mirror of https://github.com/msberends/AMR.git
112 lines
4.4 KiB
Plaintext
Executable File
112 lines
4.4 KiB
Plaintext
Executable File
---
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title: "How to predict antimicrobial resistance"
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author: "Matthijs S. Berends"
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date: '`r format(Sys.Date(), "%d %B %Y")`'
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output:
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rmarkdown::html_vignette:
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toc: true
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vignette: >
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%\VignetteIndexEntry{How to predict antimicrobial resistance}
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%\VignetteEncoding{UTF-8}
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%\VignetteEngine{knitr::rmarkdown}
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editor_options:
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chunk_output_type: console
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---
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```{r setup, include = FALSE, results = 'markup'}
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knitr::opts_chunk$set(
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collapse = TRUE,
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comment = "#",
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fig.width = 7.5,
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fig.height = 4.5
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)
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```
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## Needed R packages
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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.
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Our `AMR` package depends on these packages and even extends their use and functions.
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```{r lib packages, message = FALSE}
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library(dplyr)
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library(ggplot2)
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library(AMR)
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# (if not yet installed, install with:)
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# install.packages(c("tidyverse", "AMR"))
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```
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## Prediction analysis
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Our package contains a function `resistance_predict()`, which takes the same input as functions for [other AMR analysis](./articles/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:
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```{r, eval = FALSE}
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# resistance prediction of piperacillin/tazobactam (pita):
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resistance_predict(tbl = septic_patients, col_date = "date", col_ab = "pita")
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# or:
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septic_patients %>%
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resistance_predict(col_ab = "pita")
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# to bind it to object 'predict_pita' for example:
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predict_pita <- septic_patients %>%
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resistance_predict(col_ab = "pita")
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```
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```{r, echo = FALSE}
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predict_pita <- septic_patients %>%
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resistance_predict(col_ab = "pita")
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```
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The function will look for a data column itself if `col_date` is not set. The result is nothing more than a `data.frame`, containing the years, number of observations, actual observed resistance, the estimated resistance and the standard error below and above the estimation:
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```{r}
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predict_pita
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```
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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}
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plot(predict_pita)
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```
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We also support the `ggplot2` package with the function `ggplot_rsi_predict()`:
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```{r}
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library(ggplot2)
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ggplot_rsi_predict(predict_pita)
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```
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### Choosing the right model
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Resistance is not easily predicted; if we look at vancomycin resistance in Gram positives, the spread (i.e. standard error) is enormous:
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```{r}
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septic_patients %>%
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filter(mo_gramstain(mo) == "Gram positive") %>%
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resistance_predict(col_ab = "vanc", year_min = 2010, info = FALSE) %>%
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plot()
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```
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Vancomycin resistance could be 100% in ten years, but might also stay around 0%.
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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.
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Valid values are:
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| Input values | Function used by R | Type of model |
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|----------------------------------------|-------------------------------|-----------------------------------------------------|
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| `"binomial"` or `"binom"` or `"logit"` | `glm(..., family = binomial)` | Generalised linear model with binomial distribution |
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| `"loglin"` or `"poisson"` | `glm(..., family = poisson)` | Generalised linear model with poisson distribution |
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| `"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 observed years:
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```{r}
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septic_patients %>%
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filter(mo_gramstain(mo) == "Gram positive") %>%
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resistance_predict(col_ab = "vanc", year_min = 2010, info = FALSE, model = "linear") %>%
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plot()
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```
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This seems more likely, doesn't it?
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