Enhancing the Reliability of Real-World Evidence and Clinical Decision-Making: Robust, Calibrated, and Uncertainty-Aware Methods for Observational Healthcare Research
type: CreativeWork
description: Real-world data (RWD) has become instrumental in clinical and public health research, providing opportunities for more generalizable and practical insights compared to traditional randomized controlled trials (RCTs). Nevertheless, observational studies inherently encounter critical challenges, such as model misspecification, systematic biases, and uncertainties in predictions, potentially compromising the reliability and validity of generated evidence. Ensuring reliability is particularly vital for real-world decision-making, where trustworthy evidence must explicitly account for model uncertainty. This work presents novel methodologies specifically designed to enhance the reliability and robustness of evidence derived from RWD across diverse healthcare research contexts. In Chapter 2, we introduce the Assumption-lean Projection-based Heterogeneity-aware Algorithm (ALPHA), a robust method specifically designed to mitigate risks arising from model misspecification in large-scale distributed research settings. ALPHA efficiently estimates treatment effects without stringent modeling assumptions and accommodates site-specific heterogeneity across decentralized data networks without sharing individual-level information. Applying ALPHA to electronic health record data from eight national pediatric hospitals, we quantify the impact of COVID-19 infection on healthcare utilization outcomes, demonstrating ALPHA’s effectiveness in consistently reducing variance and systematic errors while accurately capturing substantial heterogeneity across clinical sites. Building upon the need to address systematic biases in multi-site observational research, Chapter 3 introduces a novel multi-site empirical calibration framework. This framework employs negative control outcomes (NCOs) to correct residual biases effectively, explicitly accounting for heterogeneity across both clinical sites and outcomes. By assessing cardiovascular outcomes associated with second-line diabetes therapies acr…
abstract: Real-world data (RWD) has become instrumental in clinical and public health research, providing opportunities for more generalizable and practical insights compared to traditional randomized controlled trials (RCTs). Nevertheless, observational studies inherently encounter critical challenges, such as model misspecification, systematic biases, and uncertainties in predictions, potentially compromising the reliability and validity of generated evidence. Ensuring reliability is particularly vital for real-world decision-making, where trustworthy evidence must explicitly account for model uncertainty. This work presents novel methodologies specifically designed to enhance the reliability and robustness of evidence derived from RWD across diverse healthcare research contexts. In Chapter 2, we introduce the Assumption-lean Projection-based Heterogeneity-aware Algorithm (ALPHA), a robust method specifically designed to mitigate risks arising from model misspecification in large-scale distributed research settings. ALPHA efficiently estimates treatment effects without stringent modeling assumptions and accommodates site-specific heterogeneity across decentralized data networks without sharing individual-level information. Applying ALPHA to electronic health record data from eight national pediatric hospitals, we quantify the impact of COVID-19 infection on healthcare utilization outcomes, demonstrating ALPHA’s effectiveness in consistently reducing variance and systematic errors while accurately capturing substantial heterogeneity across clinical sites. Building upon the need to address systematic biases in multi-site observational research, Chapter 3 introduces a novel multi-site empirical calibration framework. This framework employs negative control outcomes (NCOs) to correct residual biases effectively, explicitly accounting for heterogeneity across both clinical sites and outcomes. By assessing cardiovascular outcomes associated with second-line diabetes therapies acr…
author: Shen, Yishan
language: en
keywords: Mathematics
date published: 2025
date modified: 2026-07-24T03:45:26Z
provider: University of Pennsylvania
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Pennsylvania