From e42907b9f89b34018cf2d3487a90cc5dc413893a Mon Sep 17 00:00:00 2001 From: ottiP Date: Wed, 27 Dec 2023 12:41:16 +0100 Subject: [PATCH] Modify files for submission --- .DS_Store | Bin 6148 -> 14340 bytes .../core_table_droprace_filled.csv | 30241 -- Data/No_pharma_cov/core_table_filled.csv | 30241 -- Data/No_pharma_cov/suppl_6mo_table_filled.csv | 368641 --------------- Data/core_table_droprace_filled.csv | 30241 -- Data/core_table_filled.csv | 30241 -- Data/suppl_6mo_table_filled.csv | 368641 --------------- Figures/Figure1.png | Bin 0 -> 255068 bytes PCV_analysis.Rmd | 205 - PCV_analysis_with_pharma_dataset1.Rmd | 86 - PCV_analysis_with_pharma_dataset2.Rmd | 41 - PCV_analysis_with_pharma_dataset3.Rmd | 216 - PCV_analysis_with_pharma_exploratory.Rmd | 50 - R/Example_logistic_reg_agg_data.R | 18 - R/Plotting_paper.R | 110 + R/Tests.R | 20 - R/functions.R | 28 - Results/m.table.mod1 | Bin 0 -> 356 bytes Results/m.table.mod2 | Bin 0 -> 354 bytes Results/m.table.mod3 | Bin 0 -> 355 bytes Results/m.table.mod4 | Bin 0 -> 356 bytes 21 files changed, 110 insertions(+), 858910 deletions(-) delete mode 100644 Data/No_pharma_cov/core_table_droprace_filled.csv delete mode 100644 Data/No_pharma_cov/core_table_filled.csv delete mode 100644 Data/No_pharma_cov/suppl_6mo_table_filled.csv delete mode 100644 Data/core_table_droprace_filled.csv delete mode 100644 Data/core_table_filled.csv delete mode 100644 Data/suppl_6mo_table_filled.csv create mode 100644 Figures/Figure1.png delete mode 100644 PCV_analysis.Rmd delete mode 100644 PCV_analysis_with_pharma_dataset1.Rmd delete mode 100644 PCV_analysis_with_pharma_dataset2.Rmd delete mode 100644 PCV_analysis_with_pharma_dataset3.Rmd delete mode 100644 PCV_analysis_with_pharma_exploratory.Rmd delete mode 100644 R/Example_logistic_reg_agg_data.R create mode 100644 R/Plotting_paper.R delete mode 100644 R/Tests.R delete mode 100644 R/functions.R create mode 100644 Results/m.table.mod1 create mode 100644 Results/m.table.mod2 create 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z462{mo@eO_7;PGIC73Q@Vn)o_i#BxHd#j5 z_%|SH5S2xn(Bb|`{^ja}ToHUQck}F#67V&>1!7uj#;P7w<9fc0p-T$`1p$b>r3HYa z$hqv_zQK<*rr-xrG{`#LfEL^K1OTm!PU$|s{pp(G7*H)wlB1`fVhe<@#;wD{*BT(O zGk$D+4V1=r$RM;>_K)Gga;hA%YGknM0kB6GguOK(VShi<=09CduvQx&bvHiC3j=>6 zt}_o|kP-yID;lPX)mJ8#<48*ZLD-dje{0HV~-&9OjerfBd@oyqU!*N*5 z%zi`}H)Aug2_z3!DfQk~rucK+Z&ukYo=MC(fFD1-^dP9<@nbEpui&vdWXH~4X!ef0 z_>k!#dIt3elzn~m$7{3_JL{tA!~?`M>O$;0_!ut;K7jp!bL9A@<{?H##>v%Hn~{-` z$>n7WKR-XTwYBx);^HI*;}jheqwfyob_^ZKiHWI?@Hf1?yiL2iyYq{SZu|wjZca|& zwcEt4`uh6TZf=#lQ-8cI<)815{KKQ0*uveiz+GT8y4oHayJ6{3B+bA69xg;tV{CE~ zP0Ycam60^IDBTc5ngds@jz9Kx%3!47S@Ckcgu;Nt_cgf>h{>$n(cWCim8bSy~3g* zePd(eo}M1JZnn}h$esD+&vD#Dt9NT-EZ0(TZewBTrJ6_lxPia^+3znra{Ly~9@QIB z+~3~v@Ba43`@MeffcZU#)Zg9B?~m=bPi2Ms`eB&v?|=DsAAJ1ikt@P3oqzL1|IJki zUZRXO8Ok{M+eiQ1yMKQs|9px6=wAKvB>uBi`_FRvXF2^{VE!Jj{)tZiStS4GocNE< z)PI)KKg;RgFO+{y?0-(|e}5ML6Jr1U&iR*G?f=10, this specific covariate is explained by all other covariates to a large extent (~90%). There could be a problem of collinearity. -```{r} -m1$vif -``` -## Second part of the analysis: restrict to people with severe non-respiratory vs non-severe outcomes for Covid-19 -```{r} -list_out <- c("non_resp_severe","non_severe") -m2<-run_model(core_table,list_cov,list_out) -View(m2$m.table) -``` -##Check collinearity -```{r} -m2$vif -``` - -## Third part of the analysis: restrict to people with ICU critical care vs non-severe outcomes for Covid-19 -```{r} -list_out <- c("ICU_crit_care","non_severe") -m3<-run_model(core_table,list_cov,list_out) -View(m3$m.table) -``` - -##Check collinearity -```{r} -m3$vif -``` - -## Fourth part of the analysis: restrict to people with ICU critical care vs respiratory severe outcomes for Covid-19 - -```{r} -list_out <- c("ICU_crit_care","resp_severe") -m4<-run_model(core_table,list_cov,list_out) -View(m4$m.table) -``` - -##Check collinearity -```{r} -m4$vif -``` - - diff --git a/PCV_analysis_with_pharma_dataset2.Rmd b/PCV_analysis_with_pharma_dataset2.Rmd deleted file mode 100644 index c662e0f..0000000 --- a/PCV_analysis_with_pharma_dataset2.Rmd +++ /dev/null @@ -1,41 +0,0 @@ ---- -title: "PCV_analysis -- Dataset 3" -output: html_document ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -## Load required R packages -```{r} -library(table1) -library(dplyr) -library(ggplot2) -#library(GGally) -library(car) -``` - -```{r} -### Load the data: -supp_table <- read.csv("~/Documents/Ottavia/Data_pharma/suppl_table_filled.csv") -``` - -## Check that individuals are the same across dataframes -```{r} -print(paste0("Total number of individuals: ")) -sum(supp_table$count) -``` -## Define covariates -```{r} -list_cov <- c("year_month","age","patient_gender_code","race_code","PCV_combined") #removed "income_est_mod" -## drop covariates -drop.cols <- c("bmi_30_plus","income_est_mod") -df_table <- supp_table %>% select(-all_of(drop.cols)) -``` -## Run the first logistic regression analysis looking at outcome: severe respiratory outcome for Covid-19 versus non-severe outcomes -```{r} -list_out <- c("non_severe","resp_severe") -m1<-run_model(df_table,list_cov,list_out) -View(m1$m.table) -``` \ No newline at end of file diff --git a/PCV_analysis_with_pharma_dataset3.Rmd b/PCV_analysis_with_pharma_dataset3.Rmd deleted file mode 100644 index 0e9a882..0000000 --- a/PCV_analysis_with_pharma_dataset3.Rmd +++ /dev/null @@ -1,216 +0,0 @@ ---- -title: "PCV_analysis -- Dataset 1 and 2" -output: html_document ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -## Load required R packages -```{r} -library(table1) -library(dplyr) -library(ggplot2) -#library(GGally) -library(car) -``` - -```{r} -### Load the data: -core_table <- read.csv("./Data/core_table_filled.csv") -core_table_norace <- read.csv("./Data/core_table_droprace_filled.csv") -``` - -## Check that individuals are the same across dataframes -```{r} -print(paste0("Total number of individuals: ")) -sum(core_table$count) -print("\n") -sum(core_table_norace$count) -``` -## Factor variables in the data -```{r} -## Factor categorical variable and change reference level for race (set White as the ref level) -col_names <- c("age","patient_gender_code","race_code","income_est_mod","PCV_combined") -supp_table_filled[col_names] <- lapply(supp_table_filled[col_names], factor) -supp_table_filled$race_code <- relevel(supp_table_filled$race_code,ref="W") -supp_table_filled$PCV_combined <- relevel(supp_table_filled$PCV_combined,ref="U") -``` - -## First step is to create another columnn of no response -```{r} -### Prepare the data and do some exploration -#baseline demographic characteristics (Table 1) -data <- supp_table_filled[supp_table_filled$COVID_severity %in% c("non_severe","resp_severe"),] -data1 <- supp_table_filled[supp_table_filled$COVID_severity == "non_severe",] -data2 <- supp_table_filled[supp_table_filled$COVID_severity =="resp_severe",] -data_comb<-merge(data1,data2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod")) -``` -```{r} -d <- supp_table_filled[supp_table_filled$COVID_severity %in% c("non_severe","resp_severe"),] -d1 <- supp_table_filled[supp_table_filled$COVID_severity == "non_severe",] -d2 <- supp_table_filled[supp_table_filled$COVID_severity =="resp_severe",] -d_comb<-merge(d1,d2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod")) - -``` - - - - -```{r} -## Exploratory analysis of the dataset: -print(paste0("PCV13 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_only"])/sum(data$count))) -print(paste0("PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PPSV23_10yrs"])/sum(data$count))) -print(paste0("PCV13 and PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_PPSV23_10yrs"])/sum(data$count))) -print(paste0("Unknown PCV vaccine coverage: ",sum(data$count[data$PCV_combined=="U"])/sum(data$count))) -print(paste0("Flu vaccine: ",sum(data$count[data$"flu_vacc"=="TRUE"])/sum(data$count))) -print(paste0("Zooster vaccine: ",sum(data$count[data$"zoster_vacc"=="TRUE"])/sum(data$count))) -``` - -## Run the first logistic regression analysis looking at outcome: severe respiratory outcome for Covid-19 versus non-severe outcomes -```{r} -m1<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit")) -#summary(m1) -m1.ci<-confint(m1) -``` - -```{r} -m1.table <- cbind(coef(m1),m1.ci) -colnames(m1.table) <- c("estimate","lower","upper") -m1.table <- exp(m1.table) -View(m1.table) -``` - -##Check collinearity among the covariates using variance inflation factor (VIF): VIF answers the question: "How well is one of my covariates x_i explained by all other covariates x jointly?". -The VIF is defined as 1/(1-R_squared); this means that for VIF>=10, this specific covariate is explained by all other covariates to a large extent (~90%). There could be a problem of collinearity. - -```{r} -#X<-data_comb[,2:10] -#ggpairs(X) -vif(m1) -``` - - -## Second part of the analysis: restrict to people with severe non-respiratory vs non-severe outcomes for Covid-19 -```{r} -### Prepare the data and do some exploration -#baseline demographic characteristics (Table 1) -data <- supp_table_filled[supp_table_filled$COVID_severity %in% c("non_severe","non_resp_severe"),] -data1 <- supp_table_filled[supp_table_filled$COVID_severity == "non_severe",] -data2 <- supp_table_filled[supp_table_filled$COVID_severity =="non_resp_severe",] -data_comb<-merge(data1,data2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod")) -``` - -```{r} -## Exploratory analysis of the dataset: -print(paste0("Total number of individuals: ",sum(data$count))) -print(paste0("PCV13 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_only"])/sum(data$count))) -print(paste0("PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PPSV23_10yrs"])/sum(data$count))) -print(paste0("PCV13 and PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_PPSV23_10yrs"])/sum(data$count))) -print(paste0("Unknown PCV vaccine coverage: ",sum(data$count[data$PCV_combined=="U"])/sum(data$count))) -print(paste0("Flu vaccine: ",sum(data$count[data$"flu_vacc"=="TRUE"])/sum(data$count))) -print(paste0("Zooster vaccine: ",sum(data$count[data$"zoster_vacc"=="TRUE"])/sum(data$count))) -``` - -## Logistic regression -```{r} -m2<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit")) -#summary(m2) -m2.ci<-confint(m2) -``` - -##Check collinearity -```{r} -vif(m2) -``` - - -```{r} -m2.table <- cbind(coef(m2),m2.ci) -colnames(m2.table) <- c("estimate","lower","upper") -m2.table <- exp(m2.table) -View(m2.table) -``` - -## Third part of the analysis: restrict to people with ICU critical care vs non-severe outcomes for Covid-19 -```{r} -### Prepare the data and do some exploration -#baseline demographic characteristics (Table 1) -data <- supp_table_filled[supp_table_filled$COVID_severity %in% c("non_severe","ICU_crit_care"),] -data1 <- supp_table_filled[supp_table_filled$COVID_severity == "non_severe",] -data2 <- supp_table_filled[supp_table_filled$COVID_severity =="ICU_crit_care",] -data_comb<-merge(data1,data2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod")) -``` - -```{r} -## Exploratory analysis of the dataset: -print(paste0("Total number of individuals: ",sum(data$count))) -print(paste0("PCV13 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_only"])/sum(data$count))) -print(paste0("PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PPSV23_10yrs"])/sum(data$count))) -print(paste0("PCV13 and PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_PPSV23_10yrs"])/sum(data$count))) -print(paste0("Unknown PCV vaccine coverage: ",sum(data$count[data$PCV_combined=="U"])/sum(data$count))) -print(paste0("Flu vaccine: ",sum(data$count[data$"flu_vacc"=="TRUE"])/sum(data$count))) -print(paste0("Zooster vaccine: ",sum(data$count[data$"zoster_vacc"=="TRUE"])/sum(data$count))) -``` - -## Logistic regression -```{r} -m3<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit")) -#summary(m3) -m3.ci<-confint(m3) -``` - -```{r} -m3.table <- cbind(coef(m3),m3.ci) -colnames(m3.table) <- c("estimate","lower","upper") -m3.table <- exp(m3.table) -View(m3.table) -``` - -##Check collinearity -```{r} -vif(m3) -``` - -## Fourth part of the analysis: restrict to people with ICU critical care vs respiratory severe outcomes for Covid-19 -```{r} -### Prepare the data and do some exploration -#baseline demographic characteristics (Table 1) -data <- supp_table_filled[supp_table_filled$COVID_severity %in% c("ICU_crit_care","resp_severe"),] -data1 <- supp_table_filled[supp_table_filled$COVID_severity == "ICU_crit_care",] -data2 <- supp_table_filled[supp_table_filled$COVID_severity =="resp_severe",] -data_comb<-merge(data1,data2,by=c("year_month","age","patient_gender_code","race_code","PCV_combined","flu_vacc","zoster_vacc","bmi_30_plus","comorbidities","income_est_mod")) -``` - -```{r} -## Exploratory analysis of the dataset: -print(paste0("Total number of individuals: ",sum(data$count))) -print(paste0("PCV13 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_only"])/sum(data$count))) -print(paste0("PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PPSV23_10yrs"])/sum(data$count))) -print(paste0("PCV13 and PPSV23 vaccine coverage: ",sum(data$count[data$PCV_combined=="PCV13_PPSV23_10yrs"])/sum(data$count))) -print(paste0("Unknown PCV vaccine coverage: ",sum(data$count[data$PCV_combined=="U"])/sum(data$count))) -print(paste0("Flu vaccine: ",sum(data$count[data$"flu_vacc"=="TRUE"])/sum(data$count))) -print(paste0("Zooster vaccine: ",sum(data$count[data$"zoster_vacc"=="TRUE"])/sum(data$count))) -``` - -## Logistic regression -```{r} -m4<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit")) -#summary(m4) -m4.ci<-confint(m4) -``` - -```{r} -m4.table <- cbind(coef(m4),m4.ci) -colnames(m4.table) <- c("estimate","lower","upper") -m4.table <- exp(m4.table) -View(m4.table) -``` - -##Check collinearity -```{r} -vif(m4) -``` - - diff --git a/PCV_analysis_with_pharma_exploratory.Rmd b/PCV_analysis_with_pharma_exploratory.Rmd deleted file mode 100644 index cc50b2b..0000000 --- a/PCV_analysis_with_pharma_exploratory.Rmd +++ /dev/null @@ -1,50 +0,0 @@ ---- -title: "PCV_analysis" -output: html_document ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -## Load required R packages -```{r} -library(table1) -library(dplyr) -library(ggplot2) -``` - -```{r} -### Load the data: -core_table <- read.csv("./Data/core_table_filled.csv") -core_table_norace <- read.csv("./Data/core_table_droprace_filled.csv") -supp_table_filled <-read.csv("~/Documents/Ottavia/Data_pharma/suppl_table_filled.csv") -supp_table_6mo_filled <- read.csv("./Data/suppl_6mo_table_filled.csv") -``` - -## Check that individuals are the same across dataframes -```{r} -print(paste0("Total number of individuals: ")) -sum(core_table$count) -print("\n") -sum(core_table_norace$count) -print("\n") -sum(supp_table_filled$count) -print("\n") -sum(supp_table_6mo_filled$count) -``` - -## Exploratory analysis: descriptive stats -```{r} -Tot_ind <- sum(supp_table_filled$count) -vax_by_age<-supp_table_filled %>% group_by(age,PCV_combined) %>% summarise(prop = sum(count)/Tot_ind) -vax_by_com<-supp_table_filled %>% group_by(comorbidities,PCV_combined) %>% summarise(prop = sum(count)/Tot_ind) -``` - -## Plot histograms -```{r} -ggplot(vax_by_age,aes(age,prop,fill=PCV_combined))+ - geom_bar(stat="identity",position='dodge') -ggplot(vax_by_com,aes(comorbidities,prop,fill=PCV_combined))+ - geom_bar(stat="identity",position='dodge') -``` diff --git a/R/Example_logistic_reg_agg_data.R b/R/Example_logistic_reg_agg_data.R deleted file mode 100644 index d6e99e2..0000000 --- a/R/Example_logistic_reg_agg_data.R +++ /dev/null @@ -1,18 +0,0 @@ -library(tidyverse) -set.seed(5) -df<-tibble(Gender = as.factor(sample(c("m","f"), 200, replace = TRUE, prob=c(0.6,0.4))), - Age_Group = as.factor(sample(c("[<30]","[30-65]", "[65+]"), 200, replace = TRUE, prob=c(0.3,0.6,0.1))), - Response = rbinom(200, 1, prob = 0.2)) -df -model1<-glm(Response ~ Gender+Age_Group, data = df, family = binomial("logit")) -summary(model1) - -df_agg<-df%>%group_by(Gender, Age_Group)%>%summarise(Impressions=n(), Responses=sum(Response))%>% - ungroup()%>%mutate(RR=Responses/Impressions) -df_agg - -df_agg$No_Responses <- df_agg$Impressions- df_agg$Responses -df_agg - -m3<-glm(cbind(Responses, No_Responses) ~ Gender+Age_Group, data=df_agg, family = binomial("logit")) -summary(m3) \ No newline at end of file diff --git a/R/Plotting_paper.R b/R/Plotting_paper.R new file mode 100644 index 0000000..f92a0fa --- /dev/null +++ b/R/Plotting_paper.R @@ -0,0 +1,110 @@ +######Plotting +library(dplyr) +library(ggplot2) +library(duckdb) +library(arrow) +library(lubridate) +library(reshape2) +library(vtable) +library(tidyr) +#library(plyr) +library(data.table) +library(forestplot) +library(metafor) +library(patchwork) +library(ggplotify) + +#### Load data +mod1<-readRDS("./Results/m.table.mod1") +mod2<-readRDS("./Results/m.table.mod2") +mod3<-readRDS("./Results/m.table.mod3") +mod4<-readRDS("./Results/m.table.mod4") +colnames(mod1)<-c("mean","lower","upper") +colnames(mod2)<-c("mean","lower","upper") +colnames(mod3)<-c("mean","lower","upper") +colnames(mod4)<-c("mean","lower","upper") + + +mod1<-as.data.frame(mod1[c(1,2,3,6,7),]) +mod1$model<-c("PPSV23 >5yrs","PPSV23 <=5yrs","PCV13 only","PCV13 and PPSV23 <=5yrs","PCV13 and PPSV23 >5yrs") +mod1$OR<-c("1.01 (0.95,1.06)","1.05 (1.01,1.08)","0.91 (0.88,0.93)","0.95 (0.91,0.99)","0.91 (0.85,0.97)") +mod2<-as.data.frame(mod2[c(1,2,3,6,7),]) +mod2$model<-c("PPSV23 >5yrs","PPSV23 <=5yrs","PCV13 only","PCV13 and PPSV23 <=5yrs","PCV13 and PPSV23 >5yrs") +mod2$OR<-c("0.99 (0.90,1.10)","1.12 (1.05,1.20)","1.08 (1.02,1.14)","1.21 (1.13,1.29)","1.07 (0.96,1.19)")#round(mod2$mean,2) +mod3<-as.data.frame(mod3[c(1,2,3,6,7),]) +mod3$model<-c("PPSV23 >5yrs","PPSV23 <=5yrs","PCV13 only","PCV13 and PPSV23 <=5yrs","PCV13 and PPSV23 >5yrs") +mod3$OR<-c("0.96 (0.88,1.06)","1.03 (0.97,1.10)","0.92 (0.88,0.97)","0.96 (0.88,1.03)","0.89 (0.79,0.99)")#round(mod3$mean,2) +mod4<-as.data.frame(mod4[c(1,2,3,6,7),]) +mod4$model<-c("PPSV23 >5yrs","PPSV23 <=5yrs","PCV13 only","PCV13 and PPSV23 <=5yrs","PCV13 and PPSV23 >5yrs") +mod4$OR<-c("0.97 (0.89,1.06)","1.07 (1.01,1.14)","0.85 (0.81,0.90)","0.92 (0.85,0.98)","0.83 (0.73,0.92)")#round(mod4$mean,2) + + +#par(mfrow = c(2, 2)) # 1-by-2 grid of plots +g1<-mod1 %>% forestplot(labeltext=c(model,OR), + zero=1, + title="A) Severe respiratory or ICU vs non-severe outcomes", + boxsize = 0.1, + xlab="OR", + xticks=c(0.8,1.0,1.2,1.6)) %>% + fp_set_style(box = "royalblue", + line = "darkblue", + summary = "royalblue") %>% + fp_add_header(OR="OR (95%CI)") +g2<-mod2 %>% forestplot(labeltext=c(model,OR), + zero=1, + title="B) Severe respiratory or ICU vs severe non-resp outcomes", + boxsize = 0.1, + xlab="OR", + xticks=c(0.8,1.0,1.2,1.6)) %>% + fp_set_style(box = "royalblue", + line = "darkblue", + summary = "royalblue") %>% + fp_add_header(OR="OR (95%CI)") +g3<-mod3 %>% forestplot(labeltext=c(model,OR), + zero=1, + title="C) ICU vs severe respiratory outcomes", + boxsize = 0.1, + xlab="OR", + xticks=c(0.8,1.0,1.2,1.6)) %>% + fp_set_style(box = "royalblue", + line = "darkblue", + summary = "royalblue") %>% + fp_add_header(OR="OR (95%CI)") +g4<-mod4 %>% forestplot(labeltext=c(model,OR), + zero=1, + title="D) ICU vs non-severe outcomes", + boxsize = 0.1, + xlab="OR", + xticks=c(0.8,1.0,1.2,1.6)) %>% + fp_set_style(box = "royalblue", + line = "darkblue", + summary = "royalblue") %>% + fp_add_header(OR="OR (95%CI)") + + + + +g1<-grid2grob(print(g1)) +g2<-grid2grob(print(g2)) +g3<-grid2grob(print(g3)) +g4<-grid2grob(print(g4)) + +wrap_elements(g1)*wrap_elements(g2)/(wrap_elements(g3)*wrap_elements(g4)) + + + +ggsave(gsave,"./Figures/Figure1.png") + +forest(x=mod1$mean, + ci.lb = mod1$lower, + ci.ub = mod1$upper, + cex = 1.5, + annotate=FALSE, + at=seq(0.8,1,1.2), + xlim=c(0.8,1.2), + xlab="OR" + #slab=rownames(dat), + #psize=psize*.7, + #atransf=exp +) + diff --git a/R/Tests.R b/R/Tests.R deleted file mode 100644 index 23999fa..0000000 --- a/R/Tests.R +++ /dev/null @@ -1,20 +0,0 @@ -data[data$year_month=="2021-12" & data$age=="0" & data$patient_gender_code=="F"&data$race_code=="U"&data$PCV_combined=="U"&data$flu_vacc - =="FALSE"&data$zoster_vacc=="FALSE"&data$bmi_30_plus=="FALSE"&data$comorbidities=="FALSE"&data$income_est_mod=="U",] - - -data_comb[data_comb$year_month=="2021-12" & data_comb$age=="0" & data_comb$patient_gender_code=="F"&data_comb$race_code=="U"&data_comb$PCV_combined=="U"&data_comb$flu_vacc - =="FALSE"&data_comb$zoster_vacc=="FALSE"&data_comb$bmi_30_plus=="FALSE"&data_comb$comorbidities=="FALSE"&data_comb$income_est_mod=="U",] - - - - -supp_table_filled[supp_table_filled$year_month=="2021-12" & supp_table_filled$age=="1" & supp_table_filled$patient_gender_code=="F"& supp_table_filled$race_code=="U"&supp_table_filled$PCV_combined=="U"& - supp_table_filled$flu_vacc - =="FALSE"&supp_table_filled$zoster_vacc=="FALSE"&supp_table_filled$bmi_30_plus=="FALSE"&supp_table_filled$comorbidities=="FALSE"&supp_table_filled$income_est_mod=="U" - &supp_table_filled$COVID_severity!="non_resp_severe"& supp_table_filled$COVID_severity!="ICU_crit_care",] - - -core_table[core_table$year_month=="2021-12" & core_table$age=="1" & core_table$patient_gender_code=="F"& core_table$race_code=="U"&core_table$PCV_combined=="U"& - core_table$flu_vacc - =="FALSE"&core_table$zoster_vacc=="FALSE"&core_table$bmi_30_plus=="FALSE"&core_table$comorbidities=="FALSE"&core_table$income_est_mod=="U" - &core_table$COVID_severity!="non_resp_severe"& core_table$COVID_severity!="ICU_crit_care",] diff --git a/R/functions.R b/R/functions.R deleted file mode 100644 index f091982..0000000 --- a/R/functions.R +++ /dev/null @@ -1,28 +0,0 @@ -run_model <- function(df,list_cov,list_out) - {## Convert columns of list_cov to factors - col_names <- list_cov[2:length(list_cov)] - df[col_names] <- lapply(df[col_names], factor) - df$race_code <- relevel(df$race_code,ref="W") - df$PCV_combined <- relevel(df$PCV_combined,ref="U") - ## Create df of response and non-responses to be fed to the logistic regression - df1 <- df[df$COVID_severity %in% list_out,] - data1 <- df1[df1$COVID_severity == list_out[1],] - data2 <- df1[df1$COVID_severity ==list_out[2],] - #list_cov_tot <- paste(c("year_month",list_cov)) - data_comb<-merge(data1,data2,all.x=TRUE,all.y=TRUE) - ## - if(sum(df1$count)==sum(data_comb$count.x)+sum(data_comb$count.y)){print("OK")} - else print("ERROR") - ## Run logistic regression model - data_comb$COVID_severity.x<-NULL - data_comb$COVID_severity.y<-NULL - m<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+PCV_combined+. -year_month, data=data_comb, family = binomial("logit")) - #summary(m1) - m.ci<-confint(m) - m.table <- cbind(coef(m),m.ci) - colnames(m.table) <- c("estimate","lower","upper") - m.table <- exp(m.table) - m.vif <- vif(m) - result <- list("m.table"=m.table,"vif"=m.vif) - return(result) -} \ No newline at end of file diff --git a/Results/m.table.mod1 b/Results/m.table.mod1 new file mode 100644 index 0000000000000000000000000000000000000000..ef6de423f58a6de68d6fb03cd15f1c3706fd37bd GIT binary patch literal 356 zcmV-q0h|6GiwFP!000001B>8dU|?WoU}0opU}gm}8CXL@+;lA%7?}8g98voZVqZ7P z@E)-Luz&aQ9=mx4q6d?W_Fvg2RlA<8$+iQcWw1ilM^XP+=q?)&S=)Lt_{xF{dCS5ynYL%_)I# z*nrA_n&IYvd0^9_HZ#LqmRekrnVVRW3Ns}qzdW@F#wjf*0CE@@{s908DXp8j0RRB} CI8dU|?WoU}0opU}gm}8CXL@+;lA%7?}8g98vrCXP!89 zrXR5X_*TMLQ0tuiM-ThwS{_&Izl8Bxs<5fp|A_T!Y^fHu{~UA2dh0Pg|8Z7sd82rf{bw`ouu}yjEv)R^NUhVD~pPu!rV|{Bq7!S=P*NK7$-5OAR`gRNlDEqfpOS? z%7B{T=74!%)1fvq!(5hHT#}iaSdt1eB`3c;wFt&3EhqqT7#RKm0F)@N8dU|?WoU}0opU}gm}8CXL@+;lA%7?}8g98vpsm%Qyh z+Irf5=zfr0Dt*uX?Zj!DT9tO#e{%_LN%y*J|9jfy$_p||_V1>M*|cA4v42x_&U#bl zF8eq9!TX9k7TCX+{3*X8>xlguPDO<&n$PXObZ_u7)4gy1Gfs)&{*m+cZ`@}-lz6<% z{-xH_+<+fJ_8(sSmO6TPqy5KOB0HVzrR?A9PwZZPcdh*owZ3qcH?j8r*m|Wa>$B`X z6nJ|7PT6Sx{?v9Z5osj`1{Nludl?y67&t*5O3BOxQoKM65@rYc5mgBXP)S~5ZfY@* zEeusB09Ff;=MM-74l^>2&&@ANHLWZvh6-~-g^`3<1DwMQjbWU`oPvx*7$+q)rv%1f z11bY*hMNQCflY_n%nWl`YH>+sZemF)%#@t`^3)<2r?j8|$YEgk2LO*EXQH|R003a{ Brquud literal 0 HcmV?d00001 diff --git a/Results/m.table.mod4 b/Results/m.table.mod4 new file mode 100644 index 0000000000000000000000000000000000000000..98d3e3a09767e7941269a590df8e841afe3d2ee8 GIT binary patch literal 356 zcmV-q0h|6GiwFP!000001B>8dU|?WoU}0opU}gm}8CXL@+;lA%7?}8g98vrCte(4f zZ(*|ks3%|KvT~*UYd^tdtIBuT|Bib9F7x_*`(KR$WtLBP?B52ixF`PH-~LsFidp^T zQ}%CiD%S5)-e&*7eD6W7`X}};|6Dos(0!Tx&$tSP%7@T_lW)9*89ii9OSkCv(Dte#_w0{ z-@-{e#zW1_l-;pnDk^SQt1#9!kl~1yZ~~3=(Ds`w>+M2T(~~Vs2_N zkSz>VCjeFpk>?Kx2o5tcj?c|6N;R!4DuxPkLxquqSOc8H42@x&#GHbRL>MO}HKzo| zVFM}yYKEHw=7CLz+RO}dS!!`fW^Q6hD$JCe{PNTy7^k$L0LWor_y+(>Fv|hD0RRA> Caj|s( literal 0 HcmV?d00001