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Analytic Approaches for Assessing Long-Term Treatment Effects

Examples of Empirical Applications and Findings

UCLA Drug Abuse Research Center

Haikang Shen

UCLA Drug Abuse Research Center

Chih-Ping Chou

University of Southern California

Stephen C. Messer

Westat

M. Douglas Anglin

UCLA Drug Abuse Research Center

Analytic approaches, including the structural equation model (autoregressive panel model), hierarchical linear model, latent growth curve model, survival/event history analysis, latent transition model, and time-series analysis (interrupted time series, multivariate time-series analysis) are discussed for their applicability to data of different structures and their utility in evaluating temporal effects of treatment. Methods are illustrated by presenting applications of the various approaches in previous studies examining temporal patterns of treatment effects. Recent advancements in these longitudinal modeling approaches and the accompanying computer software development offer tremendous flexibility in examining long-term treatment effects through longitudinal data with varying numbers and intervals of assessment and types of measures. A multimethod assessment will contribute to a more complete understanding of the complex phenomena of the long-term courses of substance use and its treatment.

Evaluation Review, Vol. 25, No. 2, 233-262 (2001)
DOI: 10.1177/0193841X0102500206


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