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

Abstract

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 LTCfocus.org 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 LTCfocus.org is a valuable source of baseline data.

Trial registration number and trial register: ClinicalTrials.gov Identifier: NCT03244917.

Keywords: Cluster randomized trial; Covariate-constrained randomization; Minimization; Study design.

© 2020 The Author(s).

Figures

Fig. 1

Estimated number of black residents versus actual number of black residents enrolled for each nursing home. Points are jittered to avoid overlap. The dashed line represents the median estimated number of black residents for all eligible TRAIN-AD facilities at the start of study recruitment. The solid line is the line of agreement (y = x). Intervention arm is partially masked, and reported as Arms 1 and 2.

Fig. 2

Estimated number of residents versus actual number of residents enrolled for each nursing home. Points are jittered to avoid overlap. The dashed line represents the median estimated number of residents for all eligible TRAIN-AD facilities at the start of study recruitment. The solid line is the line of agreement (y = x). Intervention arm is partially masked, and reported as Arms 1 and 2.

Fig. 3

Distribution of imbalance score for 10,000 simulations randomizing 24 nursing homes from the list of all eligible facilities for TRAIN-AD at the start of study recruitment using the original randomization scheme for TRAIN-AD of four waves of six nursing homes.