Selected article for: "logistic regression and propensity score matching"

Author: Choi, Juwhan; Oh, Jee Youn; Lee, Young Seok; Hur, Gyu Young; Lee, Sung Yong; Shim, Jae Jeong; Kang, Kyung Ho; Min, Kyung Hoon
Title: Pseudomonas aeruginosa infection increases the readmission rate of COPD patients
  • Document date: 2018_10_2
  • ID: 6rbkhf9l_9
    Snippet: To compensate for bias and differences in baseline characteristics between the two groups, we performed propensity score matching. Propensity scores were calculated for each patient using multivariable logistic regression based on the covariates (all variables in Tables 1 and 2) . Matching was performed using the nearest neighbor method to select for the most similar propensity scores. We performed 1:1 matching and reported a standardized mean d.....
    Document: To compensate for bias and differences in baseline characteristics between the two groups, we performed propensity score matching. Propensity scores were calculated for each patient using multivariable logistic regression based on the covariates (all variables in Tables 1 and 2) . Matching was performed using the nearest neighbor method to select for the most similar propensity scores. We performed 1:1 matching and reported a standardized mean difference (d) effect size to express the suitability of matching.

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