perfomed LR analysis and tidyed up clinical formatting code
This commit is contained in:
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08e01abfb5
commit
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5 changed files with 296 additions and 301 deletions
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@ -8,7 +8,7 @@ getwd()
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########################################################################
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########################################################################
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clinical_cols = c("mosaic"
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clinical_cols_data = c("mosaic"
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, "ia_exac_copd"
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, "death"
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#, "obese2" #inc peaeds, but once you subset data for adults, its the same!
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@ -17,7 +17,7 @@ clinical_cols = c("mosaic"
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, "sfluv"
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, "h1n1v"
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, "age"
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, "gender"
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, "gender"
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, "asthma"
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, "vl_pfu_ul_npa1"
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, "los"
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@ -36,6 +36,14 @@ clinical_cols = c("mosaic"
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, "inresp_sev"
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, "steroid")
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clinical_cols_added = c("age_bins"
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, "o2_sat_bin"
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, "onset_initial_bin"
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, "steroid_ics"
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, "t1_resp_recoded" )
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clinical_cols = c(clinical_cols_data, clinical_cols_added)
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sig_npa_cols = c("eotaxin_npa1"
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, "eotaxin3_npa1"
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, "eotaxin3_npa2"
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@ -28,34 +28,27 @@ clinical_ics = read.csv(infile_ics)
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str(clinical_ics)
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########################################################################
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# quick sanity checks
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table(adult_df$ia_exac_copd==1 & adult_df$asthma == 1) # check this is 4
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table(fp_adults$ia_exac_copd==1 & fp_adults$asthma == 1) # check this is 3
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# clear unnecessary variables
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rm(all_df, adult_df, metadata_all)
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table(fp_adults$ia_exac_copd==1 & fp_adults$asthma == 1)
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########################################################################
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# Clinical_data extraction
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########################################################################
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cat("\nExtracting:", length(clinical_cols), "cols from fp_adults")
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#cat("\nExtracting:", length(clinical_cols), "cols from fp_adults")
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clinical_df = fp_adults[, clinical_cols]
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#clinical_df = fp_adults[, clinical_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("\nPASS: binary data obs are complete, n =", nrow(clinical_df))
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}else{
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cat("\nFAIL: Incomplete data for binary outcomes. Please check and decide!")
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quit()
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}
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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("\nPASS: binary data obs are complete, n =", nrow(clinical_df))
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#}else{
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# cat("\nFAIL: 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$ia_exac_copd)
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#table(clinical_df$ia_exac_copd)
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str(clinical_df)
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#str(clinical_df)
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#clinical_df$o2_sat_suppl
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########################################################################
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#==================================
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# Check asthma and copd conflict
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@ -82,40 +75,39 @@ if ( table(fp_adults$ia_exac_copd, fp_adults$asthma) [[2,2]] == 0){
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cat("Check status again...")
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}
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#=====================================================================
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#=================================
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# resp scores: In, max and t1 & t2
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#=================================
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# count the resp scores
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max_resp_score_table<- table(clinical_df$max_resp_score)
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max_resp_score_table<- table(fp_adults$max_resp_score)
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max_resp_score_table
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T1_resp_score_table<- table(clinical_df$T1_resp_score)
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T1_resp_score_table<- table(fp_adults$T1_resp_score)
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T1_resp_score_table
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T2_resp_score_table<- table(clinical_df$T2_resp_score)
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T2_resp_score_table<- table(fp_adults$T2_resp_score)
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T2_resp_score_table
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Inresp_sev<- table(clinical_df$inresp_sev)
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Inresp_sev<- table(fp_adults$inresp_sev)
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Inresp_sev
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# Reassign the resp score so all 4 are replace by 3
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clinical_df$max_resp_score[clinical_df$max_resp_score == 4 ] <- 3
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revised_resp_score_table<- table(clinical_df$max_resp_score)
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fp_adults$max_resp_score[fp_adults$max_resp_score == 4 ] <- 3
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revised_resp_score_table<- table(fp_adults$max_resp_score)
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revised_resp_score_table
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clinical_df$T1_resp_score[clinical_df$T1_resp_score ==4 ] <- 3
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revised_T1_resp_score_table<- table(clinical_df$T1_resp_score)
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fp_adults$T1_resp_score[fp_adults$T1_resp_score ==4 ] <- 3
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revised_T1_resp_score_table<- table(fp_adults$T1_resp_score)
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revised_T1_resp_score_table
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clinical_df$T2_resp_score[clinical_df$T2_resp_score == 4]<- 3
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revised_T2_resp_score_table<- table(clinical_df$T2_resp_score)
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fp_adults$T2_resp_score[fp_adults$T2_resp_score == 4]<- 3
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revised_T2_resp_score_table<- table(fp_adults$T2_resp_score)
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revised_T2_resp_score_table
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clinical_df$inresp_sev[clinical_df$inresp_sev == 4]<- 3
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revised_Inresp_sev<- table(clinical_df$inresp_sev)
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fp_adults$inresp_sev[fp_adults$inresp_sev == 4]<- 3
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revised_Inresp_sev<- table(fp_adults$inresp_sev)
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revised_Inresp_sev
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#=====================================================================
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# Remove these after checking
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@ -130,32 +122,32 @@ rm(max_resp_score_table, T1_resp_score_table, T2_resp_score_table, Inresp_sev
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# age
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#========
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# Create categories of variables
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clinical_df$age_int = round(clinical_df$age, digits = 0)
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table(clinical_df$age_int)
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table(clinical_df$asthma, clinical_df$age_int)
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min(clinical_df$age_int); max(clinical_df$age_int)
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fp_adults$age_int = round(fp_adults$age, digits = 0)
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table(fp_adults$age_int)
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table(fp_adults$asthma, fp_adults$age_int)
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min(fp_adults$age_int); max(fp_adults$age_int)
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max_age_interval = round_any(max(clinical_df$age_int), 10, f = ceiling)
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max_age_interval = round_any(max(fp_adults$age_int), 10, f = ceiling)
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max_age_interval
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min_age = min(clinical_df$age_int); min_age #19
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min_age = min(fp_adults$age_int); min_age #19
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min_age_interval = min_age - 1; min_age_interval
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#age_bins = cut(clinical_df$age_int, c(0,18,30,40,50,60,70,80,90))
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age_bins = cut(clinical_df$age_int, c(min_age_interval, 30, 40, 50, 60, 70, max_age_interval))
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clinical_df$age_bins = age_bins
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dim(clinical_df) # 133 28
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#age_bins = cut(fp_adults$age_int, c(0,18,30,40,50,60,70,80,90))
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age_bins = cut(fp_adults$age_int, c(min_age_interval, 30, 40, 50, 60, 70, max_age_interval))
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fp_adults$age_bins = age_bins
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dim(fp_adults) # 133 28
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# age_bins (to keep consistent with the results table)
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class(clinical_df$age_bins)
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levels(clinical_df$age_bins)
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class(fp_adults$age_bins)
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levels(fp_adults$age_bins)
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#"(18,30]" "(30,40]" "(40,50]" "(50,60]" "(60,70]" "(70,80]"
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table(clinical_df$asthma, clinical_df$age_bins)
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table(fp_adults$asthma, fp_adults$age_bins)
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# (18,30] (30,40] (40,50] (50,60] (60,70] (70,80]
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#0 25 17 25 14 11 1
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#1 11 8 12 5 2 2
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if (sum(table(clinical_df$asthma, clinical_df$age_bins)) == nrow(clinical_df) ){
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if (sum(table(fp_adults$asthma, fp_adults$age_bins)) == nrow(fp_adults) ){
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cat("\nPASS: age_bins assigned successfully")
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}else{
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cat("\nFAIL: no. mismatch when assigning age_bins")
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@ -163,37 +155,37 @@ if (sum(table(clinical_df$asthma, clinical_df$age_bins)) == nrow(clinical_df) ){
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}
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# reassign levels
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class(clinical_df$age_bins)
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levels(clinical_df$age_bins) <- c("(18,30]","(30,40]","(40,50]","(50,80]","(50,80]","(50,80]")
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table(clinical_df$asthma, clinical_df$age_bins)
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table(clinical_df$asthma, clinical_df$age_bins)
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class(fp_adults$age_bins)
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levels(fp_adults$age_bins) <- c("(18,30]","(30,40]","(40,50]","(50,80]","(50,80]","(50,80]")
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table(fp_adults$asthma, fp_adults$age_bins)
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table(fp_adults$asthma, fp_adults$age_bins)
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# (18,30] (30,40] (40,50] (50,80]
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#0 25 17 25 26
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#1 11 8 12 9
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sum(table(clinical_df$asthma, clinical_df$age_bins)) == nrow(clinical_df)
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table(clinical_df$age_int)
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clinical_df = subset(clinical_df, select = -c(age_int))
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table(clinical_df$age_int)
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sum(table(fp_adults$asthma, fp_adults$age_bins)) == nrow(fp_adults)
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table(fp_adults$age_int)
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fp_adults = subset(fp_adults, select = -c(age_int))
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table(fp_adults$age_int)
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class(clinical_df$age_bins)
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clinical_df$age_bins
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class(fp_adults$age_bins)
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fp_adults$age_bins
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#===========================
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# O2 saturation binning
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#===========================
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clinical_df$o2_sat_admis
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n1 = sum(is.na(clinical_df$o2_sat_admis))
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fp_adults$o2_sat_admis
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n1 = sum(is.na(fp_adults$o2_sat_admis))
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clinical_df$o2_sat_admis = round(clinical_df$o2_sat_admis, digits = 0)
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table(clinical_df$o2_sat_admis)
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tot_o2 = sum(table(clinical_df$o2_sat_admis))- table(clinical_df$o2_sat_admis)[["-1"]]
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fp_adults$o2_sat_admis = round(fp_adults$o2_sat_admis, digits = 0)
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table(fp_adults$o2_sat_admis)
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tot_o2 = sum(table(fp_adults$o2_sat_admis))- table(fp_adults$o2_sat_admis)[["-1"]]
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tot_o2
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n_text_code = table(clinical_df$o2_sat_admis)[["-1"]]
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n_text_code = table(fp_adults$o2_sat_admis)[["-1"]]
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clinical_df$o2_sat_admis[clinical_df$o2_sat_admis <0] <- NA
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n2 = sum(is.na(clinical_df$o2_sat_admis))
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fp_adults$o2_sat_admis[fp_adults$o2_sat_admis <0] <- NA
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n2 = sum(is.na(fp_adults$o2_sat_admis))
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if (n2 == n1 + n_text_code) {
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cat ("PASS: -1 code converted to NA")
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@ -201,75 +193,75 @@ if (n2 == n1 + n_text_code) {
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cat("FAIL: something went wrong!")
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}
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o2_sat_bin = cut(clinical_df$o2_sat_admis, c(0,92,100))
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clinical_df$o2_sat_bin = o2_sat_bin
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table(clinical_df$o2_sat_bin)
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o2_sat_bin = cut(fp_adults$o2_sat_admis, c(0,92,100))
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fp_adults$o2_sat_bin = o2_sat_bin
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table(fp_adults$o2_sat_bin)
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sum(table(clinical_df$o2_sat_bin)) == tot_o2
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sum(table(fp_adults$o2_sat_bin)) == tot_o2
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#===========================
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# Onset to initial binning
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#===========================
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clinical_df$onset_2_initial
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fp_adults$onset_2_initial
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max_in = max(clinical_df$onset_2_initial); max_in #23
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min_in = min(clinical_df$onset_2_initial) - 1 ; min_in # -6
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max_in = max(fp_adults$onset_2_initial); max_in #23
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min_in = min(fp_adults$onset_2_initial) - 1 ; min_in # -6
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tot_onset2ini = sum(table(clinical_df$onset_2_initial))
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tot_onset2ini = sum(table(fp_adults$onset_2_initial))
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tot_onset2ini
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onset_initial_bin = cut(clinical_df$onset_2_initial, c(min_in, 4, max_in))
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clinical_df$onset_initial_bin = onset_initial_bin
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sum(table(clinical_df$onset_initial_bin)) == tot_onset2ini
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onset_initial_bin = cut(fp_adults$onset_2_initial, c(min_in, 4, max_in))
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fp_adults$onset_initial_bin = onset_initial_bin
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sum(table(fp_adults$onset_initial_bin)) == tot_onset2ini
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#=======================
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# seasonal flu: sfluv
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#=======================
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# reassign as 0 and 1
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table(clinical_df$sfluv)
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table(clinical_df$asthma, clinical_df$sfluv)
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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$sfluv)
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table(fp_adults$sfluv)
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table(fp_adults$asthma, fp_adults$sfluv)
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fp_adults$sfluv = ifelse(fp_adults$sfluv == "yes", 1, 0)
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table(fp_adults$sfluv)
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table(fp_adults$asthma, fp_adults$sfluv)
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# convert to integer
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str(clinical_df$sfluv)
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clinical_df$sfluv = as.integer(clinical_df$sfluv)
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str(clinical_df$sfluv)
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str(fp_adults$sfluv)
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fp_adults$sfluv = as.integer(fp_adults$sfluv)
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str(fp_adults$sfluv)
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#=======================
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# h1n1v
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#=======================
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# reassign as 0 and 1
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table(clinical_df$h1n1v)
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table(clinical_df$asthma, clinical_df$h1n1v)
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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$h1n1v)
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table(fp_adults$h1n1v)
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table(fp_adults$asthma, fp_adults$h1n1v)
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fp_adults$h1n1v = ifelse(fp_adults$h1n1v == "yes", 1, 0)
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table(fp_adults$h1n1v)
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table(fp_adults$asthma, fp_adults$h1n1v)
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# convert to integer
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str(clinical_df$h1n1v)
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clinical_df$h1n1v = as.integer(clinical_df$h1n1v)
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str(clinical_df$h1n1v)
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str(fp_adults$h1n1v)
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fp_adults$h1n1v = as.integer(fp_adults$h1n1v)
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str(fp_adults$h1n1v)
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#=======================
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# ethnicity
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#=======================
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class(clinical_df$ethnicity) # integer
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table(clinical_df$ethnicity)
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table(clinical_df$asthma, clinical_df$ethnicity)
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class(fp_adults$ethnicity) # integer
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table(fp_adults$ethnicity)
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table(fp_adults$asthma, fp_adults$ethnicity)
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clinical_df$ethnicity[clinical_df$ethnicity == 4] <- 2
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table(clinical_df$ethnicity)
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table(clinical_df$asthma, clinical_df$ethnicity)
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fp_adults$ethnicity[fp_adults$ethnicity == 4] <- 2
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table(fp_adults$ethnicity)
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table(fp_adults$asthma, fp_adults$ethnicity)
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#=======================
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# pneumonia
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#=======================
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table(clinical_df$ia_cxr)
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class(clinical_df$ia_cxr) # integer
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table(fp_adults$ia_cxr)
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class(fp_adults$ia_cxr) # integer
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# ia_cxr 2 ---> yes pneumonia (1)
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# 1 ---> no (0)
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# ! 1 or 2 -- > "unknown"
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@ -283,29 +275,29 @@ class(clinical_df$ia_cxr) # integer
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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(clinical_df$ia_cxr)
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table(fp_adults$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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clinical_df$ia_cxr[clinical_df$ia_cxr == -3 | clinical_df$ia_cxr == -1 | clinical_df$ia_cxr == 0 | clinical_df$ia_cxr == 3 ] <- NA
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table(clinical_df$ia_cxr)
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fp_adults$ia_cxr[fp_adults$ia_cxr == -3 | fp_adults$ia_cxr == -1 | fp_adults$ia_cxr == 0 | fp_adults$ia_cxr == 3 ] <- NA
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table(fp_adults$ia_cxr)
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# 1 2
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#69 47 17
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sum(is.na(clinical_df$ia_cxr))
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sum(is.na(fp_adults$ia_cxr))
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clinical_df$ia_cxr[clinical_df$ia_cxr == 1] <- 0
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clinical_df$ia_cxr[clinical_df$ia_cxr == 2] <- 1
|
||||
table(clinical_df$ia_cxr)
|
||||
fp_adults$ia_cxr[fp_adults$ia_cxr == 1] <- 0
|
||||
fp_adults$ia_cxr[fp_adults$ia_cxr == 2] <- 1
|
||||
table(fp_adults$ia_cxr)
|
||||
# 0 1
|
||||
#69 47 17
|
||||
|
||||
#=======================
|
||||
# smoking [tricky one]
|
||||
#=======================
|
||||
class(clinical_df$smoking) # integer
|
||||
table(clinical_df$asthma, clinical_df$smoking)
|
||||
class(fp_adults$smoking) # integer
|
||||
table(fp_adults$asthma, fp_adults$smoking)
|
||||
|
||||
# orig
|
||||
# -3 -1 1 2 3 4
|
||||
|
|
@ -330,20 +322,20 @@ table(clinical_df$asthma, clinical_df$smoking)
|
|||
#-2: n/a specified by the clinician =====> categ blank (NA)
|
||||
#-3: unknown specified by clinician=====> categ blank (NA)
|
||||
|
||||
table(clinical_df$smoking)
|
||||
table(fp_adults$smoking)
|
||||
#-3 -1 1 2 3 4
|
||||
#19 11 35 2 19 47
|
||||
|
||||
# reassign the smoking codes
|
||||
clinical_df$smoking[clinical_df$smoking == 4 | clinical_df$smoking == 2 ] <- 0
|
||||
clinical_df$smoking[clinical_df$smoking == 1 | clinical_df$smoking == 3 ] <- 1
|
||||
clinical_df$smoking[clinical_df$smoking == -1 | clinical_df$smoking == -2 | clinical_df$smoking == -3 ] <- NA
|
||||
fp_adults$smoking[fp_adults$smoking == 4 | fp_adults$smoking == 2 ] <- 0
|
||||
fp_adults$smoking[fp_adults$smoking == 1 | fp_adults$smoking == 3 ] <- 1
|
||||
fp_adults$smoking[fp_adults$smoking == -1 | fp_adults$smoking == -2 | fp_adults$smoking == -3 ] <- NA
|
||||
|
||||
table(clinical_df$smoking); sum(is.na(clinical_df$smoking))
|
||||
table(fp_adults$smoking); sum(is.na(fp_adults$smoking))
|
||||
# 0 1
|
||||
#30 49 54
|
||||
|
||||
table(clinical_df$asthma, clinical_df$smoking)
|
||||
table(fp_adults$asthma, fp_adults$smoking)
|
||||
|
||||
# orig
|
||||
# 0 1
|
||||
|
|
@ -352,24 +344,24 @@ table(clinical_df$asthma, clinical_df$smoking)
|
|||
################################################################
|
||||
|
||||
#=========================
|
||||
# Merge: clinical_df and infile ics
|
||||
# Merge: fp_adults and infile ics
|
||||
#=========================
|
||||
merging_cols = intersect( names(clinical_df), names(clinical_ics) )
|
||||
merging_cols = intersect( names(fp_adults), names(clinical_ics) )
|
||||
merging_cols
|
||||
|
||||
clinical_df_ics = merge(clinical_df, clinical_ics, by = merging_cols, all = T); clinical_df_ics
|
||||
fp_adults_ics = merge(fp_adults, clinical_ics, by = merging_cols, all = T); fp_adults_ics
|
||||
|
||||
colnames(clinical_df_ics)
|
||||
colnames(fp_adults_ics)
|
||||
|
||||
if (nrow(clinical_df_ics) == nrow(clinical_df) & nrow(clinical_ics)){
|
||||
cat("\nPASS: No. of rows match, nrow =", nrow(clinical_df_ics)
|
||||
if (nrow(fp_adults_ics) == nrow(fp_adults) & nrow(clinical_ics)){
|
||||
cat("\nPASS: No. of rows match, nrow =", nrow(fp_adults_ics)
|
||||
, "\nChecking ncols...")
|
||||
if ( ncol(clinical_df_ics) == ncol(clinical_df) + ncol(clinical_ics) - length(merging_cols) ){
|
||||
cat("\nPASS: No. of cols match, ncol =", ncol(clinical_df_ics))
|
||||
if ( ncol(fp_adults_ics) == ncol(fp_adults) + ncol(clinical_ics) - length(merging_cols) ){
|
||||
cat("\nPASS: No. of cols match, ncol =", ncol(fp_adults_ics))
|
||||
} else {
|
||||
cat("\nFAIL: ncols mismatch"
|
||||
, "Expected ncols:", ncol(clinical_df) + ncol(clinical_ics) - length(merging_cols)
|
||||
, "\nGot:", ncol(clinical_df_ics))
|
||||
, "Expected ncols:", ncol(fp_adults) + ncol(clinical_ics) - length(merging_cols)
|
||||
, "\nGot:", ncol(fp_adults_ics))
|
||||
}
|
||||
} else {
|
||||
cat("\nFAIL: nrows mismatch"
|
||||
|
|
@ -379,49 +371,54 @@ if (nrow(clinical_df_ics) == nrow(clinical_df) & nrow(clinical_ics)){
|
|||
#=========================
|
||||
# add binary outcome for T1 resp score
|
||||
#=========================
|
||||
table(clinical_df_ics$T1_resp_score)
|
||||
table(fp_adults_ics$T1_resp_score)
|
||||
|
||||
clinical_df_ics$t1_resp_recoded = ifelse(clinical_df_ics$T1_resp_score <3, 0, 1)
|
||||
table(clinical_df_ics$t1_resp_recoded)
|
||||
#table(clinical_df_ics$steroid)
|
||||
table(clinical_df_ics$steroid_ics)
|
||||
fp_adults_ics$t1_resp_recoded = ifelse(fp_adults_ics$T1_resp_score <3, 0, 1)
|
||||
table(fp_adults_ics$t1_resp_recoded)
|
||||
#table(fp_adults_ics$steroid)
|
||||
table(fp_adults_ics$steroid_ics)
|
||||
|
||||
#=========================
|
||||
# change the factor vars to integers
|
||||
#=========================
|
||||
#str(clinical_df_ics)
|
||||
#factor_vars = lapply(clinical_df_ics, class) == "factor"
|
||||
#str(fp_adults_ics)
|
||||
#factor_vars = lapply(fp_adults_ics, class) == "factor"
|
||||
#table(factor_vars)
|
||||
|
||||
#clinical_df_ics[, factor_vars] <- lapply(clinical_df_ics[, factor_vars], as.integer)
|
||||
#fp_adults_ics[, factor_vars] <- lapply(fp_adults_ics[, factor_vars], as.integer)
|
||||
#table(factor_vars)
|
||||
|
||||
#str(clinical_df_ics)
|
||||
#str(fp_adults_ics)
|
||||
|
||||
#=========================
|
||||
# remove cols
|
||||
#=========================
|
||||
|
||||
clinical_df_ics = subset(clinical_df_ics, select = -c(onset_2_initial))
|
||||
fp_adults_ics = subset(fp_adults_ics, select = -c(onset_2_initial))
|
||||
|
||||
#======================
|
||||
# writing output file
|
||||
#======================
|
||||
outfile_name_reg = "clinical_df_recoded.csv"
|
||||
outfile_name_reg = "fp_adults_recoded.csv"
|
||||
outfile_reg = paste0(outdir, outfile_name_reg)
|
||||
|
||||
cat("\nWriting clinical file for regression:", outfile_reg)
|
||||
#write.csv(clinical_df_ics, file = outfile_reg)
|
||||
#write.csv(fp_adults_ics, file = outfile_reg)
|
||||
|
||||
#=========================
|
||||
# clinical_df_ics: without asthma
|
||||
# fp_adults_ics: without asthma
|
||||
#=========================
|
||||
clinical_df_ics_na = clinical_df_ics[clinical_df_ics$asthma == 0,]
|
||||
fp_adults_ics_na = fp_adults_ics[fp_adults_ics$asthma == 0,]
|
||||
|
||||
|
||||
#=========================
|
||||
# clinical_df only
|
||||
#=========================
|
||||
clinical_df_ics = fp_adults[, clinical_cols]
|
||||
################################################################
|
||||
rm(age_bins, max_age_interval, max_in, min_in
|
||||
, o2_sat_bin, onset_initial_bin, tot_o2
|
||||
, n_text_code, n1, n2, tot_onset2ini, infile_ics
|
||||
, tot_onset2ini, meta_data_cols
|
||||
, clinical_df, clinical_ics)
|
||||
, fp_adults, clinical_ics)
|
||||
################################################################
|
||||
|
|
|
|||
|
|
@ -214,7 +214,7 @@ comb_stats_categ_df_f = comb_stats_categ_df[order(comb_stats_categ_df$p_signif
|
|||
# write output file
|
||||
#******************
|
||||
cat("Chisq and fishers test results in:", outfile_clin_categ)
|
||||
write.csv(comb_stats_categ_df_f, outfile_clin_categ, row.names = FALSE)
|
||||
#write.csv(comb_stats_categ_df_f, outfile_clin_categ, row.names = FALSE)
|
||||
|
||||
#==================
|
||||
#0 date not recorded
|
||||
|
|
|
|||
|
|
@ -13,21 +13,25 @@ getwd()
|
|||
source("data_extraction_formatting_clinical.R")
|
||||
|
||||
# quick sanity checks
|
||||
table(clinical_df_ics$ia_exac_copd==1 & clinical_df_ics$asthma == 1)
|
||||
table(fp_adults$ia_exac_copd==1 & fp_adults$asthma == 1)
|
||||
table(fp_adults_na$ia_exac_copd==1 & fp_adults_na$asthma == 1)
|
||||
table(fp_adults_ics$ia_exac_copd==1 & fp_adults_ics$asthma == 1)
|
||||
table(fp_adults_ics$ia_exac_copd==1 & fp_adults_ics$asthma == 1)
|
||||
table(fp_adults_ics_na$ia_exac_copd==1 & fp_adults_ics_na$asthma == 1)
|
||||
|
||||
table(clinical_df_ics$asthma)
|
||||
table(fp_adults_ics$asthma)
|
||||
|
||||
if ( length(cols_to_extract) == length(clinical_cols) + length(sig_npa_cols) ){
|
||||
cat("PASS: extracting clinical and sign npa cols")
|
||||
cat("PASS: extracting clinical and sign npa cols for regression")
|
||||
} else{
|
||||
cat("FAIL: could not find cols to extract")
|
||||
cat("FAIL: could not find cols to extract for regression")
|
||||
quit()
|
||||
}
|
||||
|
||||
fp_adults_reg = fp_adults[, cols_to_extract]
|
||||
fp_adults_reg_na = fp_adults_na[, cols_to_extract]
|
||||
|
||||
#fp_adults_reg = fp_adults_ics[, cols_to_extract]
|
||||
fp_adults_reg = fp_adults_ics[, colnames(fp_adults_ics)%in%cols_to_extract]
|
||||
cols_to_extract[!cols_to_extract%in%colnames(fp_adults_reg)]
|
||||
|
||||
fp_adults_reg_na = fp_adults_ics_na[, colnames(fp_adults_ics_na)%in%cols_to_extract]
|
||||
|
||||
#--------------------
|
||||
# Data reassignment
|
||||
|
|
@ -38,26 +42,23 @@ my_data_na = fp_adults_reg_na
|
|||
table(my_data$ia_exac_copd==1 & my_data$asthma == 1)
|
||||
table(my_data_na$ia_exac_copd==1 & my_data_na$asthma == 1)
|
||||
|
||||
# clear variables
|
||||
rm(fp_adults, fp_adults_na, clinical_df_ics, clinical_df_ics_na)
|
||||
|
||||
cols_to_omit = c("mosaic", "flustat", "onset_2_initial", "ia_exac_copd", "onsfindeath", "o2_sat_admis")
|
||||
my_clinical_cols = clinical_cols[!clinical_cols%in%cols_to_omit]
|
||||
my_clinical_cols
|
||||
|
||||
#########################################################################
|
||||
#============================
|
||||
# Identifying column types: Reg data
|
||||
#===========================
|
||||
cols_to_omit = c("mosaic", "flustat", "onset_2_initial", "ia_exac_copd")
|
||||
my_vars = colnames(my_data)
|
||||
|
||||
my_reg_data = my_data[!colnames(my_data)%in%cols_to_omit]
|
||||
lapply(my_data, class)
|
||||
|
||||
my_vars = colnames(my_reg_data)
|
||||
my_vars
|
||||
|
||||
lapply(my_reg_data, class)
|
||||
|
||||
check_int_vars = my_vars[lapply(my_reg_data, class)%in%c("integer")]
|
||||
check_num_vars = my_vars[lapply(my_reg_data, class)%in%c("numeric")]
|
||||
check_charac_vars = my_vars[lapply(my_reg_data, class)%in%c("character")]
|
||||
check_factor_vars = my_vars[lapply(my_reg_data, class)%in%c("factor")]
|
||||
check_int_vars = my_vars[lapply(my_data, class)%in%c("integer")]
|
||||
check_num_vars = my_vars[lapply(my_data, class)%in%c("numeric")]
|
||||
check_charac_vars = my_vars[lapply(my_data, class)%in%c("character")]
|
||||
check_factor_vars = my_vars[lapply(my_data, class)%in%c("factor")]
|
||||
|
||||
cat("\nNo. of int cols:", length(check_int_vars)
|
||||
, "\nNo. of num cols:", length(check_num_vars)
|
||||
|
|
@ -65,44 +66,56 @@ cat("\nNo. of int cols:", length(check_int_vars)
|
|||
, "\nNo. of factor cols:", length(check_factor_vars)
|
||||
)
|
||||
|
||||
# convert char vals to int as these should be int
|
||||
my_reg_data[,check_charac_vars] = lapply(my_reg_data[,check_charac_vars], as.integer)
|
||||
str(my_reg_data$sfluv)
|
||||
|
||||
|
||||
numerical_vars = c("age"
|
||||
#=======================================
|
||||
# changing dtypes in cols
|
||||
#=======================================
|
||||
# what I need to be numerical explicitly
|
||||
my_numerical_vars = c("age"
|
||||
, "vl_pfu_ul_npa1"
|
||||
, "los"
|
||||
, "onset2final"
|
||||
, "onsfindeath"
|
||||
, "o2_sat_admis")
|
||||
|
||||
my_data[my_numerical_vars] <- lapply(my_data[my_numerical_vars], as.numeric)
|
||||
|
||||
num_vars = my_vars[lapply(my_data, class)%in%c("numeric")]
|
||||
num_vars
|
||||
|
||||
# what I need to be integer explicitly
|
||||
not_int_vars = c(my_numerical_vars, sig_npa_cols)
|
||||
|
||||
my_int_vars = my_vars[!my_vars%in%not_int_vars]
|
||||
my_int_vars
|
||||
|
||||
#my_int_vars = my_clinical_cols[int_vars_extract%in%check_int_vars]
|
||||
#my_int_vars
|
||||
|
||||
my_reg_data[numerical_vars] <- lapply(my_reg_data[numerical_vars], as.numeric)
|
||||
# convert int cols to factor
|
||||
my_data[my_int_vars] <- lapply(my_data[my_int_vars], as.factor)
|
||||
|
||||
my_reg_params = my_vars
|
||||
factor_vars = my_vars[lapply(my_data, class)%in%c("factor")]
|
||||
factor_vars
|
||||
check_factor_vars
|
||||
|
||||
# check again
|
||||
lapply(my_data, class)
|
||||
#======================================================================
|
||||
my_reg_data = my_data[!colnames(my_data)%in%cols_to_omit]
|
||||
|
||||
my_reg_params = colnames(my_reg_data)
|
||||
#length(factor_vars) + length(num_vars); ncol(my_reg_data)
|
||||
#my_reg_params = c(factor_vars, num_vars)
|
||||
|
||||
na_count = sapply(my_reg_data, function(x) sum(is.na(x)));na_count
|
||||
names(na_count)[na_count>0]
|
||||
|
||||
vars_to_factor = my_vars[!my_vars%in%numerical_vars]
|
||||
|
||||
# convert to factor
|
||||
lapply(my_reg_data, class)
|
||||
my_reg_data[vars_to_factor] <- lapply(my_reg_data[vars_to_factor], as.factor)
|
||||
factor_vars <- colnames(my_reg_data)[lapply(my_reg_data, class) == "factor"]
|
||||
table(factor_vars)
|
||||
|
||||
# check again
|
||||
lapply(my_reg_data, class)
|
||||
|
||||
|
||||
# all parasm for reg
|
||||
my_reg_params = c("age"
|
||||
my_reg_params_DEL = c("age"
|
||||
, "age_bins"
|
||||
, "vl_pfu_ul_npa1"
|
||||
, "los"
|
||||
, "onset2final"
|
||||
|
|
@ -123,11 +136,12 @@ my_reg_params = c("age"
|
|||
, "T2_resp_score"
|
||||
, "inresp_sev"
|
||||
, "steroid"
|
||||
, "age_bins"
|
||||
|
||||
, "o2_sat_bin"
|
||||
, "onset_initial_bin"
|
||||
, "steroid_ics"
|
||||
, "t1_resp_recoded")
|
||||
, "t1_resp_recoded"
|
||||
, sig_npa_cols)
|
||||
|
||||
#=================
|
||||
# reg data prepare
|
||||
|
|
@ -139,15 +153,15 @@ pv2 = "t1_resp_recoded"
|
|||
#reg_params_mixed = my_vars[!my_vars%in%pv1]
|
||||
|
||||
########################################################################
|
||||
#=================
|
||||
# outcome2
|
||||
#=================
|
||||
# outcome: death
|
||||
########################################################################
|
||||
#-----------------------------
|
||||
# outcome: death + obesity
|
||||
# data: fp adults
|
||||
#-----------------------------
|
||||
my_reg_params1 = my_reg_params[!my_reg_params%in%c("death", "obesity")]
|
||||
|
||||
sink(file = "reg_output_out1.txt", append = T)
|
||||
for(i in my_reg_params1) {
|
||||
#print (i)
|
||||
p_form = as.formula(paste("death ~ obesity + ", i ,sep = ""))
|
||||
|
|
@ -159,16 +173,20 @@ for(i in my_reg_params1) {
|
|||
print(nobs(model_reg))
|
||||
cat("=================================================================================\n")
|
||||
}
|
||||
sink()
|
||||
|
||||
#-----------------------------
|
||||
# outcome: death
|
||||
# outcome: death + obesity
|
||||
# data: fp adults
|
||||
#-----------------------------
|
||||
my_reg_params1v2 = my_reg_params[!my_reg_params%in%c("death")]
|
||||
my_reg_params2 = my_reg_params[!my_reg_params%in%c( "death"
|
||||
, "obesity"
|
||||
, "asthma")]
|
||||
|
||||
for(i in my_reg_params1v2) {
|
||||
sink(file = "reg_output_out1_ob_as.txt", append = T)
|
||||
for(i in my_reg_params2) {
|
||||
#print (i)
|
||||
p_form = as.formula(paste("death ~ ", i ,sep = ""))
|
||||
p_form = as.formula(paste("death ~ obesity + asthma +", i ,sep = ""))
|
||||
print(p_form)
|
||||
model_reg = glm(p_form , family = binomial, data = my_reg_data)
|
||||
print(summary(model_reg))
|
||||
|
|
@ -177,20 +195,45 @@ for(i in my_reg_params1v2) {
|
|||
print(nobs(model_reg))
|
||||
cat("=================================================================================\n")
|
||||
}
|
||||
sink()
|
||||
|
||||
|
||||
#-------------------
|
||||
# Full model
|
||||
#-------------------
|
||||
full_mod = glm(death ~ obesity +
|
||||
asthma +
|
||||
#age +
|
||||
age_bins +
|
||||
#t1_resp_recoded +
|
||||
com_noasthma +
|
||||
eotaxin3_npa1 +
|
||||
il1_npa1+
|
||||
il8_2_npa1+
|
||||
il12p70_npa1
|
||||
, family = "binomial"
|
||||
, data = my_reg_data)
|
||||
|
||||
print(summary(full_mod))
|
||||
print(exp(cbind(OR = coef(full_mod), confint(full_mod))))
|
||||
print(nobs(full_mod))
|
||||
|
||||
cat("=================================================================================\n")
|
||||
|
||||
########################################################################
|
||||
# outcome: t1_resp_recoded
|
||||
########################################################################
|
||||
#=================
|
||||
# outcome2
|
||||
#=================
|
||||
#-----------------------------
|
||||
# outcome: t1_resp_recoded + obesity
|
||||
# data: fp adults
|
||||
#-----------------------------
|
||||
my_reg_params2 = my_reg_params[!my_reg_params%in%c("death"
|
||||
, "obesity"
|
||||
, "t1_resp_recoded"
|
||||
, "T1_resp_score")]
|
||||
my_reg_params3 = my_reg_params[!my_reg_params%in%c("t1_resp_recoded"
|
||||
, "T1_resp_score"
|
||||
, "death"
|
||||
, "obesity")]
|
||||
|
||||
for(i in my_reg_params2) {
|
||||
sink(file = "reg_output_out2.txt", append = T)
|
||||
for(i in my_reg_params3) {
|
||||
#print (i)
|
||||
p_form = as.formula(paste("t1_resp_recoded ~ obesity + ", i ,sep = ""))
|
||||
print(p_form)
|
||||
|
|
@ -201,20 +244,22 @@ for(i in my_reg_params2) {
|
|||
print(nobs(model_reg))
|
||||
cat("=================================================================================\n")
|
||||
}
|
||||
|
||||
sink()
|
||||
|
||||
#-----------------------------
|
||||
# outcome: t1_resp_recoded
|
||||
# outcome: t1_resp_recoded + obesity
|
||||
# data: fp adults
|
||||
#-----------------------------
|
||||
my_reg_params2v2 = my_reg_params[!my_reg_params%in%c("death"
|
||||
#, "obesity"
|
||||
, "t1_resp_recoded"
|
||||
, "T1_resp_score")]
|
||||
my_reg_params4 = my_reg_params[!my_reg_params%in%c( "t1_resp_recoded"
|
||||
, "T1_resp_score"
|
||||
, "obesity"
|
||||
, "death"
|
||||
, "asthma")]
|
||||
|
||||
for(i in my_reg_params2v2) {
|
||||
sink(file = "reg_output_out2_ob_as.txt", append = T)
|
||||
for(i in my_reg_params4) {
|
||||
#print (i)
|
||||
p_form = as.formula(paste("t1_resp_recoded ~ ", i ,sep = ""))
|
||||
p_form = as.formula(paste("t1_resp_recoded ~ obesity + asthma +", i ,sep = ""))
|
||||
print(p_form)
|
||||
model_reg = glm(p_form , family = binomial, data = my_reg_data)
|
||||
print(summary(model_reg))
|
||||
|
|
@ -223,86 +268,23 @@ for(i in my_reg_params2v2) {
|
|||
print(nobs(model_reg))
|
||||
cat("=================================================================================\n")
|
||||
}
|
||||
sink()
|
||||
|
||||
########################################################################
|
||||
#-------------------
|
||||
# Full model
|
||||
########################################################################
|
||||
#-------------------
|
||||
#full_mod2 = glm(death ~ obesity +
|
||||
# asthma +
|
||||
# #age +
|
||||
# #age_bins +
|
||||
#
|
||||
#
|
||||
# , family = "binomial"
|
||||
# , data = my_reg_data)
|
||||
#
|
||||
#print(summary(full_mod2))
|
||||
#print(exp(cbind(OR = coef(full_mod2), confint(full_mod2))))
|
||||
#print(nobs(full_mod2))
|
||||
|
||||
full_mod = glm(death ~ obesity +
|
||||
age +
|
||||
#age_bins +
|
||||
obesity +
|
||||
asthma +
|
||||
t1_resp_recoded +
|
||||
#ia_cxr
|
||||
, family = "binomial", data = my_reg_data)
|
||||
cat("=================================================================================\n")
|
||||
|
||||
summary(full_mod)
|
||||
|
||||
|
||||
|
||||
########################################################################
|
||||
# mediators
|
||||
########################################################################
|
||||
sig_npa_cols = c("mosaic", sig_npa_cols)
|
||||
|
||||
my_med_sig = fp_adults[, sig_npa_cols]
|
||||
|
||||
my_reg_data_med = merge(clinical_df_ics, my_med_sig
|
||||
, by = intersect(names(clinical_df_ics), names(my_med_sig))
|
||||
)
|
||||
|
||||
#-----------------------------
|
||||
# outcome: death + obesity
|
||||
# data: fp adults
|
||||
#-----------------------------
|
||||
#my_reg_params_meds = c(my_reg_params, sig_npa_cols)
|
||||
my_reg_params_meds = colnames(my_reg_data_med)
|
||||
my_reg_params_meds1 = my_reg_params_meds[!my_reg_params_meds%in%c("mosaic", "flustat"
|
||||
, "onset_2_initial"
|
||||
, "onsfindeath"
|
||||
, "ia_exac_copd"
|
||||
, "death"
|
||||
, "obesity")]
|
||||
|
||||
|
||||
|
||||
for(i in my_reg_params_meds1) {
|
||||
#print (i)
|
||||
p_form = as.formula(paste("death ~ obesity + ", i ,sep = ""))
|
||||
print(p_form)
|
||||
model_reg = glm(p_form , family = binomial, data = my_reg_data_med)
|
||||
print(summary(model_reg))
|
||||
print(exp(cbind(OR = coef(model_reg), confint(model_reg))))
|
||||
#print (PseudoR2(model_reg))
|
||||
print(nobs(model_reg))
|
||||
cat("=================================================================================\n")
|
||||
}
|
||||
|
||||
#-----------------------------
|
||||
# outcome: t1_resp_recoded ~ obesity + asthma
|
||||
# data: fp adults
|
||||
#-----------------------------
|
||||
my_reg_params_meds2v2 = my_reg_params_meds[!my_reg_params_meds%in%c("mosaic"
|
||||
, "flustat"
|
||||
, "onset_2_initial"
|
||||
, "onsfindeath"
|
||||
, "ia_exac_copd"
|
||||
, "death"
|
||||
, "t1_resp_recoded"
|
||||
, "T1_resp_score"
|
||||
, "asthma")]
|
||||
|
||||
|
||||
|
||||
for(i in my_reg_params_meds2v2) {
|
||||
#print (i)
|
||||
p_form = as.formula(paste("t1_resp_recoded ~ obesity + asthma + ", i ,sep = ""))
|
||||
print(p_form)
|
||||
model_reg = glm(p_form , family = binomial, data = my_reg_data_med)
|
||||
print(summary(model_reg))
|
||||
print(exp(cbind(OR = coef(model_reg), confint(model_reg))))
|
||||
#print (PseudoR2(model_reg))
|
||||
print(nobs(model_reg))
|
||||
cat("=================================================================================\n")
|
||||
}
|
||||
36
read_data.R
36
read_data.R
|
|
@ -66,10 +66,6 @@ if (table(fp_adults$flustat == 1)[[1]] == nrow(fp_adults) ){
|
|||
} else{
|
||||
cat ("\nFAIL: adult df number mismatch!")
|
||||
}
|
||||
|
||||
#=============================================
|
||||
# FLU positive adult patients: without asthma
|
||||
#=============================================
|
||||
#-----------------------------------
|
||||
# asthma and copd status correction
|
||||
# for conflicting field!
|
||||
|
|
@ -101,18 +97,21 @@ if ( table(fp_adults$ia_exac_copd, fp_adults$asthma) [[2,2]] == 0 ){
|
|||
quit()
|
||||
}
|
||||
|
||||
cat("\nExtracting flu positive without asthma")
|
||||
table(fp_adults$asthma)
|
||||
cat("\nNo. of asthmatics:", table(fp_adults$asthma)[[2]]
|
||||
, "\nNo. of non-asthmatics:", table(fp_adults$asthma)[[1]])
|
||||
str(fp_adults$asthma)
|
||||
#=============================================
|
||||
# FLU positive adult patients: without asthma
|
||||
#=============================================
|
||||
#cat("\nExtracting flu positive without asthma")
|
||||
#table(fp_adults$asthma)
|
||||
#cat("\nNo. of asthmatics:", table(fp_adults$asthma)[[2]]
|
||||
# , "\nNo. of non-asthmatics:", table(fp_adults$asthma)[[1]])
|
||||
#str(fp_adults$asthma)
|
||||
|
||||
table(fp_adults$obesity)
|
||||
table(fp_adults$obesity, fp_adults$asthma)
|
||||
#table(fp_adults$obesity)
|
||||
#table(fp_adults$obesity, fp_adults$asthma)
|
||||
|
||||
fp_adults_na = fp_adults[fp_adults$asthma == 0,]
|
||||
table(fp_adults_na$obesity)
|
||||
table(fp_adults_na$obesity, fp_adults_na$asthma)
|
||||
#fp_adults_na = fp_adults[fp_adults$asthma == 0,]
|
||||
#table(fp_adults_na$obesity)
|
||||
#table(fp_adults_na$obesity, fp_adults_na$asthma)
|
||||
|
||||
#============
|
||||
# hc
|
||||
|
|
@ -121,3 +120,12 @@ table(fp_adults_na$obesity, fp_adults_na$asthma)
|
|||
#str(hc_data)
|
||||
#table(hc_data$Timepoint, hc_data$Sample)
|
||||
########################################################################
|
||||
|
||||
# quick sanity checks
|
||||
table(adult_df$ia_exac_copd==1 & adult_df$asthma == 1) # check this is 4
|
||||
|
||||
table(fp_adults$ia_exac_copd==1 & fp_adults$asthma == 1) # check this is 3
|
||||
|
||||
# clear unnecessary variables
|
||||
rm(metadata_all)
|
||||
rm(all_df, adult_df)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue