Statistics in medicine
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Statistics in medicine · Jun 2006
A model-based approach in the estimation of the maximum tolerated dose in phase I cancer clinical trials.
The primary aim of a phase I cancer clinical trial is to determine the maximum tolerated dose (MTD) of a new agent. The MTD is determined as the highest dose level of a potential therapeutic agent at which the patients have experienced an acceptable level of dose limiting toxicity. Although many other types of designs have been proposed in recent years, the traditional algorithm-based designs, especially the 3+3 designs, are still widely used due to their practical simplicity. ⋯ In this paper, we propose a model-based approach in the estimation of the MTD following a traditional 3+3 design. Simulation results indicate that our model-based approach produces much less biased estimates of the MTD compared to the estimates obtained from the traditional 3+3 designs. Furthermore, our model-based approach can be easily extended to any traditional A+B design.
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Statistics in medicine · Jun 2006
Generalizing the TITE-CRM to adapt for early- and late-onset toxicities.
Due to the staggered entry of subjects in phase I trials, some subjects will only be partially through the study when others are ready to be enrolled. Nonetheless, many phase I designs focus solely upon whether or not subjects experience toxicity, thereby determining the maximum tolerated dose (MTD) with a binomial likelihood using data from fully observed subjects. The time-to-event continual reassessment method (TITE-CRM) was the first attempt to incorporate information from partially observed subjects by using a weighted binomial likelihood in which the weights are based upon the actual toxicity time distribution. ⋯ The value of theta allows us to reflect the occurrence of early- or late-onset toxicities without correctly specifying the actual distribution of toxicity times. Through this model, we do not necessarily expect to improve identification of the MTD, but rather hope to reduce the exposure of subjects to overly toxic doses. Through simulation, we examine how well our model identifies the MTD and allocates dose assignments in three scenarios investigated by previous publications.
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Statistics in medicine · Apr 2006
On non-inferiority margin and statistical tests in active control trials.
The problem of selecting a non-inferiority margin and the corresponding statistical test for non-inferiority in active control trials is considered. For selection of non-inferiority margin, the guideline by the International Conference on Harmonization (ICH) recommends that the non-inferiority margin should be chosen in such a way that if the non-inferiority of the test therapy to the active control agent is claimed, the test therapy is not only non-inferior to the active control agent, but also superior to the placebo. ⋯ Statistical tests for non-inferiority designed in the situation where the non-inferiority margin is an unknown parameter are derived. An example concerning a cancer trail for testing non-inferiority with the primary study endpoint of the time to disease progression is presented to illustrate the proposed method.
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Statistics in medicine · Dec 2005
Comparative StudyComparison of Cox's and relative survival models when estimating the effects of prognostic factors on disease-specific mortality: a simulation study under proportional excess hazards.
In many prognostic studies focusing on mortality of persons affected by a particular disease, the cause of death of individual patients is not recorded. In such situations, the conventional survival analytical methods, such as the Cox's proportional hazards regression model, do not allow to discriminate the effects of prognostic factors on disease-specific mortality from their effects on all-causes mortality. In the last decade, the relative survival approach has been proposed to deal with the analyses involving population-based cancer registries, where the problem of missing information on the cause of death is very common. ⋯ In contrast, the effect of higher cancer stages was under-estimated by 8-28 per cent. In contrast to crude survival, relative survival model largely reduced such problems and handled well even such challenging tasks as separating the opposite effects of the same variable on cancer-related versus other-causes mortality. Specifically, in all the cases discussed above, the relative bias in the estimates from the Esteve et al.'s model was always below 10 per cent, with the coverage rates above 81 per cent.