arXiv subjects

Agbessi Amouzou

Publications and source records attributed to Agbessi Amouzou.

3 recordsLinked to original sources

arXiv2026

VA-Calibration: Correcting for Algorithmic Misclassification in Estimating Cause Distributions

Accurate estimation of cause-specific mortality fractions (CSMFs), the percentage of deaths attributable to each cause in a population, is essential for global health monitoring. Challenge arises because computer-coded verbal autopsy (CCVA) algorithms, commonly used to estimate CSMFs, frequently misclassify the cause of death (COD). This misclassification is further complicated by structured patterns and substantial variation across countries. To address this, we introduce the R package 'vacalibration'. It implements a modular Bayesian framework to correct for the misclassification, thereby yielding more accurate CSMF estimates from verbal autopsy (VA) questionnaire data. The package utilizes uncertainty-quantified CCVA misclassification matrix estimates derived from data collected in the CHAMPS project and available on the 'CCVA-Misclassification-Matrices' GitHub repository. Currently, these matrices cover three CCVA algorithms (EAVA, InSilicoVA, and InterVA) and two age groups (neonates aged 0-27 days, and children aged 1-59 months) across countries (specific estimates for Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa, and a combined estimate for all other countries), enabling global calibration. The 'vacalibration' package also supports ensemble calibration when multiple algorithms are available. Implemented using the 'RStan', the package offers rapid computation, uncertainty quantification, and seamless compatibility with openVA, a leading COD analysis software ecosystem. We demonstrate the package's flexibility with two real-world applications in COMSA-Mozambique and CA CODE. The package and its foundational methodology applies more broadly and can calibrate any discrete classifier or their ensemble.

stat.AP↗

arXiv2021

Generalized Bayes Quantification Learning under Dataset Shift

Quantification learning is the task of prevalence estimation for a test population using predictions from a classifier trained on a different population. Quantification methods assume that the sensitivities and specificities of the classifier are either perfect or transportable from the training to the test population. These assumptions are inappropriate in the presence of dataset shift, when the misclassification rates in the training population are not representative of those for the test population. Quantification under dataset shift has been addressed only for single-class (categorical) predictions and assuming perfect knowledge of the true labels on a small subset of the test population. We propose generalized Bayes quantification learning (GBQL) that uses the entire compositional predictions from probabilistic classifiers and allows for uncertainty in true class labels for the limited labeled test data. Instead of positing a full model, we use a model-free Bayesian estimating equation approach to compositional data based only on a first-moment assumption. The idea will be useful in Bayesian compositional data analysis in general as it is robust to different generating mechanisms for compositional data and includes categorical outputs as a special case. We show how our method yields existing quantification approaches as special cases. Extension to an ensemble GBQL that uses predictions from multiple classifiers yielding inference robust to inclusion of a poor classifier is discussed. We outline a fast and efficient Gibbs sampler using a rounding and coarsening approximation to the loss functions. We also establish posterior consistency, asymptotic normality and valid coverage of interval estimates from GBQL, as well as finite sample posterior concentration rate. Empirical performance of GBQL is demonstrated through simulations and analysis of real data with evident dataset shift.

stat.ME↗

arXiv2019

Regularized Bayesian transfer learning for population level etiological distributions

Computer-coded verbal autopsy (CCVA) algorithms predict cause of death from high-dimensional family questionnaire data (verbal autopsies) of a deceased individual. CCVA algorithms are typically trained on non-local data, then used to generate national and regional estimates of cause-specific mortality fractions. These estimates may be inaccurate if the non-local training data is different from the local population of interest. This problem is a special case of transfer learning. However, most transfer learning classification approaches are concerned with individual (e.g. a person's) classification within a target domain (e.g. a particular population) with training performed in data from a source domain. Epidemiologists are often more interested in estimating population-level etiological distributions, using datasets much smaller than those used in common transfer learning applications. We present a parsimonious hierarchical Bayesian transfer learning framework to directly estimate population-level class probabilities in a target domain. To address small sample sizes, we introduce a novel shrinkage prior for the transfer error rates guaranteeing that, in absence of any labeled target domain data or when the baseline classifier has zero transfer error, the calibrated estimate of class probabilities coincides with the naive estimates from the baseline classifier, thereby subsuming the default practice as a special case. A novel Gibbs sampler using data-augmentation enables fast implementation. We extend our approach to use not one, but an ensemble of baseline classifiers. Theoretical and empirical results demonstrate how the ensemble model favors the most accurate baseline classifier. We present extensions allowing class probabilities to vary with covariates, and an EM-algorithm-based MAP estimation. An R-package implementing this method is developed.

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