README.md

In zhenkewu/doubletree: Nested Latent Class Models for Domain-Adaptive and Semisupervised Learning of Cause-of-Deaths using Verbal Autopsy

doubletree:

An R package for Empowering Domain-Adaptive Probabilistic Cause-of-Death Assignment using Verbal Autopsy via Double-Tree Shrinkage

Maintainer: Zhenke Wu, [e-mail]

References: If you are using doubletree for tree-informed Bayesian domain adaptation, please cite the following preprint:

| | Citation | Paper Link | ------------- | ------------- | ------------- | | 1. | Wu Z, Li RZ, Chen I, Li M (2023+). Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy. |Arxiv|

Table of content

• 1. Installation
• 2. Overview
• 3. Examples

Installation -------------- wzxhzdk:0

Overview ---------- Ascertaining the causes of deaths absent vital registries presents a major barrier to under-standing cause-specific burdens of mortality and public health management. Verbal autopsy(VA) surveys are increasingly adopted in these resource poor settings. Relatives of a deceased person are interviewed about symptoms leading up to the death. Statistical methods have been devised for estimating the cause-specific mortality fractions (CSMF). However, expansion of VA from established to new sites has raised acute needs of domain-adaptive and accurate CSMF estimation methods that can handle imbalanced and sparse death counts in many cause-domain combinations. In this paper, we propose a method that starts with a hierarchy for the domains and adaptively learns the between-domain similarity in the conditional distribution of the survey responses given a cause. In addition, the method uses a second pre-specified cause hierarchy to borrow information across cause groups so that each group has similar conditional response probabilities. Through simulation studies, the method, referred to as “double-tree shrinkage”,is shown to improve the precision and reduce the asymptotic bias in estimating CSMFs as a result of the flexible and adaptive conditional dependence structure estimation. We also evaluate and illustrate the method using PHMRC VA data. The paper concludes with a discussion on limitations, extensions and highlights the central role of domain adaptivity offered by the proposed method in ongoing VA survey research. The `doubletree` package works with the following scenarios: * Scenario a: No missing leaf labels in tree1 or tree2, for all observations; So this is reduced to a nested latent class model estimation prorblem with parameter shrinkage according to the two trees. * Scenario b: All missing tree1 leaf label occurred for in a single leaf node in tree2 (say v2): 1. Scenario b1: No observation with observed tree1 label in leaf v2; 2. Scenario b2: More than 1 observations have observed tree1 label in leaf v2. * Scenario c: Missing tree1 leaf labels occurred for observations in 2 or more leaf nodes in tree2, say the leaf node set S: 1. Sub-scenarios: 0,1,2,... leaf/leaves in S have partially observed leaf label in tree1

Examples --------- * main function `nlcm_doubletree` * A simple workflow using simulated data and two trees obtained from [PHMRC data](http://ghdx.healthdata.org/record/ihme-data/population-health-metrics-research-consortium-gold-standard-verbal-autopsy-data-2005-2011); also check out the `openVA` package that processed the raw data into binary indicators: [openVA](https://github.com/verbal-autopsy-software/openVA). 1. in`R`, check the example by `example(nlcm_doubletree)`; note that this example uses the hierarchies, but simulates data based on a set of true parameters and sample sizes. 2. Trajectory of the lower bound of ELBO  * the hierarchy for 35 causes and the hierarchy for 6 domains is shown below. The numbers in the cells indicate the number of physician-coded cause-of-death (COD) in a particular site ("domain"). In the most common situation this package is designed for, we would not observe the membership of some deaths to the cells. For example, in `AP` site, we may not be able to see the tabulation by the CODs (by row), but just a total number of deaths in `AP` (the column sum for AP in the table shown here). `doubletree` will provide an estimate of 1. the population cause-specific mortality fraction in `AP` (a vector of length 35 that sums to 1); 2. individual-specific posterior probability of CODs (a vector of length 35 that sums to 1), based on which we may obtain, e.g., _maximum a posteriori_ COD for each death given the survey responses. .

zhenkewu/doubletree documentation built on Oct. 21, 2023, 7:04 a.m.