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Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study

Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study

To compensate for the aperiodicity of the data, the distribution of data points was increased by computing a simple combination method as follows: C(n,k)=P(n, k)k!=n!(n−k)!k! (1) where P indicates the permutation function, n is the number of data points, and k is the number of selected sequential data points (4 in this study).

Byungeun Shon, Sook Jin Seong, Eun Jung Choi, Mi-Ri Gwon, Hae Won Lee, Jaechan Park, Ho-Young Chung, Sungmoon Jeong, Young-Ran Yoon

JMIR AI 2025;4:e64845

Readdressing the Ongoing Challenge of Missing Data in Youth Ecological Momentary Assessment Studies: Meta-Analysis Update

Readdressing the Ongoing Challenge of Missing Data in Youth Ecological Momentary Assessment Studies: Meta-Analysis Update

Similarly, standardized residuals larger than the 100×(1−0.05/(2×k))th percentile of a standard normal distribution indicated outliers [63]. Expecting widespread missingness across reported variables, we conducted separate meta-regressions with single predictors.

Konstantin Drexl, Vanisha Ralisa, Joëlle Rosselet-Amoussou, Cheng K Wen, Sébastien Urben, Kerstin Jessica Plessen, Jennifer Glaus

J Med Internet Res 2025;27:e65710