collaborators
Showing stat.MEShow all

7 papers · 1 filter

stat.ME2026

Target-Aware Sequential Inference: Pooled versus Stratified Anytime-Valid Designs

Subir Hait

Sequential studies with heterogeneous strata may define the estimand under a target mixture that differs from the sampling distribution. We organize the design problem by guarantee…

stat.ME2026

Beyond Actor-Partner Associations: A Target-Trial Framework for Causal Spillover Effects in Longitudinal Dyadic Data

Subir Hait

Actor-partner interdependence models (APIMs) describe how one member's exposure is associated with that member's and the partner's outcomes, but actor and partner coefficients are…

stat.ME2026

Is the Linear Threshold Good Enough? A Scale-Free Parameter and Adequacy Test for Curvature-Induced Threshold Displacement

Subir Hait

Applied work often locates a threshold by linearizing a smooth function about a reference point and solving for the crossing. When the function is curved, the linear crossing can b…

stat.ME2026

Aggregation Distortion in Multilevel Mediation: Estimand Geometry, Design Dependence, and Diagnostics

Subir Hait

Researchers often summarize mediation in clustered data with a single-level product-of-coefficients estimator. We show what this pooled analysis estimates and why the target depend…

stat.ME2026

Design-Indexed Heterogeneity in Meta-Analysis: A Constrained Location-Scale Specification

Subir Hait

An analyst should not assume that a design score improves a meta-analysis simply because it is labeled as robustness. This paper treats declining heterogeneity along a prespecified…

stat.ME2026

Orthogonal Validation-Augmented Cox Regression with Internally Validated Failure Indicators Cross-Fitted Estimation, Risk-Set Linearization, and Validation Design

Subir Hait

Incomplete adjudication and endpoint misclassification arise when gold-standard event classification is available only for an internal validation sample. With accurate follow-up ti…

stat.ME2026

Evidence, Calibration, and Stability: A Triadic Framework for Hypothesis Testing Under Model Uncertainty

Subir Hait

Statistical tests are often asked to do too much. A single reported result is expected to describe what the observed data say, reassure readers about repeated-sampling behavior, an…