Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine.
Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine.
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DOI：
10.3758/s13428-022-02012-1
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发表时间：
2023-12
影响因子：
5.4
通讯作者：
Ridenour TA
中科院分区：
文献类型：
--
作者：
Tueller S;Ramirez D;Cance JD;Ye A;Wheeler AC;Fan Z;Hornik C;Ridenour TA
功效分析为行为和医学研究的先验规划提供信息，包括规律性的随机临床试验（即旨在根据个体差异向一般人群推断结果的研究）。可用于遵循具体方法的临床试验功效分析的调查和资源要少得多，这种方法强调基线（对照）阶段与一个或多个治疗阶段之间的个体差异。我们测试了对统计功效的影响，以检测研究人员控制下的四个具体试验设计因素的治疗结果，假设采用多基线设计：样本大小、每个参与者的观察数量、基线阶段的观察比例以及竞争统计模型（即分层建模与分段回归）。我们还测试了很大程度上超出研究人员控制范围的四个因素的影响：人口规模、由于残差导致的个体变异比例、治疗效果大小以及治疗阶段的结果形式（阶段跳跃与渐变）。使用所有因素组合的蒙特卡洛模拟是从 200、1750 和 3500 名参与者的有限群体中进行替换抽样的。在保持所有其他因素不变的情况下，分析描述了每个因素单独和所有两因素组合的独特相对影响。每个因素都会影响功效，其中最大的影响来自较大的治疗效果大小，其次是每个参与者的更多观察、更大的样本、较小的残差方差，以及与将接近 50% 的观察值分配到基线阶段相关的功效的意外改善。这项研究的技术和 R 包更好地实现了针对罕见疾病、精准医学和其他小样本研究的具体临床试验的先验严格设计。在线版本包含可在 10.3758/s13428-022-02012-1 获取的补充材料。
Power analysis informs a priori planning of behavioral and medical research, including for randomized clinical trials that are nomothetic (i.e., studies designed to infer results to the general population based on interindividual variabilities). Far fewer investigations and resources are available for power analysis of clinical trials that follow an idiographic approach, which emphasizes intraindividual variabilities between baseline (control) phase versus one or more treatment phases. We tested the impact on statistical power to detect treatment outcomes of four idiographic trial design factors that are under researchers’ control, assuming a multiple baseline design: sample size, number of observations per participant, proportion of observations in the baseline phase, and competing statistical models (i.e., hierarchical modeling versus piecewise regression). We also tested the impact of four factors that are largely outside of researchers’ control: population size, proportion of intraindividual variability due to residual error, treatment effect size, and form of outcomes during the treatment phase (phase jump versus gradual change). Monte Carlo simulations using all combinations of the factors were sampled with replacement from finite populations of 200, 1750, and 3500 participants. Analyses characterized the unique relative impact of each factor individually and all two-factor combinations, holding all others constant. Each factor impacted power, with the greatest impact being from larger treatment effect sizes, followed respectively by more observations per participant, larger samples, less residual variance, and the unexpected improvement in power associated with assigning closer to 50% of observations to the baseline phase. This study’s techniques and R package better enable a priori rigorous design of idiographic clinical trials for rare diseases, precision medicine, and other small-sample studies. The online version contains supplementary material available at 10.3758/s13428-022-02012-1.
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影响因子：
3.5
作者：
Ridenour, Ty A.;Pineo, Thomas Z.;Molina, Mildred M. Maldonado;Lich, Kristen Hassmiller
通讯作者：
Lich, Kristen Hassmiller
DOI：
10.1037/amp0000707
发表时间：
2021-04
期刊：
The American psychologist
影响因子：
--
作者：
Gruber J;Prinstein MJ;Clark LA;Rottenberg J;Abramowitz JS;Albano AM;Aldao A;Borelli JL;Chung T;Davila J;Forbes EE;Gee DG;Hall GCN;Hallion LS;Hinshaw SP;Hofmann SG;Hollon SD;Joormann J;Kazdin AE;Klein DN;La Greca AM;Levenson RW;MacDonald AW;McKay D;McLaughlin KA;Mendle J;Miller AB;Neblett EW;Nock M;Olatunji BO;Persons JB;Rozek DC;Schleider JL;Slavich GM;Teachman BA;Vine V;Weinstock LM
通讯作者：
Weinstock LM
影响因子：
--
作者：
Ridenour TA;Chen SK;Liu HY;Bobashev GV;Hill K;Cooper R
通讯作者：
Cooper R
影响因子：
--
作者：
Baquet, CR;Commiskey, P;Mishra, SI
通讯作者：
Mishra, SI
影响因子：
5.8
作者：
Kreidler, Sarah M.;Muller, Keith E.;Glueck, Deborah H.
通讯作者：
Glueck, Deborah H.