Missing data in substance abuse treatment research: Current methods and modern approaches.

Two common procedures for the treatment of missing information, listwise deletion and positive urine analysis (UA) imputation (e.g., if the participant fails to provide urine for analysis, then score the UA positive), may result in significant biases during the interpretation of treatment effects. To compare these approaches and to offer a possible alternative, these two procedures were compared to the multiple imputation (MI) procedure with publicly available data from a recent clinical trial (National Drug Abuse Treatment Clinical Trials Network protocol CTN-0003, Ling et al, 2009). Listwise deletion, single imputation (i.e., positive UA imputation), and MI missing data procedures were used to comparatively examine the effect of the protocol’s two different buprenorphine/naloxone tapering schedules (7- or 28-days) for opioid addiction on the likelihood of a positive UA. The listwise deletion of missing data resulted in a nonsignificant effect for the taper while the positive UA imputation procedure resulted in a significant effects, replicating the original findings by Ling et al (2009). Although the MI procedure also resulted in a significant effect, the effect size was meaningfully smaller and the standard errors meaningfully larger when compared to the positive UA procedure. This study demonstrates that the researcher can obtain markedly different results depending on how the missing data are handled. Missing data theory suggests that listwise deletion and single imputation procedures should not be used to account for missing information, and that MI has advantages with respect to internal and external validity when the assumption of missing at random can be reasonably supported. Consistent with previous investigation of missing data in substance abuse treatment, the authors encourage researchers to understand and report the missing data mechanism as well as use newer procedures for the treatment of missing information (i.e., MI or direct maximum likelihood procedures) that are based on a research-specified “best estimate” of the missing values.

Related protocols: CTN-0003