From 950d3d6baf779707e8e95dd508b03ecad95b02b6 Mon Sep 17 00:00:00 2001 From: altonrus Date: Fri, 19 Nov 2021 10:45:19 -0500 Subject: [PATCH] Final published update --- 2_scripts/0_preprocess.R | 103 +++ 2_scripts/2_ensemble_and_model_selection.R | 43 +- 2_scripts/3_feature_importance.R | 14 + 2_scripts/5_individual_trajectories.R | 91 +- .../6_analysis_of_first_return_outcomes.R | 71 ++ 3_intermediate/corr_table_data.csv | 71 ++ 3_intermediate/data_summary_prelabelling.html | 104 +-- 4_output/figs/Fig1_ROC_compare.png | Bin 0 -> 54786 bytes 4_output/figs/Fig2_variable_importance.png | Bin 0 -> 49011 bytes .../figs/Fig3_indiv_traject_archetypes.png | Bin 0 -> 31279 bytes 4_output/figs/Fig4_indiv_traject_plots.png | Bin 0 -> 269646 bytes ...> Fig5_average_traject_by_iron_status.png} | Bin 4_output/figs/ROC_compare.pdf | Bin 0 -> 527952 bytes 4_output/figs/ROC_compare.png | Bin 55579 -> 54786 bytes .../figs/ae_traject_by_Venous_HGB_tertile.png | Bin 94491 -> 0 bytes 4_output/figs/any_ae_traject.pdf | Bin 0 -> 267585 bytes 4_output/figs/any_ae_traject.png | Bin 69616 -> 57327 bytes 4_output/figs/each_ae_traject.pdf | Bin 0 -> 1978985 bytes 4_output/figs/each_ae_traject.png | Bin 151616 -> 269646 bytes 4_output/figs/feat_imp_both_top15.png | Bin 48955 -> 49011 bytes 4_output/figs/feat_imp_mini.png | Bin 0 -> 19620 bytes 4_output/figs/indiv_plots_archetypes.pdf | Bin 0 -> 66761 bytes 4_output/figs/indiv_plots_archetypes.png | Bin 31429 -> 31279 bytes 4_output/figs/trajectories_3.pdf | Bin 0 -> 19623 bytes 4_output/figs/trajectories_3.png | Bin 4791 -> 4783 bytes 4_output/figs/trajectories_60_random.pdf | Bin 0 -> 314415 bytes 4_output/figs/trajectories_60_random.png | Bin 59460 -> 59360 bytes 4_output/table_by_outcome.csv | 6 + 5_manuscript/iron_trajectories.Rmd | 129 ++- 5_manuscript/iron_trajectories.docx | Bin 1083272 -> 1195478 bytes 5_manuscript/tailored_idi.bib | 867 ++++++++++-------- 31 files changed, 991 insertions(+), 508 deletions(-) create mode 100644 2_scripts/6_analysis_of_first_return_outcomes.R create mode 100644 3_intermediate/corr_table_data.csv create mode 100644 4_output/figs/Fig1_ROC_compare.png create mode 100644 4_output/figs/Fig2_variable_importance.png create mode 100644 4_output/figs/Fig3_indiv_traject_archetypes.png create mode 100644 4_output/figs/Fig4_indiv_traject_plots.png rename 4_output/figs/{ae_traject_by_Index_donation_iron_status.png => Fig5_average_traject_by_iron_status.png} (100%) create mode 100644 4_output/figs/ROC_compare.pdf delete mode 100644 4_output/figs/ae_traject_by_Venous_HGB_tertile.png create mode 100644 4_output/figs/any_ae_traject.pdf create mode 100644 4_output/figs/each_ae_traject.pdf create mode 100644 4_output/figs/feat_imp_mini.png create mode 100644 4_output/figs/indiv_plots_archetypes.pdf create mode 100644 4_output/figs/trajectories_3.pdf create mode 100644 4_output/figs/trajectories_60_random.pdf create mode 100644 4_output/table_by_outcome.csv diff --git a/2_scripts/0_preprocess.R b/2_scripts/0_preprocess.R index 4b50cd2..cbcd788 100644 --- a/2_scripts/0_preprocess.R +++ b/2_scripts/0_preprocess.R @@ -492,10 +492,12 @@ d.all[, outcome := fifelse(DER_VisitResult == 3, 0#1=no adverse event ))))] +##Main analysis: Index donations must have >150ml RBC lost, not including 2RBC d.all[, iron_loss_visit := ifelse(DER_RBCLoss_mL > 55, 1, 0)] d.all[, index_donation := ifelse(DER_RBCLoss_mL > 150 & DER_RBCLoss_Units < 2, 1, 0)] + d.labeled <- cbind(d.all[is.na(RandID)], "time_to_fu" = numeric(), "fu_outcome"= character()) @@ -524,6 +526,75 @@ for (row_idx in 1:nrow(d.all)){ } } + + +# ##Separate analysis for 2RBC: Donations with >150ml RBC lost and are classified as 2 units lost +# d.all[, iron_loss_visit := ifelse(DER_RBCLoss_mL > 55, 1, 0)] +# d.all[, index_donation := ifelse(DER_RBCLoss_mL > 150 & DER_RBCLoss_Units == 2, 1, 0)] +# +# +# +# d.labeled <- cbind(d.all[is.na(RandID)], +# "time_to_fu" = numeric(), +# "fu_outcome"= character()) +# +# +# for (row_idx in 1:nrow(d.all)){ +# if(d.all[row_idx, index_donation]==1){ +# #if qualifies as index visit, get all subsequent visits from donor +# d.index_visit <- d.all[row_idx] +# d.fu_visits <- d.all[RandID==d.index_visit$RandID & VisitNum > d.index_visit$VisitNum] +# reached_iron_loss_visit <- FALSE +# row_fu <- 1 +# while (reached_iron_loss_visit == FALSE & row_fu <= nrow(d.fu_visits)){ +# d.labeled <- rbind( +# d.labeled, +# cbind(d.index_visit, +# "time_to_fu" = d.fu_visits[row_fu, VisitDate] - d.index_visit$VisitDate, +# "fu_outcome" = d.fu_visits[row_fu, outcome] +# ) +# ) +# +# reached_iron_loss_visit <- fifelse(d.fu_visits[row_fu]$iron_loss_visit == 1, TRUE, FALSE) +# row_fu = row_fu+1 +# } +# +# } +# } +# d.firstreturn <- cbind(d.all[is.na(RandID)], +# "time_to_fu" = numeric(), +# "fu_outcome"= character()) +# +# for (row_idx in 1:nrow(d.all)){ +# if(d.all[row_idx, index_donation]==1){ +# #if qualifies as index visit, get all subsequent visits from donor +# d.index_visit <- d.all[row_idx] +# d.fu_visits <- d.all[RandID==d.index_visit$RandID & VisitNum > d.index_visit$VisitNum] +# fu_visit_added <- FALSE +# row_fu <- 1 +# while (fu_visit_added == FALSE & row_fu <= nrow(d.fu_visits)){ +# if( +# d.fu_visits[row_fu, DER_VisitResult == 3 | DER_RBCLoss_mL >= 55] +# ){ +# d.firstreturn <- rbind( +# d.firstreturn, +# cbind(d.index_visit, +# "time_to_fu" = d.fu_visits[row_fu, VisitDate] - d.index_visit$VisitDate, +# "fu_outcome" = d.fu_visits[row_fu, outcome] +# ) +# ) +# fu_visit_added <- TRUE +# +# } +# +# row_fu = row_fu+1 +# } +# +# } +# } + + + #Distribution of outcome including undetermined table(d.labeled$fu_outcome) d.labeled[, .N/nrow(d.labeled), by=fu_outcome] @@ -587,3 +658,35 @@ view(dfSummary(d.firstreturn), file="./3_intermediate/data_summary_firstreturn.h fwrite(d.firstreturn, "./1_data/first_return_dataset.csv") + + +# Covariance matrix ---------------------------- +dt.md <- fread( "./1_data/model_dev_data/ml_training_data.csv") +identifiers <- c("RandID", "VisitDate", "VisitNum", "DER_VisitResult", "DV_Donproc", "DER_RBCLoss_Units") +#remove extraneous fields +dt.md[, c(identifiers) := NULL] +#remove index donations missing hemoglobin +dt.md<- dt.md[!is.na(FingerstickHGB_equiv)] + +dt.md.OH <- data.table(model.matrix(fu_outcome~ ., data=dt.md)) +dt.md.OH[,`(Intercept)`:=NULL] +#run a correlation and drop the insignificant ones +corr <- cor(dt.md.OH) +#prepare to drop duplicates and correlations of 1 +corr[lower.tri(corr,diag=TRUE)] <- NA +#drop perfect correlations +corr[corr == 1] <- NA +#turn into a 3-column table +corr <- as.data.frame(as.table(corr)) +#remove the NA values from above +corr <- na.omit(corr) +#select significant values +corr <- subset(corr, abs(Freq) > .5) +#sort by highest correlation +corr <- corr[order(-abs(corr$Freq)),] +#print table +corr <- as.data.table(corr) + +setnames(corr, c("Var1", "Var2", "Freq"),c("Variable 1", "Variable 2","Correlation coefficient")) + +fwrite(corr, "./3_intermediate/corr_table_data.csv") diff --git a/2_scripts/2_ensemble_and_model_selection.R b/2_scripts/2_ensemble_and_model_selection.R index afb106b..27bd6b3 100644 --- a/2_scripts/2_ensemble_and_model_selection.R +++ b/2_scripts/2_ensemble_and_model_selection.R @@ -48,7 +48,7 @@ fwrite(tune_results_all, "./3_intermediate/tuning_results/tuning_results_all.csv # ylab("Mean overall AUC\ncross validation across 15 tuning sets")+xlab("Model types")+ # scale_x_discrete(labels = c("Elastic Net", "Elastic net\nwith interactions", "Random forest", "Regression trees", "Gradient\nboosted trees")) # - +tune_results_all <- fread("./3_intermediate/tuning_results/tuning_results_all.csv") mod_names <- c("Gradient boosted trees", "Random forest", "Elastic net with interactions", @@ -264,7 +264,8 @@ ggsave("./4_output/figs/AUC_top_models.png", # # # # # #### ASSESS TOP MODELS (withXB and noXB) on outer folds #### # # # # # - +base_mod_spec_XB <- readRDS("./1_data/base_mod_spec_XB.RDS") +base_mod_spec_noXB <- readRDS("./1_data/base_mod_spec_noXB.RDS") #outer_fold_assess(base_mod_spec = base_mod_spec_noXB$random_forest.39, version="withXB") #outer_fold_assess(base_mod_spec = base_mod_spec_noXB$random_forest.39, version="noXB") outer_fold_assess_ensemble(base_model_specs = base_mod_spec_XB, @@ -273,6 +274,9 @@ outer_fold_assess_ensemble(base_model_specs = base_mod_spec_XB, outer_fold_assess_ensemble(base_model_specs = base_mod_spec_noXB, path = "./3_intermediate/", version="noXB") + + + #TOP OVERALL MODEL #Multiclass AUCs (avg pairwise Hand and Till 2001) dt_outer_preds_all_repeats <- rbind( @@ -317,16 +321,16 @@ for (vsn in c("withXB", "noXB")){ -ROC_1vall_withXB<- ggroc(roc_objects$withXB) + +ROC_1vall_withXB<- ggroc(roc_objects$withXB, aes=c("linetype", "color")) + geom_segment(aes(x = 1, xend = 0, y = 0, yend = 1), color="grey", linetype="dashed") + theme(legend.position = "none")+ ggtitle("Extra biomarkers")+ #guides(col = guide_legend(nrow = 2))+ scale_color_manual( - values = c("turquoise2", "turquoise2", "turquoise2", - "yellow2", "yellow2", "yellow2", - "darkorange1", "darkorange1", "darkorange1", - "red1","red1", "red1"), + values = rep(c("#4affff", "#e8c700", "#d97900", "#b80202"),each=3), + labels = rep(c("None", "HGB deferral", "Low iron donation", "Absent iron donation"),each=3))+ + scale_linetype_manual( + values = rep(c("solid", "longdash", "dotdash", "twodash"),each=3), labels = rep(c("None", "HGB deferral", "Low iron donation", "Absent iron donation"),each=3))+ xlab("Specificity")+ylab("Sensitivity")+ geom_point(aes(x=0.75, y=0.75), fill="black", color="black", size = 2) @@ -336,16 +340,16 @@ ROC_1vall_withXB<- ggroc(roc_objects$withXB) + # height = 4.5, # unit = "in") -ROC_1vall_noXB<- ggroc(roc_objects$noXB) + +ROC_1vall_noXB<- ggroc(roc_objects$noXB, aes=c("linetype", "color")) + geom_segment(aes(x = 1, xend = 0, y = 0, yend = 1), color="grey", linetype="dashed") + theme(legend.position = "none")+ ggtitle("Standard biomarkers")+ #guides(col = guide_legend(nrow = 2))+ scale_color_manual( - values = c("turquoise2", "turquoise2", "turquoise2", - "yellow2", "yellow2", "yellow2", - "darkorange1", "darkorange1", "darkorange1", - "red1","red1", "red1"), + values = rep(c("#4affff", "#e8c700", "#d97900", "#b80202"),each=3), + labels = rep(c("None", "HGB deferral", "Low iron donation", "Absent iron donation"),each=3))+ + scale_linetype_manual( + values = rep(c("solid", "longdash", "dotdash", "twodash"),each=3), labels = rep(c("None", "HGB deferral", "Low iron donation", "Absent iron donation"),each=3))+ xlab("Specificity")+ylab("Sensitivity")+ geom_point(aes(x=0.75, y=0.75), fill="black", color="black", size = 2) @@ -361,10 +365,13 @@ g_legend<-function(a.gplot){ mylegend<-g_legend( ggplot(data = data.table(Cat=factor(c("No adverse outcome", "HGB deferral", "Low iron donation", "Absent iron donation"), levels = c("No adverse outcome", "HGB deferral", "Low iron donation", "Absent iron donation"))))+ - geom_bar(aes(x=Cat, fill=Cat))+ - scale_fill_manual(values=c("turquoise2","yellow2","darkorange1","red1"), + geom_line(aes(x=Cat, color=Cat, linetype=Cat, y=Cat))+ + scale_color_manual(values=c("#4affff", "#e8c700", "#d97900", "#b80202"), name="")+ - guides(fill=guide_legend(nrow=1)) + scale_linetype_manual(values=c("solid", "longdash","dotdash", "twodash"), + name="")+ + guides(color=guide_legend(nrow=1), + linetype = guide_legend(nrow=1)) ) @@ -375,6 +382,12 @@ ggsave("./4_output/figs/ROC_compare.png", width = 6.5, height = 4, unit = "in") +ggsave("./4_output/figs/ROC_compare.pdf", + plot=grid.arrange(arrangeGrob(ROC_1vall_noXB,ROC_1vall_withXB,nrow=1), + mylegend,nrow=2, heights=c(7,1)), + width = 6.5, + height = 4, + unit = "in") diff --git a/2_scripts/3_feature_importance.R b/2_scripts/3_feature_importance.R index 52b1c37..7b68551 100644 --- a/2_scripts/3_feature_importance.R +++ b/2_scripts/3_feature_importance.R @@ -132,3 +132,17 @@ featimp_median <- rbind( ) fwrite(featimp_median, "./4_output/feature_importance_medians.csv") + +#Mini plot for Grant +feats_for_mini <- c("Ferritin", median_noXB[median_imp >.005]$display_name) +dt_mini <- featimp_both[display_name %in% feats_for_mini] +dt_mini[, mod:=ifelse(mod=="Extra biomarkers", "With\nferritin", "Without")] + +ggplot(dt_mini)+ + geom_boxplot(aes(x=reorder(display_name, AUC_multi_pctchg, FUN = median), y=AUC_multi_pctchg))+ + facet_grid(cols = vars(mod))+ + coord_flip()+geom_hline(yintercept=0, color="red")+ + scale_y_continuous(labels = label_percent(accuracy=1), breaks = c(0, .03, .06), name = "Relative variable\nimportance")+ + xlab("") +ggsave("./4_output/figs/feat_imp_mini.png", + width = 3.4, height = 2, units = "in") diff --git a/2_scripts/5_individual_trajectories.R b/2_scripts/5_individual_trajectories.R index 20c01a1..7d534f8 100644 --- a/2_scripts/5_individual_trajectories.R +++ b/2_scripts/5_individual_trajectories.R @@ -21,6 +21,8 @@ dt.fr <- fread("./1_data/first_return_dataset.csv") dt.fr <- dt.fr[!is.na(FingerstickHGB_equiv)] dt.fr<-dt.fr[time_to_fu >=56] dt.frXB <- dt.fr[!is.na(ARUP_Ferritin)] + + ## # GENERATE RISK TRAJECTORIES MATRIX ------------------- ## @@ -188,15 +190,17 @@ dt_long_risk_matrix_sm[, ex_group := ifelse(Idx_donation %in% tail(Idx_slow_rec dt_any_AE <- cbind(dt_long_risk_matrix_sm[outcome=="None", ]) dt_any_AE[, Risk := 1-Risk] dt_any_AE[, outcome := "Any adverse event"] + p_top <- ggplot(dt_any_AE, aes(x=time_to_fu, y=Risk))+ geom_line(aes(group = Idx_donation, color = ex_group, - alpha= ex_group, size = ex_group))+ + alpha= ex_group, size = ex_group, linetype = ex_group))+ facet_wrap(vars(outcome), nrow=1)+ - scale_y_continuous(name="Probability", labels=percent_format())+ - scale_color_manual(values=c("grey", "red2", "springgreen4", "darkorange3"), name="Example trajectories")+ + scale_y_continuous(name=element_blank(), labels=percent_format())+ + scale_color_manual(values=c("grey", "red2", "springgreen3", "darkorange3"), name="Example trajectories")+ scale_x_continuous(limits = c(56, 250), breaks = c(56, 150, 250), name="")+ + scale_linetype_manual(values=c("solid", "solid", "longdash","solid"),name="Example\nTrajectories")+ scale_alpha_manual(values=c(0.4, 1, 1, 1), name="Example trajectories")+ - scale_size_manual(values=c(0.2, 1, 1, 1))+ + scale_size_manual(values=c(0.2, 0.6,0.6,0.6))+ theme(legend.position = "None") outcome_labs <-c( @@ -208,32 +212,43 @@ outcome_labs <-c( p_bottom <- ggplot(dt_long_risk_matrix_sm[outcome!="None"], aes(x=time_to_fu, y=Risk))+ geom_line(aes(group = Idx_donation, color = ex_group, - alpha= ex_group, size = ex_group))+ + alpha= ex_group, size = ex_group, linetype = ex_group))+ facet_wrap(vars(outcome), nrow=1, labeller = as_labeller(outcome_labs))+ - scale_y_continuous(name="Probability", labels=percent_format())+ - scale_x_continuous(limits = c(56, 250), breaks = c(56, 100, 150, 200, 250), name="Days until next donation")+ - scale_color_manual(values=c("grey", "red2", "springgreen4", "darkorange3"), name="Example\nTrajectories")+ + scale_y_continuous(name=element_blank(), labels=percent_format())+ + scale_x_continuous(limits = c(56, 250), breaks = c(56, 100, 150, 200, 250), name="Days until next donation attempt")+ + scale_color_manual(values=c("grey", "red2", "springgreen3", "darkorange3"), name="Example\nTrajectories")+ scale_alpha_manual(values=c(0.4, 1, 1, 1), name="Example\nTrajectories")+ - scale_size_manual(values=c(0.2, 1, 1, 1), name="Example\nTrajectories")+ + scale_size_manual(values=c(0.2, 0.6,0.6,0.6), name="Example\nTrajectories")+ + scale_linetype_manual(values=c("solid", "solid", "longdash","solid"),name="Example\nTrajectories")+ theme(legend.position = "bottom")#+ #guides(color=guide_legend(nrow=2,byrow=TRUE)) -grid.arrange(p_top, p_bottom, ncol=1) +yleft <- textGrob("Probability of adverse event", + rot = 90, gp = gpar(fontsize = 11), just = 0.4) + ggsave("4_output/figs/each_ae_traject.png", - grid.arrange(p_top, p_bottom, ncol=1), + plot = grid.arrange(p_top, p_bottom, ncol=1, left = yleft), + width = 6, + height = 5, + units = "in" +) + +ggsave("4_output/figs/each_ae_traject.pdf", + plot = grid.arrange(p_top, p_bottom, ncol=1, left = yleft), width = 6, height = 5, units = "in" ) + ggplot(dt_any_AE, aes(x=time_to_fu, y=Risk))+ geom_line(aes(group = Idx_donation, color = ex_group, alpha= ex_group, size = ex_group))+ #facet_wrap(vars(outcome), nrow=1)+ scale_y_continuous(name="Prob. adverse event", labels=percent_format())+ scale_x_continuous(name="Days until next donation attempt")+ - scale_color_manual(values=c("grey", "red2", "springgreen4", "darkorange3"), name="")+ + scale_color_manual(values=c("grey", "red2", "springgreen", "darkorange3"), name="")+ scale_alpha_manual(values=c(0.4, 1, 1, 1), name="")+ scale_size_manual(values=c(0.2, 1, 1, 1), name="")+ theme(legend.position = "bottom") @@ -245,6 +260,12 @@ ggsave("4_output/figs/any_ae_traject.png", units = "in" ) +ggsave("4_output/figs/any_ae_traject.pdf", + width = 5, + height = 2.3, + units = "in" +) + @@ -269,7 +290,7 @@ for(plot_num in 1:60){ axis.line.y = element_blank(), axis.title = element_blank(), axis.text.x = element_text(size=10))+ - scale_fill_manual(values = c("#00FFFF", "#FBD808", "#FF9005", "#FF0000")) + scale_fill_manual(values = c("#b8ffff", "#e8c700", "#d97900", "#b80202")) #xlab("Days until donation attempt")+ #ylab("Probability of outcome") @@ -296,6 +317,24 @@ ggsave("./4_output/figs/trajectories_60_random.png", height = 8.9, units = "in" ) +ggsave("./4_output/figs/trajectories_60_random.pdf", + plot = plot_grid(plot_traj_1, plot_traj_2, plot_traj_3, plot_traj_4, plot_traj_5, + plot_traj_6, plot_traj_7, plot_traj_8, plot_traj_9, plot_traj_10, + plot_traj_11, plot_traj_12, plot_traj_13, plot_traj_14, plot_traj_15, + plot_traj_16, plot_traj_17, plot_traj_18, plot_traj_19, plot_traj_20, + plot_traj_21, plot_traj_22, plot_traj_23, plot_traj_24, plot_traj_25, + plot_traj_26, plot_traj_27, plot_traj_28, plot_traj_29, plot_traj_30, + plot_traj_31, plot_traj_32, plot_traj_33, plot_traj_34, plot_traj_35, + plot_traj_36, plot_traj_37, plot_traj_38, plot_traj_39, plot_traj_40, + plot_traj_41, plot_traj_42, plot_traj_43, plot_traj_44, plot_traj_45, + plot_traj_46, plot_traj_47, plot_traj_48, plot_traj_49, plot_traj_40, + plot_traj_51, plot_traj_52, plot_traj_53, plot_traj_54, plot_traj_55, + plot_traj_56, plot_traj_57, plot_traj_58, plot_traj_59, plot_traj_60, + ncol = 5), + width = 6.5, + height = 8.9, + units = "in" +) ggsave("./4_output/figs/trajectories_3.png", plot = plot_grid(plot_traj_4, plot_traj_29, plot_traj_6, @@ -305,6 +344,14 @@ ggsave("./4_output/figs/trajectories_3.png", units = "in" ) +ggsave("./4_output/figs/trajectories_3.pdf", + plot = plot_grid(plot_traj_4, plot_traj_29, plot_traj_6, + ncol = 3), + width = 5, + height = 1, + units = "in" +) + #Trajectories for 3 archetypes archetype_donor_nums <- c(Idx_chronic[1:10], @@ -325,7 +372,7 @@ for(donor_num in archetype_donor_nums){ axis.line.y = element_blank(), axis.title = element_blank(), axis.text.x = element_text(size=10))+ - scale_fill_manual(values = c("#00FFFF", "#FBD808", "#FF9005", "#FF0000"), + scale_fill_manual(values = c("#b8ffff", "#e8c700", "#d97900", "#b80202"), labels = c("No adverse outcome", "Hemoglobin deferral", "Low iron donation", @@ -353,7 +400,7 @@ title2 <- ggdraw() + x = 0, hjust = 0 ) -plot_row_sr <- plot_grid(plot_archetype_121, plot_archetype_394,plot_archetype_787,plot_archetype_1781, +plot_row_sr <- plot_grid(plot_archetype_121, plot_archetype_394,plot_archetype_787,plot_archetype_856, ncol = 4) title3 <- ggdraw() + draw_label( @@ -387,7 +434,19 @@ ggsave( rel_heights = c(rep(c(0.22, 1),3),.5)), width = 5, height = 4.5, units="in" ) - +ggsave( + "./4_output/figs/indiv_plots_archetypes.pdf", + plot_grid(title1, + plot_row_fr, + title2, + plot_row_sr, + title3, + plot_row_chr, + legend, + ncol =1, + rel_heights = c(rep(c(0.22, 1),3),.5)), + width = 5, height = 4.5, units="in" +) diff --git a/2_scripts/6_analysis_of_first_return_outcomes.R b/2_scripts/6_analysis_of_first_return_outcomes.R new file mode 100644 index 0000000..9576d17 --- /dev/null +++ b/2_scripts/6_analysis_of_first_return_outcomes.R @@ -0,0 +1,71 @@ +dt.fr <- fread("./1_data/first_return_dataset.csv") +dt.fr <- dt.fr[!is.na(FingerstickHGB_equiv)] +dt.fr<-dt.fr[time_to_fu >=56] +dt.frXB <- dt.fr[!is.na(ARUP_Ferritin)] + +##Plot actual returns + outcomes +dt.plt <- dt.frXB[, c("time_to_fu", "fu_outcome")] + +ggplot(dt.plt)+ + geom_boxplot(aes(x = time_to_fu, y = factor(fu_outcome))) + +#Average IDI by outcomes +dt.frXB[, outcome := ifelse(fu_outcome==-1, + "Unknown iron status donation", + ifelse(fu_outcome==0, + "No adverse event", + ifelse(fu_outcome==1, + "HGB deferral", + ifelse(fu_outcome==2, + "Low iron donation", + "Absent iron donation"))))] +dt.frXB[,outcome := factor(outcome, + levels = c("HGB deferral", + "Absent iron donation", + "Low iron donation", + "No adverse event", + "Unknown iron status donation"))] + +#Calculate expected distribution +sim_outcome_dist <- table(dt.frXB$fu_outcome) +frac_of_completed <- sim_outcome_dist[c("0", "2", "3")]/sum(sim_outcome_dist[c("0", "2", "3")]) + +target_dist <- c(sim_outcome_dist["0"] + frac_of_completed["0"]*sim_outcome_dist["-1"], + sim_outcome_dist["1"], + sim_outcome_dist["2"] + frac_of_completed["2"]*sim_outcome_dist["-1"], + sim_outcome_dist["3"] + frac_of_completed["3"]*sim_outcome_dist["-1"] ) + +#Create data.table +dt.target<-data.table(cbind("target_dist" = c(0, target_dist), + "fu_outcome" = -1:3)) +#Calculate percent +dt.target[, "Number (%) after proportionally allocating unknown iron donations" := + paste0(round(target_dist,digits=0)," (",percent(target_dist/sum(target_dist),accuracy=0.1),")")] + +#calculate outcomes by outcome +dt.byoutcome <- dt.frXB[,list("Number (%) in first return dataset" = paste0(.N," (",percent(.N/nrow(dt.frXB),accuracy=0.1),")"), + "Median (mean) time to follow-up visit" = paste0(quantile(time_to_fu,.5)," (", round(mean(time_to_fu),digits=1),")") + ), + by=c("fu_outcome", "outcome")] +#Bring in target dist +dt.byoutcome<-dt.byoutcome[dt.target[,c(2,3)], on="fu_outcome"] +#Order the table by outcome +dt.byoutcome<-dt.byoutcome[order(outcome)] +dt.byoutcome<-dt.byoutcome[, c(2,3,5,4)] +setnames(dt.byoutcome, "outcome", "Follow-up visit outcome") + +fwrite(dt.byoutcome, "./4_output/table_by_outcome.csv") + + + +#Distributions for labeled vs. unlabeled ferritin + +dt.plt <- dt.plt[, .N, by=c("time_to_fu", "fu_outcome")] +#truncate at 250 day IDI +dt.plt[,time_to_fu:=ifelse(time_to_fu >250,250,time_to_fu)] +dt.plt <- dt.plt[order(time_to_fu)] +dt.plt[, csum := cumsum(N), by=fu_outcome] + +#library(ggbeeswarm) +ggplot(dt.plt, aes(x = time_to_fu, y = csum, color = factor(fu_outcome)))+ + geom_line() diff --git a/3_intermediate/corr_table_data.csv b/3_intermediate/corr_table_data.csv new file mode 100644 index 0000000..ed96466 --- /dev/null +++ b/3_intermediate/corr_table_data.csv @@ -0,0 +1,71 @@ +Variable 1,Variable 2,Correlation coefficient +Gender_F,gender_menstrating_cohortsM,-1 +DER_ARUP_log_STfR_Ferr,DER_BodyIron,-0.999875255884321 +DER_ARUP_log_Ferr,DER_BodyIron,0.966851674449308 +DER_EBV,DER_PercentRBCLoss,-0.966706989874747 +DER_ARUP_log_Ferr,DER_ARUP_log_STfR_Ferr,-0.962965023433004 +RQ18_Menstrual_Flow,menstrual_flow_times_freq,0.959392035668053 +RQ17_NumberOfPeriods,gender_menstrating_cohortsF_no_menstr,0.955280212619143 +DER_RBCLoss_mL,DER_AdjVenousHgb,0.953759807613697 +RQ17_NumberOfPeriods,menstrual_flow_times_freq,0.947597838862075 +RQ18_Menstrual_Flow,gender_menstrating_cohortsF_no_menstr,0.943388345486681 +DER_EBV,DER_RedCellVolume,0.916181429828993 +RQ17_NumberOfPeriods,RQ18_Menstrual_Flow,0.905188648924197 +menstrual_flow_times_freq,gender_menstrating_cohortsF_no_menstr,0.905151643982919 +DER_Weight,DER_EBV,0.898927063044855 +DER_RBC_Last12months,DER_RBC_Last24months,0.891527865075752 +RQ20_NumberOfPregnancies,RQ21_NumberOfLiveBirths,0.888544840204153 +DER_RedCellVolume,DER_PercentRBCLoss,-0.888279481016513 +DER_Weight,BMI,0.873639191806455 +DER_Weight,DER_PercentRBCLoss,-0.87299479584732 +RQ19_Ever_Pregnant,RQ20_NumberOfPregnancies,0.788710212317898 +RQ19_Ever_Pregnant,RQ21_NumberOfLiveBirths,0.784786329435819 +DER_Weight,DER_RedCellVolume,0.77382559220681 +DER_Height,DER_EBV,0.754632300558391 +ARUP_Ferritin,DER_ARUP_log_Ferr,0.744822701367628 +DER_Height,DER_PercentRBCLoss,-0.740357905181658 +DER_RedCellVolume,Gender_F,-0.73807691910793 +DER_RedCellVolume,gender_menstrating_cohortsM,0.73807691910793 +DER_Height,DER_RedCellVolume,0.729574297800767 +ARUP_Ferritin,DER_BodyIron,0.691043960888679 +DER_EBV,Gender_F,-0.688397534697162 +DER_EBV,gender_menstrating_cohortsM,0.688397534697162 +ARUP_Ferritin,DER_ARUP_log_STfR_Ferr,-0.686844108162957 +RQ11_Beef,compositeIronScore,0.685338549956494 +DER_RBC_Last12months,DER_DaysRBCLoss,-0.683802607838875 +DER_Height,Gender_F,-0.681716239338963 +DER_Height,gender_menstrating_cohortsM,0.681716239338963 +DER_RBC_Last24months,cumLifetimeDonations,0.679501448074564 +ARUP_STR,DER_ARUP_log_STfR_Ferr,0.665854588996086 +DER_RBC_Last12months,cumLifetimeDonations,0.657995020514207 +ARUP_STR,DER_BodyIron,-0.65669572164567 +DER_PercentRBCLoss,Gender_F,0.653158044792335 +DER_PercentRBCLoss,gender_menstrating_cohortsM,-0.653158044792335 +DER_AdjVenousHgb,DER_RedCellVolume,0.652164799628803 +DER_RBCLoss_mL,DER_RedCellVolume,0.639915384511414 +DER_RBCLoss_mL,FingerstickHGB_equiv,0.633761413276713 +Gender_F,RQ19_Ever_Pregnant,0.631009223315227 +RQ19_Ever_Pregnant,gender_menstrating_cohortsM,-0.631009223315227 +FingerstickHGB_equiv,DER_AdjVenousHgb,0.627602978507992 +supp_iron_pct_of_daily,multivitamins_per_week,0.618844963543539 +DD_RacethH,DD_RacethW,-0.609090088197738 +RQ11_OMSS,RQ11_OtrFish,0.598673740505898 +DER_RBC_Last24months,DER_DaysRBCLoss,-0.592490279621532 +RQ11_Eggs,compositeIronScore,0.589204097995791 +BMI,DER_EBV,0.588843752499703 +BMI,DER_PercentRBCLoss,-0.578100488798477 +DER_AdjVenousHgb,DER_ARUP_log_Ferr,0.544538141223542 +DER_AdjVenousHgb,DER_BodyIron,0.534941115483051 +DER_AdjVenousHgb,DER_ARUP_log_STfR_Ferr,-0.532784317708947 +DER_Age,gender_menstrating_cohortsF_no_menstr,-0.520677316439658 +DER_RBC_Last24months,DER_BodyIron,-0.515116000506707 +DER_DaysRBCLoss,cumLifetimeDonations,-0.514563948336087 +DER_RBC_Last24months,DER_ARUP_log_STfR_Ferr,0.514346865883664 +RQ11_LPCT,compositeIronScore,0.512997289053331 +DER_RedCellVolume,DER_ARUP_log_Ferr,0.512330826831801 +DER_DaysRBCLoss,RQ1_Ever_Donated,-0.511262024624973 +DER_RBC_Last24months,DER_ARUP_log_Ferr,-0.505020012841747 +DER_RBCLoss_mL,DER_ARUP_log_Ferr,0.503914193901343 +DER_RBC_Last12months,DER_BodyIron,-0.50126103815807 +DER_RBC_Last12months,DER_ARUP_log_STfR_Ferr,0.500366200269063 +DER_Age,RQ18_Menstrual_Flow,-0.500179646797015 diff --git a/3_intermediate/data_summary_prelabelling.html b/3_intermediate/data_summary_prelabelling.html index bf6dbb7..011ae18 100644 --- a/3_intermediate/data_summary_prelabelling.html +++ b/3_intermediate/data_summary_prelabelling.html @@ -217,7 +217,7 @@

d.all

0 < 3 < 7 IQR (CV) : 2 (0.5)
0:980(7.9%)
1:1181(9.5%)
2:1892(15.2%)
3:2443(19.7%)
4:3173(25.5%)
5:2039(16.4%)
6:712(5.7%)
7:3(0.0%)
- + 12423 (100.0%) 0 @@ -232,7 +232,7 @@

d.all

0 < 6 < 13 IQR (CV) : 5 (0.5) 14 distinct values - + 12423 (100.0%) 0 @@ -247,7 +247,7 @@

d.all

0 < 1 < 2 IQR (CV) : 0 (0.5)
0:1677(13.5%)
1:9455(76.1%)
2:1291(10.4%)
- + 12423 (100.0%) 0 @@ -262,7 +262,7 @@

d.all

0 < 212 < 478 IQR (CV) : 34 (0.5) 188 distinct values - + 12423 (100.0%) 0 @@ -277,7 +277,7 @@

d.all

3 < 91 < 3650 IQR (CV) : 80 (2.6) 534 distinct values - + 12423 (100.0%) 0 @@ -292,7 +292,7 @@

d.all

7 < 3650 < 3650 IQR (CV) : 0 (0.4) 610 distinct values - + 12423 (100.0%) 0 @@ -306,7 +306,7 @@

d.all

Mean : 1 Max : 1
0:206(1.7%)
1:12217(98.3%)
- + 12423 (100.0%) 0 @@ -321,7 +321,7 @@

d.all

0 < 7 < 19 IQR (CV) : 4 (0.4) 20 distinct values - + 12423 (100.0%) 0 @@ -336,7 +336,7 @@

d.all

7.4 < 13.8 < 20.4 IQR (CV) : 1.6 (0.1) 253 distinct values - + 11396 (91.7%) 1027 @@ -355,7 +355,7 @@

d.all

7. O- 8. O+
748(6.0%)
3567(28.7%)
82(0.7%)
307(2.5%)
221(1.8%)
1219(9.8%)
1408(11.3%)
4871(39.2%)
- + 12423 (100.0%) 0 @@ -370,7 +370,7 @@

d.all

7.2 < 13.7 < 21 IQR (CV) : 1.9 (0.1) 1070 distinct values - + 11396 (91.7%) 1027 @@ -385,7 +385,7 @@

d.all

108 < 174 < 320 IQR (CV) : 50 (0.2) 165 distinct values - + 12423 (100.0%) 0 @@ -400,7 +400,7 @@

d.all

59 < 67 < 77 IQR (CV) : 6 (0.1) 21 distinct values - + 12423 (100.0%) 0 @@ -415,7 +415,7 @@

d.all

17.4 < 26.5 < 56.2 IQR (CV) : 6.1 (0.2) 866 distinct values - + 12423 (100.0%) 0 @@ -430,7 +430,7 @@

d.all

2.9 < 4.8 < 8.8 IQR (CV) : 1.5 (0.2) 986 distinct values - + 12423 (100.0%) 0 @@ -445,7 +445,7 @@

d.all

704 < 1803 < 3992 IQR (CV) : 706 (0.2) 1914 distinct values - + 12423 (100.0%) 0 @@ -460,7 +460,7 @@

d.all

0 < 11.9 < 47 IQR (CV) : 4.9 (0.5) 8293 distinct values - + 12423 (100.0%) 0 @@ -474,7 +474,7 @@

d.all

Mean : 1 Max : 1
0:523(4.2%)
1:11900(95.8%)
- + 12423 (100.0%) 0 @@ -489,7 +489,7 @@

d.all

18 < 52 < 87 IQR (CV) : 19 (0.3) 70 distinct values - + 12423 (100.0%) 0 @@ -503,7 +503,7 @@

d.all

Mean : 0.5 Max : 1
0:6081(48.9%)
1:6342(51.1%)
- + 12423 (100.0%) 0 @@ -519,7 +519,7 @@

d.all

4. O 5. W
310(2.5%)
428(3.4%)
341(2.7%)
163(1.3%)
11181(90.0%)
- + 12423 (100.0%) 0 @@ -533,7 +533,7 @@

d.all

Mean : 0.4 Max : 1
0:7683(61.8%)
1:4740(38.2%)
- + 12423 (100.0%) 0 @@ -547,7 +547,7 @@

d.all

Mean : 0.1 Max : 1
0:11166(89.9%)
1:1257(10.1%)
- + 12423 (100.0%) 0 @@ -562,7 +562,7 @@

d.all

0 < 0 < 5.5 IQR (CV) : 0.5 (2)
0.00:9213(74.2%)
0.50:2799(22.5%)
1.00:391(3.1%)
2.00:9(0.1%)
3.50:7(0.1%)
5.50:4(0.0%)
- + 12423 (100.0%) 0 @@ -577,7 +577,7 @@

d.all

0 < 2 < 14 IQR (CV) : 2.5 (0.7)
0.00:529(4.3%)
0.50:1861(15.0%)
1.00:2384(19.2%)
2.00:4007(32.3%)
3.50:2868(23.1%)
5.50:576(4.6%)
7.00:165(1.3%)
14.00:33(0.3%)
- + 12423 (100.0%) 0 @@ -592,7 +592,7 @@

d.all

0 < 3.5 < 14 IQR (CV) : 1.5 (0.6)
0.00:393(3.2%)
0.50:542(4.4%)
1.00:1331(10.7%)
2.00:3095(24.9%)
3.50:5204(41.9%)
5.50:1386(11.2%)
7.00:386(3.1%)
14.00:86(0.7%)
- + 12423 (100.0%) 0 @@ -607,7 +607,7 @@

d.all

0 < 0 < 5.5 IQR (CV) : 0.5 (1.4)
0.00:7329(59.0%)
0.50:4861(39.1%)
1.00:150(1.2%)
2.00:68(0.5%)
3.50:11(0.1%)
5.50:4(0.0%)
- + 12423 (100.0%) 0 @@ -622,7 +622,7 @@

d.all

0 < 0.5 < 14 IQR (CV) : 0 (1.1)
0.00:3058(24.6%)
0.50:7936(63.9%)
1.00:1174(9.5%)
2.00:187(1.5%)
3.50:48(0.4%)
5.50:9(0.1%)
7.00:6(0.0%)
14.00:5(0.0%)
- + 12423 (100.0%) 0 @@ -637,7 +637,7 @@

d.all

0 < 1 < 14 IQR (CV) : 0.5 (0.9)
0.00:999(8.0%)
0.50:4916(39.6%)
1.00:4414(35.5%)
2.00:1570(12.6%)
3.50:452(3.6%)
5.50:55(0.4%)
7.00:12(0.1%)
14.00:5(0.0%)
- + 12423 (100.0%) 0 @@ -652,7 +652,7 @@

d.all

0 < 1 < 14 IQR (CV) : 1 (0.9)
0.00:240(1.9%)
0.50:2759(22.2%)
1.00:3902(31.4%)
2.00:3030(24.4%)
3.50:1707(13.7%)
5.50:469(3.8%)
7.00:284(2.3%)
14.00:32(0.3%)
- + 12423 (100.0%) 0 @@ -667,7 +667,7 @@

d.all

0 < 7 < 14 IQR (CV) : 10.5 (0.6)
0.00:49(0.4%)
0.50:374(3.0%)
1.00:458(3.7%)
2.00:794(6.4%)
3.50:1897(15.3%)
5.50:1742(14.0%)
7.00:3528(28.4%)
14.00:3581(28.8%)
- + 12423 (100.0%) 0 @@ -682,7 +682,7 @@

d.all

0 < 12.7 < 113.5 IQR (CV) : 8.1 (0.5) 519 distinct values - + 12423 (100.0%) 0 @@ -697,7 +697,7 @@

d.all

0 < 0 < 1 IQR (CV) : 1 (1.3)
0.00  :7144(57.5%)
0.29 !:880(7.1%)
0.57 !:30(0.2%)
0.71 !:842(6.8%)
1.00  :3527(28.4%)
! rounded
- + 12423 @@ -714,7 +714,7 @@

d.all

0 < 5 < 7 IQR (CV) : 7 (0.9)
0:4678(37.7%)
2:1315(10.6%)
5:1485(12.0%)
7:4945(39.8%)
- + 12423 (100.0%) 0 @@ -729,7 +729,7 @@

d.all

0 < 0 < 14 IQR (CV) : 0 (1.9) 15 distinct values - + 12423 (100.0%) 0 @@ -744,7 +744,7 @@

d.all

0 < 0 < 6 IQR (CV) : 0 (1.9)
0:9449(76.1%)
1:30(0.2%)
2:414(3.3%)
3:915(7.4%)
4:1198(9.6%)
5:370(3.0%)
6:47(0.4%)
- + 12423 (100.0%) 0 @@ -759,7 +759,7 @@

d.all

0 < 0 < 1.2 IQR (CV) : 0 (1.9) 42 distinct values - + 12423 (100.0%) 0 @@ -773,7 +773,7 @@

d.all

Mean : 0.4 Max : 1
0:7889(63.5%)
1:4534(36.5%)
- + 12423 (100.0%) 0 @@ -788,7 +788,7 @@

d.all

0 < 0 < 7 IQR (CV) : 2 (1.6)
0:7889(63.5%)
1:747(6.0%)
2:1572(12.7%)
3:1118(9.0%)
4:653(5.3%)
5:218(1.8%)
6:99(0.8%)
7:127(1.0%)
- + 12423 (100.0%) 0 @@ -803,7 +803,7 @@

d.all

0 < 0 < 5 IQR (CV) : 2 (1.6)
0:8270(66.6%)
1:791(6.4%)
2:1984(16.0%)
3:933(7.5%)
4:300(2.4%)
5:145(1.2%)
- + 12423 (100.0%) 0 @@ -817,7 +817,7 @@

d.all

2. F_no_menstr 3. M
3368(27.1%)
2974(23.9%)
6081(48.9%)
- + 12423 (100.0%) 0 @@ -832,7 +832,7 @@

d.all

1 < 23 < 2136 IQR (CV) : 31 (1.6) 268 distinct values - + 6178 (49.7%) 6245 @@ -847,7 +847,7 @@

d.all

0.6 < 2.9 < 82.5 IQR (CV) : 1.3 (0.5) 108 distinct values - + 6178 (49.7%) 6245 @@ -862,7 +862,7 @@

d.all

0 < 1.4 < 3.3 IQR (CV) : 0.6 (0.3) 268 distinct values - + 6178 (49.7%) 6245 @@ -877,7 +877,7 @@

d.all

0.1 < 2.1 < 3.8 IQR (CV) : 0.7 (0.2) 1968 distinct values - + 6178 (49.7%) 6245 @@ -892,7 +892,7 @@

d.all

-9.9 < 4.1 < 20.7 IQR (CV) : 5.3 (1) 2554 distinct values - + 6178 (49.7%) 6245 @@ -914,7 +914,7 @@

d.all

10. 378-0827-9 [ 2144 others ]
27(0.2%)
21(0.2%)
18(0.1%)
17(0.1%)
17(0.1%)
15(0.1%)
15(0.1%)
14(0.1%)
13(0.1%)
13(0.1%)
12253(98.6%)
- + 12423 (100.0%) 0 @@ -929,7 +929,7 @@

d.all

0 < 240 < 743 IQR (CV) : 349 (0.8) 668 distinct values - + 12423 (100.0%) 0 @@ -944,7 +944,7 @@

d.all

1 < 4 < 27 IQR (CV) : 4 (0.6) 27 distinct values - + 12423 (100.0%) 0 @@ -959,7 +959,7 @@

d.all

1 < 1 < 5 IQR (CV) : 0 (0.6)
1:10912(87.8%)
2:197(1.6%)
3:945(7.6%)
4:268(2.2%)
5:101(0.8%)
- + 12423 (100.0%) 0 @@ -981,7 +981,7 @@

d.all

10. P3 [ 3 others ]
9555(76.9%)
1304(10.5%)
1288(10.4%)
111(0.9%)
49(0.4%)
46(0.4%)
28(0.2%)
14(0.1%)
10(0.1%)
10(0.1%)
8(0.1%)
- + 12423 (100.0%) 0 @@ -989,7 +989,7 @@

d.all

-

Generated by summarytools 0.9.9 (R version 4.0.4)
2021-07-19

+

Generated by summarytools 0.9.9 (R version 4.0.4)
2021-10-20

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