The analysis of failure time data in crossover studies.
Explore the source record for details and available documents.
Angina Pectoris↗
Biomedical subjects
D R Bristol
Publications and source records attributed to D R Bristol.
Testing equality of treatment variances in a two-by-two crossover study.
Comparative clinical trials are usually conducted to compare the means associated with the treatments. However, it is often also of interest to compare the variances. Here the problem of testing the equality of the variances associated with two treatments in a two-by-two crossover design is examined.
Analysis of Variance↗
A confidence interval for the ratio of treatment variances in a two-by-two crossover study.
The distance between two variances is usually measured using the ratio, and a confidence interval for this ratio can be used to measure its magnitude. For a two-by-two crossover study, special considerations must be made because the observations for any subject are correlated. Here a confidence interval for the ratio of the treatment variances in a two-by-two crossover study is presented.
Analysis of Variance↗
Survival analysis techniques in angina pectoris trials.
The variable 'walking time to moderate angina' on an exercise stress test is the primary means to judge the efficacy of new treatments for angina pectoris. Unfortunately, 'walking time to moderate angina' is often censored by fatigue or other reasons for premature termination of the exercise stress test. If time to fatigue is not treatment-dependent, we propose use of survival analysis techniques in such trials. We present an example from a placebo-controlled multicentre clinical trial and results of simulations that compare various methods of analysis.
Analysis of Variance↗
A Markovian model for comparing incidences of side effects.
For clinical trials that entail observations at successive visits for the occurrence of a side effect, this paper considers a likelihood-based method to compare side effect incidence rates. The method, which employs the assumption of a Markov chain of order one for the vectors of binary responses, handles missing data due to premature withdrawals. An actual numerical example and a simulated example illustrate the technique.
Clinical Trials as Topic↗
Sample sizes for constructing confidence intervals and testing hypotheses.
Although estimation and confidence intervals have become popular alternatives to hypothesis testing and p-values, statisticians usually determine sample sizes for randomized clinical trials by controlling the power of a statistical test at an appropriate alternative, even those statisticians who recommend the use of confidence intervals for inference. There is merit in achieving consistency in the techniques for data analysis and sample size determination. To that end, this paper compares sample size determination with use of the length of the confidence interval with that obtained by control of power.
Angina Pectoris↗
Designing clinical trials for two-sided multiple comparisons with a control.
When a clinical trial is to be conducted to compare more than one experimental treatment to a control treatment, Dunnett's two-sided multiple comparison procedure may be proposed to perform the analysis. During the planning stage, the problem of determining the appropriate sample size must be resolved. Here a solution to this problem is derived by controlling the power of the corresponding testing procedure that assumes that the common variance is known.
Arthritis, Rheumatoid↗
A clinical trial with an interim analysis.
Explore the source record for details and available documents.
Clinical Trials as Topic↗
A one-sided interim analysis with binary outcomes.
Wieand and Therneau (Controlled Clin Trials 8:20-28, 1987) proposed a technique for conducting a clinical trial to test the equality of response rates for two treatments using a one-sided test with an option of terminating the trial at the single interim analysis if it appears that the test treatment will offer no improvement over the control treatment. Chi, Bristol, and Castellana (Stat Med 5:387-392, 1986) considered a clinical trial with the same option when the treatments are compared with respect to the means of variables with normal distributions with a common known variance. Here a decision rule similar to the one proposed in the latter is employed for the problem examined in the former. This decision rule, a generalization of the one proposed by Wieand and Therneau, consists of a one-sided test at the final analysis with a one-sided test at the interim analysis, which is performed in the direction opposite to the one at the final analysis. It is shown that the proposed generalization may result in desirable properties regarding the probability of stopping the trial at the interim analysis in some situations.
Analysis of Variance↗
A clinical trial with an interim analysis.
We consider a fixed-sample parallel-group clinical trial with an interim analysis that tests H0:mu x = mu y against H1:mu x less than mu y. If we do not reject at the interim analysis, then the probability of making a type I error by rejecting H0 in favour of H2:mu x greater than mu y and the power at the final analysis are not appreciably affected by performing the interim analysis for certain relevant critical regions.
Biometry↗