correcting dtype for sfluv and h1n1v for data formatting clinical
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4 changed files with 52 additions and 461 deletions
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@ -225,36 +225,35 @@ sum(table(clinical_df$onset_initial_bin)) == tot_onset2ini
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#=======================
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# seasonal flu: sfluv
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#=======================
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if (! is.factor(clinical_df$sfluv)){
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clinical_df$sfluv = as.factor(clinical_df$sfluv)
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}
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class(clinical_df$sfluv)
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levels(clinical_df$sfluv)
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# reassign as 0 and 1
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table(clinical_df$sfluv)
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table(clinical_df$asthma, clinical_df$sfluv)
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# reassign
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levels(clinical_df$sfluv) <- c("0", "0", "1")
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clinical_df$sfluv = ifelse(clinical_df$sfluv == "yes", 1, 0)
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table(clinical_df$sfluv)
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table(clinical_df$asthma, clinical_df$sfluv)
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# convert to integer
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str(clinical_df$sfluv)
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clinical_df$sfluv = as.integer(clinical_df$sfluv)
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str(clinical_df$sfluv)
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#=======================
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# h1n1v
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#=======================
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if (! is.factor(clinical_df$h1n1v)){
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clinical_df$h1n1v = as.factor(clinical_df$h1n1v)
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}
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class(clinical_df$h1n1v)
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levels(clinical_df$h1n1v)
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# reassign as 0 and 1
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table(clinical_df$h1n1v)
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table(clinical_df$asthma, clinical_df$h1n1v)
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# reassign
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levels(clinical_df$h1n1v) <- c("0", "0", "1")
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clinical_df$h1n1v = ifelse(clinical_df$h1n1v == "yes", 1, 0)
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table(clinical_df$h1n1v)
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table(clinical_df$asthma, clinical_df$h1n1v)
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# convert to integer
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str(clinical_df$h1n1v)
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clinical_df$h1n1v = as.integer(clinical_df$h1n1v)
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str(clinical_df$h1n1v)
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#=======================
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# ethnicity
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#=======================
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@ -1,329 +0,0 @@
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#!/usr/bin/Rscript
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getwd()
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setwd('~/git/mosaic_2020/')
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getwd()
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########################################################################
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# TASK: Extract relevant columns from mosaic adults data
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# npa
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########################################################################
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#====================
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# Input: source data for clinical
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#====================
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source("data_extraction_formatting_clinical.R")
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#source("colnames_clinical_meds.R")
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#=======================================
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# Data for mediator to include in regression
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#=======================================
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cat("Extracting", length(sig_npa_cols), "mediator cols from fp_adults")
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med_df = fp_adults[, c("mosaic", sig_npa_cols)]
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# sanity checks
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if ( sum(table(clinical_df$obesity)) & sum(table(clinical_df$age>=18)) & sum(table(clinical_df$death)) & sum(table(clinical_df$asthma)) == nrow(clinical_df) ){
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cat("PASS: binary data obs are complete, n =", nrow(clinical_df))
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}else{
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cat("FAIL: Incomplete data for binary outcomes. Please check and decide!")
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quit()
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}
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table(clinical_df$ia_exac_copd)
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########################################################################
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# Data extraction for regression
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########################################################################
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common_cols = names(clinical_df)[names(clinical_df)%in%names(med_df)]
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cat("\nMerging clinical and mediator data for regression"
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,"\nMerging on column:", common_cols)
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reg_data = merge(clinical_df, med_df
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, by = common_cols)
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if (nrow(reg_data) == nrow(clinical_df) & nrow(med_df)){
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cat("\nNo. of rows match, nrow =", nrow(clinical_df)
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, "\nChecking ncols...")
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if ( ncol(reg_data) == ncol(clinical_df) + ncol(med_df) - length(common_cols) ){
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cat("\nNo. of cols match, ncol =", ncol(reg_data))
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} else {
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cat("FAIL: ncols mismatch"
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, "Expected ncols:", ncol(clinical_df) + ncol(med_df) - length(common_cols)
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, "\nGot:", ncol(reg_data))
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}
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} else {
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cat("FAIL: nrows mismatch"
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, "\nExpected nrows:", nrow(fp_adults))
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}
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########################################################################
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# Reassign the copd and asthma status and do some checks
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table(reg_data$ia_exac_copd); sum(is.na(reg_data$ia_exac_copd))
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reg_data$ia_exac_copd[reg_data$ia_exac_copd< 1]<- 0
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reg_data$ia_exac_copd[is.na(reg_data$ia_exac_copd)] <- 0
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table(reg_data$ia_exac_copd); sum(is.na(reg_data$ia_exac_copd))
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# check copd and asthma status
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table(reg_data$ia_exac_copd, reg_data$asthma)
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check_copd_and_asthma_1<- subset(reg_data, ia_exac_copd ==1 & asthma == 1) # check this is 3
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# reassign these 3 so these are treated as non-asthmatics as copd with asthma is NOT TRUE asthma
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reg_data$asthma[reg_data$ia_exac_copd == 1 & reg_data$asthma == 1]= 0
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table(reg_data$ia_exac_copd, reg_data$asthma)
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foo<- subset(reg_data, asthma==1 & ia_exac_copd ==1) # check that its 0
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rm(check_copd_and_asthma_1, foo)
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#=====================================================================
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# count the resp scores
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max_resp_score_table<- table(reg_data$max_resp_score)
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max_resp_score_table
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T1_resp_score_table<- table(reg_data$T1_resp_score)
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T1_resp_score_table
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T2_resp_score_table<- table(reg_data$T2_resp_score)
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T2_resp_score_table
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Inresp_sev<- table(reg_data$inresp_sev)
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Inresp_sev
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# Reassign the resp score so all 4 are replace by 3
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reg_data$max_resp_score[reg_data$max_resp_score ==4 ] <- 3
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revised_resp_score_table<- table(reg_data$max_resp_score)
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revised_resp_score_table
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reg_data$T1_resp_score[reg_data$T1_resp_score ==4 ] <- 3
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revised_T1_resp_score_table<- table(reg_data$T1_resp_score)
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revised_T1_resp_score_table
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reg_data$T2_resp_score[reg_data$T2_resp_score == 4]<- 3
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revised_T2_resp_score_table<- table(reg_data$T2_resp_score)
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revised_T2_resp_score_table
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reg_data$inresp_sev[reg_data$inresp_sev == 4]<- 3
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revised_Inresp_sev<- table(reg_data$inresp_sev)
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revised_Inresp_sev
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#=====================================================================
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# Remove these after checking
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rm(max_resp_score_table, T1_resp_score_table, T2_resp_score_table, Inresp_sev
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, revised_resp_score_table, revised_T1_resp_score_table, revised_T2_resp_score_table, revised_Inresp_sev)
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#=====================================================================
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# Binning
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# "(": not inclusive
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# "]": inclusive
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#========
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# age
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#========
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# Create categories of variables
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reg_data$age = round(reg_data$age, digits = 0)
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table(reg_data$age)
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table(reg_data$asthma, reg_data$age)
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min(reg_data$age); max(reg_data$age)
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max_age_interval = round_any(max(reg_data$age), 10, f = ceiling)
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max_age_interval
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min_age = min(reg_data$age); min_age #19
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min_age_interval = min_age - 1; min_age_interval
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#age_bins = cut(reg_data$age, c(0,18,30,40,50,60,70,80,90))
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age_bins = cut(reg_data$age, c(min_age_interval, 30, 40, 50, 60, 70, max_age_interval))
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reg_data$age_bins = age_bins
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dim(reg_data) # 133 27
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# age_bins (to keep consistent with the results table)
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class(reg_data$age_bins)
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levels(reg_data$age_bins)
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#"(18,30]" "(30,40]" "(40,50]" "(50,60]" "(60,70]" "(70,80]"
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table(reg_data$asthma, reg_data$age_bins)
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# (18,30] (30,40] (40,50] (50,60] (60,70] (70,80]
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#0 25 17 25 14 11 1
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#1 11 8 12 5 3 2
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if (sum(table(reg_data$asthma, reg_data$age_bins)) == nrow(reg_data) ){
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cat("PASS: age_bins assigned successfully")
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}else{
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cat("FAIL: no. mismatch when assigning age_bins")
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quit()
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}
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# reassign
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class(reg_data$age_bins)
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levels(reg_data$age_bins) <- c("(18,30]","(30,40]","(40,50]","(50,80]","(50,80]","(50,80]")
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table(reg_data$asthma, reg_data$age_bins)
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table(reg_data$asthma, reg_data$age_bins)
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# (18,30] (30,40] (40,50] (50,80]
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#0 25 17 25 26
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#1 11 8 12 9
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sum(table(reg_data$asthma, reg_data$age_bins)) == nrow(reg_data)
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#===========================
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# O2 saturation binning
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#===========================
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reg_data$o2_sat_admis = round(reg_data$o2_sat_admis, digits = 0)
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table(reg_data$o2_sat_admis)
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tot_o2 = sum(table(reg_data$o2_sat_admis))- table(reg_data$o2_sat_admis)[["-1"]]
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tot_o2
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o2_sat_bin = cut(reg_data$o2_sat_admis, c(0,92,100))
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reg_data$o2_sat_bin = o2_sat_bin
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table(reg_data$o2_sat_bin)
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sum(table(reg_data$o2_sat_bin)) == tot_o2
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#===========================
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# Onset to initial binning
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#===========================
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max_in = max(reg_data$onset_2_initial); max_in #23
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min_in = min(reg_data$onset_2_initial) - 1 ; min_in # -6
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tot_onset2ini = sum(table(reg_data$onset_2_initial))
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tot_onset2ini
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onset_initial_bin = cut(reg_data$onset_2_initial, c(min_in, 4, max_in))
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reg_data$onset_initial_bin = onset_initial_bin
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sum(table(reg_data$onset_initial_bin)) == tot_onset2ini
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#=======================
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# seasonal flu: sfluv
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#=======================
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# should be a factor
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if (! is.factor(reg_data$sfluv)){
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reg_data$sfluv = as.factor(reg_data$sfluv)
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}
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class(reg_data$sfluv) #[1] "factor"
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levels(reg_data$sfluv)
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table(reg_data$asthma, reg_data$sfluv)
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# reassign
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levels(reg_data$sfluv) <- c("0", "0", "1")
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table(reg_data$asthma, reg_data$sfluv)
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#=======================
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# h1n1v
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#=======================
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# should be a factor
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if (! is.factor(reg_data$h1n1v)){
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reg_data$h1n1v = as.factor(reg_data$h1n1v)
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}
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class(reg_data$h1n1v) #[1] "factor"
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levels(reg_data$h1n1v)
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table(reg_data$asthma, reg_data$h1n1v)
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# reassign
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levels(reg_data$h1n1v) <- c("0", "0", "1")
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table(reg_data$asthma, reg_data$h1n1v)
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#=======================
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# ethnicity
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#=======================
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class(reg_data$ethnicity) # integer
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table(reg_data$asthma, reg_data$ethnicity)
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reg_data$ethnicity[reg_data$ethnicity == 4] <- 2
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table(reg_data$asthma, reg_data$ethnicity)
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#=======================
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# pneumonia
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#=======================
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class(reg_data$ia_cxr) # integer
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# ia_cxr 2 ---> yes pneumonia (1)
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# 1 ---> no (0)
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# ! 1 or 2 -- > "unkown"
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# reassign the pneumonia codes
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#0: not performed
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#1: normal
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#2: findings consistent with pneumonia
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#3: abnormal
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#-1: not recorded
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#-2: n/a specified by the clinician # not in the data...
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#-3: unknown specified by clinician
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table(reg_data$ia_cxr)
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#-3 -1 0 1 2 3
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#5 48 13 47 17 3
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# change these first else recoding 0 will be a problem as 0 already exists, mind you -2 categ doesn't exist
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reg_data$ia_cxr[reg_data$ia_cxr == -3 | reg_data$ia_cxr == -1 | reg_data$ia_cxr == 0 | reg_data$ia_cxr == 3 ] <- ""
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table(reg_data$ia_cxr)
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# 1 2
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#69 47 17
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reg_data$ia_cxr[reg_data$ia_cxr == 1] <- 0
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reg_data$ia_cxr[reg_data$ia_cxr == 2] <- 1
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table(reg_data$ia_cxr)
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# 0 1
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#69 47 17
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#=======================
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# smoking [tricky one]
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#=======================
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class(reg_data$smoking) # integer
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table(reg_data$asthma, reg_data$smoking)
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# orig
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# -3 -1 1 2 3 4
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#0 15 9 22 2 15 30
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#1 4 2 13 0 4 17
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# -3 -1 1 2 3 4
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#0 14 9 20 2 15 30
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#1 5 2 15 0 4 17
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# never smoking, 4 and 2 -- > no (0)
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#1 and 3 ---> yes (1)
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#!-3 and -1 ---- > NA
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################# smoking
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#1: current daily ===> categ smoker(1)
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#2: occasional =====> categ no smoker(0)
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#3: ex-smoker ===> categ smoker(1)
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#4: never =====> categ no smoker(0)
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#-1: not recorded =====> categ blank (NA)
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#-2: n/a specified by the clinician =====> categ blank (NA)
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#-3: unknown specified by clinician=====> categ blank (NA)
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table(reg_data$smoking)
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#-3 -1 1 2 3 4
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#19 11 35 2 19 47
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# reassign the smoking codes
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reg_data$smoking[reg_data$smoking == 4 | reg_data$smoking == 2 ] <- 0
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reg_data$smoking[reg_data$smoking == 1 | reg_data$smoking == 3 ] <- 1
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reg_data$smoking[reg_data$smoking == -1 | reg_data$smoking == -2 | reg_data$smoking == -3 ] <- ""
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table(reg_data$smoking)
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# 0 1
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#30 49 54
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table(reg_data$asthma, reg_data$smoking)
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# orig
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# 0 1
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#0 24 32 37
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#1 6 17 17
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# 0 1
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#0 23 32 35
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#1 7 17 19
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################################################################
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#==================
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# writing output file
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#==================
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outfile_name_reg = "reg_data_recoded_with_NA.csv"
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outfile_reg = paste0(outdir, outfile_name_reg)
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cat("Writing clinical file for regression:", outfile_reg)
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#write.csv(reg_data, file = outfile_reg)
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################################################################
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rm(age_bins, max_age_interval, max_in, min_in, o2_sat_bin, onset_initial_bin, tot_o2, tot_onset2ini, meta_data_cols)
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@ -4,14 +4,8 @@ setwd('~/git/mosaic_2020/')
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getwd()
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########################################################################
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# TASK: Run regression analysis
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# npa
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# clinical params and npa meds
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########################################################################
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#=================================================================================
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# TO DO:
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# Simple stats b/w obesity and non-obesity to consider including in reg analysis
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# Include NPA statistically sign params
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# Rerun graphs and plots without asthma?
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#=================================================================================
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#====================
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# Input: source data
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@ -25,27 +19,29 @@ table(fp_adults_na$ia_exac_copd==1 & fp_adults_na$asthma == 1)
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table(clinical_df_ics$asthma)
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if ( length(cols_to_extract) == length(clinical_cols) + length(sig_npa_cols) ){
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cat("PASS: extracting clinical and sign npa cols")
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} else{
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cat("FAIL: could not find cols to extract")
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quit()
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}
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fp_adults_reg = fp_adults[, cols_to_extract]
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fp_adults_reg_na = fp_adults_na[, cols_to_extract]
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#--------------------
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# Data reassignment
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#--------------------
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my_data = clinical_df_ics
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my_data_na = clinical_df_ics_na
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my_data = fp_adults_reg
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my_data_na = fp_adults_reg_na
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table(my_data$ia_exac_copd==1 & my_data$asthma == 1)
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table(my_data_na$ia_exac_copd==1 & my_data_na$asthma == 1)
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# clear variables
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#rm(fp_adults, fp_adults_na)
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rm(fp_adults, fp_adults_na, clinical_df_ics, clinical_df_ics_na)
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#########################################################################
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if ( names(which(lapply(my_data, class) == "character")) == "mosaic" ){
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cat("Character class for 1 column only:", "mosaic")
|
||||
}else{
|
||||
cat("More than one character class detected: Resolve!")
|
||||
quit()
|
||||
}
|
||||
|
||||
#============================
|
||||
# Identifying column types: Reg data
|
||||
#===========================
|
||||
|
|
@ -57,6 +53,23 @@ my_vars = colnames(my_reg_data)
|
|||
my_vars
|
||||
|
||||
lapply(my_reg_data, class)
|
||||
|
||||
check_int_vars = my_vars[lapply(my_reg_data, class)%in%c("integer")]
|
||||
check_num_vars = my_vars[lapply(my_reg_data, class)%in%c("numeric")]
|
||||
check_charac_vars = my_vars[lapply(my_reg_data, class)%in%c("character")]
|
||||
check_factor_vars = my_vars[lapply(my_reg_data, class)%in%c("factor")]
|
||||
|
||||
cat("\nNo. of int cols:", length(check_int_vars)
|
||||
, "\nNo. of num cols:", length(check_num_vars)
|
||||
, "\nNo. of char cols:", length(check_charac_vars)
|
||||
, "\nNo. of factor cols:", length(check_factor_vars)
|
||||
)
|
||||
|
||||
# convert char vals to int as these should be int
|
||||
my_reg_data[,check_charac_vars] = lapply(my_reg_data[,check_charac_vars], as.integer)
|
||||
str(my_reg_data$sfluv)
|
||||
|
||||
|
||||
numerical_vars = c("age"
|
||||
, "vl_pfu_ul_npa1"
|
||||
, "los"
|
||||
|
|
@ -64,6 +77,11 @@ numerical_vars = c("age"
|
|||
, "onsfindeath"
|
||||
, "o2_sat_admis")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
my_reg_data[numerical_vars] <- lapply(my_reg_data[numerical_vars], as.numeric)
|
||||
|
||||
my_reg_params = my_vars
|
||||
|
|
|
|||
|
|
@ -1,97 +0,0 @@
|
|||
#!/usr/bin/Rscript
|
||||
getwd()
|
||||
setwd('~/git/mosaic_2020/')
|
||||
getwd()
|
||||
########################################################################
|
||||
# TASK: Run regression analysis
|
||||
# npa
|
||||
########################################################################
|
||||
#=================================================================================
|
||||
# TO DO:
|
||||
# Simple stats b/w obesity and non-obesity to consider including in reg analysis
|
||||
# Include NPA statistically sign params
|
||||
# Rerun graphs and plots without asthma?
|
||||
#=================================================================================
|
||||
|
||||
#====================
|
||||
# Input: source data
|
||||
#====================
|
||||
source("data_extraction_formatting_clinical.R")
|
||||
|
||||
rm(fp_adults, metadata_all)
|
||||
|
||||
########################################################################
|
||||
my_data = reg_data
|
||||
#########################################################################
|
||||
# check factor of each column
|
||||
lapply(my_data, class)
|
||||
|
||||
character_vars <- lapply(my_data, class) == "character"
|
||||
character_vars
|
||||
table(character_vars)
|
||||
|
||||
factor_vars <- lapply(my_data, class) == "factor"
|
||||
table(factor_vars)
|
||||
|
||||
my_data[, character_vars] <- lapply(my_data[, character_vars], as.factor)
|
||||
factor_vars <- lapply(my_data, class) == "factor"
|
||||
factor_vars
|
||||
table(factor_vars)
|
||||
|
||||
# check again
|
||||
lapply(my_data, class)
|
||||
|
||||
table(my_data$ethnicity)
|
||||
my_data$ethnicity = as.factor(my_data$ethnicity)
|
||||
class(my_data$ethnicity)
|
||||
|
||||
colnames(my_data)
|
||||
reg_param = c("age"
|
||||
, "age_bins"
|
||||
#, "death" # outcome
|
||||
, "asthma"
|
||||
, "obesity"
|
||||
, "gender"
|
||||
, "los"
|
||||
, "o2_sat_admis"
|
||||
#, "logistic_outcome"
|
||||
#, "steroid_ics"
|
||||
, "ethnicity"
|
||||
, "smoking"
|
||||
, "sfluv"
|
||||
, "h1n1v"
|
||||
, "ia_cxr"
|
||||
, "max_resp_score"
|
||||
, "T1_resp_score"
|
||||
, "com_noasthma"
|
||||
, "onset_initial_bin")
|
||||
|
||||
for(i in reg_param) {
|
||||
# print (i)
|
||||
p_form = as.formula(paste("death ~ ", i ,sep = ""))
|
||||
model_reg = glm(p_form , family = binomial, data = my_data)
|
||||
print(summary(model_reg))
|
||||
print(exp(cbind(OR = coef(model_reg), confint(model_reg))))
|
||||
#print (PseudoR2(model_reg))
|
||||
cat("=================================================================================\n")
|
||||
}
|
||||
|
||||
|
||||
full_mod = glm(death ~ asthma +
|
||||
gender +
|
||||
age_bins +
|
||||
los +
|
||||
#ethnicity +
|
||||
onset_initial_bin +
|
||||
o2_sat_bin +
|
||||
com_noasthma +
|
||||
obesity +
|
||||
#ia_cxr +
|
||||
smoking +
|
||||
#sfluv +
|
||||
#h1n1v
|
||||
max_resp_score +
|
||||
T1_resp_score +
|
||||
, family = "binomial", data = my_data)
|
||||
|
||||
summary(full_mod)
|
||||
Loading…
Add table
Add a link
Reference in a new issue