Methods for handling missing binary data in substance use disorder trials.
Drug Alcohol Depend
; 250: 110897, 2023 09 01.
Article
in En
| MEDLINE
| ID: mdl-37544038
ABSTRACT
Missing data are a ubiquitous problem in longitudinal substance use disorder (SUD) clinical trials. In particular, the rates of missingness are often high and study participants often intermittently skip their scheduled outcome assessments, leading to so-called "non-monotone" missing data patterns. Moreover, when the primary outcome is a measure of substance use, study investigators often have strong prior beliefs based on their clinical experience that those participants with missing data are more likely to be using substances at those occasions, i.e., data are missing not at random (MNAR). Although approaches for handling missing data are well-developed when the missing data patterns are monotone, arising primarily from study participants withdrawing from the trial prematurely, fewer methods are available for non-monotone missingness. In this paper we review some conventional, as well as more novel, methods for handling non-monotone missingness in SUD trials when the repeatedly measured outcome variable is binary (e.g., denoting presence/absence of substance use). We compare and contrast the different approaches using data from a longitudinal clinical trial of four psychosocial treatments from the Collaborative Cocaine Treatment Study. We conclude by making some recommendations to the SUD research community concerning how more principled methods for handling missing data can be incorporated in the analysis and reporting of trial results.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Models, Statistical
/
Substance-Related Disorders
Type of study:
Clinical_trials
/
Guideline
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Drug Alcohol Depend
Year:
2023
Document type:
Article
Affiliation country:
United States