Minimizing error in estimates of the effect of interventions by accounting for baseline measurements: A simulation study analyzing effects on child growth

Abstract Interventions to reduce childhood stunting burden require clinical trials with a primary outcome of linear growth. When growth is measured longitudinally, there are several options for including baseline measurements in the analysis. This study compares the performance of several methods. Randomized controlled trials evaluating a hypothetical intervention to improve length‐for‐age z‐score (LAZ) from birth through 24 months of age were simulated. The intervention effect was evaluated using linear regression and five methods for handling baseline measurements: comparing final measurements only (FINAL), comparing final measurement adjusted for baseline (ADJUST), comparing the change in the measurement over time (DELTA), adjusting for baseline when comparing the changes over time (DELTA+ADJUST) and adjusting for baseline in two‐step residuals approach (RESIDUALS). We calculated bias, precision and power of each method for scenarios with and without a baseline imbalance in LAZ. Using a 0.15 effect size at 18 months, FINAL and DELTA required 1200 and 1500 enroled participants, respectively, to reach 80% power, whereas ADJUST, DELTA+ADJUST and RESIDUALS only required 900 participants. The adjusted models also produced unbiased estimates when there was a baseline imbalance, whereas the FINAL and DELTA methods produced biased estimates, as large as 0.07 lower and higher, respectively, than the true effect. Adjusted methods required smaller sample size and produced more precise results than both DELTA and FINAL methods in all test scenarios. If randomization fails, and there is an imbalance in LAZ at baseline, DELTA and FINAL methods can produce biased estimates, but adjusted models remain unbiased. These results warn against using the FINAL or DELTA methods