Missing Data in Marginal Structural Models: A Plasmode Simulation Study Comparing Multiple Imputation and Inverse Probability Weighting
Department of Quantitative Health Sciences, Division of Epidemiology of Chronic Diseases and Vulnerable Populations
Biostatistics | Epidemiology | Health Services Research | Quantitative, Qualitative, Comparative, and Historical Methodologies | Statistics and Probability
BACKGROUND: The use of marginal structural models (MSMs) to adjust for time-varying confounding has increased in epidemiologic studies. However, in the setting of MSMs, recommendations for how best to handle missing data are contradictory. We present a plasmode simulation study to compare the validity and precision of MSMs estimates using complete case analysis (CC), multiple imputation (MI), and inverse probability weighting (IPW) in the presence of missing data on time-independent and time-varying confounders.
MATERIALS AND METHODS: Simulations were based on a cohort substudy using data from the Osteoarthritis Initiative which estimated the marginal causal effect of intra-articular injection use on yearly changes in knee pain. We simulated 81 scenarios with parameter values varied on missing mechanisms (MCAR, MAR, and MNAR), percentages of missing (10%, 20%, and 30%), type of confounders (time-independent, time-varying, either or both), and analytical approaches (CC, IPW, and MI). The performance of CC, IPW, and MI methods was compared using relative bias, mean squared error of the estimates of interest, and empirical power.
RESULTS: Across scenarios defined by missing data mechanism, extent of missing data, and confounder type, MI generally produced less biased estimates (range: 1.2%-6.7%) with better precision (range: 0.17-0.18) compared with IPW (relative bias: -5.3% to 8.0%; precision: 0.19-0.53). Empirical power was constant across the scenarios using MI.
CONCLUSIONS: Under simple yet realistically constructed scenarios, MI seems to confer an advantage over IPW in MSMs applications.
missing data, marginal structural models, plasmode simulation, multiple imputation, inverse probability weighting
DOI of Published Version
Med Care. 2019 Mar;57(3):237-243. doi: 10.1097/MLR.0000000000001063. Link to article on publisher's site
Liu, Shao-Hsien; Chrysanthopoulou, Stavroula A.; Chang, Qiuzhi; Hunnicutt, Jacob N.; and Lapane, Kate L., "Missing Data in Marginal Structural Models: A Plasmode Simulation Study Comparing Multiple Imputation and Inverse Probability Weighting" (2019). Population and Quantitative Health Sciences Publications. 1277.