The use of propensity scores to assess the generalizability of results from randomized trials
Randomized trials remain the most accepted design for estimating the effects of interventions, but they do not necessarily answer a question of primary interest: will the programme be effective in a target population in which it may be implemented? In other words, are the results generalizable? Ther...
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| Published in | Journal of the Royal Statistical Society. Series A, Statistics in society Vol. 174; no. 2; pp. 369 - 386 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford, UK
Blackwell Publishing Ltd
01.04.2011
Blackwell Publishing Blackwell Royal Statistical Society Oxford University Press |
| Series | Journal of the Royal Statistical Society Series A |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0964-1998 1467-985X |
| DOI | 10.1111/j.1467-985X.2010.00673.x |
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| Abstract | Randomized trials remain the most accepted design for estimating the effects of interventions, but they do not necessarily answer a question of primary interest: will the programme be effective in a target population in which it may be implemented? In other words, are the results generalizable? There has been very little statistical research on how to assess the generalizability, or `external validity', of randomized trials. We propose the use of propensity-score-based metrics to quantify the similarity of the participants in a randomized trial and a target population. In this setting the propensity score model predicts participation in the randomized trial, given a set of covariates. The resulting propensity scores are used first to quantify the difference between the trial participants and the target population, and then to match, subclassify or weight the control group outcomes to the population, assessing how well the propensity-score-adjusted outcomes track the outcomes that are actually observed in the population. These metrics can serve as a first step in assessing the generalizability of results from randomized trials to target populations. The paper lays out these ideas, discusses the assumptions underlying the approach and illustrates the metrics by using data on the evaluation of a schoolwide prevention programme called `Positive behavioral interventions and supports'. |
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| AbstractList | Randomized trials remain the most accepted design for estimating the effects of interventions, but they do not necessarily answer a question of primary interest: will the programme be effective in a target population in which it may be implemented? In other words, are the results generalizable? There has been very little statistical research on how to assess the generalizability, or 'external validity', of randomized trials. We propose the use of propensity-score-based metrics to quantify the similarity of the participants in a randomized trial and a target population. In this setting the propensity score model predicts participation in the randomized trial, given a set of covariates. The resulting propensity scores are used first to quantify the difference between the trial participants and the target population, and then to match, subclassify or weight the control group outcomes to the population, assessing how well the propensity-score-adjusted outcomes track the outcomes that are actually observed in the population. These metrics can serve as a first step in assessing the generalizability of results from randomized trials to target populations. The paper lays out these ideas, discusses the assumptions underlying the approach and illustrates the metrics by using data on the evaluation of a schoolwide prevention programme called 'Positive behavioral interventions and supports'. Randomized trials remain the most accepted design for estimating the effects of interventions, but they do not necessarily answer a question of primary interest: will the programme be effective in a target population in which it may be implemented? In other words, are the results generalizable? There has been very little statistical research on how to assess the generalizability, or 'external validity', of randomized trials. We propose the use of propensity-score-based metrics to quantify the similarity of the participants in a randomized trial and a target population. In this setting the propensity score model predicts participation in the randomized trial, given a set of covariates. The resulting propensity scores are used first to quantify the difference between the trial participants and the target population, and then to match, subclassify or weight the control group outcomes to the population, assessing how well the propensity-score-adjusted outcomes track the outcomes that are actually observed in the population. These metrics can serve as a first step in assessing the generalizability of results from randomized trials to target populations. The paper lays out these ideas, discusses the assumptions underlying the approach and illustrates the metrics by using data on the evaluation of a schoolwide prevention programme called 'Positive behavioral interventions and supports'. [PUBLICATION ABSTRACT] Randomized trials remain the most accepted design for estimating the effects of interventions, but they do not necessarily answer a question of primary interest: Will the program be effective in a target population in which it may be implemented? In other words, are the results generalizable? There has been very little statistical research on how to assess the generalizability, or "external validity," of randomized trials. We propose the use of propensity-score-based metrics to quantify the similarity of the participants in a randomized trial and a target population. In this setting the propensity score model predicts participation in the randomized trial, given a set of covariates. The resulting propensity scores are used first to quantify the difference between the trial participants and the target population, and then to match, subclassify, or weight the control group outcomes to the population, assessing how well the propensity score-adjusted outcomes track the outcomes actually observed in the population. These metrics can serve as a first step in assessing the generalizability of results from randomized trials to target populations. This paper lays out these ideas, discusses the assumptions underlying the approach, and illustrates the metrics using data on the evaluation of a schoolwide prevention program called Positive Behavioral Interventions and Supports.Randomized trials remain the most accepted design for estimating the effects of interventions, but they do not necessarily answer a question of primary interest: Will the program be effective in a target population in which it may be implemented? In other words, are the results generalizable? There has been very little statistical research on how to assess the generalizability, or "external validity," of randomized trials. We propose the use of propensity-score-based metrics to quantify the similarity of the participants in a randomized trial and a target population. In this setting the propensity score model predicts participation in the randomized trial, given a set of covariates. The resulting propensity scores are used first to quantify the difference between the trial participants and the target population, and then to match, subclassify, or weight the control group outcomes to the population, assessing how well the propensity score-adjusted outcomes track the outcomes actually observed in the population. These metrics can serve as a first step in assessing the generalizability of results from randomized trials to target populations. This paper lays out these ideas, discusses the assumptions underlying the approach, and illustrates the metrics using data on the evaluation of a schoolwide prevention program called Positive Behavioral Interventions and Supports. Randomized trials remain the most accepted design for estimating the effects of interventions, but they do not necessarily answer a question of primary interest: Will the program be effective in a target population in which it may be implemented? In other words, are the results generalizable? There has been very little statistical research on how to assess the generalizability, or "external validity," of randomized trials. We propose the use of propensity-score-based metrics to quantify the similarity of the participants in a randomized trial and a target population. In this setting the propensity score model predicts participation in the randomized trial, given a set of covariates. The resulting propensity scores are used first to quantify the difference between the trial participants and the target population, and then to match, subclassify, or weight the control group outcomes to the population, assessing how well the propensity score-adjusted outcomes track the outcomes actually observed in the population. These metrics can serve as a first step in assessing the generalizability of results from randomized trials to target populations. This paper lays out these ideas, discusses the assumptions underlying the approach, and illustrates the metrics using data on the evaluation of a schoolwide prevention program called Positive Behavioral Interventions and Supports. Randomized trials remain the most accepted design for estimating the effects of interventions, but they do not necessarily answer a question of primary interest: will the programme be effective in a target population in which it may be implemented? In other words, are the results generalizable? There has been very little statistical research on how to assess the generalizability, or 'external validity', of randomized trials. We propose the use of propensity-score-based metrics to quantify the similarity of the participants in a randomized trial and a target population. In this setting the propensity score model predicts participation in the randomized trial, given a set of covariates. The resulting propensity scores are used first to quantify the difference between the trial participants and the target population, and then to match, subclassify or weight the control group outcomes to the population, assessing how well the propensity-score-adjusted outcomes track the outcomes that are actually observed in the population. These metrics can serve as a first step in assessing the generalizability of results from randomized trials to target populations. The paper lays out these ideas, discusses the assumptions underlying the approach and illustrates the metrics by using data on the evaluation of a schoolwide prevention programme called 'Positive behavioral interventions and supports'. Reprinted by permission of Blackwell Publishers |
| Author | Stuart, Elizabeth A. Bradshaw, Catherine P. Cole, Stephen R. Leaf, Philip J. |
| AuthorAffiliation | 4 Department of Mental Health and Center for the Prevention of Youth Violence, Johns Hopkins Bloomberg School of Public Health, 624 N Broadway, Baltimore, MD 3 Department of Epidemiology, Gillings School of Global Public Health and Center for AIDS Research, University of North Carolina, Chapel Hill, NC 2 Johns Hopkins Bloomberg School of Public Health Departments of Mental Health and Biostatistics, 624 N Broadway, 8th Floor, Baltimore, MD; estuart@jhsph.edu ; 410-502-6222 |
| AuthorAffiliation_xml | – name: 2 Johns Hopkins Bloomberg School of Public Health Departments of Mental Health and Biostatistics, 624 N Broadway, 8th Floor, Baltimore, MD; estuart@jhsph.edu ; 410-502-6222 – name: 3 Department of Epidemiology, Gillings School of Global Public Health and Center for AIDS Research, University of North Carolina, Chapel Hill, NC – name: 4 Department of Mental Health and Center for the Prevention of Youth Violence, Johns Hopkins Bloomberg School of Public Health, 624 N Broadway, Baltimore, MD |
| Author_xml | – sequence: 1 givenname: Elizabeth A. surname: Stuart fullname: Stuart, Elizabeth A. – sequence: 2 givenname: Stephen R. surname: Cole fullname: Cole, Stephen R. – sequence: 3 givenname: Catherine P. surname: Bradshaw fullname: Bradshaw, Catherine P. – sequence: 4 givenname: Philip J. surname: Leaf fullname: Leaf, Philip J. |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23929204$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/24926156$$D View this record in MEDLINE/PubMed http://econpapers.repec.org/article/blajorssa/v_3a174_3ay_3a2011_3ai_3a2_3ap_3a369-386.htm$$DView record in RePEc |
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| SubjectTerms | Applications Causal inference Combinatorics Combinatorics. Ordered structures Control groups Designs and configurations Effects Elementary schools Empirical research Estimating techniques Estimation Exact sciences and technology Experimental design Experimentation Experiments External validity General topics Generalizability Generalization Inference Intervention Mathematics Methodology Observational studies Outcomes of education Population estimates Population mean Positive behavioral interventions and supports Prevention programs Probability and statistics Propensity Random sampling Research synthesis Sampling methods Sciences and techniques of general use State schools Statistical analysis Statistical methods Statistics Valuation |
| Title | The use of propensity scores to assess the generalizability of results from randomized trials |
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