Covariate-constrained randomization for cluster randomized trials in the long-term care setting: Application to the TRAIN-AD trial

摘要

Little has been reported on strategies to ensure key covariate balance in cluster randomized trials in the nursing home setting. Facilities vary widely on key characteristics, small numbers may be randomized, and staggered enrollment is often necessary. A covariate-constrained algorithm was used to randomize facilities in the Trial to Reduce Antimicrobial use In Nursing home residents with Alzheimer's Disease and other Dementias (TRAIN-AD), an ongoing trial in Boston-area facilities (14 facilities/arm). Publicly available 2015 data were leveraged to inform the distribution of key facility-level covariates. The algorithm was applied in waves (2–8 facilities/wave) June 2017–March 2019. To examine the algorithm's general performance, simulations calculated an imbalance score (minimum 0) for similar trial designs. The algorithm provided good balance for profit status (Arm 1, 7 facilities; Arm 2, 6 facilities). Arm 2 was allocated more nursing homes with the number of severely cognitive impaired residents above the median (Arm 1, 7 facilities; Arm 2, 10 facilities), resulting in an imbalance in total number of residents enrolled (Arm 1, 196 residents; Arm 2, 228 residents). Facilities with number of black residents above the median were balanced (7 facilities/arm), while the numbers of black residents enrolled differed slightly between arms (Arm 1, 26 residents (13%); Arm 2, 22 residents (10%)). Simulations showed the median imbalance for TRAIN-AD's original randomization scheme (score = 3), was similar to the observed imbalance (score = 4). Covariate-constrained randomization flexibly accommodates logistical complexities of cluster trials in the nursing home setting, where is a valuable source of baseline data.