Stephen G West ^1*, Naihua Duan ², Willo Pequegnat ³, Paul Gaist ⁴, Don C Des Jarlais ⁵, David Holtgrave ⁶, Jose Szapocznik ⁷, Martin Fishbein ⁸, Bruce Rapkin ⁹, Michael Clatts ¹⁰, Patricia Dolan Mullen ¹¹

¹ Arizona State University
² Columbia Univesity and New York State Psychiatric Institute
³ National Institute of Mental Health
⁴ National Institutes of Health
⁵ Beth Israel Medical Center, New York
⁶ Johns Hopkins Bloomberg School of Public Health
⁷ University of Miami School of Medicine
⁸ University of Pennsylvania, Annenberg School for Communication
⁹ Memorial Sloan-Kettering Cancer Center
¹⁰ National Development and Research Institutes, Inc., New York
¹¹ University of Texas, School of Public Health, Houston

^* To whom correspondence should be addressed. E-mail: sgwest{at}asu.edu.

	Abstract

Public health researchers are addressing new research questions (e.g., effects of environmental tobacco smoke, Hurrican Katrina) for which the randomized control trial (RCT) may not be a feasible option. Drawing on the potential outcomes framework (Rubin causal model) and Campbellian perspectives, we consider alternative research designs that permit relatively strong causal inferences. In randomized encouragement designs, participants are randomly invited to participate in one of the treatment conditions but are allowed to decide whether to receive treatment. In quantitative assignment designs, treatment is assigned on the basis of a quantitative measure (e.g., need, merit, risk). In observational studies, treatment assignment is unknown and presumed to be nonrandom. Major threats to the validity of each design and statistical strategies for mitigating those threats are presented.

Key Words: Statistics/Evaluation/Research

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