vacalibration (version 2.0)

Mmat_champs: Misclassification Estimates Based on CHAMPS Data

Description

Estimates of misclassification matrices using the modeling framework from Pramanik et al. (2025) and the limited paired MITS-VA data from the Child Health and Mortality Prevention Surveillance (CHAMPS) project.

Usage

Mmat_champs

Format

A nested list.

age_group

"neonate" for 0-27 days, and "child" for 1-59 months

va_algo

"eava", "insilicova", and "interva"

estimate types

"postsumm" contains posterior summaries, "postmean" contains the posterior means, and "asDirich" contains Dirichlet approximation for each CHAMPS cause and country.

country

"Bangladesh", "Ethiopia", "Kenya", "Mali", "Mozambique", "Sierra Leone", "South Africa", "other"

version

Date stamp for version control of tracking updates. Only for package maintainers.

Details

Mmat_champs[[age_group]][[va_algo]][["postsumm"]][[country]] contains posterior summaries of misclassification matrix for the a desired age_group, va_algo, and country. It is an array of dimension the number of posterior summaries X CHAMPS broad cause X VA broad cause. For example, if analyzing "neonate" age group using "insilicova" algorithm in "Mozambique",

• Mmat_champs$neonate$insilicova$postsumm$Mozambique[,"pneumonia","pneumonia"] are posterior summaries of the sensitivity for "pneumonia".

• Mmat_champs$neonate$insilicova$postsumm$Mozambique[,"pneumonia","ipre"] are posterior summaries of the false negative rate for CHAMPS broad cause "pneumonia" and VA broad cause "ipre".

Posterior samples are available from the GitHub repository https://github.com/sandy-pramanik/Mmat_champs.

.rda file is available under the release: https://github.com/sandy-pramanik/Mmat_champs/releases/tag/20241004.

Mmat_champs[[age_group]][[va_algo]][["postmean"]][[country]] contains posterior means.

Mmat_champs[[age_group]][[va_algo]][["asDirich"]][[country]] contains Dirichlet approximations of its posterior.

They are matrices of dimension CHAMPS broad cause X VA broad cause. For example, if analyzing "neonate" age group using "insilicova" algorithm in "Mozambique",

• Mmat_champs$neonate$insilicova$postmean$Mozambique["pneumonia","pneumonia"] is the posterior mean of sensitivity for "pneumonia".

• Mmat_champs$neonate$insilicova$postmean$Mozambique["pneumonia","ipre"] is the posterior mean of false negative rate for CHAMPS broad cause "pneumonia" and VA broad cause "ipre".

Similarly, Mmat_champs$neonate$insilicova$asDirich$Mozambique["pneumonia",] are parameters of Dirichlet distribution approximating the posterior of classification rates of different broad causes for the CHAMPS broad cause "pneumonia".

References

Pramanik, S, et al. (2025). Modeling structure and country-specific heterogeneity in misclassification matrices of verbal autopsy-based cause of death classifiers. Annals of Applied Statistics, 19(2):1214–1239. ISSN 1932-6157.

Taylor, A, et al. (2020). Initial findings from a novel population-based child mortality surveillance approach: a descriptive study. Lancet Glob Health, 8(7):e909-e919.

Examples

# \donttest{

## misclassification estimates
data(Mmat_champs)

# misclassification estimates for "neonate" age group and "insilicova" algorithm in Mozambique
## posterior summaries of the sensitivity of "pneumonia"
Mmat_champs$neonate$insilicova$postsumm$Mozambique[,"pneumonia","pneumonia"]

## posterior summaries of the false negative rates
## CHAMPS cause "pneumonia" and VA cause "ipre"
Mmat_champs$neonate$insilicova$postsumm$Mozambique[,"pneumonia","ipre"]

# COMSA-Mozambique: Example (Publicly Available Version)
# Individual-Level Specific (High-Resolution) Cause of Death Data
data(comsamoz_public_openVAout)
head(comsamoz_public_openVAout$data)  # head of the data

## VA-calibration for the "neonate" age group and "insilicova" algorithm
calib_out1 = vacalibration(va_data =
                                     setNames(list(comsamoz_public_openVAout$data),
                                              list(comsamoz_public_openVAout$va_algo)),
                           age_group = comsamoz_public_openVAout$age_group,
                           country = "Mozambique")

calib_out2 = vacalibration(va_data =
                                     setNames(list(comsamoz_public_openVAout$data),
                                              list(comsamoz_public_openVAout$va_algo)),
                           age_group = comsamoz_public_openVAout$age_group,
                           country = "Mozambique",
  Mmat.asDirich = list("insilicova" = Mmat_champs$neonate$insilicova$asDirich$Mozambique))
## By default the function fetches the desired misclassification estimates from
## the stored Mmat_champs.

## So calib_out1 (where we don't specify the misclassification) and
## calib_out2 (where we specify) are identical.

# }