arXiv subjects

Kelly Van Lancker

Publications and source records attributed to Kelly Van Lancker.

14 recordsLinked to original sources

arXiv2026

Robust Covariate Adjustment in Multi-Center Randomized Trials

Augmented inverse probability weighting and G-computation with canonical generalized linear models have become increasingly popular for estimating the average treatment effect (ATE) in randomized experiments. These methods leverage outcome prediction models to adjust for imbalances in baseline covariates across treatment arms, improving power compared to unadjusted analyses, while controlling Type I error, even when models are misspecified. In multi-center trials they are often implemented without accounting for clustering by centers. We investigate how ignoring center-level correlation can impair estimation, degrade coverage of confidence intervals, and obscure interpretation. We find these issues to be especially acute for estimators of counterfactual means, as shown through simulations and clarified via theoretical arguments. To address these challenges, we develop semiparametric efficient estimators of counterfactual means and ATE defined for a randomly sampled center and patient. These estimators leverage outcome prediction models to improve efficiency yet retain large-sample unbiasedness under model misspecification. We further introduce an inference framework, inspired by random-effects meta-analysis, tailored to settings with many small centers. Incorporating center effects into the prediction models yields substantial efficiency gains, particularly when treatment effects vary across centers. Simulations and application to the WASH Benefits Bangladesh trial illustrate strong finite-sample performance of the proposed methods.

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arXiv2026

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

While randomization justifies the use of unadjusted effect estimators in randomized trials, there is growing interest in covariate adjustment to improve precision. Adjusting for baseline variables that are prognostic of the outcome can reduce estimator variance, resulting in narrower confidence intervals and increased statistical power. Recent guidance by the U.S. Food and Drug Administration supports fixed adjustment for prognostic covariates using parametric regression models. However, this guidance does not address more flexible approaches using data-adaptive or machine learning methods. We offer our perspectives on covariate adjustment to improve analytic precision. We focus on estimating the average effect for the target population in trials with minimal outcome missingness. We provide a non-technical overview of effect estimators that are unadjusted and effect estimators using fixed versus data-adaptive adjustment. We offer practical suggestions for conducting adjusted analyses that are data-adaptive, fully pre-specified, transparently and reproducibly implemented, robust to model misspecification, and guaranteed to improve precision relative to unadjusted analyses --- all while preserving statistical validity and the causal effect of interest. We hope that sharing our perspectives will foster broader discussion and eventual acceptance of principled, pre-specified, data-adaptive covariate adjustment in randomized trials.

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arXiv2026

Automated, efficient and model-free inference for randomized clinical trials via data-driven covariate adjustment

In 2023, the U.S. Food and Drug Administration issued guidance for adjustment of covariates in randomized clinical trials, emphasizing its role in enhancing precision and power through prognostic baseline variables. Despite its potential, many trials underutilize this method partly due to challenges in pre-specifying optimal baseline covariates and their functional forms. We explore the potential of automated, data-adaptive methods-including stepwise regression, Lasso and flexible machine learning algorithms-for covariate adjustment, addressing the challenge of pre-specification. Our approach ensures valid and interpretable treatment effect estimates and standard errors, even when outcome models are misspecified or biased outcome predictions are used. This differs from most competing methods, which assume correctly specified models for consistent standard errors. Our estimators require cross-fitting for reliable standard error estimation, though it can be omitted when variable selection is used, provided the outcome model satisfies an ultra-sparsity assumption. As such, we arrive at simple estimators and standard errors for marginal treatment effects in randomized clinical trials (or similar studies like A/B-testing), exploiting data-adaptive predictions from prognostic baseline covariates, with little (or no) bias in finite samples even when predictions are biased. Empirical and methodological results demonstrate promise of automated covariate adjustment for improving statistical power of trial analyses.

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arXiv2026

Robust evaluation of treatment effects in longitudinal studies with truncation by death or other intercurrent events

Intercurrent events, such as treatment switching, rescue medication, dropout, or truncation by death, frequently complicate intention-to-treat analyses in randomized clinical trials. Existing causal inference frameworks typically target hypothetical or principal stratum estimands (e.g., survivor average causal effects), which rely on unverifiable assumptions and can be sensitive to unmeasured confounders or positivity violations. We propose a novel approach that mitigates this sensitivity by using only information measured prior to the intercurrent event. Our key idea is to compare treated and untreated individuals, matched on baseline covariates, at the most recent time point before either experiences an intercurrent event. We call these contrasts Pairwise Last Observation Time (PLOT) estimands. PLOT estimands are identified in randomized trials without structural assumptions, even under severe positivity violations. Although PLOT-based tests may theoretically be susceptible to residual selection bias, we show this bias vanishes under standard conditions and remains negligible in extensive simulations. We develop asymptotically efficient, model-free tests and treatment effect estimators using data-adaptive nuisance parameter estimation. We evaluate performance via simulation and apply the method to re-analyze the DEVOTE trial, affected by truncation by death. PLOT offers a robust, data-driven alternative for evaluating treatment efficacy in the presence of complex intercurrent events.

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arXiv2024

Chasing Shadows: How Implausible Assumptions Skew Our Understanding of Causal Estimands

The ICH E9 (R1) addendum on estimands, coupled with recent advancements in causal inference, has prompted a shift towards using model-free treatment effect estimands that are more closely aligned with the underlying scientific question. This represents a departure from traditional, model-dependent approaches where the statistical model often overshadows the inquiry itself. While this shift is a positive development, it has unintentionally led to the prioritization of an estimand's ability to perfectly answer the key scientific question over its practical learnability from data under plausible assumptions. We illustrate this by scrutinizing assumptions in the recent clinical trials literature on principal stratum estimands, demonstrating that some popular assumptions are not only implausible but often inevitably violated. We advocate for a more balanced approach to estimand formulation, one that carefully considers both the scientific relevance and the practical feasibility of estimation under realistic conditions.

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arXiv2023

The Use of Covariate Adjustment in Randomized Controlled Trials: An Overview

There has been a growing interest in covariate adjustment in the analysis of randomized controlled trials in past years. For instance, the U.S. Food and Drug Administration recently issued guidance that emphasizes the importance of distinguishing between conditional and marginal treatment effects. Although these effects coincide in linear models, this is not typically the case in other settings, and this distinction is often overlooked in clinical trial practice. Considering these developments, this paper provides a review of when and how to utilize covariate adjustment to enhance precision in randomized controlled trials. We describe the differences between conditional and marginal estimands and stress the necessity of aligning statistical analysis methods with the chosen estimand. Additionally, we highlight the potential misalignment of current practices in estimating marginal treatment effects. Instead, we advocate for the utilization of standardization, which can improve efficiency by leveraging the information contained in baseline covariates while remaining robust to model misspecification. Finally, we present practical considerations that have arisen in our respective consultations to further clarify the advantages and limitations of covariate adjustment.

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arXiv2022

Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

Clinical trials disruption has always represented a non negligible part of the ending of interventional studies. While the SARS-CoV-2 (COVID-19) pandemic has led to an impressive and unprecedented initiation of clinical research, it has also led to considerable disruption of clinical trials in other disease areas, with around 80% of non-COVID-19 trials stopped or interrupted during the pandemic. In many cases the disrupted trials will not have the planned statistical power necessary to yield interpretable results. This paper describes methods to compensate for the information loss arising from trial disruptions by incorporating additional information available from auxiliary data sources. The methods described include the use of auxiliary data on baseline and early outcome data available from the trial itself and frequentist and Bayesian approaches for the incorporation of information from external data sources. The methods are illustrated by application to the analysis of artificial data based on the Primary care pediatrics Learning Activity Nutrition (PLAN) study, a clinical trial assessing a diet and exercise intervention for overweight children, that was affected by the COVID-19 pandemic. We show how all of the methods proposed lead to an increase in precision relative to use of complete case data only.

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arXiv2022

Combining Covariate Adjustment with Group Sequential, Information Adaptive Designs to Improve Randomized Trial Efficiency

In clinical trials, there is potential to improve precision and reduce the required sample size by appropriately adjusting for baseline variables in the statistical analysis. This is called covariate adjustment. Despite recommendations by regulatory agencies in favor of covariate adjustment, it remains underutilized leading to inefficient trials. We address two obstacles that make it challenging to use covariate adjustment. A first obstacle is the incompatibility of many covariate adjusted estimators with commonly used boundaries in group sequential designs (GSDs). A second obstacle is the uncertainty at the design stage about how much precision gain will result from covariate adjustment. We propose a method that modifies the original estimator so that it becomes compatible with GSDs, while increasing or leaving unchanged the estimator's precision. Our approach allows the use of any asymptotically linear estimator, which covers many estimators used in randomized trials. Building on this, we propose using an information adaptive design, that is, continuing the trial until the required information level is achieved. Such a design adapts to the amount of precision gain and can lead to faster, more efficient trials, without sacrificing validity or power. We evaluate estimator performance in simulations that mimic features of a completed stroke trial.

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arXiv2022

Estimands and their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

The COVID-19 pandemic continues to affect the conduct of clinical trials globally. Complications may arise from pandemic-related operational challenges such as site closures, travel limitations and interruptions to the supply chain for the investigational product, or from health-related challenges such as COVID-19 infections. Some of these complications lead to unforeseen intercurrent events in the sense that they affect either the interpretation or the existence of the measurements associated with the clinical question of interest. In this article, we demonstrate how the ICH E9(R1) Addendum on estimands and sensitivity analyses provides a rigorous basis to discuss potential pandemic-related trial disruptions and to embed these disruptions in the context of study objectives and design elements. We introduce several hypothetical estimand strategies and review various causal inference and missing data methods, as well as a statistical method that combines unbiased and possibly biased estimators for estimation. To illustrate, we describe the features of a stylized trial, and how it may have been impacted by the pandemic. This stylized trial will then be re-visited by discussing the changes to the estimand and the estimator to account for pandemic disruptions. Finally, we outline considerations for designing future trials in the context of unforeseen disruptions.

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arXiv2021

Ensuring valid inference for hazard ratios after variable selection

The problem of how to best select variables for confounding adjustment forms one of the key challenges in the evaluation of exposure effects in observational studies, and has been the subject of vigorous recent activity in causal inference. A major drawback of routine procedures is that there is no finite sample size at which they are guaranteed to deliver exposure effect estimators and associated confidence intervals with adequate performance. In this work, we will consider this problem when inferring conditional causal hazard ratios from observational studies under the assumption of no unmeasured confounding. The major complication that we face with survival data is that the key confounding variables may not be those that explain the censoring mechanism. In this paper, we overcome this problem using a novel and simple procedure that can be implemented using off-the-shelf software for penalized Cox regression. In particular, we will propose tests of the null hypothesis that the exposure has no effect on the considered survival endpoint, which are uniformly valid under standard sparsity conditions. Simulation results show that the proposed methods yield valid inferences even when covariates are high-dimensional.

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arXiv2020

Efficient, Doubly Robust Estimation of the Effect of Dose Switching for Switchers in a Randomised Clinical Trial

Motivated by a clinical trial conducted by Janssen Pharmaceuticals in which a flexible dosing regimen is compared to placebo, we evaluate how switchers in the treatment arm (i.e., patients who were switched to the higher dose) would have fared had they been kept on the low dose. This in order to understand whether flexible dosing is potentially beneficial for them. Simply comparing these patients' responses with those of patients who stayed on the low dose is unsatisfactory because the latter patients are usually in a better health condition. Because the available information in the considered trial is too scarce to enable a reliable adjustment, we will instead transport data from a fixed dosing trial that has been conducted concurrently on the same target, albeit not in an identical patient population. In particular, we will propose an estimator which relies on an outcome model and a propensity score model for the association between study and patient characteristics. The proposed estimator is asymptotically unbiased if at least one of both models is correctly specified, and efficient (under the model defined by the restrictions on the propensity score) when both models are correctly specified. We show that the proposed method for using results from an external study is generically applicable in studies where a classical confounding adjustment is not possible due to positivity violation (e.g., studies where switching takes place in a deterministic manner). Monte Carlo simulations and application to the motivating study demonstrate adequate performance.

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arXiv2020

Principled Selection of Baseline Covariates to Account for Censoring in Randomized Trials with a Survival Endpoint

The analysis of randomized trials with time-to-event endpoints is nearly always plagued by the problem of censoring. As the censoring mechanism is usually unknown, analyses typically employ the assumption of non-informative censoring. While this assumption usually becomes more plausible as more baseline covariates are being adjusted for, such adjustment also raises concerns. Pre-specification of which covariates will be adjusted for (and how) is difficult, thus prompting the use of data-driven variable selection procedures, which may impede valid inferences to be drawn. The adjustment for covariates moreover adds concerns about model misspecification, and the fact that each change in adjustment set, also changes the censoring assumption and the treatment effect estimand. In this paper, we discuss these concerns and propose a simple variable selection strategy that aims to produce a valid test of the null in large samples. The proposal can be implemented using off-the-shelf software for (penalized) Cox regression, and is empirically found to work well in simulation studies and real data analyses.

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arXiv2020

Clinical trials impacted by the COVID-19 pandemic: Adaptive designs to the rescue?

Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for the ongoing clinical trials in non-COVID-19 conditions. Motivated by four currently ongoing clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to utilize early readouts; and (v) how to utilize Bayesian techniques. In more detail approaches to resizing a trial affected by the pandemic are developed including considerations to stop a trial early, the use of group-sequential designs or sample size adjustment. All methods considered are implemented in a freely available R shiny app. Furthermore, regulatory and operational issues including the role of data monitoring committees are discussed.

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arXiv2019

Improving interim decisions in randomized trials by exploiting information on short-term outcomes and prognostic baseline covariates

Conditional power calculations are frequently used to guide the decision whether or not to stop a trial for futility or to modify planned sample size. These ignore the information in short-term endpoints and baseline covariates, and thereby do not make fully efficient use of the information in the data. We therefore propose an interim decision procedure based on the conditional power approach which exploits the information contained in baseline covariates and short-term outcomes. We will realise this by considering the estimation of the treatment effect at the interim analysis as a missing data problem. This problem is addressed by employing specific prediction models for the long-term endpoint which enable the incorporation of baseline covariates and multiple short-term endpoints. We show that the proposed procedure leads to an efficiency gain and a reduced sample size, without compromising the Type I error rate of the procedure, even when the adopted prediction models are misspecified. In particular, implementing our proposal in the conditional power approach allows earlier decisions relative to standard approaches, whilst controlling the probability of an incorrect decision. This time gain results in a lower expected number of recruited patients in case of stopping for futility, such that fewer patients receive the futile regimen. We explain how these methods can be used in adaptive designs with unblinded sample size reassessment based on the inverse normal $p$-value combination method to control type I error. We support the proposal by Monte Carlo simulations based on data from a real clinical trial.

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