Skip to content

feat(concordance): add minor error (mE) metric #111

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
efosternyarko wants to merge 1 commit into
base: main
Choose a base branch
from
Open

feat(concordance): add minor error (mE) metric #111

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
37 changes: 30 additions & 7 deletions R/concordance.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -26,8 +26,8 @@
#' user-defined field providing predictions).
#' This genotypic prediction is then
#' compared to the observed phenotypes using standard classification metrics
#' (via the `yardstick` pkg) and AMR-specific error rates (major error, ME
#' and very major error, VME) per ISO 20776-2 (and see
#' (via the `yardstick` pkg) and AMR-specific error rates (major error, ME,
#' very major error, VME, and minor error, mE) per ISO 20776-2 (and see
#' [FDA definitions](https://www.fda.gov/medical-devices/guidance-documents-medical-devices-and-radiation-emitting-products/antimicrobial-susceptibility-test-ast-systems-class-ii-special-controls-guidance-industry-and-fda)).
#' Supports evaluating both R and NWT outcomes
#' in a single call, with flexible prediction rules and marker inclusion options.
Expand Down Expand Up @@ -98,7 +98,12 @@
#' - **VME** (Very Major Error): FN / (TP + FN) = 1 - sensitivity. Proportion of
#' truly resistant isolates not predicted as such from genotype.
#' - **ME** (Major Error): FP / (TN + FP) = 1 - specificity. Proportion of
#' truly susceptible isolates incorrectly predicted resistant from genotype.
#' truly susceptible/intermediate isolates incorrectly predicted resistant from genotype.
#' - **mE** (Minor Error): For the R outcome, the proportion of intermediate (I)
#' isolates predicted as resistant: count(I=1 & pred=1) / count(I=1). Returns
#' `NA` for the NWT outcome (no intermediate ECOFF category) or when no `I`
#' column is present in `binary_matrix`. Captures I vs R discordances as defined
#' in ISO 20776-2 and [Wanger et al. (1999)](https://pubmed.ncbi.nlm.nih.gov/10325331/).
#'
#' @return An S3 object of class `"amr_concordance"`, a list containing:
#' - `conf_mat`: Named list of yardstick confusion matrix objects (e.g.
Expand Down Expand Up @@ -429,17 +434,26 @@ concordance <- function(binary_matrix,
yardstick::f_meas(df, truth = truth_value, estimate = geno_prediction)
)

# --- compute AMR-specific metrics (VME and ME) ---
# --- compute AMR-specific metrics (VME, ME, mE) ---
sensitivity <- ys_metrics$.estimate[ys_metrics$.metric == "sens"]
specificity <- ys_metrics$.estimate[ys_metrics$.metric == "spec"]

vme <- 1 - sensitivity # FN / (TP + FN)
me <- 1 - specificity # FP / (TN + FP)

# minor error: I isolates predicted as R (R outcome only, when I column present)
mE <- NA_real_
if (outcome == "R" && "I" %in% colnames(df)) {
n_I <- sum(df$I == 1, na.rm = TRUE)
if (n_I > 0) {
mE <- sum(df$I == 1 & df[[pred_col]] == 1, na.rm = TRUE) / n_I
}
}

amr_metrics <- tibble(
.metric = c("VME", "ME"),
.estimator = c("binary", "binary"),
.estimate = c(vme, me)
.metric = c("VME", "ME", "mE"),
.estimator = c("binary", "binary", "binary"),
.estimate = c(vme, me, mE)
)

# --- clean metrics format ---
Expand Down Expand Up @@ -556,6 +570,10 @@ print.amr_concordance <- function(x, ...) {
message(" F-measure : ", fmt("f_meas"))
message(" VME : ", fmt("VME"))
message(" ME : ", fmt("ME"))
mE_val <- m$estimate[m$metric == "mE"]
if (length(mE_val) > 0 && !is.na(mE_val)) {
message(" mE : ", fmt("mE"))
}
}

invisible(x)
Expand Down Expand Up @@ -638,6 +656,11 @@ concordance_from_tables <- function(pheno_table,
!!sym(true_SIR_col) == "I" ~ 0,
!!sym(true_SIR_col) == "S" ~ 0,
TRUE ~ NA
)) %>%
mutate(I = case_when(
!!sym(true_SIR_col) == "I" ~ 1L,
!!sym(true_SIR_col) %in% c("R", "S") ~ 0L,
TRUE ~ NA_integer_
))
drugs_R <- obs_pred %>%
filter(!is.na(R) & !is.na(predR)) %>%
Expand Down
Loading