arXiv subjects

Shafayet Khan Shafee

Publications and source records attributed to Shafayet Khan Shafee.

3 recordsLinked to original sources

arXiv2026

Quantifying Inconsistent Mediation: The Mediated Magnitude Share and the Degree of Suppression

The proportion mediated (PM), defined as the indirect effect divided by the total effect, is intuitive when direct and indirect effects operate in the same direction. Under inconsistent mediation, however, opposing pathways partially cancel each other, causing PM to fall outside the unit interval or become unstable as the total effect approaches zero. We introduce two complementary effect-scale measures: the Mediated Magnitude Share (MMS), which quantifies the absolute magnitude of an indirect pathway relative to the combined absolute magnitudes of the direct and indirect pathways, and the degree of suppression, which quantifies the fraction of this combined magnitude that is cancelled by opposing pathways. We establish the bounds and limiting behavior of both measures in the two-pathway case, show that MMS reduces to PM under consistent mediation, and extend both measures to settings with multiple indirect pathways. For binary outcomes, we extend the framework to odds-ratio and risk-ratio scales by using logarithms to convert multiplicative effects into additive ones, enabling direct application of MMS and the degree of suppression. Together, these measures provide bounded and interpretable characterizations of mediation systems in which pathway directions are not uniform.

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arXiv2026

On the estimation of the median odds ratio for measuring contextual effects in multilevel binary data from complex survey designs

In studies with clustered or hierarchical data structures, quantifying between-cluster heterogeneity, referred to as contextual effects, is crucial for valid cluster-level inference. The median odds ratio (MOR), derived from random effects (RE) logistic regression models for clustered binary data, provides an intuitive assessment of contextual effects. Most existing research focuses on point estimation of the MOR for two-level models, with limited exploration of its statistical properties under complex multilevel structures. However, the development of corresponding interval estimators is essential for statistical inference. Moreover, many real-world datasets, particularly those from multistage surveys, involve hierarchical structures beyond two levels, where contextual effects at each level are of interest. This paper discusses the estimation of MOR for both the two-and three-level binary data, with particular emphasis on interval estimation. Since the MOR is a post-estimation measure based on variance components of the RE logit model, its confidence interval is derived using the Delta method, treating the log-transformed MOR as asymptotically normal. The approach is demonstrated across different model specifications in two-and three-level settings. An extensive simulation study evaluated the performance of the MOR estimators across diverse scenarios in hierarchical data settings. The results showed that the estimators exhibited negligible bias and satisfactory coverage probability of a 95% confidence interval for moderate to large samples, with small-sample bias mainly due to variance component estimation. An application of the methods for estimating the contextual effect on C-section delivery demonstrated that the proposed framework enhances interpretability and supports more informed statistical and policy-oriented analyses.

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arXiv2026

G-computation for causal effect estimation from observational hierarchical data with unmeasured cluster context

Observational studies frequently involve hierarchical data structures in which individuals are nested within higher-level units. In such settings, unmeasured cluster-level factors may confound the treatment-outcome relationship and may additionally induce treatment effect heterogeneity across clusters, complicating causal effect estimation. We formalize the use of g-computation for hierarchical observational data by incorporating random-effects models (REM) as outcome models and propose a within-group g-computation strategy designed to reduce bias arising from unmeasured cluster context. The approach groups clusters according to their observed treatment prevalence and performs g-computation within groups before aggregating group-specific estimates. Through extensive Monte Carlo simulations, we compare the standard and within-group implementations of g-computation using both linear models and REM. Results show that both standard and within-group REM-based implementations substantially reduce bias when the unmeasured cluster-level variable acts solely as a confounder, whereas the proposed within-group REM estimator achieves the lowest RMSE when the unmeasured cluster-level factor acts as both a confounder and a source of treatment effect heterogeneity. We apply the proposed within-group REM estimator to estimate the causal effect of adolescent pregnancy on the child height-for-age Z-score using 2019 Bangladesh MICS data, obtaining an estimated effect of -0.12 (95% bootstrap CI: [-0.18, -0.06]). The proposed within-group g-computation framework offers a strategy for reducing bias from unmeasured cluster-level confounding and treatment effect heterogeneity in hierarchical observational studies.

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