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| Original file line number | Diff line number | Diff line change |
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| --- | ||
| title: "PCV_analysis" | ||
| output: html_document | ||
| --- | ||
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| ```{r setup, include=FALSE} | ||
| knitr::opts_chunk$set(echo = TRUE) | ||
| ``` | ||
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| ## Load required R packages | ||
| ```{r} | ||
| library(table1) | ||
| library(dplyr) | ||
| ``` | ||
|
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| ```{r} | ||
| ### Load the data: | ||
| core_table <- read.csv("./Data/core_table_filled.csv") | ||
| core_table_norace <- read.csv("./Data/core_table_droprace_filled.csv") | ||
| supp_table_filled <- read.csv("./Data/suppl_table_filled.csv") | ||
| supp_table_6mo_filled <- read.csv("./Data/suppl_6mo_table_filled.csv") | ||
| ``` | ||
|
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| ## First step is to create another columnn of no response | ||
| ```{r} | ||
| ### Prepare the data and do some exploration | ||
| #baseline demographic characteristics (Table 1) | ||
| supp_table_filled$age <- as.factor(core_table$age) | ||
| supp_table_filled$patient_gender_code <- as.factor(core_table$patient_gender_code) | ||
| data <- supp_table_filled[supp_table_filled$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")) | ||
| ``` | ||
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| ```{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$count))) | ||
| ``` | ||
|
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| ## Run the first logistic regression analysis looking at outcome: severe respiratory outcome for Covid-19 versus non-severe outcomes | ||
| ```{r} | ||
| ## Factor categorical variable and change reference level for race (set White as the ref level) | ||
| col_names <- c("patient_gender_code","race_code","income_est_mod","PCV_combined") | ||
| data_comb[col_names] <- lapply(data_comb[col_names], factor) | ||
| data_comb$race_code <- relevel(data_comb$race_code,ref="W") | ||
| data_comb$PCV_combined <- relevel(data_comb$PCV_combined,ref="U") | ||
| ``` | ||
|
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| ```{r} | ||
| m1<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit")) | ||
| #summary(m1) | ||
| m1.ci<-confint(m1) | ||
| ``` | ||
| ```{r} | ||
| m1.table <- cbind(coef(m1),m1.ci) | ||
| colnames(m1.table) <- c("estimate","lower","upper") | ||
| m1.table <- exp(m1.table) | ||
| View(m1.table) | ||
| ``` | ||
|
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|
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| ## Second part of the analysis: restrict to people with severe non-respiratory vs non-severe outcomes for Covid-19 | ||
| ```{r} | ||
| ### Prepare the data and do some exploration | ||
| #baseline demographic characteristics (Table 1) | ||
| supp_table_filled$age <- as.factor(core_table$age) | ||
| supp_table_filled$patient_gender_code <- as.factor(core_table$patient_gender_code) | ||
| data <- supp_table_filled[supp_table_filled$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")) | ||
| ``` | ||
|
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||
| ```{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$count))) | ||
| ``` | ||
|
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||
| ## Run the first logistic regression analysis looking at outcome: severe respiratory outcome for Covid-19 versus non-severe respiratory outcome | ||
| ```{r} | ||
| ## Factor categorical variable and change reference level for race (set White as the ref level) | ||
| col_names <- c("patient_gender_code","race_code","income_est_mod","PCV_combined") | ||
| data_comb[col_names] <- lapply(data_comb[col_names], factor) | ||
| data_comb$race_code <- relevel(data_comb$race_code,ref="W") | ||
| data_comb$PCV_combined <- relevel(data_comb$PCV_combined,ref="U") | ||
| ``` | ||
|
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| ```{r} | ||
| m2<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit")) | ||
| #summary(m2) | ||
| m2.ci<-confint(m2) | ||
| ``` | ||
|
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| ```{r} | ||
| m2.table <- cbind(coef(m2),m2.ci) | ||
| colnames(m2.table) <- c("estimate","lower","upper") | ||
| m2.table <- exp(m2.table) | ||
| View(m2.table) | ||
| ``` | ||
|
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| ## Second part of the analysis: restrict to people with severe non-respiratory vs non-severe outcomes for Covid-19 | ||
| ```{r} | ||
| ### Prepare the data and do some exploration | ||
| #baseline demographic characteristics (Table 1) | ||
| supp_table_filled$age <- as.factor(core_table$age) | ||
| supp_table_filled$patient_gender_code <- as.factor(core_table$patient_gender_code) | ||
| data <- supp_table_filled[supp_table_filled$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")) | ||
| ``` | ||
|
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||
| ```{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$count))) | ||
| ``` | ||
|
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||
| ## Run the first logistic regression analysis looking at outcome: severe respiratory outcome for Covid-19 versus non-severe respiratory outcome | ||
| ```{r} | ||
| ## Factor categorical variable and change reference level for race (set White as the ref level) | ||
| col_names <- c("patient_gender_code","race_code","income_est_mod","PCV_combined") | ||
| data_comb[col_names] <- lapply(data_comb[col_names], factor) | ||
| data_comb$race_code <- relevel(data_comb$race_code,ref="W") | ||
| data_comb$PCV_combined <- relevel(data_comb$PCV_combined,ref="U") | ||
| ``` | ||
|
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| ```{r} | ||
| m3<-glm(cbind(count.x, count.y) ~ age+patient_gender_code+race_code+PCV_combined+flu_vacc+zoster_vacc+bmi_30_plus+comorbidities+income_est_mod, data=data_comb, family = binomial("logit")) | ||
| #summary(m3) | ||
| m3.ci<-confint(m3) | ||
| ``` | ||
|
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| ```{r} | ||
| m3.table <- cbind(coef(m3),m3.ci) | ||
| colnames(m3.table) <- c("estimate","lower","upper") | ||
| m3.table <- exp(m3.table) | ||
| View(m3.table) | ||
| ``` | ||
|
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| ## Fourth part of the analysis: restrict to people with severe non-respiratory vs non-severe outcomes for Covid-19 | ||
| ```{r} | ||
| ### Prepare the data and do some exploration | ||
| #baseline demographic characteristics (Table 1) | ||
| supp_table_filled$age <- as.factor(core_table$age) | ||
| supp_table_filled$patient_gender_code <- as.factor(core_table$patient_gender_code) | ||
| data <- supp_table_filled[supp_table_filled$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")) | ||
| ``` | ||
|
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||
| ```{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$count))) | ||
| ``` | ||
|
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| ## Run the first logistic regression analysis looking at outcome: severe respiratory outcome for Covid-19 versus non-severe respiratory outcome | ||
| ```{r} | ||
| ## Factor categorical variable and change reference level for race (set White as the ref level) | ||
| col_names <- c("patient_gender_code","race_code","income_est_mod","PCV_combined") | ||
| data_comb[col_names] <- lapply(data_comb[col_names], factor) | ||
| data_comb$race_code <- relevel(data_comb$race_code,ref="W") | ||
| data_comb$PCV_combined <- relevel(data_comb$PCV_combined,ref="U") | ||
| ``` | ||
|
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| ```{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) | ||
| ``` | ||
|
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| ```{r} | ||
| m4.table <- cbind(coef(m4),m4.ci) | ||
| colnames(m4.table) <- c("estimate","lower","upper") | ||
| m4.table <- exp(m4.table) | ||
| View(m4.table) | ||
| ``` | ||
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| Version: 1.0 | ||
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| RestoreWorkspace: Default | ||
| SaveWorkspace: Default | ||
| AlwaysSaveHistory: Default | ||
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| EnableCodeIndexing: Yes | ||
| UseSpacesForTab: Yes | ||
| NumSpacesForTab: 2 | ||
| Encoding: UTF-8 | ||
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| RnwWeave: Sweave | ||
| LaTeX: pdfLaTeX |
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