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A common challenge in designing empirical studies is determining an appropriate sample size. When more complex models are used, estimates of power can only be obtained using Monte Carlo simulations. In this tutorial, we introduce the R package mlpwr to perform simulation-based power analysis based on surrogate modeling. Surrogate modeling is a powerful tool in guiding the search for study design parameters that imply a desired power or meet a cost threshold (e.g., in terms of monetary cost). mlpwr can be used to search for the optimal allocation when there are multiple design parameters, e.g., when balancing the number of participants and the number of groups in multilevel modeling. At the same time, the approach can take into account the cost of each design parameter, and aims to find a cost-efficient design. We introduce the basic functionality of the package, which can be applied to a wide range of statistical models and study designs. Additionally, we provide two examples based on empirical studies for illustration: one for sample size planning when using an item response theory model, and one for assigning the number of participants and the number of countries for a study using multilevel modeling., (© 2023. The Author(s).)

Importance: Body mass index (BMI) of the 95th or greater percentile for age and sex is common among young people, and its prevalence has increased in recent decades., Objective: To examine the benefits and harms of weight management interventions initiated in health care settings among children and adolescents with high BMI., Data Sources: MEDLINE via Ovid, PsycINFO via Ovid, and the Cochrane Central Registry of Controlled Trials through January 12, 2023; ongoing surveillance through January 26, 2024., Study Selection: English-language studies of weight management interventions (behavioral and pharmacologic, including liraglutide, semaglutide, orlistat, and phentermine/topiramate) among children aged 2 to 18 years with high BMI (eg, ≥85th or ≥95th percentile for age and sex) conducted in or recruited from health care settings., Data Extraction and Synthesis: One investigator abstracted data; a second checked for accuracy. Outcomes with sufficient evidence for meta-analysis were pooled using random-effects models., Main Outcomes and Measures: BMI and other weight-related outcomes, cardiometabolic measures, quality of life, physical activity, dietary pattern scores, and harms., Results: Fifty-eight randomized clinical trials (RCTs) were included (N = 10 143). Behavioral interventions were associated with small reductions in BMI and other weight outcomes after 6 to 12 months (28 RCTs [n = 4494]; mean difference in change between groups, -0.7 [95% CI, -1.0 to -0.3]). Larger effects were seen in interventions with higher contact hours and that offered physical activity sessions. Reporting was sparse for outcomes other than BMI, with few significant findings. Semaglutide and phentermine/topiramate had the largest effects on BMI (eg, 1 RCT [n = 201] for semaglutide; mean difference, -6.0 [95% CI, -7.3 to -4.6]). The very few studies that evaluated outcomes after medication discontinuation showed immediate weight regain. Gastrointestinal adverse effects were common with liraglutide, semaglutide, and orlistat. Serious adverse effects were rare, but no studies had follow-up longer than 17 months., Conclusions and Relevance: In the short term, weight management interventions led to lower BMI in children and adolescents, with no evidence of serious harm. Evidence is lacking about how weight management interventions affect BMI beyond 1 year and after medication discontinuation and about longer-term effects on other outcomes.