Predict antimicrobial resistance

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(tbl, col_ab, col_date, year_min = NULL,
  year_max = NULL, year_every = 1, minimum = 30,
  model = "binomial", I_as_R = TRUE, preserve_measurements = TRUE,
  info = TRUE)

rsi_predict(tbl, col_ab, col_date, year_min = NULL, year_max = NULL,
  year_every = 1, minimum = 30, model = "binomial", I_as_R = TRUE,
  preserve_measurements = TRUE, info = TRUE)

Arguments

tbl	a `data.frame` containing isolates.
col_ab	column name of `tbl` with antimicrobial interpretations (`R`, `I` and `S`)
col_date	column name of the date, will be used to calculate years if this column doesn't consist of years already
year_min	lowest year to use in the prediction model, dafaults the lowest year in `col_date`
year_max	highest year to use in the prediction model, defaults to 15 years after today
year_every	unit of sequence between lowest year found in the data and `year_max`
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. Valid values are `"binomial"` (or `"binom"` or `"logit"`) or `"loglin"` or `"linear"` (or `"lin"`).
I_as_R	treat `I` as `R`
preserve_measurements	logical to indicate whether predictions of years that are actually available in the data should be overwritten with the original data. The standard errors of those years will be `NA`.
info	print textual analysis with the name and `summary` of the model.

Value

data.frame 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
se_max the upper bound of the standard error with a maximum of 1
observations, the total number of observations, i.e. S + I + R
observed, the original observed values
estimated, the estimated values, calculated by the model

Examples

# NOT RUN {
# use it with base R:
resistance_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
                   col_ab = "amcl", col_date = "date")

# or use dplyr so you can actually read it:
library(dplyr)
tbl %>%
  filter(first_isolate == TRUE,
         genus == "Haemophilus") %>%
  resistance_predict(amcl, date)
# }# NOT RUN {

# real live example:
library(dplyr)
septic_patients %>%
  # get bacteria properties like genus and species
  left_join_microorganisms("mo") %>%
  # calculate first isolates
  mutate(first_isolate = first_isolate(.)) %>%
  # filter on first E. coli isolates
  filter(genus == "Escherichia",
         species == "coli",
         first_isolate == TRUE) %>%
  # predict resistance of cefotaxime for next years
  resistance_predict(col_ab = "cfot",
                     col_date = "date",
                     year_max = 2025,
                     preserve_measurements = TRUE,
                     minimum = 0)

# create nice plots with ggplot
if (!require(ggplot2)) {

  data <- septic_patients %>%
    filter(mo == as.mo("E. coli")) %>%
    resistance_predict(col_ab = "amox",
                       col_date = "date",
                       info = FALSE,
                        minimum = 15)

  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 = "%IR",
         x = "Year") +
    theme_minimal(base_size = 13)
}
# }

Arguments

Value

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See also

Examples

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