Selected article for: "logistic regression and mixed model"
Author: Ingrid Arevalo-Rodriguez; Diana Buitrago-Garcia; Daniel Simancas-Racines; Paula Zambrano-Achig; Rosa del Campo; Agustin Ciapponi; Omar Sued; Laura Martinez-Garcia; Anne Rutjes; Nicola Low; Jose A Perez-Molina; Javier Zamora
Title: FALSE-NEGATIVE RESULTS OF INITIAL RT-PCR ASSAYS FOR COVID-19: A SYSTEMATIC REVIEW
  • Document date: 2020_4_21
    • ID: g8h6trql_27
    Snippet: For all included studies, we extracted data about the number of cases initially considered as negative 165 (i.e. false-negative cases) as well as the total of confirmed cases in further investigations. We 166 presented the results of estimated proportions (with 95% CIs) in a forest plot, in order to assess the 167 between-study variability. We aimed to calculate the false-negative rate with the corresponding 168 95% CI using a multilevel mixed-ef.....
    Document: For all included studies, we extracted data about the number of cases initially considered as negative 165 (i.e. false-negative cases) as well as the total of confirmed cases in further investigations. We 166 presented the results of estimated proportions (with 95% CIs) in a forest plot, in order to assess the 167 between-study variability. We aimed to calculate the false-negative rate with the corresponding 168 95% CI using a multilevel mixed-effect logistic regression model implemented in Stata 16®'s 169 metaprop_one command. This allowed us to estimate the between-study heterogeneity from the 170 variance of study-specific random intercepts. We assessed the heterogeneity between the results 171 of the primary studies using the Tau-square statistic. A probability value less than 0.1 (p<0.1) was 172 considered to suggest statistically significant heterogeneity and preclude a pooled result of 173 numerical data. 174

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