A case study of competing risk analysis in the presence of missing data

Observational data with missing or incomplete data are common in biomedical research. Multiple imputation is an e ff ective approach to handle missing data with the ability to decrease bias while increasing statistical power and e ffi ciency. In recent years propensity score (PS) matching has been increasingly used in observational studies to estimate treatment e ff ect as it can reduce confounding due to measured baseline covariates. In this paper, we describe in detail approaches to competing risk analysis in the setting of incomplete observational data when using PS matching. First, we used multiple imputation to impute several missing variables simultaneously, then conducted propensity-score matching to match statin-exposed patients with those unexposed. Afterwards, we assessed the e ff ect of statin exposure on the risk of heart failure-related hospitalizations or emergency visits by estimating both relative and absolute e ff ects. Collectively, we provided a general methodological framework to assess treatment e ff ect in incomplete observational data. In addition, we presented a practical approach to produce overall cumulative incidence function (CIF) based on estimates from multiple imputed and PS-matched samples.