Poor response to treatment is a defining characteristic of reading disorder. In the present systematic review and meta-analysis, we found that the overall average effect size for treatment efficacy was modest, with a mean standardized difference of 0.38. Small true effects, combined with the difficulty to recruit large samples, seriously challenge researchers planning to test treatment efficacy in dyslexia and potentially in other learning disorders. Nonetheless, most published studies claim effectiveness, generally based on liberal use of multiple testing. This inflates the risk that most statistically significant results are associated with overestimated effect sizes. To enhance power, we propose the strategic use of repeated measurements with mixed-effects modelling. This novel approach would enable us to estimate both individual parameters and population-level effects more reliably. We suggest assessing a reading outcome not once, but three times, at pre-treatment and three times at post-treatment. Such design would require only modest additional efforts compared to current practices. Based on this, we performed ad hoc a priori design analyses via simulation studies. Results showed that using the novel design may allow one to reach adequate power even with low sample sizes of 30–40 participants (i.e., 15–20 participants per group) for a typical effect size of d = 0.38. Nonetheless, more conservative assumptions are warranted for various reasons, including a high risk of publication bias in the extant literature. Our considerations can be extended to intervention studies of other types of neurodevelopmental disorders.

对治疗的不良反应是阅读障碍的一个决定性特征。在目前的系统评价和荟萃分析中，我们发现治疗效果的总体平均效应大小是适度的，平均标准化差异为 0.38。小的真实效果，加上难以招募大样本，严重挑战了计划测试阅读障碍和其他学习障碍的治疗效果的研究人员。尽管如此，大多数已发表的研究都声称其有效性，通常是基于对多重测试的自由使用。这夸大了大多数统计显着结果与高估效应大小相关的风险。为了增强功效，我们建议战略性地使用具有混合效应建模的重复测量。这种新颖的方法将使我们能够更可靠地估计个体参数和群体水平的影响。我们建议评估阅读结果不是一次，而是三次，在治疗前和治疗后三次。与目前的做法相比，这种设计只需要适度的额外努力。基于此，我们通过模拟研究进行了特别的先验设计分析。结果表明，即使在 30-40 名参与者（即每组 15-20 名参与者）的低样本量下，使用这种新颖的设计也可以达到足够的功效，典型的效果大小为 基于此，我们通过模拟研究进行了特别的先验设计分析。结果表明，即使在 30-40 名参与者（即每组 15-20 名参与者）的低样本量下，使用这种新颖的设计也可以达到足够的功效，典型的效果大小为 基于此，我们通过模拟研究进行了特别的先验设计分析。结果表明，即使在 30-40 名参与者（即每组 15-20 名参与者）的低样本量下，使用这种新颖的设计也可以达到足够的功效，典型的效果大小为d = 0.38。尽管如此，出于各种原因，包括现有文献中存在发表偏倚的高风险，仍需要更保守的假设。我们的考虑可以扩展到其他类型的神经发育障碍的干预研究。