[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations RSI values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables.","code":"library(dplyr) library(ggplot2) library(AMR) library(cleaner) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\", \"cleaner\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"creation-of-data","dir":"Articles","previous_headings":"","what":"Creation of data","title":"How to conduct AMR data analysis","text":"create fake example data use analysis. AMR data analysis, need least: patient ID, name code microorganism, date antimicrobial results (antibiogram). also include specimen type (e.g.filter blood urine), ward type (e.g.filter ICUs). additional columns (like hospital name, patients gender even [well-defined] clinical properties) can comparative analysis, tutorial demonstrate .","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"patients","dir":"Articles","previous_headings":"Creation of data","what":"Patients","title":"How to conduct AMR data analysis","text":"start patients, need unique list patients. LETTERS object available R - ’s vector 26 characters: Z. patients object just created now vector length 260, values (patient IDs) varying A1 Z10. Now also set gender patients, putting ID gender table: first 135 patient IDs now male, 125 female.","code":"patients <- unlist(lapply(LETTERS, paste0, 1:10)) patients_table <- data.frame( patient_id = patients, gender = c( rep(\"M\", 135), rep(\"F\", 125) ) )"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"dates","dir":"Articles","previous_headings":"Creation of data","what"
