Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score

@article{Rosenbaum1985ConstructingAC,
  title={Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score},
  author={Paul R. Rosenbaum and Donald B. Rubin},
  journal={The American Statistician},
  year={1985},
  volume={39},
  pages={33-38}
}
Abstract Matched sampling is a method for selecting units from a large reservoir of potential controls to produce a control group of modest size that is similar to a treated group with respect to the distribution of observed covariates. We illustrate the use of multivariate matching methods in an observational study of the effects of prenatal exposure to barbiturates on subsequent psychological development. A key idea is the use of the propensity score as a distinct matching variable. 
View via Publisher
5,774 Citations
Highly Influential Citations
447
Background Citations
1,334
Methods Citations
1,697
Results Citations
61

5,774 Citations

Selection of Control Variables in Propensity Score Matching: Evidence from a Simulation Study

Propensity score matching is a widely-used method to measure the effect of a treatment in social as well as health sciences. An important issue in propensity score matching is how to select…

Matching with Propensity Scores to Reduce Bias in Observational Studies

In observational studies subjects are not randomly assigned to treatment and control groups. Therefore, both groups can be significantly different from each other and their differences after applying…

Matching With Multiple Control Groups With Adjustment for Group Differences

When estimating causal effects from observational data, it is desirable to approximate a randomized experiment as closely as possible. This goal can often be achieved by choosing a subsample from the…

Using Multiple Control Groups and Matching to Address Unobserved Biases in Comparative Effectiveness Research

Two analytical strategies to bolster inferences of the effectiveness of policy interventions based on observational data are introduced, which match subjects using a strategy that finely balances the distributions of key categorical covariates and stochastically balances on other covariates.

Propensity Score Methods for Bias Reduction in Observational Studies of Treatment Effect.

Bias Reduction in Observational Cohort Studies-obtaining matched cohorts using propensity scores

Matched cohort designs where different treatments are compared offer a lot of challenges with regard to the selection of variables for matching. One popular method of getting around the issues…

Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group.

This tutorial discusses the uses of propensity score methods for bias reduction, gives references to the literature and illustrates the uses through applied examples.

Propensity score methods for bias reduction in the comparison of a treatment to a non‐randomized control group

In observational studies, investigators have no control over the treatment assignment. The treated and non-treated (that is, control) groups may have large differences on their observed covariates,…

TriMatch: An R Package for Propensity Score Matching of Non-Binary Treatments

This talk introduces the TriMatch package for R that provides a method for determining matched triplets and examples from educational and medical contexts will be discussed.

Estimating Causal Effects from Large Data Sets Using Propensity Scores

Propensity score methods generalize subclassification in the presence of many confounding covariates, such as age, region of the country, and sex, in a study of smoking and mortality.
...

References

SHOWING 1-10 OF 28 REFERENCES

Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies

Abstract Monte Carlo methods are used to study the efficacy of multivariate matched sampling and regression adjustment for controlling bias due to specific matching variables X when dependent…

The Bias Due to Incomplete Matching

SUMMARY Observational studies comparing groups of treated and control units are often used to estimate the effects caused by treatments. Matching is a method for sampling a large reservoir of…

Matching when covariables are normally distributed

SUMMARY The use of matched pairs to reduce effects of concomitant variation in observational studies is considered when covariables have multivariate normal distributions. A case is matched by the…

Stratification by a multivariate confounder score.

The complexity and inefficiency of multiple cross-classification as a means of controlling confounding in etiologic research may be avoided upon summarizing the pattern of confounding factors for…

Controlling Bias in Observational Studies: A Review.

Abstract : This paper reviews work on the effectiveness of different methods of matched sampling and statistical adjustment, alone and in combination, in reducing bias due to confounding x-variables…

Matched Sampling for Causal Effects: The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies

The ability of matched sampling and linear regression adjustment to reduce the bias of an estimate of the treatment eff ect in two sample observational studies is investigated for a simple matching…

Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome

This paper proposes a simple technique for assessing the range of plausible causal con- clusions from observational studies with a binary outcome and an observed categorical covariate. The technique…

Caliper pair-matching on a continuous variable in case-control studies

The use of matched pairs has been criticized as being less efficient than estimators based on random samples. This paper compares the mean square error of an analysis of covariance estimator based on…

From Association to Causation in Observational Studies: The Role of Tests of Strongly Ignorable Treatment Assignment

Abstract If treatment assignment is strongly ignorable, then adjustment for observed covariates is sufficient to produce consistent estimates of treatment effects in observational studies. A general…

Conditional Permutation Tests and the Propensity Score in Observational Studies

Abstract In observational studies, the distribution of treatment assignments is unknown, and therefore randomization tests are not generally applicable. However, permutation tests that condition on…