Optimizing disease progression study designs for drug effect discrimination
Optimizing disease progression study designs for drug effect discrimination
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DOI：
10.1007/s10928-013-9331-3
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发表时间：
2013-10-01
影响因子：
2.5
通讯作者：
Hooker, Andrew C.
中科院分区：
文献类型：
--
作者：
Ueckert, Sebastian;Hennig, Stefanie;Hooker, Andrew C.
研究直接优化临床试验设计的统计功效的可能性，以检测药物效应，并与关注参数精密度的最佳设计进行比较。使用Wald近似的一般公式推导出的改进统计量预测疾病进展研究的给定试验设计的统计功效。将预测值与经典Wald统计量一起与通过临床试验模拟确定的基于I型误差校正模型的把握度进行比较。在第二步中，通过直接最大化新统计量来确定最大功效的研究设计。将基于经验功效计算的功效优化设计及其相应性能与侧重于参数精度的设计进行比较。经验确定的权力和新开发的统计数据的比较，显示出良好的协议在所有的情况下调查。这与经典Wald统计相反，后者始终过度预测参考功率，偏差高达90%。使用所提出的度量最大化的设计不同于传统的最优设计，并且在随后的临床试验模拟中显示出相等或高达20%的功效。此外，通过同时优化研究规模和研究设计，使用所提出的方法最大限度地减少达到80%把握度所需的个体数量。在随后的模拟研究中确认了80%的目标功效。开发了一种新的统计量，允许在统计功效方面明确优化临床试验设计。
Investigate the possibility to directly optimize a clinical trial design for statistical power to detect a drug effect and compare to optimal designs that focus on parameter precision. An improved statistic derived from the general formulation of the Wald approximation was used to predict the statistical power for given trial designs of a disease progression study. The predicted value was compared, together with the classical Wald statistic, to a type I error-corrected model-based power determined via clinical trial simulations. In a second step, a study design for maximal power was determined by directly maximizing the new statistic. The resulting power-optimal designs and their corresponding performance based on empirical power calculations were compared to designs focusing on parameter precision. Comparisons of empirically determined power and the newly developed statistic, showed excellent agreement across all scenarios investigated. This was in contrast to the classical Wald statistic, which consistently over-predicted the reference power with deviations of up to 90 %. Designs maximized using the proposed metric differed from traditional optimal designs and showed equal or up to 20 % higher power in the subsequent clinical trial simulations. Furthermore, the proposed method was used to minimize the number of individuals required to achieve 80 % power through a simultaneous optimization of study size and study design. The targeted power of 80 % was confirmed in subsequent simulation study. A new statistic was developed, allowing for the explicit optimization of a clinical trial design with respect to statistical power.