust to misspecification of the variance. We propose a t-test for a two-treatment comparison based on Stein's two-stage test which involves the use of an internal pilot to estimate variance and thus the final sample size required. We evaluate our procedure's performance and show that it controls the type I and II error rates more closely than existing methods for the same problem. We also propose a rule for choosing the size of the internal pilot, and show that this is reasonable in terms of the efficiency of the procedure.

Clinical Trials, Phase II as Topic↗

PubMedDate not supplied

Sample size recalculation using conditional power.

The sample size required to achieve a given power at a prespecified absolute difference in mean response may depend on one or more nuisance parameters, which are usually unknown. Proposed methods for using an internal pilot to recalculate the sample size using estimates of these parameters have been well studied. Most of these methods ignore the fact that data on the parameter of interest from within this internal pilot will contribute towards the value of the final test statistic. We propose a method which involves recalculating the target sample size by computing the number of further observations required to maintain the probability of rejecting the null hypothesis at the end of the study under the prespecified absolute difference in mean response conditional on the data observed so far. We do this within the framework of a two-group error-spending sequential test, modified so as to prevent inflation of the type I error rate.

Breast Neoplasms↗