PhDDesk

PhDDesk is the working research desk for Precision Maternal Epidemiology in Obstetric HIV: A Biostatistical Framework for Latent Risk Stratification and Dynamic Prediction. It brings together the core dissertation materials, including the proposal abstract, metadata and data documentation, and the methodological components that support latent phenotype modeling, longitudinal inference, and dynamic prediction.

PhD Team

Marothi Peter Letsoalo

Role: PhD researcher

ORCID SMATER.ME

Danielle Jade Roberts

Role: Primary supervisor

ORCID

Nonhlanhla Yende-Zuma

Role: Co-supervisor

ORCID

Open standalone PhDDesk project

Proposal Abstract

Proposal
Biostatistics
Obstetric HIV
Maternal Health
Latent Phenotypes
Dynamic Prediction
Causal Inference

Introduces the PhD research problem, defines the core aims, and sets out how latent phenotype modeling, causal methods, and dynamic prediction are integrated in the obstetric HIV program.

Mar 25, 2026
Marothi Peter Letsoalo, Danielle Jade Roberts, Nonhlanhla Yende-Zuma

Metadata & Data Documentation

Metadata
Data Documentation
Variable Registry
Mplus
CDISC-style Naming
Longitudinal Data
Reproducibility
Obstetric HIV
Severe Maternal Outcomes

Documents the PhD data architecture by defining the core variable registry, source tables, join logic, Mplus-ready four-character aliases, and stream-specific use of data for reproducible obstetric HIV analysis.

Mar 25, 2026
Marothi Peter Letsoalo, Danielle Jade Roberts, Nonhlanhla Yende-Zuma

Latent Trajectory Modeling in Maternal Health in Low- and Middle-Income Countries: A Systematic Review and Methodological Audit of Statistical Integrity and Reporting Quality

Systematic Review
Methodological Audit
Maternal Health
Latent Trajectories
Reporting Quality

Will bring together the review protocol, search and screening workflow, extraction framework, and methodological audit criteria for latent trajectory studies in maternal health.

Mar 25, 2026
Marothi Peter Letsoalo, Danielle Jade Roberts, Nonhlanhla Yende-Zuma

Social Risk Phenotypes and the Maternal Burden of Obstetric HIV: A Causal Mixture Modeling Approach

Social Risk Phenotypes
Causal Mixture Modeling
Obstetric HIV
Severe Maternal Outcomes
Latent Classes

Will define the social-risk phenotype framework, baseline covariate structure, and causal mixture modeling strategy for severe maternal outcome analysis in obstetric HIV.

Mar 25, 2026
Marothi Peter Letsoalo, Danielle Jade Roberts, Nonhlanhla Yende-Zuma

Multivariate Maternal Risk Phenotyping: Handling Time-Unstructuredness in Growth Mixture Models

Maternal Risk Phenotyping
Growth Mixture Models
Longitudinal Data
Biomarkers
Obstetric HIV

Will develop the irregular longitudinal biomarker framework, continuous-time structure, and phenotype derivation strategy for multivariate maternal risk trajectories.

Mar 25, 2026
Marothi Peter Letsoalo, Danielle Jade Roberts, Nonhlanhla Yende-Zuma

Multidimensional Latent Burden and Translational Stratification: A Dynamic Prediction Framework

Dynamic Prediction
Translational Stratification
Joint Modeling
Obstetric HIV
Time-to-Event Analysis

Will integrate latent burden profiles, longitudinal markers, and time-to-event processes for individualized dynamic prediction and translational maternal risk stratification.

Mar 25, 2026
Marothi Peter Letsoalo, Danielle Jade Roberts, Nonhlanhla Yende-Zuma
No matching items