Alternatives to Randomized Trials for Estimating Treatment Effects
Thomas B. Newman, Michael A. Kohn
Abstract
Thomas B. Newman, Michael A. Kohn
Abstract
We said in Chapter 8 that randomized blinded trials are the best way to estimate treatment effects because they minimize the potential for confounding, co-interventions, and bias, thus maximizing the strength of causal inference. However, sometimes observational studies can be attractive alternatives to randomized trials because they may be more feasible, ethical, or elegant. Of course, the issue of inferring causality from observational studies is a major topic in classical risk factor epidemiology. In this chapter, we focus on observational studies of treatments rather than risk factors, describing methods of reducing or assessing confounding that are particularly applicable to such studies.
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We said in Chapter 8 that randomized blinded trials are the best way to estimate treatment effects because they minimize the potential for confounding, co-interventions, and bias, thus maximizing the strength of causal inference. However, sometimes observational studies can be attractive alternatives to randomized trials because they may be more feasible, ethical, or elegant. Of course, the issue of inferring causality from observational studies is a major topic in classical risk factor epidemiology. In this chapter, we focus on observational studies of treatments rather than risk factors, describing methods of reducing or assessing confounding that are particularly applicable to such studies.
Key concepts: Observational study, Causal inference, Confounding, Randomized controlled trial, Causality (physics), Psychological intervention, Marginal structural model, Randomized experiment