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+4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PPSV23_10yrs,ICU_crit_care,0 +4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PPSV23_10yrs,non_resp_severe,0 +4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PPSV23_10yrs,non_severe,0 +4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,PPSV23_10yrs,resp_severe,0 +4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,U,ICU_crit_care,0 +4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,U,non_resp_severe,0 +4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,U,non_severe,0 +4,5,U,W,TRUE,TRUE,TRUE,TRUE,U,U,resp_severe,0 diff --git a/Example_logistic_reg_agg_data.R b/Example_logistic_reg_agg_data.R new file mode 100644 index 0000000..d6e99e2 --- /dev/null +++ b/Example_logistic_reg_agg_data.R @@ -0,0 +1,18 @@ +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$Responses +df_agg + +m3<-glm(cbind(Responses, No_Responses) ~ Gender+Age_Group, data=df_agg, family = binomial("logit")) +summary(m3) \ No newline at end of file diff --git a/PCV_analysis_with_pharma.Rmd b/PCV_analysis_with_pharma.Rmd index d9eed78..8e5ce99 100644 --- a/PCV_analysis_with_pharma.Rmd +++ b/PCV_analysis_with_pharma.Rmd @@ -11,16 +11,49 @@ knitr::opts_chunk$set(echo = TRUE) ```{r} library(table1) library(dplyr) +library(ggplot2) +library(GGally) ``` ```{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("./Data/suppl_table_filled.csv") +supp_table_filled <- read.csv("~/Documents/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$count) +vax_by_age<-supp_table_filled %>% group_by(age,PCV_combined) %>% summarise(prop = sum(count)/Tot_ind) +vax_by_com<-supp_table_filled %>% group_by(comorbidities,PCV_combined) %>% summarise(prop = sum(count)/Tot_ind) + +``` + +## Plot histograms +```{r} +ggplot(vax_by_age,aes(age,prop,fill=PCV_combined))+ + geom_bar(stat="identity",position='dodge') +ggplot(vax_by_com,aes(comorbidities,prop,fill=PCV_combined))+ + geom_bar(stat="identity",position='dodge') +``` + + + + ## First step is to create another columnn of no response ```{r} ### Prepare the data and do some exploration @@ -35,7 +68,6 @@ data_comb<-merge(data1,data2,by=c("year_month","age","patient_gender_code","race ```{r} ## Exploratory analysis of the dataset: -print(paste0("Total number of individuals: ",sum(data$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$count))) @@ -58,6 +90,14 @@ m1<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined #summary(m1) m1.ci<-confint(m1) ``` +##Check collinearity among the covariates using pair-wise correlation matrix +```{r} +X<-data_comb[,2:10] +ggpairs(X) + +``` + + ```{r} m1.table <- cbind(coef(m1),m1.ci) diff --git a/Tests.R b/Tests.R new file mode 100644 index 0000000..0f751a1 --- /dev/null +++ b/Tests.R @@ -0,0 +1,8 @@ +data[data$year_month=="2021-12" & data$age=="1" & data$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$income_est_mod=="U",] + + +supp_table_filled[supp_table_filled$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",]