Estimating treatment effects in observational studies.

In randomized treatment studies the randomization of subjects to the different treatment conditions ensures that the treatment groups are comparable in their baseline characteristics—measured or unmeasured—so we can confidently attribute differences in treatment outcomes to the assigned treatments. In contrast, subjects in observational studies are not randomly assigned to the treatment groups so differences in treatment outcomes could be due to differences in baseline characteristics between the treatment groups. For example, if we wished to compare the outcome of high-intensity treatment for depression (i.e., many visits in the prior 12 months) versus the outcome of low-intensity treatment for depression (i.e., few visits in the prior 12 months) and included subjects from both primary care and specialty mental health clinics, any observed differences in the outcomes for lowintensity and high-intensity treatment could be due to differences in the proportions of subjects that were treated in the two types of clinics. When the treatments being compared (e.g., low versus high intensity of care) and other factors that can affect the outcome (e.g., type of clinic or patient characteristics) are associated with each other, there is confounding. Confounding makes it difficult to determine whether the treatment of interest truly causes the outcome because the apparent treatment effect could be partly due to its association with the confounding variables. Without appropriate adjustment for confounding variables one may come to biased and misleading conclusions about the effect of the treatment of interest.