PhD
Precision Maternal Epidemiology in Obstetric HIV: A Biostatistical Framework for Latent Risk Stratification and Dynamic Prediction
Why This Work Matters
- Maternal outcomes in HIV remain shaped by interacting biological, clinical, and structural risk.
- Existing population-average models can hide high-risk subgroups in routine antenatal care (Naicker et al. 2022; Serunjogi et al. 2025).
- Multi-country longitudinal evidence supports the need for stronger phenotype-based risk stratification (Taha et al. 2022).
Scientific Problem
- Severe maternal outcomes arise from layered biosocial mechanisms that are not well represented by static models.
- Longitudinal antenatal records are often irregular in timing, complicating inference (Asparouhov and Muthen 2024; Wickrama et al. 2016).
- Classification uncertainty in latent approaches can bias downstream associations if not explicitly corrected (Bauer and Curran 2003; Bakk and Vermunt 2014).
Research Question and Statistical Framing
Primary research question:
How can person-centered latent phenotype modeling improve individualized severe maternal outcome risk stratification in obstetric HIV under real-world, irregular longitudinal follow-up?
Supporting statistical questions:
- How can latent-variable methods identify unobserved maternal risk subphenotypes without overextraction bias?
- How can social risk phenotypes be linked to severe maternal outcomes while correcting for classification error and baseline confounding?
- How can we preserve valid longitudinal inference when antenatal visits are asynchronous across participants and sites?
- How can joint latent class prediction provide updated, clinically interpretable risk estimates at decision timepoints?
Program Structure
| Aim | Focus | Statistical Core |
|---|---|---|
| 1 | Methodological audit | GRoLTS + SMART-aligned reporting diagnostics |
| 2 | Social risk phenotypes | Multilevel LCA, BCH correction, causal weighting |
| 3 | Longitudinal burden phenotypes | Continuous-time mixture modeling |
| 4 | Dynamic prediction | Joint latent class and landmark-style updating |
References: (Van de Schoot et al. 2017; Lissa et al. 2024; Asparouhov and Muthen 2014; Rizopoulos et al. 2017)
Aim 1: Methodological Audit
- Audit class extraction, random starts, fit diagnostics, and reporting quality.
- Evaluate sensitivity to distributional misspecification and overextraction risk (Bauer and Curran 2003).
- Enforce transparent latent trajectory reporting standards (Van de Schoot et al. 2017; Lissa et al. 2024).
Aim 3: Longitudinal Physiological Burden
- Use continuous-time modeling to preserve asynchronous visit structure (Asparouhov and Muthen 2024).
- Estimate multivariate trajectory phenotypes that reflect evolving clinical burden (Wickrama et al. 2016).
- Connect biosocial profiles to phenotype evolution across gestational time.
Aim 4: Dynamic Translational Prediction
- Use joint latent class modeling to combine marker history with time-to-event risk (Rizopoulos et al. 2017; Proust-Lima et al. 2023).
- Produce updated individual risk profiles at clinical landmarks.
- Evaluate practical utility for triage decisions and clinical benefit (Heagerty and Zheng 2005; Vickers and Elkin 2006).
Expected Contribution
- A reproducible precision epidemiology framework for obstetric HIV.
- Better early identification of high-risk maternal profiles.
- A stronger bridge between methodological rigor and actionable maternal health decision support.
References
Asparouhov, Tihomir, and Bengt Muthen. 2014. “Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using Mplus.” Structural Equation Modeling: A Multidisciplinary Journal 21 (3): 329–41. https://doi.org/10.1080/10705511.2014.915181.
Asparouhov, Tihomir, and Bengt Muthen. 2024. “Mixture Modeling with Individual Measurement Times.” Mplus Technical Report. https://www.statmodel.com/download/Tscores.pdf.
Bakk, Zoltan, and Jeroen K Vermunt. 2014. “Robustness of Stepwise Latent Class Modeling with Continuous Distal Outcomes.” Structural Equation Modeling: A Multidisciplinary Journal 21 (1): 20–35. https://doi.org/10.1080/10705511.2014.955104.
Bauer, Daniel J., and Patrick J. Curran. 2003. “Distributional Assumptions of Growth Mixture Models: Implications for Overextraction of Latent Trajectory Classes.” Psychological Methods 8 (3): 338–63. https://doi.org/10.1037/1082-989X.8.3.338.
Heagerty, Patrick J, and Yingye Zheng. 2005. “Survival Model Predictive Accuracy and ROC Curves.” Biometrics 61 (1): 92–105. https://doi.org/10.1111/j.0006-341X.2005.030814.x.
Le, Khoa, Johannes Clouth, and Jeroen K Vermunt. 2024. “Causal Latent Class Analysis with Distal Outcomes: A Modified Three-Step Method Using Inverse Propensity Weighting.” Multivariate Behavioral Research 59 (5): 1006–26. https://doi.org/10.1080/00273171.2024.2367485.
Lissa, C. J. Van, M. Garnier-Villarreal, and D. Anadria. 2024. “Recommended Practices in Latent Class Analysis Using the Open-Source r-Package tidySEM.” Structural Equation Modeling: A Multidisciplinary Journal 31 (3): 526–34. https://doi.org/10.1080/10705511.2023.2250920.
Naicker, Nivashnee et al. 2022. “Pregnancy Rates and Outcomes in a Longitudinal HIV Cohort in the Context of Evolving Antiretroviral Treatment Provision in South Africa.” BMC Pregnancy and Childbirth 22 (1): 596. https://doi.org/10.1186/s12884-022-04829-2.
Proust-Lima, Cecile, Tiphaine Saulnier, Viviane Philipps, et al. 2023. “Describing Complex Disease Progression Using Joint Latent Class Models for Multivariate Longitudinal Markers and Clinical Endpoints.” Statistics in Medicine 42 (22): 3996–4014. https://doi.org/10.1002/sim.9844.
Rizopoulos, Dimitris, Geert Molenberghs, and Emmanuel MEH Lesaffre. 2017. “Dynamic Predictions with Time-Dependent Covariates in Survival Analysis Using Joint Modeling and Landmarking.” Biometrical Journal 59 (6): 1261–76. https://doi.org/10.1002/bimj.201600238.
Serunjogi, Robert et al. 2025. “Risk of Adverse Birth Outcomes and Birth Defects Among Women Living with HIV on Antiretroviral Therapy and HIV-Negative Women in Uganda, 2015–2021.” Journal of Acquired Immune Deficiency Syndromes 98 (5): 434. https://doi.org/10.1097/QAI.0000000000003596.
Taha, Taha E, Nonhlanhla Yende-Zuma, Stephen S Brummel, et al. 2022. “Effects of Long-Term Antiretroviral Therapy in Reproductive-Age Women in Sub-Saharan Africa (the PEPFAR PROMOTE Study): A Multi-Country Observational Cohort Study.” The Lancet HIV 9 (6): e394–403. https://doi.org/10.1016/S2352-3018(22)00037-6.
Van de Schoot, Rens, Marit Sijbrandij, Sonja D. Winter, Sarah Depaoli, and Jeroen K. Vermunt. 2017. “The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies.” Structural Equation Modeling: A Multidisciplinary Journal 24 (3): 451–67. https://doi.org/10.1080/10705511.2016.1247646.
Vickers, Andrew J, and Elena B Elkin. 2006. “Decision Curve Analysis: A Novel Method for Evaluating Prediction Models.” Medical Decision Making 26 (6): 565–74. https://doi.org/10.1177/0272989X06295361.
Wickrama, Kandauda AS, Tae Kyoung Lee, Catherine Walker O’Neal, and Frederick O Lorenz. 2016. Higher-Order Growth Curves and Mixture Modeling with Mplus: A Practical Guide. Routledge. https://doi.org/10.4324/9781315642741.