Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns `se_min`

and `se_max`

. See *Examples* for a real live example.

```
resistance_predict(
x,
col_ab,
col_date = NULL,
year_min = NULL,
year_max = NULL,
year_every = 1,
minimum = 30,
model = NULL,
I_as_S = TRUE,
preserve_measurements = TRUE,
info = interactive(),
...
)
rsi_predict(
x,
col_ab,
col_date = NULL,
year_min = NULL,
year_max = NULL,
year_every = 1,
minimum = 30,
model = NULL,
I_as_S = TRUE,
preserve_measurements = TRUE,
info = interactive(),
...
)
# S3 method for resistance_predict
plot(x, main = paste("Resistance Prediction of", x_name), ...)
ggplot_rsi_predict(
x,
main = paste("Resistance Prediction of", x_name),
ribbon = TRUE,
...
)
# S3 method for resistance_predict
autoplot(
object,
main = paste("Resistance Prediction of", x_name),
ribbon = TRUE,
...
)
```

x | a data.frame containing isolates. Can be left blank for automatic determination, see |
---|---|

col_ab | column name of |

col_date | column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class |

year_min | lowest year to use in the prediction model, dafaults to the lowest year in |

year_max | highest year to use in the prediction model, defaults to 10 years after today |

year_every | unit of sequence between lowest year found in the data and |

minimum | minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model. |

model | the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using `glm(..., family = binomial)``, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See |

I_as_S | a logical to indicate whether values |

preserve_measurements | a logical to indicate whether predictions of years that are actually available in the data should be overwritten by the original data. The standard errors of those years will be |

info | a logical to indicate whether textual analysis should be printed with the name and |

... | arguments passed on to functions |

main | title of the plot |

ribbon | a logical to indicate whether a ribbon should be shown (default) or error bars |

object | model data to be plotted |

A data.frame with extra class `resistance_predict`

with columns:

`year`

`value`

, the same as`estimated`

when`preserve_measurements = FALSE`

, and a combination of`observed`

and`estimated`

otherwise`se_min`

, the lower bound of the standard error with a minimum of`0`

(so the standard error will never go below 0%)`se_max`

the upper bound of the standard error with a maximum of`1`

(so the standard error will never go above 100%)`observations`

, the total number of available observations in that year, i.e. \(S + I + R\)`observed`

, the original observed resistant percentages`estimated`

, the estimated resistant percentages, calculated by the model

Furthermore, the model itself is available as an attribute: `attributes(x)$model`

, see *Examples*.

Valid options for the statistical model (argument `model`

) are:

`"binomial"`

or`"binom"`

or`"logit"`

: a generalised linear regression model with binomial distribution`"loglin"`

or`"poisson"`

: a generalised log-linear regression model with poisson distribution`"lin"`

or`"linear"`

: a linear regression model

The lifecycle of this function is **stable**. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.

If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.

In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (https://www.eucast.org/newsiandr/).

**R = Resistant**

A microorganism is categorised as*Resistant*when there is a high likelihood of therapeutic failure even when there is increased exposure. Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.**S = Susceptible**

A microorganism is categorised as*Susceptible, standard dosing regimen*, when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.**I = Increased exposure, but still susceptible**

A microorganism is categorised as*Susceptible, Increased exposure*when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.

This AMR package honours this (new) insight. Use `susceptibility()`

(equal to `proportion_SI()`

) to determine antimicrobial susceptibility and `count_susceptible()`

(equal to `count_SI()`

) to count susceptible isolates.

On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR data analysis, the complete documentation of all functions and an example analysis using WHONET data.

The `proportion()`

functions to calculate resistance

```
x <- resistance_predict(example_isolates,
col_ab = "AMX",
year_min = 2010,
model = "binomial")
plot(x)
# \donttest{
if (require("ggplot2")) {
ggplot_rsi_predict(x)
}
# using dplyr:
if (require("dplyr")) {
x <- example_isolates %>%
filter_first_isolate() %>%
filter(mo_genus(mo) == "Staphylococcus") %>%
resistance_predict("PEN", model = "binomial")
plot(x)
# get the model from the object
mymodel <- attributes(x)$model
summary(mymodel)
}
# create nice plots with ggplot2 yourself
if (require("dplyr") & require("ggplot2")) {
data <- example_isolates %>%
filter(mo == as.mo("E. coli")) %>%
resistance_predict(col_ab = "AMX",
col_date = "date",
model = "binomial",
info = FALSE,
minimum = 15)
autoplot(data)
ggplot(data,
aes(x = year)) +
geom_col(aes(y = value),
fill = "grey75") +
geom_errorbar(aes(ymin = se_min,
ymax = se_max),
colour = "grey50") +
scale_y_continuous(limits = c(0, 1),
breaks = seq(0, 1, 0.1),
labels = paste0(seq(0, 100, 10), "%")) +
labs(title = expression(paste("Forecast of Amoxicillin Resistance in ",
italic("E. coli"))),
y = "%R",
x = "Year") +
theme_minimal(base_size = 13)
}
# }
```