Results 21 to 30 of about 131,451 (287)
Adding propensity scores to pure prediction models fails to improve predictive performance [PDF]
PeerJ, 2013 Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to
Amy S. Nowacki +3 more
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Effect of the Extent of Resection on Survival Outcome in Glioblastoma: Propensity Score Approach
Brazilian Neurosurgery, 2021 Objective To evaluate the effectiveness of the extent of resection (EOR) on survival outcome using propensity score-based approaches. Materials and Methods A retrospective cohort study was performed in patients with newly diagnosed ...
Thara Tunthanathip, Suphavadee Madteng
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Estimating General Parameters from Non-Probability Surveys Using Propensity Score Adjustment
Mathematics, 2020 This study introduces a general framework on inference for a general parameter using nonprobability survey data when a probability sample with auxiliary variables, common to both samples, is available.
Luis Castro-Martín +2 more
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Propensity score weighting for covariate adjustment in randomized clinical trials [PDF]
Statistics in Medicine, 2020 Chance imbalance in baseline characteristics is common in randomized clinical trials. Regression adjustment such as the analysis of covariance (ANCOVA) is often used to account for imbalance and increase precision of the treatment effect estimate. An objective alternative is through inverse probability weighting (IPW) of the propensity scores. Although
Shuxi Zeng, Fan Li, Rui Wang, Fan Li
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Adjusting for unmeasured spatial confounding with distance adjusted propensity score matching [PDF]
Biostatistics, 2018 31 ...
Papadogeorgou, Georgia +2 more
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On variance estimate for covariate adjustment by propensity score analysis [PDF]
Statistics in Medicine, 2016 Propensity score (PS) methods have been used extensively to adjust for confounding factors in the statistical analysis of observational data in comparative effectiveness research. There are four major PS-based adjustment approaches: PS matching, PS stratification, covariate adjustment by PS, and PS-based inverse probability weighting (IPW).
Zou, Baiming +5 more
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Adjusting for indirectly measured confounding using large-scale propensity score
Journal of Biomedical Informatics, 2022 Confounding remains one of the major challenges to causal inference with observational data. This problem is paramount in medicine, where we would like to answer causal questions from large observational datasets like electronic health records (EHRs) and administrative claims. Modern medical data typically contain tens of thousands of covariates.
Linying Zhang +4 more
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PLoS ONE, 2021 This study calculates the effect of different types of land circulation on farmers' decision-making regarding agricultural planting structure, using field survey data involving 1,120 households in Hubei province, China, and PSM (propensity score matching)
Jiquan Peng +5 more
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Stats, 2021 Without randomization of treatments, valid inference of treatment effects from observational studies requires controlling for all confounders because the treated subjects generally differ systematically from the control subjects.
Tingting Zhou +2 more
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BMC Medicine, 2011 Background Drug-eluting stents (DES) reduce rates of restenosis compared with bare metal stents (BMS). A number of observational studies have also found lower rates of mortality and non-fatal myocardial infarction with DES compared with BMS, findings not
McCulloch Charles E +3 more
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