Statistics in medicine
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Statistics in medicine · Sep 2005
Sample size for a two-group comparison of repeated binary measurements using GEE.
Controlled clinical trials often randomize subjects to two treatment groups and repeatedly evaluate them at baseline and intervals across a treatment period of fixed duration. A popular primary objective in these trials is to compare the change rates in the repeated measurements between treatment groups. Repeated measurements usually involve missing data and a serial correlation within each subject. ⋯ In this paper, we propose a closed form sample size formula for comparing the change rates of binary repeated measurements using GEE for a two-group comparison. The sample size formula is derived incorporating missing patterns, such as independent missing and monotone missing, and correlation structures, such as AR(1) model. We also propose an algorithm to generate correlated binary data with arbitrary marginal means and a Markov dependency and use it in simulation studies.
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Statistics in medicine · Aug 2005
Diagnostics for assumptions in moderate to large simple clinical trials: do they really help?
In this article, primarily we look at a case study, where prior to conducting the major efficacy analysis, one performs a diagnostic test for assumptions, and acts upon the result if the diagnostic test rejects the assumptions. Specifically, we show by an example that a hybrid approach of using a diagnostic test for equality of variance in a two-sample t-test situation can adversely affect, rather than protect, the operating characteristics of the study. ⋯ Secondarily, we present rationale as to why the classical tests (or slightly modified versions) can be viewed as asymptotically non-parametric, and can actually be more robust against failure of assumptions than rank tests. Readers are cautioned that this illustration is limited to efficacy analysis, and is not meant as a criticism of other analyses, such as modelling or exploratory ones.
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Statistics in medicine · Jul 2005
Comparative StudyFlexible Bayesian methods for cancer phase I clinical trials. Dose escalation with overdose control.
We examine a large class of prior distributions to model the dose-response relationship in cancer phase I clinical trials. We parameterize the dose-toxicity model in terms of the maximum tolerated dose (MTD) gamma and the probability of dose limiting toxicity (DLT) at the initial dose rho(0). The MTD is estimated using the EWOC (escalation with overdose control) method of Babb et al. We show through simulations that a candidate joint prior for (rho0,gamma) with negative a priori correlation structure results in a safer trial than the one that assumes independent priors for these two parameters while keeping the efficiency of the estimate of the MTD essentially unchanged.
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Statistics in medicine · Jul 2005
Dose escalation trial designs based on a molecularly targeted endpoint.
Traditional phase I dose-finding studies for chemotoxic agents base dose escalation on toxicity, with escalation continuing until unacceptable toxicity is observed. Recent development of molecularly targeted agents that have little or no toxicity in the therapeutic dose range has raised questions over the best study designs for phase I studies. Two types of designs are proposed and evaluated in this paper. ⋯ One design is developed to ensure that if the true target response rate is low there will be a high probability of escalating and if the true target response rate is high there will be a low probability of escalating. The other design is developed to continue to escalate as long as the true response rate is increasing and to stop escalating when the response rate plateaus or decreases. A limited simulation study is performed and the designs are compared with respect to the dose level at the end of escalation and the number of patients treated on study.