Design and Analysis Issues in Community-Based Drug Abuse Prevention
Bibliographic Data
| ID | 3756969 |
|---|---|
| Authors | David M Murray (0000-0003-0797-9269, University of Minnesota), Joel M Moskowitz (0000-0002-1001-5260, University of California, Berkeley), Clyde W Dent (University of Southern California) |
| Year | 1996 |
| Volume | 39 |
| Issue | 7 |
| Pages | 853-867 |
| Publication date | 1996-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Behavioral Scientist (JOURNAL) |
| Journal identifiers | ISSN: 0002-7642 • E-ISSN: 1552-3381 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/0002764296039007007 |
| OpenAlex | W1964670133 |
| Language | EN |
| Citations received | 8 |
| References cited | 27 |
This article reviews the design and analysis issues that face community-based drug abuse prevention trials. Such trials allocate intact social groups to study conditions and fall within the general class of studies called "community trials." Special concerns for community trials include selection bias, differential mortality and maturation, contamination, poorly defined constructs, weak interventions, violation of assumptions underlying analysis methods, and low power. Potential solutions include careful selection of comparison units, tight control over distribution of intervention materials, matching, randomization, adding more units to each condition or more frequent observations in each unit, monitoring exposure to intervention-like activities in all sites, regression adjustment for covariates, modeling time, and selection of an appropriate analysis model. Four approaches to analysis are illustrated in an example based on an adolescent tobacco use prevention study
Clinical study design · Clinical trial · Environmental health · Intervention (counseling) · Matching (statistics) · Psychiatry · Psychological intervention · Risk analysis (engineering) · Selection bias · Substance abuse · Advanced Causal Inference Techniques · Health Policy Implementation Science · Medicine · Psychology · Statistical Methods and Bayesian Inference
English language use as a risk factor for smoking initiation among Hispanic and Asian American adolescents
Random-Effects Modeling of Categorical Response Data
Implementing research‐based substance abuse prevention in communities
Project FLAVOR
Longitudinal Relations Among Depression, Stress, and Coping in High Risk Youth
Drug Abuse Prevention Programming
Estimating the Effects of Interventions That are Deployed in Many Places
Methodological and Substantive Issues in Substance Abuse Prevention Research
Random-Effects Models for Longitudinal Data
Approximate Inference in Generalized Linear Mixed Models
Longitudinal Data Analysis for Discrete and Continuous Outcomes
Random-effects regression models for clustered data with an example from smoking prevention research.
Longitudinal data analysis using generalized linear models
Hierarchical linear models
An Analysis of Variance Pitfall
The Unit of Analysis
| Unique citing works | 8 |
|---|---|
| Citations per year | 0,27 |
| Citation span | 1996 - 2005 (10) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 8 |