Department of Pediatric Rheumatology/Immunology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Department of Pediatrics, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Behav Res Methods. 2024 Mar;56(3):2569-2580. doi: 10.3758/s13428-023-02167-5. Epub 2023 Aug 1.
The Permutation Distancing Test (PDT) is a nonparametric test for evaluating treatment effects in dependent single-case observational design (SCOD) AB-phase data without linear trends. Monte Carlo methods were used to estimate the PDT power and type I error rate, and to compare them to those of the Single-Case Randomization Test (SCRT) assuming a randomly determined intervention point and the traditional permutation test assuming full exchangeability. Data were simulated without linear trends for five treatment effect levels (- 2, - 1, 0, 1, 2), five autocorrelation levels (0, .15, .30, .45, .60), and four observation number levels (30, 60, 90, 120). The power was calculated multiple times for all combinations of factor levels each generating 1000 replications. With 30 observations, the PDT showed sufficient power (≥ 80%) to detect medium treatment effects up to autocorrelation ≤ .45. Using 60 observations, the PDT showed sufficient power to detect medium treatment effects regardless of autocorrelation. With ≥ 90 observations, the PDT could also detect small treatment effects up to autocorrelation ≤ .30. With 30 observations, the type I error rate was 5-7%. With 60 observations and more, the type I error rate was ≤ 5% with autocorrelation < .60. The PDT outperformed the SCRT regarding power, particularly with a small number of observations. The PDT outperformed the traditional permutation test regarding type I error rate control, especially when autocorrelation increased. In conclusion, the PDT is a useful and promising nonparametric test to evaluate treatment effects in dependent SCOD AB-phase data without linear trends.
置换距离检验（PDT）是一种非参数检验方法，用于评估具有依赖性的单案例观察性设计（SCOD）AB 阶段数据中没有线性趋势的治疗效果。蒙特卡罗方法用于估计 PDT 的功效和Ⅰ类错误率，并将其与假设干预点随机确定的单案例随机化检验（SCRT）和假设完全可交换的传统置换检验进行比较。在没有线性趋势的情况下，对五种治疗效果水平（-2、-1、0、1、2）、五种自相关水平（0、.15、.30、.45、.60）和四种观测数水平（30、60、90、120）进行了数据模拟。对于每个因素水平组合，都进行了多次功效计算，每个组合生成 1000 次重复。在 30 次观测的情况下，PDT 具有足够的功效（≥80%），可以检测到自相关≤.45 的中等治疗效果。使用 60 次观测，PDT 具有足够的功效，可以检测到无论自相关如何的中等治疗效果。使用≥90 次观测，PDT 还可以检测到自相关≤.30 的小治疗效果。在 30 次观测的情况下，Ⅰ类错误率为 5-7%。在 60 次观测或更多观测的情况下，当自相关<.60 时，Ⅰ类错误率≤5%。PDT 在功效方面优于 SCRT，尤其是在观测次数较少的情况下。PDT 在控制Ⅰ类错误率方面优于传统的置换检验，尤其是自相关增加时。总之，PDT 是一种有用且有前途的非参数检验方法，可用于评估没有线性趋势的依赖性 SCOD AB 阶段数据中的治疗效果。
