P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2025-07-09 18:22:02 +02:00
Code Releases Activity

new WISCA vignette

Browse Source
This commit is contained in:
github-actions[bot]
2025-04-30 17:33:03 +02:00
parent 04aec39371
commit c70ac149ff
8 changed files with 284 additions and 79 deletions
Download Patch File Download Diff File Expand all files Collapse all files

37
man/antibiogram.Rd
View File

@ -306,42 +306,11 @@ You can also use functions from specific 'table reporting' packages to transform
\section{Explaining WISCA}{
WISCA, as outlined by Bielicki \emph{et al.} (\doi{10.1093/jac/dkv397}), stands for Weighted-Incidence Syndromic Combination Antibiogram, which estimates the probability of adequate empirical antimicrobial regimen coverage for specific infection syndromes. This method leverages a Bayesian decision model with random effects for pathogen incidence and susceptibility, enabling robust estimates in the presence of sparse data.
WISCA (Weighted-Incidence Syndromic Combination Antibiogram) estimates the probability of empirical coverage for combination regimens.
The Bayesian model assumes conjugate priors for parameter estimation. For example, the coverage probability \eqn{\theta} for a given antimicrobial regimen is modelled using a Beta distribution as a prior:
It weights susceptibility by pathogen prevalence within a clinical syndrome and provides credible intervals around the expected coverage.
\deqn{\theta \sim \text{Beta}(\alpha_0, \beta_0)}
where \eqn{\alpha_0} and \eqn{\beta_0} represent prior successes and failures, respectively, informed by expert knowledge or weakly informative priors (e.g., \eqn{\alpha_0 = 1, \beta_0 = 1}). The likelihood function is constructed based on observed data, where the number of covered cases for a regimen follows a binomial distribution:
\deqn{y \sim \text{Binomial}(n, \theta)}
Posterior parameter estimates are obtained by combining the prior and likelihood using Bayes' theorem. The posterior distribution of \eqn{\theta} is also a Beta distribution:
\deqn{\theta | y \sim \text{Beta}(\alpha_0 + y, \beta_0 + n - y)}
Pathogen incidence, representing the proportion of infections caused by different pathogens, is modelled using a Dirichlet distribution, which is the natural conjugate prior for multinomial outcomes. The Dirichlet distribution is parameterised by a vector of concentration parameters \eqn{\alpha}, where each \eqn{\alpha_i} corresponds to a specific pathogen. The prior is typically chosen to be uniform (\eqn{\alpha_i = 1}), reflecting an assumption of equal prior probability across pathogens.
The posterior distribution of pathogen incidence is then given by:
\deqn{\text{Dirichlet}(\alpha_1 + n_1, \alpha_2 + n_2, \dots, \alpha_K + n_K)}
where \eqn{n_i} is the number of infections caused by pathogen \eqn{i} observed in the data. For practical implementation, pathogen incidences are sampled from their posterior using normalised Gamma-distributed random variables:
\deqn{x_i \sim \text{Gamma}(\alpha_i + n_i, 1)}
\deqn{p_i = \frac{x_i}{\sum_{j=1}^K x_j}}
where \eqn{x_i} represents unnormalised pathogen counts, and \eqn{p_i} is the normalised proportion for pathogen \eqn{i}.
For hierarchical modelling, pathogen-level effects (e.g., differences in resistance patterns) and regimen-level effects are modelled using Gaussian priors on log-odds. This hierarchical structure ensures partial pooling of estimates across groups, improving stability in strata with small sample sizes. The model is implemented using Hamiltonian Monte Carlo (HMC) sampling.
Stratified results can be provided based on covariates such as age, sex, and clinical complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities) using \code{dplyr}'s \code{\link[dplyr:group_by]{group_by()}} as a pre-processing step before running \code{\link[=wisca]{wisca()}}. Posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:
\deqn{\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{covariate}})}{\exp(\beta_0)}}
By combining empirical data with prior knowledge, WISCA overcomes the limitations of traditional combination antibiograms, offering disease-specific, patient-stratified estimates with robust uncertainty quantification. This tool is invaluable for antimicrobial stewardship programs and empirical treatment guideline refinement.
\strong{Note:} WISCA never gives an output on the pathogen/species level, as all incidences and susceptibilities are already weighted for all species.
For more background, interpretation, and examples, see \href{https://amr-for-r.org/articles/WISCA.html}{the WISCA vignette}.
}
\examples{