Alternatives to the randomized controlled trial.

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.

[1]  K. Amico Percent total attrition: a poor metric for study rigor in hosted intervention designs. , 2009, American journal of public health.

[2]  L. Bickman,et al.  The Sage Handbook of Social Research Methods , 2008 .

[3]  C. S. Reichardt,et al.  Quasi-Experimental and Correlational Designs: Methods for the Real World When Random Assignment isn't Feasible , 2008 .

[4]  Paul R Rosenbaum,et al.  Combining propensity score matching and group-based trajectory analysis in an observational study. , 2007, Psychological methods.

[5]  Anthony Shakeshaft,et al.  The multiple baseline design for evaluating population-based research. , 2007, American journal of preventive medicine.

[6]  Christopher Winship,et al.  Counterfactuals and Causal Inference: Methods and Principles for Social Research , 2007 .

[7]  James Price,et al.  The impact of a smoking ban on hospital admissions for coronary heart disease. , 2007, Preventive medicine.

[8]  C. Bonell,et al.  Should structural interventions be evaluated using RCTs? The case of HIV prevention. , 2006, Social science & medicine.

[9]  Charles S Reichardt,et al.  The principle of parallelism in the design of studies to estimate treatment effects. , 2006, Psychological methods.

[10]  D. Rubin Matched Sampling for Causal Effects , 2006 .

[11]  Douglas L. Miller,et al.  Does Head Start Improve Children's Life Chances? Evidence from a Regression Discontinuity Design , 2005, SSRN Electronic Journal.

[12]  D. Rubin Causal Inference Using Potential Outcomes , 2005 .

[13]  Don C Des Jarlais,et al.  Improving the reporting quality of nonrandomized evaluations of behavioral and public health interventions: the TREND statement. , 2004, American journal of public health.

[14]  D. McCaffrey,et al.  Propensity score estimation with boosted regression for evaluating causal effects in observational studies. , 2004, Psychological methods.

[15]  Donald B. Rubin,et al.  Principal Stratification Approach to Broken Randomized Experiments , 2003 .

[16]  Booil Jo,et al.  Statistical power in randomized intervention studies with noncompliance. , 2002, Psychological methods.

[17]  J. Schafer,et al.  Missing data: our view of the state of the art. , 2002, Psychological methods.

[18]  Paul R. Rosenbaum,et al.  Replicating Effects and Biases , 2001 .

[19]  D. Moher,et al.  The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomized trials. , 2001, Journal of the American Podiatric Medical Association.

[20]  D. Moher,et al.  The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomized trials. , 2001, Annals of internal medicine.

[21]  W. Shadish,et al.  Experimental and Quasi-Experimental Designs for Generalized Causal Inference , 2001 .

[22]  Stephen G. West,et al.  Causal inference and generalization in field settings: Experimental and quasi-experimental designs. , 2000 .

[23]  Christopher Winship,et al.  THE ESTIMATION OF CAUSAL EFFECTS FROM OBSERVATIONAL DATA , 1999 .

[24]  W. Shadish,et al.  Design rules: More steps towards a complete theory of quasi-experimentation , 1999 .

[25]  R. Little,et al.  Statistical Techniques for Analyzing Data from Prevention Trials: Treatment of No-Shows Using Rubin's Causal Model , 1998 .

[26]  P. R. Rosembaum,et al.  Identification of Causal Effects Using Instrumental Variables: Comment , 1996 .

[27]  H. Robbins,et al.  Clinical and prophylactic trials with assured new treatment for those at greater risk: I. A design proposal. , 1996, American journal of public health.

[28]  H Robbins,et al.  Clinical and prophylactic trials with assured new treatment for those at greater risk: II. Examples. , 1996, American journal of public health.

[29]  A. Vinokur,et al.  Impact of the JOBS intervention on unemployed workers varying in risk for depression , 1995, American journal of community psychology.

[30]  Joshua D. Angrist,et al.  Identification of Causal Effects Using Instrumental Variables , 1993 .

[31]  S. Zeger,et al.  On estimating efficacy from clinical trials. , 1991, Statistics in medicine.

[32]  T. Speed,et al.  On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9 , 1990 .

[33]  D. Rubin,et al.  Statistical Analysis with Missing Data. , 1989 .

[34]  Stephen G. West,et al.  A Multiplist Strategy for Strengthening Nonequivalent Control Group Designs , 1987 .

[35]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[36]  Ronald G. Stansfield,et al.  Sociological Methodology 1982 , 1983 .

[37]  D. Rubin Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[38]  J. Tukey The Future of Data Analysis , 1962 .

[39]  D. Campbell Factors relevant to the validity of experiments in social settings. , 1957, Psychological bulletin.

[40]  R Fisher,et al.  Design of Experiments , 1936 .