• Corpus ID: 220793153

Improved Inference for Heterogeneous Treatment Effects Using Real-World Data Subject to Hidden Confounding

@article{Yang2020ImprovedIF,
  title={Improved Inference for Heterogeneous Treatment Effects Using Real-World Data Subject to Hidden Confounding},
  author={Shu Yang and Donglin Zeng and Xiaofei Wang},
  journal={arXiv: Methodology},
  year={2020}
}
The heterogeneity of treatment effect (HTE) lies at the heart of precision medicine. Randomized clinical trials (RCTs) are gold-standard to estimate the HTE but are typically underpowered. While real-world data (RWD) have large predictive power but are often confounded due to lack of randomization of treatment. In this article, we show that the RWD, even subject to hidden confounding, may be used to empower RCTs in estimating the HTE using the confounding function. The confounding function… 

Figures and Tables from this paper

Integrative R-learner of heterogeneous treatment effects combining experimental and observational studies

An integrative R -learner is developed that provides a general framework that can accommodate various HTE models for loss minimization and is consistent and asymptotically at least as efficient as the estimator using only the RCT.

Identification and estimation of treatment effects on long-term outcomes in clinical trials with external observational data

Summary: In biomedical studies, estimating drug effects on chronic diseases requires a long follow-up period, which is difficult to meet in randomized clinical trials (RCTs). The use of a short-term

Causal inference methods for combining randomized trials and observational studies: a review

This paper first discusses identification and estimation methods that improve generalizability of randomized controlled trials (RCTs) using the representativeness of observational data, and methods that combining RCTs and observational data to improve the (conditional) average treatment effect estimation.

Generalizable survival analysis of randomized controlled trials with observational studies

In the presence of heterogeneity between the randomized controlled trial (RCT) participants and the target population, evaluating the treatment effect solely based on the RCT often leads to biased

Minimax Rates and Adaptivity in Combining Experimental and Observational Data

This paper theoretically characterize the potential efficiency gain of integrating observational data into the RCT-based analysis from a minimax point of view, and proposes a fully adaptive anchored thresholding estimator that attains the optimal rate up to poly-log factors.

Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population

In the presence of heterogeneity between the randomized controlled trial (RCT) participants and the target population, evaluating the treatment effect solely based on the RCT often leads to biased

Testing for Treatment Effect Twice Using Internal and External Controls in Clinical Trials

The equal contribution combined test provides a new method of sensitivity analysis designed for data fusion problems, which anchors at the unbiased analysis based on RCT only and spends a small proportion of the type I error to also test using the external controls.

Adaptive Combination of Conditional Average Treatment Effects Based on Randomized and Observational Data

Data from both a randomized trial and an observational study are sometimes simultaneously available for evaluating the effect of an intervention. The randomized data typically allows for reliable

A Cross-Validated Targeted Maximum Likelihood Estimator for Data-Adaptive Experiment Selection Applied to the Augmentation of RCT Control Arms with External Data

A novel approach to the integration of RCT and real-world data (RWD) based on framing the problem as one of data-adaptive experiment selection is presented, which could help analyze hybrid RCT-RWD studies when running an adequately powered RCT is infeasible.

Paradoxes and resolutions for semiparametric fusion of individual and summary data

Suppose we have available individual data from an internal study and various types of summary statistics from relevant external studies. External summary statistics have been used as constraints on

References

SHOWING 1-10 OF 59 REFERENCES

Integrative analysis of randomized clinical trials with real world evidence studies

The proposed methods to estimate the effect of adjuvant chemotherapy in early-stage resected non-small-cell lung cancer integrating data from a RCT and a sample from the National Cancer Database are applied.

Improving trial generalizability using observational studies.

A calibration weighting estimator is proposed that enforces the covariate balance between the RCT and OS, therefore improving the trial-based estimator's generalizability and exploiting semiparametric efficiency theory.

Assessing the impact of unmeasured confounding for binary outcomes using confounding functions.

The confounding function approach is a useful method for assessing the impact of unmeasured confounding, in particular when alternative approaches, e.g. external adjustment or instrumental variable approaches, cannot be applied.

Combining Multiple Observational Data Sources to Estimate Causal Effects

  • Shu YangP. Ding
  • Mathematics
    Journal of the American Statistical Association
  • 2020
This framework applies to asymptotically normal estimators, including the commonly used regression imputation, weighting, and matching estimator, and does not require a correct specification of the model relating the unmeasured confounders to the observed variables.

Sensitivity Analysis for Selection bias and unmeasured Confounding in missing Data and Causal inference models

In both observational and randomized studies, subjects commonly drop out of the study (i.e., become censored) before end of follow-up. If, conditional on the history of the observed data up to t, the

Estimating causal effects of treatments in randomized and nonrandomized studies.

A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented. The objective is to specify the benefits of randomization in estimating

The use of propensity scores to assess the generalizability of results from randomized trials

These metrics can serve as a first step in assessing the generalizability of results from randomized trials to target populations, and are illustrated using data on the evaluation of a schoolwide prevention program called Positive Behavioral Interventions and Supports.

Removing Hidden Confounding by Experimental Grounding

This work introduces a novel method of using limited experimental data to correct the hidden confounding in causal effect models trained on larger observational data, even if the observational data does not fully overlap with the experimental data.

Identification of Causal Effects Using Instrumental Variables

It is shown that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers.

Combining randomized and non‐randomized evidence in clinical research: a review of methods and applications

This review covers statistical methods that have been used for the evidence-synthesis of different study types with the same outcome and similar interventions and provides a new classification of methods, which takes into account: the inferential approach, the bias modeling, the hierarchical structure, and the use of graphical modeling.
...