TY - JOUR
T1 - Alternatives to the randomized controlled trial
AU - West, Stephen G.
AU - Duan, Naihua
AU - Pequegnat, Willo
AU - Gaist, Paul
AU - Des Jarlais, Don C.
AU - Holtgrave, David
AU - Szapocznik, José
AU - Fishbein, Martin
AU - Rapkin, Bruce
AU - Clatts, Michael
AU - Mullen, Patricia Dolan
PY - 2008/8/1
Y1 - 2008/8/1
N2 - Public health researchers are addressing new research questions (e.g., effects of environmental tobacco smoke, Hurricane Katrina) for which the randomized controlled 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.
AB - Public health researchers are addressing new research questions (e.g., effects of environmental tobacco smoke, Hurricane Katrina) for which the randomized controlled 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.
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U2 - 10.2105/AJPH.2007.124446
DO - 10.2105/AJPH.2007.124446
M3 - Review article
C2 - 18556609
AN - SCOPUS:48749107496
SN - 0090-0036
VL - 98
SP - 1359
EP - 1366
JO - American journal of public health
JF - American journal of public health
IS - 8
ER -