Statistics in medicine
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In the analysis of observational data, stratifying patients on the estimated propensity scores reduces confounding from measured variables. Confidence intervals for the treatment effect are typically calculated without acknowledging uncertainty in the estimated propensity scores, and intuitively this may yield inferences, which are falsely precise. In this paper, we describe a Bayesian method that models the propensity score as a latent variable. ⋯ A novel feature of the proposed method is that it fits models for the treatment and outcome simultaneously rather than one at a time. The method uses the outcome variable to inform the fit of the propensity model. We explore the performance of the estimated propensity scores using cross-validation.
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Statistics in medicine · Dec 2008
Comparative StudyMeta-analysis of skewed data: combining results reported on log-transformed or raw scales.
When literature-based meta-analyses involve outcomes with skewed distributions, the best available data can sometimes be a mixture of results presented on the raw scale and results presented on the logarithmic scale. We review and develop methods for transforming between these results for two-group studies, such as clinical trials and prospective or cross-sectional epidemiological studies. These allow meta-analyses to be conducted using all studies and on a common scale. ⋯ We conclude that an approach based on a log-normal assumption for the raw data is reasonably robust to different true distributions; and we provide new standard error approximations for this method. An assumption can be made that the standard deviations in the two groups are equal. This increases precision of the estimates, but if incorrect can lead to very misleading results.
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The cluster randomized cross-over design has been proposed in particular because it prevents an imbalance that may bring into question the internal validity of parallel group cluster trials. We derived a sample size formula for continuous outcomes that takes into account both the intraclass correlation coefficient (representing the clustering effect) and the interperiod correlation (induced by the cross-over design).
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Statistics in medicine · Nov 2008
Optimal phase I dose-escalation trial designs in oncology--a simulation study.
In phase I oncology trials conducted over the past few decades, the maximum tolerated dose (MTD) has usually been estimated by the traditional escalation rule (TER), which traces back to 1973. In the meantime, new methods have been proposed which hope to estimate the true MTD more precisely than the TER while using less patients. In this simulation study, TER is compared with the accelerated titration dose design (ATD), two up-and-down designs (biased coin design, r-in-a-row (RIAR)), the maximum likelihood version of the continual reassessment method (CRML), and a Bayesian method that is implemented in the software Bayesian ADEPT (assisted decision-making in early phase trials). ⋯ ADEPT turned out to be quick and accurate in determining the MTD, while TER was the safest but least accurate method. CRML was as accurate as TER, and the up-and-down designs did not excel. Bayesian ADEPT is considered a valuable tool for the conduct of phase I dose-escalation trials in oncology, but careful preparation is indispensable before its practical use.
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Receiver operating characteristic (ROC) curve is widely applied in measuring discriminatory ability of diagnostic or prognostic tests. This makes the ROC analysis one of the most active research areas in medical statistics. Many parametric and semiparametric estimation methods have been proposed for estimating the ROC curve and its functionals. ⋯ The accuracy of the estimate of the ROC curve in the simulation studies is examined by the integrated absolute error. In comparison with other existing curve estimation methods, the BB method performs well in terms of accuracy, robustness and simplicity. We also propose a procedure based on the BB approach to test the binormality assumption.