Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches | Paper Citation Record

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Unresolved cited work

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Unresolved cited work

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches locally efficient

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: A principled yet flexible approach

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This paper cites Simple, Efficient Estimators of Treatment Effects in Randomized Trials Using Generalized Linear Models to Leverage Baseline Variables.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Simple, Efficient Estimators of Treatment Effects in Randomized Trials Using Generalized Linear Models to Leverage Baseline Variables

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This paper cites Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes

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This paper cites Covariate adjustment in randomized controlled trials: General concepts and practical considerations.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Covariate adjustment in randomized controlled trials: General concepts and practical considerations

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Statistical Methods for Research Workers

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This paper cites Machine learning methods for leveraging baseline covariate information to improve the efficiency of clinical trials.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Machine learning methods for leveraging baseline covariate information to improve the efficiency of clinical trials

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This paper cites Optimising precision and power by machine learning in randomised trials with ordinal and time-to-event outcomes with an application to COVID-19.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Optimising precision and power by machine learning in randomised trials with ordinal and time-to-event outcomes with an application to COVID-19

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This paper cites Adaptive selection of the optimal strategy to improve precision and power in randomized trials.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Adaptive selection of the optimal strategy to improve precision and power in randomized trials

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This paper cites COADVISE: covariate adjustment with variable selection in randomized controlled trials.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches COADVISE: covariate adjustment with variable selection in randomized controlled trials

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This paper cites Efficient Randomized Experiments Using Foundation Models.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Efficient Randomized Experiments Using Foundation Models

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This paper cites Statistical Principles for Clinical Trials E9.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Statistical Principles for Clinical Trials E9

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Guideline on Adjustment for Baseline Covariates in Clinical Trials

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products Guidance for Industry

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Lasso adjustments of treatment effect estimates in randomized experiments

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This paper cites The LOOP Estimator: Adjusting for Covariates in Randomized Experiments.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches The LOOP Estimator: Adjusting for Covariates in Randomized Experiments

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches The Generalized Oaxaca-Blinder Estimator

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches A family of Bayesian prognostic and predictive covariate-adjusted response-adaptive randomization designs

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Asymptotic Statistics

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Stratification by a multivariate confounder score

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score

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This paper cites A new approach to causal inference in mortality studies with sustained exposure periods–application to control of the healthy worker survivor effect.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches A new approach to causal inference in mortality studies with sustained exposure periods–application to control of the healthy worker survivor effect

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models (with Rejoiner)

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Inverse probability weighted estimation for general missing data problems

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches A general form of covariate adjustment in clinical trials under covariate-adaptive randomization

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Adjusting for partially missing baseline measurements in randomized trials

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches To Adjust or not to Adjust? Estimating the Average Treatment Effect in Randomized Experiments with Missing Covariates

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Estimation of regression coefficients when some regressors are not always observed

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Unified Methods for Censored Longitudinal Data and Causality

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Variance reduction in randomised trials by inverse probability weighting using the propensity score

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Cross-Validated Targeted Minimum-Loss-Based Estimation

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Targeted Maximum Likelihood Learning

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Targeted Learning: Causal Inference for Observational and Experimental Data

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Machine learning in the estimation of causal effects: targeted minimum loss-based estimation and double/debiased machine learning

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Empirical efficiency maximization: improved locally efficient covariate adjustment in randomized experiments and survival analysis

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Double/debiased machine learning for treatment and structural parameters

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Efficient and Adaptive Estimation for Semiparametric Models

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Semiparametric Theory and Missing Data

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Some Surprising Results about Covariate Adjustment in Logistic Regression Models

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Adaptive Pre-specification in Randomized Trials With and Without Pair-Matching

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Super Learner

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Machine learning to optimize precision in the analysis of randomized trials: A journey in pre-specified, yet data-adaptive learning

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Trial Emulation, Simulation, and Augmentation Using Electronic Health Records and Generative AI

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Coadvise

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches RobinCar2: ROBust INference for Covariate Adjustment in Randomized Clinical Trials

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Automated, efficient and model-free inference for randomized clinical trials via data-driven covariate adjustment

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Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-data Applications

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This paper cites Considerations for the Integration of Randomized Controlled Trials and Real-World Data.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Considerations for the Integration of Randomized Controlled Trials and Real-World Data

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-01T06:03:28.562375Z

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Observation 0fcfc62e-76fb-405d-bd10-3ef8e45bf65a · outbound

This paper cites Robust integration of external control data in randomized trials.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Robust integration of external control data in randomized trials

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-01T06:03:28.682696Z

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Unavailable: canonical work link unavailable.

Observation fb0d0b4d-24cc-4e3c-88a7-bec05daf2c2c · outbound

This paper cites Robust integration of external control data in randomized trials.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Robust integration of external control data in randomized trials

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-01T06:03:28.865446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd5cf862-c889-40df-a0d6-08d414aee394 · outbound

This paper cites an unresolved cited work.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Unresolved cited work

Reference 460

Resolution: unresolved; no resolver link, observed 2026-08-01T06:03:25.342054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2b909b9-085d-4190-aa88-9446f1cf4aa3 · outbound

This paper cites an unresolved cited work.

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches Unresolved cited work

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-01T06:03:28.148467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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