An observational study draws inferences from a sample to a population where the independent variable is not under the control of the researcher because of ethical concerns or logistical constraints.
One common observational study is about the possible effect of a treatment on subjects, where the assignment of subjects into a treated group versus a control group is outside the control of the investigator. This is in contrast with experiments, such as randomized controlled trials, where each subject is randomly assigned to a treated group or a control group.
Although observational studies cannot be used as reliable sources to make statements of fact about the “safety, efficacy, or effectiveness” of a practice, they can still be of use for some other things, as Richard Nahin, Ph.D., M.P.H., of the National Center for Complementary and Alternative Medicine, writes in a blog post:
“They can: 1) provide information on “real world” use and practice; 2) detect signals about the benefits and risks of…[the] use [of practices] in the general population; 3) help formulate hypotheses to be tested in subsequent experiments; 4) provide part of the community-level data needed to design more informative pragmatic clinical trials; and 5) inform clinical practice.“
Types Of Observational Studies
Case-control study: study originally developed in epidemiology, in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute.
Cross-sectional study: involves data collection from a population, or a representative subset, at one specific point in time.
Longitudinal study: correlational research study that involves repeated observations of the same variables over long periods of time.
Cohort study or Panel study: a particular form of longitudinal study where a group of patients is closely monitored over a span of time.
Ecological study: an observational study in which at least one variable is measured at the group level.
In all of those cases, if a randomized experiment cannot be carried out, the alternative line of investigation suffers from the problem that the decision of which subjects receive the treatment is not entirely random and thus is a potential source of bias. A major challenge in conducting observational studies is to draw inferences that are acceptably free from influences by overt biases, as well as to assess the influence of potential hidden biases.
An observer of an uncontrolled experiment (or process) records potential factors and the data output: the goal is to determine the effects of the factors. Sometimes the recorded factors may not be directly causing the differences in the output. There may be more important factors which were not recorded but are, in fact, causal.
Also, recorded or unrecorded factors may be correlated which may yield incorrect conclusions. Finally, as the number of recorded factors increases, the likelihood increases that at least one of the recorded factors will be highly correlated with the data output simply by chance.
In lieu of experimental control, multivariate statistical techniques allow the approximation of experimental control with statistical control, which accounts for the influences of observed factors that might influence a cause-and-effect relationship. In healthcare and the social sciences, investigators may use matching to compare units that non-randomly received the treatment and control. One common approach is to use propensity score matching in order to reduce confounding.
Rosenbaum, Paul R. 2009 Design of Observational Studies New York: Springer ISBN: 978-1441912121