2022
DOI: 10.1002/sim.9437
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Propensity score methods for observational studies with clustered data: A review

Abstract: Propensity score methods are a popular approach to mitigating confounding bias when estimating causal effects in observational studies. When study units are clustered (eg, patients nested within health systems), additional challenges arise such as accounting for unmeasured confounding at multiple levels and dependence between units within the same cluster. While clustered observational data are widely used to draw causal inferences in many fields, including medicine and healthcare, extensions of propensity sco… Show more

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Cited by 26 publications
(23 citation statements)
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“…Seventh, we were unable to obtain the vaccination status in the dataset. Finally, because information on hospital identification was not available from the dataset, hospital clustering was considered when calculating the propensity score, which might cause bias ( 42 ).…”
Section: Discussionmentioning
confidence: 99%
“…Seventh, we were unable to obtain the vaccination status in the dataset. Finally, because information on hospital identification was not available from the dataset, hospital clustering was considered when calculating the propensity score, which might cause bias ( 42 ).…”
Section: Discussionmentioning
confidence: 99%
“…Propensity score (PS) of oral antiviral nonusers, molnupiravir users, and nirmatrelvir/ritonavir users was estimated by multinomial logistic regression on 18 clinical characteristics, including age, sex, baseline date, cardiovascular disease, digestive disease, diabetes, malignant tumor, nervous system disease, respiratory disease, kidney disease, hemoglobin, white blood cell, platelets, creatinine, alanine aminotransferase, albumin, total bilirubin, and number of hospitalizations in the past years; the 7 geographic clusters of nursing home was included as a random intercept. 12 Average treatment effect weighting was used for those treated with nirmatrelvir/ritonavir. The balance of clinical characteristics between groups was assessed by absolute standardized mean difference, with a value of less than 0.1 indicating a good balance.…”
Section: Discussionmentioning
confidence: 99%
“…Propensity scores, which reflect the probability of being tested positive on preoperative COVID-19 screening, were calculated using random-intercept logistic regression models accounting for the multicentre design. 13 All the following variables collected a priori were forced into this non-parsimonious PS model: age class, chronic respiratory diseases, grade of surgery ( i.e. , major vs. non-major surgery), emergency surgery, ASA class ≥3, hypertension, type of anaesthesia, vaccination against COVID-19, and surgery for cancer.…”
Section: Methodsmentioning
confidence: 99%