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Observational Study:Definition and Examples

Paul R. Rosenbaum

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Abstract

Abstract An observational study is an empiric investigation that attempts to estimate the effects caused by a treatment when it is not possible to perform an experiment. Random assignment of subjects to treatment or control in an experiment ensures that comparable groups of subjects are compared under alternative treatments. Without random assignment, in an observational study, the treatment and control groups may not be comparable prior to treatment, so that differing outcomes after treatment may not be effects caused by the treatment. Pretreatment differences that have been accurately measured are calledovert biases; they may often be controlled using analytical adjustments, such as matching, stratification, or covariance adjustment. Pretreatment differences that were not recorded are called hidden biases, and they pose substantial problems that must be addressed by other means. A well designed observational study permits detection of one or two of the most plausible hidden biases. The analysis of an observational study can measure the degree of sensitivity of conclusions to hidden biases of plausible magnitudes. Design strategies involving pattern specificity, such as Campbell's ‘multiple operationalism’ or Hill's dose‐response, may reduce sensitivity to hidden bias, and this possibility may be appraised using the design sensitivity of competing designs.

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Abstract An observational study is an empiric investigation that attempts to estimate the effects caused by a treatment when it is not possible to perform an experiment. Random assignment of subjects to treatment or control in an experiment ensures that comparable groups of subjects are compared under alternative treatments. Without random assignment, in an observational study, the treatment and control groups may not be comparable prior to treatment, so that differing outcomes after treatment may not be effects caused by the treatment. Pretreatment differences that have been accurately measured are calledovert biases; they may often be controlled using analytical adjustments, such as matching, stratification, or covariance adjustment. Pretreatment differences that were not recorded are called hidden biases, and they pose substantial problems that must be addressed by other means. A well designed observational study permits detection of one or two of the most plausible hidden biases. The analysis of an observational study can measure the degree of sensitivity of conclusions to hidden biases of plausible magnitudes. Design strategies involving pattern specificity, such as Campbell's ‘multiple operationalism’ or Hill's dose‐response, may reduce sensitivity to hidden bias, and this possibility may be appraised using the design sensitivity of competing designs.

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Available abstract

Abstract An observational study is an empiric investigation that attempts to estimate the effects caused by a treatment when it is not possible to perform an experiment. Random assignment of subjects to treatment or control in an experiment ensures that comparable groups of subjects are compared under alternative treatments. Without random assignment, in an observational study, the treatment and control groups may not be comparable prior to treatment, so that differing outcomes after treatment may not be effects caused by the treatment. Pretreatment differences that have been accurately measured are calledovert biases; they may often be controlled using analytical adjustments, such as matching, stratification, or covariance adjustment. Pretreatment differences that were not recorded are called hidden biases, and they pose substantial problems that must be addressed by other means. A well designed observational study permits detection of one or two of the most plausible hidden biases. The analysis of an observational study can measure the degree of sensitivity of conclusions to hidden biases of plausible magnitudes. Design strategies involving pattern specificity, such as Campbell's ‘multiple operationalism’ or Hill's dose‐response, may reduce sensitivity to hidden bias, and this possibility may be appraised using the design sensitivity of competing designs.

Key concepts: Observational study, Random assignment, Matching (statistics), Sensitivity (control systems), Covariance, Statistics, Analysis of covariance, Propensity score matching

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