Selected article for: "backward stepwise logistic regression and logistic regression"
Author: Zhichao Feng; Jennifer Li; Shanhu Yao; Qizhi Yu; Wenming Zhou; Xiaowen Mao; Huiling Li; Wendi Kang; Xin Ouyang; Ji Mei; Qiuhua Zeng; Jincai Liu; Xiaoqian Ma; Pengfei Rong; Wei Wang
Title: The Use of Adjuvant Therapy in Preventing Progression to Severe Pneumonia in Patients with Coronavirus Disease 2019: A Multicenter Data Analysis
  • Document date: 2020_4_10
    • ID: k65501xp_20
    Snippet: distributions were also reported using differences with 95% confidence intervals (CIs). Univariate and multivariate logistic regression with backward stepwise selection based on likelihood ratio was used to identify the risk factors for the development of severe pneumonia, and the corresponding odds ratios (ORs) and 95% CIs were determined. We also employed the propensity score matching (PSM) with a 1:1 ratio to minimize selection bias and adjust.....
    Document: distributions were also reported using differences with 95% confidence intervals (CIs). Univariate and multivariate logistic regression with backward stepwise selection based on likelihood ratio was used to identify the risk factors for the development of severe pneumonia, and the corresponding odds ratios (ORs) and 95% CIs were determined. We also employed the propensity score matching (PSM) with a 1:1 ratio to minimize selection bias and adjust for the imbalance between patients with or without chloroquine therapy. A one-to-one nearest-neighbour matching algorithm with an optimal calliper of 0.2 without replacement was used to generate pairs of patients. The analyses regarding different factors were based on non-missing data, and missing data were not imputed. All statistical analyses were performed using SPSS statistics software (version 22.0, IBM SPSS Inc, Chicago, IL, USA). A two-sided P value of less than 0.05 considered to be statistically significant.

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