README-pdf.Rmd

---
title: "Summarizer README"
output: 
  pdf_document:
    number_sections: yes
    df_print: kable
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
options(knitr.table.format = "latex")
```


#Summarise variables/factors by a categorical variable

`summary.factorlist()` is a simple wrapper used to summarise any number of variables by a single categorical variable. 
This is usually "Table 1" of a study report. 

```{r, warning=FALSE, message=FALSE}
library(summarizer)
library(dplyr)
library(stringr)
library(kableExtra)

# Load example dataset, modified version of survival::colon
data(colon_s)

# Table 1 - Patient demographics ----
explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
dependent = "perfor.factor"
colon_s %>%
  summary.factorlist(dependent, explanatory, p=T) %>% 
	kable(., booktabs = TRUE)

```



`summary.factorlist()` is also commonly used to summarise any number of variables by an *outcome variable* (say dead yes/no).  

```{r}
# Table 2 - 5 yr mortality ----
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  summary.factorlist(dependent, explanatory) %>% 
	kable(., booktabs = TRUE)
```

#Summarise regression model results in final table format

The second main feature is the ability to create final tables for logistic `glm()`, hierarchical logistic `lme4::glmer()` and 
Cox proprotional hazard `survival::coxph()` regression models.

The `summarizer()` "all-in-one" function takes a single dependent variable with a vector of explanatory variable names 
(continuous or categorical variables) to produce a final table for publication including summary statistics, 
univariable and multivariable regression analyses. The first columns are those produced by 
`summary.factorist()`. 

##glm

`glm(depdendent ~ explanatory, family="binomial")`

```{r, message=FALSE}
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  summarizer(dependent, explanatory) %>% 
	kable(., booktabs = TRUE)
```

##multi-level
Where a multivariable model contains a subset of the variables specified in the full univariable set, this can be specified. 

```{r, message=FALSE}
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
explanatory.multi = c("age.factor", "obstruct.factor")
dependent = 'mort_5yr'
colon_s %>%
  summarizer(dependent, explanatory, explanatory.multi) %>% 
	kable(., booktabs = TRUE)
```

##Random effects. 

`lme4::glmer(dependent ~ explanatory + (1 | random_effect), family="binomial")`

```{r, message=FALSE}
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
explanatory.multi = c("age.factor", "obstruct.factor")
random.effect = "hospital"
dependent = 'mort_5yr'
colon_s %>%
  summarizer(dependent, explanatory, explanatory.multi, random.effect) %>% 
	kable(., booktabs = TRUE)
```

##with metrics
`metrics=TRUE` provides common model metrics. 

```{r, message=FALSE}
colon_s %>%
  summarizer(dependent, explanatory, explanatory.multi,  metrics=TRUE) %>% 
	kable(., booktabs = TRUE)
```

##Cox proportional hazards 

`survival::coxph(dependent ~ explanatory)`

```{r, message=FALSE}
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "Surv(time, status)"

colon_s %>% 
	summarizer(dependent, explanatory) %>% 
	kable(., booktabs = TRUE)
```

#Subsets
Rather than going all-in-one, any number of subset models can be manually added on to a `summary.factorlist()` table using `summarizer.merge()`. This is particularly useful when models take a long-time to run or are complicated. 

##glm
Note requirement for `glm.id=TRUE`. `fit2df` is a subfunction extracting most common models to a dataframe. 


```{r, message=FALSE}
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
explanatory.multi = c("age.factor", "obstruct.factor")
random.effect = "hospital"
dependent = 'mort_5yr'

# Separate tables
colon_s %>%
  summary.factorlist(dependent, explanatory, glm.id=TRUE) -> example.summary

colon_s %>%
  glmuni(dependent, explanatory) %>%
  fit2df(estimate.suffix=" (univariable)") -> example.univariable

colon_s %>%
  glmmulti(dependent, explanatory) %>%
  fit2df(estimate.suffix=" (multivariable)") -> example.multivariable


colon_s %>%
  glmmixed(dependent, explanatory, random.effect) %>%
  fit2df(estimate.suffix=" (multilevel") -> example.multilevel

# Pipe together
example.summary %>% 
  summarizer.merge(example.univariable) %>% 
  summarizer.merge(example.multivariable) %>% 
  summarizer.merge(example.multilevel) %>% 
  select(-c(glm.id, index)) -> example.final
example.final %>% 
	kable(., booktabs = TRUE) %>% 
	kable_styling(latex_options = "scale_down")

```

##Cox Proportional Hazards example with separate tables merged together.

```{r, message=FALSE}
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
explanatory.multi = c("age.factor", "obstruct.factor")
dependent = "Surv(time, status)"

# Separate tables
colon_s %>%
	summary.factorlist(dependent, explanatory, glm.id=TRUE) -> example2.summary

colon_s %>%
	coxphuni(dependent, explanatory) %>%
	fit2df(estimate.suffix=" (univariable)") -> example2.univariable

colon_s %>%
  coxphmulti(dependent, explanatory.multi) %>%
  fit2df(estimate.suffix=" (multivariable)") -> example2.multivariable

# Pipe together
example2.summary %>% 
	summarizer.merge(example2.univariable) %>% 
	summarizer.merge(example2.multivariable) %>% 
	select(-c(glm.id, index)) -> example2.final
example2.final %>% 
	kable(., booktabs = TRUE)
```

#Summarise regression model results in plot

Models can be summarized with odds ratio/hazard ratio plots using `or.plot` or `hr.plot` (hr.plot not fully tested). 

```{r, fig.width=12, message=FALSE}
# OR plot
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  or.plot(dependent, explanatory)
# Previously fitted models (`glmmulti`) can be provided directly to `glmfit`  
  
# HR plot (not fully tested)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "Surv(time, status)"
colon_s %>%
  hr.plot(dependent, explanatory, dependent_label = "Survival")
# Previously fitted models (`coxphmulti`) can be provided directly using `coxfit`
```

Our own particular `Rstan` models are supported and will be documented in the future. Broadly, if you are running (hierarchical) logistic regression models in [Stan](http://mc-stan.org/users/interfaces/rstan) with coefficients specified as a vector labelled `beta`, then `fit2df()` will work directly on the `stanfit` object in a similar manner to if it was a `glm` or `glmerMod` object. 

# Notes

Use `Hmisc::label()` to assign labels to variables for tables and plots.

```{r}
label(colon_s$age.factor) = "Age (years)"
```

Export dataframe tables directly or to [R Markdown](http://rmarkdown.rstudio.com) using [`knitr::kable()`](https://yihui.name/knitr/).

Note wrapper `summary.missing()` can be useful. Wraps `mice::md.pattern`.

```{r}
colon_s %>%
  summary.missing(dependent, explanatory) %>% 
	kable(., booktabs = TRUE)
```