AMR/vignettes/resistance_predict.Rmd

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---
title: "How to predict antimicrobial resistance"
author: "Matthijs S. Berends"
date: '`r format(Sys.Date(), "%d %B %Y")`'
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output:
rmarkdown::html_vignette:
toc: true
vignette: >
%\VignetteIndexEntry{How to predict antimicrobial resistance}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
collapse = TRUE,
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comment = "#>",
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fig.width = 7.5,
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fig.height = 4.75
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)
```
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## Needed R packages
As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the [tidyverse packages](https://www.tidyverse.org) [`dplyr`](https://dplyr.tidyverse.org/) and [`ggplot2`](https://ggplot2.tidyverse.org) by [Dr Hadley Wickham](https://www.linkedin.com/in/hadleywickham/). The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.
Our `AMR` package depends on these packages and even extends their use and functions.
```{r lib packages, message = FALSE}
library(dplyr)
library(ggplot2)
library(AMR)
# (if not yet installed, install with:)
# install.packages(c("tidyverse", "AMR"))
```
## Prediction analysis
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Our package contains a function `resistance_predict()`, which takes the same input as functions for [other AMR analysis](./AMR.html). Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.
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It is basically as easy as:
```{r, eval = FALSE}
# resistance prediction of piperacillin/tazobactam (pita):
resistance_predict(tbl = septic_patients, col_date = "date", col_ab = "pita")
# or:
septic_patients %>%
resistance_predict(col_ab = "pita")
# to bind it to object 'predict_pita' for example:
predict_pita <- septic_patients %>%
resistance_predict(col_ab = "pita")
```
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The function will look for a date column itself if `col_date` is not set.
When running any of these commands, a summary of the regression model will be printed unless using `resistance_predict(..., info = FALSE)`.
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```{r, echo = FALSE}
predict_pita <- septic_patients %>%
resistance_predict(col_ab = "pita")
```
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This text is only a printed summary - the actual result (output) of the function is a `data.frame` containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:
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```{r}
predict_pita
```
The function `plot` is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:
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```{r, fig.height = 5.5}
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plot(predict_pita)
```
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This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.
We also support the `ggplot2` package with our custom function `ggplot_rsi_predict()` to create more appealing plots:
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```{r}
ggplot_rsi_predict(predict_pita)
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# choose for error bars instead of a ribbon
ggplot_rsi_predict(predict_pita, ribbon = FALSE)
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```
### Choosing the right model
Resistance is not easily predicted; if we look at vancomycin resistance in Gram positives, the spread (i.e. standard error) is enormous:
```{r}
septic_patients %>%
filter(mo_gramstain(mo) == "Gram positive") %>%
resistance_predict(col_ab = "vanc", year_min = 2010, info = FALSE) %>%
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ggplot_rsi_predict()
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```
Vancomycin resistance could be 100% in ten years, but might also stay around 0%.
You can define the model with the `model` parameter. The default model is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.
Valid values are:
| Input values | Function used by R | Type of model |
|----------------------------------------|-------------------------------|-----------------------------------------------------|
| `"binomial"` or `"binom"` or `"logit"` | `glm(..., family = binomial)` | Generalised linear model with binomial distribution |
| `"loglin"` or `"poisson"` | `glm(..., family = poisson)` | Generalised linear model with poisson distribution |
| `"lin"` or `"linear"` | `lm()` | Linear model |
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For the vancomycin resistance in Gram positive bacteria, a linear model might be more appropriate since no (left half of a) binomial distribution is to be expected based on the observed years:
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```{r}
septic_patients %>%
filter(mo_gramstain(mo) == "Gram positive") %>%
resistance_predict(col_ab = "vanc", year_min = 2010, info = FALSE, model = "linear") %>%
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ggplot_rsi_predict()
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```
This seems more likely, doesn't it?
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The model itself is also available from the object, as an `attribute`:
```{r}
model <- attributes(predict_pita)$model
summary(model)$family
summary(model)$coefficients
```