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

Sterling McPherson; Celestina Barbosa-Leiker; G Leonard Burns; Donelle Howell; John Roll

doi:10.1037/a0027146

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

Journal article

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Peer reviewed

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

Sterling McPherson, Celestina Barbosa-Leiker, G Leonard Burns, Donelle Howell and John Roll

Experimental and clinical psychopharmacology, Vol.20(3), pp.243-250

06/2012

DOI: https://doi.org/10.1037/a0027146

Handle:

https://hdl.handle.net/2376/104104

PMCID: PMC3785093

PMID: 22329556

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Abstract

Data Interpretation, Statistical

Randomized Controlled Trials as Topic - statistics & numerical data

Randomized Controlled Trials as Topic - methods

Humans

Logistic Models

Substance-Related Disorders - drug therapy

Data Collection - methods

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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Title: Missing data in substance abuse treatment research: current methods and modern approaches
Creators: Sterling McPherson - College of Nursing, Department of Psychology, Washington State University, Spokane, WA 99210-1295, USA. smcpherson05@wsu.edu
Celestina Barbosa-Leiker
G Leonard Burns
Donelle Howell
John Roll
Publication Details: Experimental and clinical psychopharmacology, Vol.20(3), pp.243-250
Academic Unit: Nursing, College of; Elson S. Floyd College of Medicine; Medical Education and Clinical Science, Department of; Psychology, Department of
Publisher: United States
Grant note: 5 U10 DA013714-10 / NIDA NIH HHS U10 DA013714 / NIDA NIH HHS
Identifiers: 99900546867301842
Language: English
Resource Type: Journal article