In many studies, multiple longitudinal outcomes are collected, and interest lies in studying the association between these outcomes. Joint modeling is then required, but full likelihood estimation becomes infeasible as the number of outcomes increases. To address this, the pairwise-fitting approach was developed. However, the robustness of this pseudo-likelihood-based approach under missing at random (MAR) remains unclear. We investigate the impact of MAR dropout on the pairwise-fitting approach through a case and simulation study and compare the results to full likelihood estimation. In the simulation study, we simulate three continuous longitudinal outcomes so that full likelihood estimation remains computationally feasible, allowing a comparison with the pairwise fitting approach. Various settings are examined, including random intercept and random intercept-and-slope models, in which we vary the standard deviation of the error terms and the degree of correlation between random effects. Our results show that bias remains limited in random intercept models and in most random intercept-and-slope models. However, when the standard deviation of the error terms becomes large compared to that of the random effects, some bias appears in the covariances between the random effects of the outcomes not driving dropout. This bias is mitigated using multiple imputation. As a case study, we analyzed data from a schizophrenia study using both full likelihood and pseudo-likelihood approaches and compared the results.
Dries De Witte, G. Verbeke, T. Neyens et al.· Pharmaceutical statistics· 0 citations
Environmental conditions are major drivers of malaria transmission, but epidemiological analyses are often constrained by temporal misalignment between health outcomes reported at coarse time scales and environmental exposures available at finer resolutions. Conventional approaches aggregate environmental data to match health outcomes, potentially obscuring delayed and nonlinear relationships. We propose a Bayesian spatio-temporal framework that addresses this limitation through a latent daily disease process linked to observed monthly malaria counts by temporal disaggregation. The framework integrates distributed lag nonlinear models for climatic effects, spatio-temporal random effects, and intervention covariates within a unified hierarchical model. The methodology was applied to malaria surveillance data from 161 districts in Mozambique between 2017 and 2024, integrating temperature, precipitation, relative humidity, vegetation, elevation, and malaria interventions. Compared with a conventional monthly model, the proposed framework improved predictive accuracy and uncertainty quantification while exploiting the temporal resolution of environmental data. Estimated relationships showed nonlinear associations between climatic variability and malaria incidence, including an optimal temperature range, increasing risk with positive vegetation anomalies, and nonlinear precipitation effects. By avoiding temporal aggregation of environmental exposures, the framework provides a flexible approach for investigating delayed environmental effects from routine surveillance data and can be extended to other environmentally sensitive diseases with mismatched temporal resolutions.
Alejandro Rozo Posada, Maxime Fajgenblat, C. Faes et al.· 0 citations
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