Recruitment for pediatric trials in Type II Diabetes Mellitus (T2DM) is very challenging, necessitating the exploration of new approaches for reducing the sample sizes of pediatric trials. This work aimed at assessing if a longitudinal Non-Linear-Mixed-Effect (NLME) analysis of T2DM trial could be more powerful and thus require fewer patients than two standard statistical analyses commonly used as primary or sensitivity efficacy analysis: Last-Observation-Carried-Forward (LOCF) followed by (co)variance (AN(C)OVA) analysis at the evaluation time-point, and Mixed-effects Model Repeated Measures (MMRM) analysis. Standard T2DM efficacy studies were simulated, with glycated hemoglobin (HbA1c) as the main endpoint, 24 weeks’ study duration, 2 arms, assuming a placebo and a treatment effect, exploring three different scenarios for the evolution of HbA1c, and accounting for a dropout phenomenon. 1000 trials were simulated, then analyzed using the 3 analyses, whose powers were compared. As expected, the longitudinal modeling MMRM analysis was found to be more powerful than the LOCF + ANOVA analysis at week 24. The NLME analysis gave slightly more accurate drug-effect estimations than the two other methods, however it tended to slightly overestimate the magnitude of the drug effect, and it was more powerful than the MMRM analysis only in some scenarios of slow HbA1c decrease. The gain in power afforded by NLME was more apparent when two additional assessments enriched the design; however, the gain was not systematic for all scenarios. Finally, this work showed that NLME analyses may help to reduce significantly the required sample sizes in T2DM pediatric studies, but only for enriched designs and slow HbA1c decrease.

中文翻译：

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评估非线性混合效应模型以减少2型糖尿病儿童试验的样本量。

II型糖尿病（T2DM）的儿科试验的招募非常具有挑战性，因此有必要探索新的方法来减少儿科试验的样本量。这项工作旨在评估T2DM试验的纵向非线性混合效果（NLME）分析是否可能更有效，从而比通常用作主要或敏感性疗效分析的两种标准统计分析所需的患者更少：最后观察在评估时间点进行正向（LOCF），然后进行（协）方差（AN（C）OVA）分析，然后进行混合效应模型重复测量（MMRM）分析。模拟了标准的T2DM疗效研究，以糖化血红蛋白（HbA1c）为主要终点，研究时间为24周，假设安慰剂和治疗效果，进行了2组治疗，探讨了HbA1c演变的三种不同情况，并考虑了辍学现象。模拟了1000个试验，然后使用3个分析进行了分析，比较了它们的功效。如预期的那样，在第24周时，发现纵向建模MMRM分析比LOCF + ANOVA分析更有效。NLME分析比其他两种方法提供了更准确的药物效应估计，但是倾向于略微高估了仅在某些HbA1c缓慢降低的情况下，它比MMRM分析更有效。当另外两次评估使设计更加丰富时，NLME所提供的功率增益就更加明显。但是，这种增益并非在所有情况下都是系统的。最后，这项工作表明NLME分析可能有助于显着减少T2DM儿科研究中所需的样本量，