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Using Individualized Treatment Effects to Assess Treatment Effect Heterogeneity

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arXiv 2025年

作者： Sechidis, Konstantinos Zhang, Cong Sun, Sophie Chen, Yao Spector, Asher Bornkamp, Björn Advanced Methodology and Data Science Novartis Pharma AG Basel Switzerland China Novartis Institutes for Bio-medical Research CO. Shanghai China Advanced Methodology and Data Science Novartis Pharmaceuticals Corporation East HanoverNJ United States Department of Statistics Stanford University StanfordCA United States

Assessing treatment effect heterogeneity (TEH) in clinical trials is crucial, as it provides insights into the variability of treatment responses among patients, influencing important decisions related to drug development. Furthermore, it can lead to personalized medicine by tailoring treatments to individual patient characteristics. This paper introduces novel methodologies for assessing treatment effects using the individual treatment effect as a basis. To estimate this effect, we use a Double Robust (DR) learner to infer a pseudo-outcome that reflects the causal contrast. This pseudo-outcome is then used to perform three objectives: (1) a global test for heterogeneity, (2) ranking covariates based on their influence on effect modification, and (3) providing estimates of the individualized treatment effect. We compare our DR-learner with various alternatives and competing methods in a simulation study, and also use it to assess heterogeneity in a pooled analysis of five Phase III trials in psoriatic arthritis. By integrating these methods with the recently proposed WATCH workflow (Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors), we provide a robust framework for analyzing TEH, offering insights that enable more informed decision-making in this challenging area. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

Peter Krusches and Frank Bretz's contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al.

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Journal of the Royal Statistical Society. Series B: Statistical methodology 2022年第1期84卷 42-43页

作者： Krusche, Peter Bretz, Frank Novartis Pharma Advanced Methodology and Data Science Basel Switzerland

Synthetic data use: exploring use cases to optimise data utility

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Discover Artificial Intelligence 2021年第1期1卷 1-13页

作者： James, Stefanie Harbron, Chris Branson, Janice Sundler, Mimmi Data Policy Director Data Office Data Science and Artificial Intelligence Biopharmaceuticals Research and Development AstraZeneca Academy House 136 Hills Road CambridgeCB2 8PA United Kingdom Expert Statistical Scientist Roche Pharmaceuticals Welwyn Garden City United Kingdom Global Head of Advanced Methodology and Data Science Novartis Basel Switzerland Head of Data and AI Governance and Policy R & amp D Data Office Data Science and Artificial Intelligence Biopharmaceuticals Research and Development AstraZeneca Cambridge United Kingdom

Synthetic data is a rapidly evolving field with growing interest from multiple industry stakeholders and European bodies. In particular, the pharmaceutical industry is starting to realise the value of synthetic data which is being utilised more prevalently as a method to optimise data utility and sharing, ultimately as an innovative response to the growing demand for improved privacy. Synthetic data is data generated by simulation, based upon and mirroring properties of an original dataset. Here, with supporting viewpoints from across the pharmaceutical industry, we set out to explore use cases for synthetic data across seven key but relatable areas for optimising data utility for improved data privacy and protection. We also discuss the various methods which can be used to produce a synthetic dataset and availability of metrics to ensure robust quality of generated synthetic datasets. Lastly, we discuss the potential merits, challenges and future direction of synthetic data within the pharmaceutical industry and the considerations for this privacy enhancing technology. © The Author(s) 2021.

关键词： Software testing

Non-proportional hazards in Immuno-oncology: is an old perspective needed?

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arXiv 2020年

作者： Magirr, Dominic Advanced Methodology and Data Science Novartis Pharma AG Basel Switzerland

A fundamental concept in two-arm non-parametric survival analysis is the comparison of observed versus expected numbers of events on one of the treatment arms (the choice of which arm is arbitrary), where the expectation is taken assuming that the true survival curves in the two arms are identical. This concept is at the heart of the counting-process theory that provides a rigorous basis for methods such as the log-rank test. It is natural, therefore, to maintain this perspective when extending the log-rank test to deal with non-proportional hazards, for example by considering a weighted sum of the "observed - expected" terms, where larger weights are given to time periods where the hazard ratio is expected to favour the experimental treatment. In doing so, however, one may stumble across some rather subtle issues, related to the difficulty in ascribing a causal interpretation to hazard ratios, that may lead to strange conclusions. An alternative approach is to view non-parametric survival comparisons as permutation tests. With this perspective, one can easily improve on the efficiency of the log-rank test, whilst thoroughly controlling the false positive rate. In particular, for the field of immuno-oncology, where researchers often anticipate a delayed treatment effect, sample sizes could be substantially reduced without loss of power. Copyright © 2020, The Authors. All rights reserved.

关键词： Oncology

Design and analysis of group-sequential clinical trials based on a Modestly-weighted log-rank test in anticipation of a delayed separation of survival curves: A practical guidance

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arXiv 2021年

作者： Magirr, Dominic Jiménez, José L. Advanced Methodology and Data Science Novartis Pharma AG Basel Switzerland

A common feature of many recent trials evaluating the effects of immunotherapy on survival is that non-proportional hazards can be anticipated at the design stage. This raises the possibility to use a statistical method tailored towards testing the purported long-term benefit, rather than applying the more standard log-rank test and/or Cox model. Many such proposals have been made in recent years, but there remains a lack of practical guidance on implementation, particularly in the context of group-sequential designs. In this article, we aim to fill this gap. We discuss how the POPLAR trial, which compared immunotherapy versus chemotherapy in non-small-cell lung cancer, might have been re-designed to be more robust to the presence of a delayed effect. We then provide step-by-step instructions on how to analyse a hypothetical realisation of the trial, based on this new design. Basic theory on weighted log-rank tests and group-sequential methods is covered, and an accompanying R package (including vignette) is provided. © 2021, CC BY.

关键词： Chemotherapy

Treatment-control comparisons in platform trials including non-concurrent controls

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arXiv 2024年

作者： Roig, Marta Bofill Krotka, Pavla Hees, Katharina Koenig, Franz Magirr, Dominic Jacko, Peter Parke, Tom Posch, Martin Center for Medical Data Science Medical University of Vienna Vienna Austria Section of Data Science and Methods Paul-Ehrlich-Institut Germany Advanced Methodology and Data Science Novartis Pharma AG Switzerland Berry Consultants United Kingdom

Shared controls in platform trials comprise concurrent and non-concurrent controls. For a given experimental arm, non-concurrent controls refer to data from patients allocated to the control arm before the arm enters the trial. The use of non-concurrent controls in the analysis is attractive because it may increase the trial’s power of testing treatment differences while decreasing the sample size. However, since arms are added sequentially in the trial, randomization occurs at different times, which can introduce bias in the estimates due to time trends. In this article, we present methods to incorporate non-concurrent control data in treatment-control comparisons allowing for time trends. We focus mainly on frequentist approaches that model the time trend and Bayesian strategies that limit the borrowing level depending on the heterogeneity between concurrent and non-concurrent controls. We examine the impact of time trends, overlap between experimental treatment arms and entry times of arms in the trial on the operating characteristics of treatment effect estimators for each method under different patterns for the time trends. We argue under which conditions the methods lead to type 1 error control and discuss the gain in power compared to trials only using concurrent controls by means of a simulation study in which methods are compared. Platform trials;External controls;Time trends Copyright © 2024, The Authors. All rights reserved.

关键词： Concurrency control

Gaussian universal likelihood ratio testing

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arXiv 2021年

作者： Dunn, Robin Ramdas, Aaditya Balakrishnan, Sivaraman Wasserman, Larry Novartis Pharmaceuticals Corporation Advanced Methodology and Data Science East HanoverNJ07936 United States Department of Statistics & Data Science Carnegie Mellon University PittsburghPA15213 United States

The classical likelihood ratio test (LRT) based on the asymptotic chi-squared distribution of the log likelihood is one of the fundamental tools of statistical inference. A recent universal LRT approach based on sample splitting provides valid hypothesis tests and confidence sets in any setting for which we can compute the split likelihood ratio statistic (or, more generally, an upper bound on the null maximum likelihood). The universal LRT is valid in finite samples and without regularity conditions. This test empowers statisticians to construct tests in settings for which no valid hypothesis test previously existed. For the simple but fundamental case of testing the population mean of d-dimensional Gaussian data with identity covariance matrix, the classical LRT itself applies. Thus, this setting serves as a perfect test bed to compare the classical LRT against the universal LRT. This work presents the first in-depth exploration of the size, power, and relationships between several universal LRT variants. We show that a repeated subsampling approach is the best choice in terms of size and power. For large numbers of subsamples, the repeated subsampling set is approximately spherical. We observe reasonable performance even in a high-dimensional setting, where the expected squared radius of the best universal LRT's confidence set is approximately 3/2 times the squared radius of the classical LRT's spherical confidence set. We illustrate the benefits of the universal LRT through testing a non-convex doughnut-shaped null hypothesis, where a universal inference procedure can have higher power than a standard approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Covariance matrix

Machine learning vs. rule-based methods for document classification of electronic health records within mental health care—A systematic literature review

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Natural Language Processing Journal

Natural Language Processing Journal 2025年 10卷

作者： Emil Rijcken Kalliopi Zervanou Pablo Mosteiro Floortje Scheepers Marco Spruit Uzay Kaymak Jheronimus Academy of Data Science Eindhoven University of Technology Sint Janssingel 92 ’s-Hertogenbosch 5211 DA The Netherlands Department of Information and Computing Sciences Utrecht University Princetonplein 5 Utrecht 3584 CC The Netherlands Department of Methodology and Statistics Utrecht University Padualaan 14 Utrecht 3584CH The Netherlands Psychiatry University Medical Center Utrecht Heidelberglaan 100 Utrecht 3584 CS The Netherlands Leiden Institute of Advanced Computer Science Niels Bohrweg 1 Leiden 2333 CA The Netherlands Public Health & Primary Care Leiden University Medical Center Albinusdreef 2 Leiden 2333 ZA The Netherlands

Document classification is a widely used task for analyzing mental healthcare texts. This systematic literature review focuses on the document classification of electronic health records in mental healthcare. Over the last decade, there has been a shift from rule-based to machine-learning methods. Despite this shift, no systematic comparison of these two approaches exists for mental healthcare applications. This review examines the evolution, applications, and performance of these methods over time. We find that for most of the last decade, rule-based methods have outperformed machine-learning approaches. However, with the development of more advanced machine-learning techniques, performance has improved. In particular, Transformer-based models enable machine learning approaches to outperform rule-based methods for the first time.

关键词： Document classification Natural language processing Electronic health records Mental healthcare Machine learning Rule-based methods

Optimal allocation strategies in platform trials

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arXiv 2023年

作者： Roig, Marta Bofill Glimm, Ekkehard Mielke, Tobias Posch, Martin Section for Medical Statistics Center for Medical Data Science Medical University of Vienna Vienna Austria Advanced Methodology and Data Science Novartis Pharma AG Basel Switzerland Statistics and Decision Sciences Janssen-Cilag GmbH Germany

Platform trials are randomized clinical trials that allow simultaneous comparison of multiple interventions, usually against a common control. Arms to test experimental interventions may enter and leave the platform over time. This implies that the number of experimental intervention arms in the trial may change over time. Determining optimal allocation rates to allocate patients to the treatment and control arms in platform trials is challenging because the change in treatment arms implies that also the optimal allocation rates will change when treatments enter or leave the platform. In addition, the optimal allocation depends on the analysis strategy used. In this paper, we derive optimal treatment allocation rates for platform trials with shared controls, assuming that a stratified estimation and testing procedure based on a regression model, is used to adjust for time trends. We consider both, analysis using concurrent controls only as well as analysis methods based on also non-concurrent controls and assume that the total sample size is fixed. The objective function to be minimized is the maximum of the variances of the effect estimators. We show that the optimal solution depends on the entry time of the arms in the trial and, in general, does not correspond to the square root of k allocation rule used in the classical multi-arm trials. We illustrate the optimal allocation and evaluate the power and type 1 error rate compared to trials using one-to-one and square root of k allocations by means of a case study. Copyright © 2023, The Authors. All rights reserved.

关键词： Regression analysis