Instrumental Variable Methods for Continuous Outcomes That Accommodate Nonignorable Missing Baseline Values.
Instrumental variable (IV) methods provide unbiased treatment effect estimation in the presence of unmeasured confounders under certain assumptions. To provide valid estimates of treatment effect, treatment effect confounders that are associated with the IV (IV-confounders) must be included in the analysis, and not including observations with missing values may lead to bias. Missing covariate data are particularly problematic when the probability that a value is missing is related to the value itself, which is known as nonignorable missingness. In such cases, imputation-based methods are biased. Using health-care provider preference as an IV method, we propose a 2-step procedure with which to estimate a valid treatment effect in the presence of baseline variables with nonignorable missing values. First, the provider preference IV value is estimated by performing a complete-case analysis using a random-effects model that includes IV-confounders. Second, the treatment effect is estimated using a 2-stage least squares IV approach that excludes IV-confounders with missing values. Simulation results are presented, and the method is applied to an analysis comparing the effects of sulfonylureas versus metformin on body mass index, where the variables baseline body mass index and glycosylated hemoglobin have missing values. Our result supports the association of sulfonylureas with weight gain.
Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, New York.|Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania.|Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics and Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Ashkan
A
Ertefaie
Department of Healthcare Policy and Research, Weill Cornell Medical College, Cornell University, New York, New York.
James H
JH
Flory
Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics and Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Sean
S
Hennessy
Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania.
Dylan S
DS
Small
D015982:Bias; D015992:Body Mass Index; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D006442:Glycated Hemoglobin / Q000187:drug effects; D006801:Humans; D007004:Hypoglycemic Agents / Q000494:pharmacology; D016018:Least-Squares Analysis; D008687:Metformin / Q000494:pharmacology; D017063:Outcome Assessment, Health Care / Q000379:methods*; D012107:Research Design; D013453:Sulfonylurea Compounds / Q000494:pharmacology; D015430:Weight Gain / Q000187:drug effects
D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents; D008687:Metformin; D013453:Sulfonylurea Compounds; C517652:hemoglobin A1c protein, human
instrumental variables; nonignorable missing values; provider preference; unmeasured confounders