50 lines
1.3 KiB
R
50 lines
1.3 KiB
R
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## Article 1 outcome group definition script
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## To be enriched from Statistics Denmark
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##
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## Based on the ItMLiHSmar2022 course
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library(Hmisc)
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library(dplyr)
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# library(daDoctoR)
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library(tidyselect)
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# Setting final primary output from "pout"
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if (pout=="drop"){
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X_tbl <- X_tbl|>
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mutate(group=pase_drop_fac)
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# print(quantile(as.numeric(X_tbl$pase_0)))
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# print(quantile(as.numeric(X_tbl$pase_6)))
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# print(summary(X_tbl$pase_0_cut))
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X_tbl_f <- X_tbl|>
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filter(pase_0_cut!=1)|>
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select(-starts_with("pase_"))
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}
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if (pout=="hop"){
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X_tbl <- X_tbl|>
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mutate(group=pase_hop_fac)
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# print(quantile(as.numeric(X_tbl$pase_0)))
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# print(quantile(as.numeric(X_tbl$pase_6)))
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# print(summary(X_tbl$pase_0_cut))
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X_tbl_f <- X_tbl|>
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filter(pase_6_cut!=1)|>
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select(-starts_with("pase_"))
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}
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# Dropping non-complete for analysis
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Xy <- X_tbl_f|>
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na.omit()|> # Keeping only complete observations
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select(-c(tci) # Left out of model as no present in drop-group
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)|>
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mutate(mrs_0=factor(ifelse(mrs_0==1,1,2))) # Sets binary mRS 0 to include in glmnet, 0 or above
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label(Xy) = as.list(var.labels[match(names(Xy), names(var.labels))])
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X<-dplyr::select(Xy,-c(group, -starts_with("pase_")) # Exclude primary outcome
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)
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y<-Xy$group
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