2004
DOI: 10.1002/pds.969
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Principles for modeling propensity scores in medical research: a systematic literature review

Abstract: Reporting of aspects related to propensity score model development is limited and raises questions about the value of these principles in developing propensity scores from which unbiased treatment effects are estimated.

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Cited by 301 publications
(255 citation statements)
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“…The propensity score method combines all measured confounders of interest in a single score used for matching. Examining the differences in distributions of confounders between matched groups is performed to verify the appropriate balance (23). Although neither this nor any other method can account for unmeasured variables, it is the best currently available for this type of analysis.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…The propensity score method combines all measured confounders of interest in a single score used for matching. Examining the differences in distributions of confounders between matched groups is performed to verify the appropriate balance (23). Although neither this nor any other method can account for unmeasured variables, it is the best currently available for this type of analysis.…”
Section: Discussionmentioning
confidence: 99%
“…Using logistic regression modeling, we derived predicted probabilities (propensity scores) for receiving immunosuppression (22,23). Given the number of patients treated, only five variables could be included in the model.…”
Section: Statistical Analysesmentioning
confidence: 99%
“…To be able to compare outcomes in patients with CS therapy with controls of similar risk, we used the propensity score. 31 Multiple methodologic choices were made in performing this analysis (Figure 1). They are as follows:…”
Section: Statistical Analysesmentioning
confidence: 99%
“…31 Matched treated and untreated groups were compared. The benefits of therapy were studied in subgroups defined by the initial eGFR, proteinuria findings, and pathology findings.…”
Section: Pathology Findingsmentioning
confidence: 99%
“…The authors used a sophisticated method to match new users and nonusers by propensity score [17], and succeeded in identifying two subgroups of COPD patients with similar distribution of measured confounders. Nonetheless, it has been reported that omission of important confounders in propensity score calculations not only leads to biased estimates of the treatment effect but also cannot be detected with model fit or discrimination analyses [18,19]. Indeed, no method can be of help when the relevant potential confounders are missing because they are not available in the original dataset.…”
mentioning
confidence: 99%