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 dplyr and ggplot2 by Dr Hadley Wickham. 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.

Prediction analysis

Our package contains a function resistance_predict(), which takes the same input as functions for other AMR analysis. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.

It is basically as easy as:

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).

#> NOTE: Using column `date` as input for `col_date`.
#> 
#> Logistic regression model (logit) with binomial distribution
#> ------------------------------------------------------------
#> 
#> Call:
#> glm(formula = df_matrix ~ year, family = binomial)
#> 
#> Deviance Residuals: 
#>     Min       1Q   Median       3Q      Max  
#> -2.9224  -1.3120   0.0170   0.7586   3.1932  
#> 
#> Coefficients:
#>               Estimate Std. Error z value Pr(>|z|)    
#> (Intercept) -222.92857   45.93922  -4.853 1.22e-06 ***
#> year           0.10994    0.02284   4.814 1.48e-06 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for binomial family taken to be 1)
#> 
#>     Null deviance: 59.794  on 14  degrees of freedom
#> Residual deviance: 35.191  on 13  degrees of freedom
#> AIC: 93.464
#> 
#> Number of Fisher Scoring iterations: 4

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:

predict_pita
#>    year      value    se_min    se_max observations   observed  estimated
#> 1  2003 0.06250000        NA        NA           32 0.06250000 0.06177594
#> 2  2004 0.08536585        NA        NA           82 0.08536585 0.06846343
#> 3  2005 0.10000000        NA        NA           60 0.10000000 0.07581637
#> 4  2006 0.05084746        NA        NA           59 0.05084746 0.08388789
#> 5  2007 0.12121212        NA        NA           66 0.12121212 0.09273250
#> 6  2008 0.04166667        NA        NA           72 0.04166667 0.10240539
#> 7  2009 0.01639344        NA        NA           61 0.01639344 0.11296163
#> 8  2010 0.09433962        NA        NA           53 0.09433962 0.12445516
#> 9  2011 0.18279570        NA        NA           93 0.18279570 0.13693759
#> 10 2012 0.30769231        NA        NA           65 0.30769231 0.15045682
#> 11 2013 0.08620690        NA        NA           58 0.08620690 0.16505550
#> 12 2014 0.15254237        NA        NA           59 0.15254237 0.18076926
#> 13 2015 0.27272727        NA        NA           55 0.27272727 0.19762493
#> 14 2016 0.25000000        NA        NA           84 0.25000000 0.21563859
#> 15 2017 0.16279070        NA        NA           86 0.16279070 0.23481370
#> 16 2018 0.25513926 0.2228376 0.2874409           NA         NA 0.25513926
#> 17 2019 0.27658825 0.2386811 0.3144954           NA         NA 0.27658825
#> 18 2020 0.29911630 0.2551715 0.3430611           NA         NA 0.29911630
#> 19 2021 0.32266085 0.2723340 0.3729877           NA         NA 0.32266085
#> 20 2022 0.34714076 0.2901847 0.4040968           NA         NA 0.34714076
#> 21 2023 0.37245666 0.3087318 0.4361815           NA         NA 0.37245666
#> 22 2024 0.39849187 0.3279750 0.4690088           NA         NA 0.39849187
#> 23 2025 0.42511415 0.3479042 0.5023241           NA         NA 0.42511415
#> 24 2026 0.45217796 0.3684992 0.5358568           NA         NA 0.45217796
#> 25 2027 0.47952757 0.3897276 0.5693275           NA         NA 0.47952757
#> 26 2028 0.50700045 0.4115444 0.6024565           NA         NA 0.50700045
#> 27 2029 0.53443111 0.4338908 0.6349714           NA         NA 0.53443111

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:

plot(predict_pita)

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:

ggplot_rsi_predict(predict_pita)

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:

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

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:

This seems more likely, doesn’t it?

The model itself is also available from the object, as an attribute: