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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. Listwise deletion, single imputation (i.e., positive UA imputation), and MI missing data procedures were used to comparatively examine the effect of two different buprenorphine/naloxone tapering schedules (7- or 28-days) for opioid addiction on the likelihood of a positive UA (Clinical Trial Network 0003; Ling et al., 2009). The listwise deletion of missing data resulted in a nonsignificant effect for the taper while the positive UA imputation procedure resulted in a significant effect, 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.

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ABSTRACT. Objective: although it has been recognized that the course of alcoholism may differ across individuals, little work has characterized drinking trajectories from drinking onset to midlife. Method: the current study examined trajectories of alcohol dependence from adolescence to the mid-50s in a sample of 420 men with a lifetime diagnosis of alcohol dependence. Men from the Vietnam era twin Registry were given the lifetime Drinking history, which assesses the patterns of alcohol consumption and diagnostic symptoms for self-defined drinking phases. phase data were converted into person-year data, with alcohol-dependence diagnosis coded as present or absent for each of 13 age groupings, starting with up to age 20 and ending with ages 54-56. Results: latent growth mixture modeling was used to define four drinking trajectories: young-adult, late-onset, severe-nonchronic, and severe-chronic alcoholics. Further analyses with other diagnostic variables, other drinking variables, alcohol expectancies, personality scales, and religiousness scores were completed to differentiate men best categorized by each trajectory. Conclusions: extension of latent growth Mixture Modeling (lgMM) into the mid-50s revealed that, although some individuals remain chronic alcohol users, others decline in alcohol problem use. Differences seen among these groups may help in the treatment of alcohol dependence, such that more energy can be spent treating those likely to be in the more severe trajectories. (J. Stud. Alcohol