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BEGIN:VEVENT

CATEGORIES:Natural Sciences
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=18832e99-2a84ac8f-012a-85e0def0-00000038:Rauch\, Lo
 ri
CREATED:20250120T203537Z
DESCRIPTION:Verbal autopsy (VA) algorithms are routinely employed in low-a
 nd middle-income countries to determine individual causes of death (COD)\
 , which are then aggregated to estimate population-level cause-specific m
 ortality fractions (CSMFs) essential for public health policymaking. Howe
 ver\, VA algorithms often misclassify COD\, introducing bias in CSMF esti
 mates. A recent method\, VA-calibration\, addresses this bias by utilizin
 g a VA misclassification rate matrix derived from limited labeled COD dat
 a collected in the CHAMPS project. Due to limited labeled samples\, the d
 ata are pooled across countries to improve estimation precision\, thereby
  implicitly assuming uniform misclassification rates. In this presentatio
 n\, I will highlight substantial cross-country heterogeneity in VA miscla
 ssification\, challenging this homogeneity assumption and revealing its i
 mpact on VA-calibration bias. To address this\, I will propose a comprehe
 nsive country-specific VA misclassification matrix modeling framework in 
 data-scarce settings. The framework introduces a novel base model that pa
 rsimoniously characterizes the misclassification matrix through two laten
 t mechanisms: intrinsic accuracy and systematic preference. We theoretica
 lly prove that these mechanisms are identifiable from the data and manife
 st as a form of invariance in misclassification odds\, a pattern evident 
 in the CHAMPS data. Building on this\, the framework then incorporates cr
 oss-country heterogeneity through interpretable effect sizes and uses shr
 inkage priors to balance the bias-variance tradeoff in misclassification 
 matrix estimation. This effort broadens VA-calibration's future applicabi
 lity and strengthens ongoing efforts of using VA for mortality surveillan
 ce. I will illustrate this through applications to mortality surveillance
  projects\, such as COMSA in Mozambique and CA CODE.
DURATION:PT1H
DTSTAMP:20250120T203537Z
DTSTART;TZID=America/New_York:20250207T153000
LAST-MODIFIED:20250120T203537Z
LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi
 stry 116
STATUS:CONFIRMED
SUMMARY:Bayesian Modeling of Misclassification Matrices for Improved Verba
 l Autopsy-Based Mortality Estimates in LMICs
UID:CAL-8a000483-92c3adf6-0194-856d8f38-000031d0demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
 ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
 er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Natural Sciences:/user/publi
 c-user/Topics/Natural Sciences
X-BEDEWORK-SPEAKER:Sandipan Pramanik
X-BEDEWORK-DUKE-SERIES:Statistical Science Seminar
X-BEDEWORK-SUBMITTEDBY:lr3 for Statistical Science (agrp_StatisticalScienc
 e)
END:VEVENT
END:VCALENDAR