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push some fixes
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@sammigd
sammigd committed Jul 18, 2022
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8 changes: 4 additions & 4 deletions PCV_analysis_with_pharma_dataset1.Rmd
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Expand Up @@ -32,13 +32,13 @@ sum(core_table_norace$count)
```
## Define covariates
```{r}
list_cov <- c("year_month","age","patient_gender_code","race_code","PCV_combined")
list_cov <- c("age","patient_gender_code","race_code","PCV_combined")
```


## 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")
list_out <- c("resp_severe","non_severe")
m1<-run_model(core_table,list_cov,list_out)
View(m1$m.table)
```
Expand All @@ -49,7 +49,7 @@ m1$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_severe","non_resp_severe")
list_out <- c("non_resp_severe","non_severe")
m2<-run_model(core_table,list_cov,list_out)
View(m2$m.table)
```
Expand All @@ -60,7 +60,7 @@ m2$vif

## Third part of the analysis: restrict to people with ICU critical care vs non-severe outcomes for Covid-19
```{r}
list_out <- c("non_severe","ICU_crit_care")
list_out <- c("ICU_crit_care","non_severe")
m3<-run_model(core_table,list_cov,list_out)
View(m3$m.table)
```
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189 changes: 12 additions & 177 deletions PCV_analysis_with_pharma_dataset2.Rmd
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@@ -1,5 +1,5 @@
---
title: "PCV_analysis -- Dataset 1 and 2"
title: "PCV_analysis -- Dataset 3"
output: html_document
---

Expand All @@ -18,189 +18,24 @@ 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")
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(core_table$count)
print("\n")
sum(core_table_norace$count)
sum(supp_table$count)
```
## Factor variables in the data
## Define covariates
```{r}
## Factor categorical variable and change reference level for race (set White as the ref level)
col_names <- c("age","patient_gender_code","race_code","income_est_mod","PCV_combined")
supp_table_filled[col_names] <- lapply(supp_table_filled[col_names], factor)
supp_table_filled$race_code <- relevel(supp_table_filled$race_code,ref="W")
supp_table_filled$PCV_combined <- relevel(supp_table_filled$PCV_combined,ref="U")
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))
```

## 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}
## 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)
```


list_out <- c("non_severe","resp_severe")
m1<-run_model(df_table,list_cov,list_out)
View(m1$m.table)
```
10 changes: 10 additions & 0 deletions PCV_analysis_with_pharma_dataset3.Rmd
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Expand Up @@ -47,6 +47,16 @@ data1 <- supp_table_filled[supp_table_filled$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:
Expand Down
13 changes: 7 additions & 6 deletions R/functions.R
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@@ -1,16 +1,17 @@
run_model <- function(df,list_cov,list_out)
{## Convert columns of list_cov to factors
col_names <- list_cov
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
df <- df[df$COVID_severity %in% list_out,]
data1 <- df[df$COVID_severity == list_out[1],]
data2 <- df[df$COVID_severity ==list_out[2],]
data_comb<-merge(data1,data2,by=c("year_month",list_cov))
df1 <- df[df$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(df$count)==sum(data_comb$count.x)+sum(data_comb$count.y)){print("OK")}
if(sum(df1$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
Expand Down

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