Statistical methods in medical research
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Stat Methods Med Res · May 2019
Comment to the reply letter: Letter to the Editor: Preference option randomized design (PORD) for comparative effectiveness research: Statistical power for testing comparative effect, preference effect, selection effect, intent-to-treat effect, and overall effect (SMMR, Vol. 28, Issue 2, 2019).
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Stat Methods Med Res · Mar 2019
Comparative StudyAdjustment for treatment changes in epilepsy trials: A comparison of causal methods for time-to-event outcomes.
When trials are subject to departures from randomised treatment, simple statistical methods that aim to estimate treatment efficacy, such as per protocol or as treated analyses, typically introduce selection bias. More appropriate methods to adjust for departure from randomised treatment are rarely employed, primarily due to their complexity and unfamiliarity. We demonstrate the use of causal methodologies for the production of estimands with valid causal interpretation for time-to-event outcomes in the analysis of a complex epilepsy trial, as an example to guide non-specialist analysts undertaking similar analyses. ⋯ In a complex trial featuring a remission outcome, underlying assumptions of the structural failure time model are likely to be violated, and inverse probability of censoring weighting may provide the most useful option, assuming availability of appropriate data and sufficient sample sizes. Recommendations are provided for analysts when considering which of these methods should be applied in a given trial setting.
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Stat Methods Med Res · Aug 2018
A graphical perspective of marginal structural models: An application for the estimation of the effect of physical activity on blood pressure.
Estimating causal effects requires important prior subject-matter knowledge and, sometimes, sophisticated statistical tools. The latter is especially true when targeting the causal effect of a time-varying exposure in a longitudinal study. Marginal structural models are a relatively new class of causal models that effectively deal with the estimation of the effects of time-varying exposures. ⋯ This graph is then validated and improved utilizing structural equation models. We estimated the aforementioned causal effect using marginal structural models for repeated measures and guided the implementation of the models with the causal graph. By employing the causal graph framework, we also show the validity of fitting conditional marginal structural models for repeated measures in the context implied by our data.
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Stat Methods Med Res · Aug 2018
A more efficient three-arm non-inferiority test based on pooled estimators of the homogeneous variance.
Hida and Tango established a statistical testing framework for the three-arm non-inferiority trial including a placebo with a pre-specified non-inferiority margin to overcome the shortcomings of traditional two-arm non-inferiority trials (such as having to choose the non-inferiority margin). In this paper, we propose a new method that improves their approach with respect to two aspects. ⋯ Furthermore, when that sample size was small or moderate, our simulation studies showed that our approach performed better than Hida and Tango's. Although both controlled the type I error rate, their test was more conservative and the statistical power of our test was higher.