Propensity score methods for estimating relative risks in cluster randomized trials with low-incidence binary outcomes and selection bias.
Despite randomization, selection bias may occur in cluster randomized trials. Classical multivariable regression usually allows for adjusting treatment effect estimates with unbalanced covariates. However, for binary outcomes with low incidence, such a method may fail because of separation problems. This simulation study focused on the performance of propensity score (PS)-based methods to estimate relative risks from cluster randomized trials with binary outcomes with low incidence. The results suggested that among the different approaches used (multivariable regression, direct adjustment on PS, inverse weighting on PS, and stratification on PS), only direct adjustment on the PS fully corrected the bias and moreover had the best statistical properties.
INSERM U1153, Paris, France; INSERM CIC 1415, Tours, France; CHRU de Tours, Tours, France.
Clémence
C
Leyrat
Agnès
A
Caille
Allan
A
Donner
Bruno
B
Giraudeau
D000328:Adult; D000368:Aged; D016000:Cluster Analysis; D003198:Computer Simulation; D005081:Exercise Therapy; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D009010:Monte Carlo Method; D015207:Osteoarthritis, Hip / Q000628:therapy; D020370:Osteoarthritis, Knee / Q000628:therapy; D010146:Pain; D057216:Propensity Score; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012044:Regression Analysis; D012306:Risk; D015983:Selection Bias
Monte Carlo simulations; binary outcomes; cluster randomized trial; propensity score; selection bias