Statistics in medicine
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Randomized Phase II or Phase III clinical trials that are powered based on clinical endpoints, such as survival time, may be prohibitively expensive, in terms of both the time required for their completion and the number of patients required. As such, surrogate endpoints, such as objective tumour response or markers including prostate specific antigen or CA-125, have gained widespread popularity in clinical trials. If an improvement in a surrogate endpoint does not itself confer patient benefit, then consideration must be given to the extent to which improvement in a surrogate endpoint implies improvement in the true clinical endpoint of interest. ⋯ One approach to the validation of surrogate endpoints involves ensuring that a valid between-group analysis of the surrogate endpoint constitutes also a valid analysis of the true clinical endpoint. The Prentice criterion is a set of conditions that essentially specify the conditional independence of the impact of treatment on the true endpoint, given the surrogate endpoint. It is shown that this criterion alone ensures that an observed effect of the treatment on the true endpoint implies a treatment effect also on the surrogate endpoint, but contrary to popular belief, it does not ensure the converse, specifically that the observation of a significant treatment effect on the surrogate endpoint can be used to infer a treatment effect on the true endpoint.
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Statistics in medicine · May 2004
Comparative StudyWhat to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data.
To compare the performance of different meta-analysis methods for pooling odds ratios when applied to sparse event data with emphasis on the use of continuity corrections. ⋯ Many routinely used summary methods provide widely ranging estimates when applied to sparse data with high imbalance between the size of the studies' arms. A sensitivity analysis using several methods and continuity correction factors is advocated for routine practice.
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Statistics in medicine · Apr 2004
Comparative StudyEight interval estimators of a common rate ratio under stratified Poisson sampling.
Under the assumption that the rate ratio (RR) is constant across strata, we consider eight interval estimators of RR under stratified Poisson sampling: the weighted least-squares (WLS) interval estimator with the logarithmic transformation, the interval estimator using the principle analogous to that of Fieller's Theorem, the interval estimators using Wald's statistic with and without the logarithmic transformation, the interval estimators using the Mantel-Haenszel statistic with and without the logarithmic transformation, the score test-based interval estimator, and the asymptotic likelihood ratio test-based interval estimator. We apply Monte Carlo simulation to evaluate and compare the performance of these estimators with respect to the coverage probability and the average length in a variety of situations. We find that the coverage probability of the commonly used WLS interval estimator tends to be smaller than the desired confidence level, especially when we have a large number of strata with a small expected total number of cases (ETNC) per stratum and the underlying RR is far away from 1 (i.e. ⋯ However, when evaluating the non-coverage probability in the two tails, we find that the former tends to shift the left, while the latter is generally not subject to this concern. We also note that interval estimator using the Mantel-Haenszel (MH) statistic with the logarithmic transformation is likely less efficient than the two test-based interval estimators using the score and the likelihood ratio tests. Finally, we use the data taken from a study of the postmenopausal hormone use on the risk of breast cancer in women as an example to illustrate the use of these interval estimators considered here.
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Statistics in medicine · Feb 2004
Comparative StudyModelling clustered survival data from multicentre clinical trials.
In randomized clinical trials, subjects are recruited at multiple study centres. Factors that vary across centres may exert a powerful independent influence on study outcomes. ⋯ This approach compares favourably with competing methods and appears minimally affected by violation of the assumption of a gamma-distributed frailty. Recent computational advances make use of the gamma frailty model a practical and appealing tool for addressing centre effects in the analysis of multicentre trials.