Shao-Hsien Liu1,2, Stavroula A Chrysanthopoulou3, Qiuzhi Chang4, Jacob N Hunnicutt5, Kate L Lapane2. 1. Clinical and Population Health Research Program, Graduate School of Biomedical Sciences. 2. Department of Quantitative Health Sciences, Division of Epidemiology of Chronic Diseases and Vulnerable Populations, University of Massachusetts Medical School, Worcester, MA. 3. Department of Biostatistics, Brown University School of Public Health, Providence, RI. 4. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA. 5. Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA.
Abstract
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.
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.
Authors: Nicholas J Greco; Allen F Anderson; Barton J Mann; Brian J Cole; Jack Farr; Carl W Nissen; James J Irrgang Journal: Am J Sports Med Date: 2009-12-31 Impact factor: 6.202