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arXiv 2024年

作者： Wolbers, Marcel Rabuñal, Mar Vázquez Li, Ke Rufibach, Kaspar Bové, Daniel Sabanés Methods Collaboration and Outreach Group Product Development Data Sciences Pharma Development Roche Basel Switzerland Product Development Data Sciences Pharma Development Roche Basel Switzerland Data Science Acceleration Product Development Data Sciences Pharma Development Roche Basel Switzerland

In randomized controlled trials, forest plots are frequently used to investigate the homogeneity of treatment effect estimates in pre-defined subgroups. However, the interpretation of subgroup-specific treatment effect estimates requires great care due to the smaller sample size of subgroups and the large number of investigated subgroups. In the literature, Bayesian shrinkage methods have been proposed to address these issues, but they often focus on disjoint subgroups while subgroups displayed in forest plots are overlapping, i.e., each subject appears in multiple subgroups. In our proposed approach, we first build a flexible Cox model based on all available observations, including categorical covariates that identify the subgroups of interest and their interactions with the treatment group variable. We explore both penalized partial likelihood estimation with a lasso or ridge penalty for treatment-by-covariate interaction terms, and Bayesian estimation with a regularized horseshoe prior. One advantage of the Bayesian approach is the ability to derive credible intervals for shrunken subgroup-specific estimates. In a second step, the Cox model is marginalized to obtain treatment effect estimates for all subgroups. We illustrate these methods using data from a randomized clinical trial in follicular lymphoma and evaluate their properties in a simulation study. In all simulation scenarios, the overall mean-squared error is substantially smaller for penalized and shrinkage estimators compared to the standard subgroup-specific treatment effect estimator but leads to some bias for heterogeneous subgroups. A naive overall sample estimator also outperforms the standard subgroup-specific estimator in terms of the overall mean-squared error for all scenarios except for a scenario with substantial heterogeneity. We recommend that subgroup-specific estimators, which are typically displayed in forest plots, are more routinely complemented by treatment effect estimators based on

关键词： Mean square error

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BAYESIAN ANALYSIS OF RESTRICTED MEAN SURVIVAL TIME ADJUSTED FOR COVARIATES USING PSEUDO-OBSERVATIONS

arXiv

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arXiv 2025年

作者： Orsini, Léa Lesaffre, Emmanuel Yin, Guosheng Brard, Caroline Dejardin, David Le Teuff, Gwénaël Oncostat U1018 Inserm University Paris-Saclay Villejuif France I-Biostat KU-Leuven Leuven Belgium Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong Ipsen Innovation Clinical Development Organisation Les Ulis France Product Development Data Sciences F. Hoffmann-La Roche AG Basel Switzerland

The difference in restricted mean survival time (RMST) is a clinically meaningful measure to quantify treatment effect in randomized controlled trials, especially when the proportional hazards assumption does not hold. Several frequentist methods exist to estimate RMST adjusted for covariates based on modeling and integrating the survival function. A more natural approach may be a regression model on RMST using pseudo-observations, which allows for a direct estimation without modeling the survival function. Only a few Bayesian methods exist, and each requires a model of the survival function. We developed a new Bayesian method that combines the use of pseudo-observations with the generalized method of moments. This offers RMST estimation adjusted for covariates without the need to model the survival function, making it more attractive than existing Bayesian methods. A simulation study was conducted with different time-dependent treatment effects (early, delayed, and crossing survival) and covariate effects, showing that our approach provides valid results, aligns with existing methods, and shows improved precision after covariate adjustment. For illustration, we applied our approach to a phase III trial in prostate cancer, providing estimates of the treatment effect on RMST, comparable to existing methods. In addition, our approach provided the effect of other covariates on RMST and determined the posterior probability of the difference in RMST exceeds any given time threshold for any covariate, allowing for nuanced and interpretable results. Copyright © 2025, The Authors. All rights reserved.

关键词： Linear regression

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A Causal Inference Framework for Leveraging External Controls in Hybrid Trials

arXiv

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arXiv 2023年

作者： Valancius, Michael Pang, Herbert Zhu, Jiawen Cole, Stephen R. Funk, Michele Jonsson Kosorok, Michael R. Department of Biostatistics University of North Carolina at Chapel Hill Chapel HillNC United States Product Development Data Sciences Genentech South San FranciscoCA United States Department of Epidemiology University of North Carolina at Chapel Hill Chapel HillNC United States

We consider the challenges associated with causal inference in settings where data from a randomized trial is augmented with control data from an external source to improve efficiency in estimating the average treatment effect (ATE). Through the development of a formal causal inference framework, we outline sufficient causal assumptions about the exchangeability between the internal and external controls to identify the ATE and establish the connection to a novel graphical criteria. We propose estimators, review efficiency bounds, develop an approach for efficient doubly-robust estimation even when unknown nuisance models are estimated with flexible machine learning methods, and demonstrate finite-sample performance through a simulation study. To illustrate the ideas and methods, we apply the framework to a trial investigating the effect of risdisplam on motor function in patients with spinal muscular atrophy for which there exists an external set of control patients from a previous trial. © 2023, CC BY.

关键词： Machine learning

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Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems 3rd

Interpretable Deep Attention Model for Multivariate Time Ser...

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3rd International Conference on Dynamic data Driven Application Systems, DDDAS 2020

作者： Gangopadhyay, Tryambak Tan, Sin Yong Jiang, Zhanhong Sarkar, Soumik Department of Mechanical Engineering Iowa State University AmesIA50011 United States Data Sciences and Product Development Johnson Controls MilwaukeeWI53202 United States

ISBN: (纸本)9783030617240

Multivariate time series prediction has important applications in the domain of energy-efficient building technology. With the buildings consuming large amounts of electrical energy, it is critical to reducing energy consumption and economic costs while ensuring a better quality of urban living standards. As sensor-actuator rich, smart buildings are becoming complex dynamic data-driven applications systems (DDDAS), accurate and interpretable data-driven decision-making tools can have immense value. In this context, we develop a novel deep learning model that can explicitly capture the temporal correlations through LSTM layers. The model can isolate the important timesteps in the input time series for prediction. Also, it is critical to identify the contributions of different variables in the multivariate input. Our proposed model based on attention mechanisms can simultaneously learn important timesteps and variables. We demonstrate the results using a public multivariate time series dataset collected from an air handling unit in a building heating, ventilation, and air-conditioning (HVAC) system. The model with enhanced interpretability does not compromise with the prediction accuracy. The interpretations are validated from a domain knowledge perspective. © 2020, Springer Nature Switzerland AG.

关键词： Time series

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Quantification of follow-up time in oncology clinical trials with a time-to-event endpoint: Asking the right questions

arXiv

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arXiv 2022年

作者： Rufibach, Kaspar Grinsted, Lynda Li, Jiang Weber, Hans Jochen Zheng, Cheng Zhou, Jiangxiu Product Development Data Sciences Hoffmann-La Roche Ltd Basel Switzerland AstraZeneca UK Ltd Cambridge Cambridgeshire United Kingdom BeiGene USA Inc. 55 Challenger Road Ridgefield ParkNJ07660 United States Clinical Development and Analytics Novartis Pharma AG Basel Switzerland Zentalis Pharmaceuticals New YorkNY United States Statistics and Decision Sciences J&J Spring HousePA United States

For the analysis of a time-to-event endpoint in a single-arm or randomized clinical trial it is generally perceived that interpretation of a given estimate of the survival function, or the comparison between two groups, hinges on some quantification of the amount of follow-up. Typically, a median of some loosely defined quantity is reported. However, whatever median is reported, is typically not answering the question(s) trialists actually have in terms of follow-up quantification. In this paper, inspired by the estimand framework, we formulate a comprehensive list of relevant scientific questions that trialists have when reporting time-to-event data. We illustrate how these questions should be answered, and that reference to an unclearly defined follow-up quantity is not needed at all. In drug development, key decisions are made based on randomized controlled trials, and we therefore also discuss relevant scientific questions not only when looking at a time-to-event endpoint in one group, but also for comparisons. We find that different thinking about some of the relevant scientific questions around follow-up is required depending on whether a proportional hazards assumption can be made or other patterns of survival functions are anticipated, e.g. delayed separation, crossing survival functions, or the potential for cure. We conclude the paper with practical *** Codes 62N02 © 2022, CC BY.

关键词： Controlled drug delivery

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A N-gram based approach to auto-extracting topics from research articles

arXiv

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arXiv 2021年

作者： Zhu, Linkai Huang, Maoyi Chen, Maomao Wang, Wennan Institute of Software Chinese Academy of Sciences Beijing China Institute of Data Science City University of Macau China Product Development Ericsson Gothenburg Sweden Department of Computer Science and Engineering University of Gothenburg Gothenburg Sweden

A lot of manual work goes into identifying a topic for an article. With a large volume of articles, the manual process can be exhausting. Our approach aims to address this issue by automatically extracting topics from the text of large numbers of articles. This approach takes into account the efficiency of the process. Based on existing N-gram analysis, our research examines how often certain words appear in documents in order to support automatic topic extraction. In order to improve efficiency, we apply custom filtering standards to our research. Additionally, delete as many noncritical or irrelevant phrases as possible. In this way, we can ensure we are selecting unique keyphrases for each article, which capture its core idea. For our research, we chose to center on the autonomous vehicle domain, since the research is relevant to our daily lives. We have to convert the PDF versions of most of the research papers into editable types of files such as TXT. This is because most of the research papers are only in PDF format. To test our proposed idea of automating, numerous articles on robotics have been selected. Next, we evaluate our approach by comparing the result with that obtained manually. Copyright © 2021, The Authors. All rights reserved.

关键词： Efficiency

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392P Investigating the data landscape of Duchenne muscular dystrophy: Answering your research questions with the right data source

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Neuromuscular Disorders 2024年 43卷 104441.120-104441.120页

作者： Furby, H. Johnson, M. Signorovitch, J. Moride, Y. Castillon, G. Simpson, A. Real-World Data Science Product Development Data Sciences Roche Products Ltd Welwyn Garden City UK Analysis Group Boston Massachusetts USA YOLARX Consultants Montreal Quebec Canada Global Access F. Hoffmann-La Roche Ltd Basel Switzerland

Duchenne muscular dystrophy (DMD) is a rare genetic disorder characterised by progressive muscle degeneration and weakness due to alterations of the dystrophin protein. Real-world data (RWD) sources can be used to contextualise clinical trial results and better understand the long-term impact of new therapies on the natural history of disease. RWD sources in DMD are fragmented and have heterogeneous data provenance. In order to generate robust real-world evidence (RWE), it is important that potential data source solutions are fully evaluated for their suitability to answer research questions of interest. A targeted literature review was conducted, augmented by web searches and expert elicitation, to gain a comprehensive understanding of the available data sources that would be fit for purpose for observational research in DMD. Included publications were those mentioning registries, retro- or prospective cohort studies and historic clinical trials with placebo arms with data collection from 2018 or later. General administrative claims or electronic health record data sources were excluded, as were small scale (N<100), single-centre studies. Sixty-four RWD sources and 7 historic clinical trials were shortlisted for evaluation. data custodians were identified from publicly available information and existing author connections. A questionnaire was then sent to data custodians which queried the data provenance, content, completeness and accessibility of data. An overview of the data sources, including variables collected, patient counts and length of follow-up, as well as a synthesis of which data sources can be used to answer certain types of research questions (e.g., external control arm studies, treatment pattern and resource utilisation studies) will be presented. Sharing this research with the scientific community will provide researchers with a head-start on data source selection which may help accelerate RWE generation in DMD.

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WCN24-461 RAPID COMPLEMENT INHIBITION WITH THE C5 INHIBITOR CROVALIMAB: TIMING ANALYSIS USING ANIMAL MODEL AND COMPOSER TRIAL data

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Kidney International Reports 2024年第4期9卷 S524-S525页

作者： Rocio Soldati Cristian Brocchieri Leigh Beveridge Muriel Buri Niels Janssen Patty Leon Yoshinori Tsuboi Simon Buatois Buenos Aires Global Product Development Argentina Hematology and Nephrology Rare Diseases F. Hoffmann-La Roche Ltd. Basel Product Development Hematology Genentech Inc. San Francisco CA Product Development (PD) Data Sciences (PDD) F. Hoffmann-La Roche Ltd. Basel Pharmaceutical Sciences pRED F. Hoffmann-La Roche Ltd. Basel PDH Rare Blood Disorders Genentech Ltd City: San Francisco CA Research Division Chugai Pharmaceutical Co. Tokyo Pharmaceutical Sciences F. Hoffmann-La Roche Ltd. Basel

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Associations of patient-specific resting state variability with CSF and clinical biomarkers: data from Alzheimer’s disease clinical trials

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Alzheimer's & Dementia 2023年第S3期19卷

作者： Susanna Gobbi Marzia A. Scelsi Paul Delmar Tobias Bittner Gregory Klein Stefano Magon F. Hoffmann-La Roche Ltd. Basel Switzerland Roche Pharma Research and Early Development Basel Switzerland Roche Products Ltd. Product Development Data Sciences Welwyn Garden City United Kingdom Roche Products Ltd. Welwyn Garden City United Kingdom Product Development Data Sciences - Data and Statistical Sciences (PDD-DSS) Basel Switzerland F. Hoffmann-La Roche Ltd Basel Switzerland gRED OMNI BD Basel Switzerland Pharma Research and Early Development F. Hoffmann-La Roche Ltd. Basel Switzerland Roche Pharma Research and Early Development Roche Innovation Center Basel Switzerland

Background Changes of brain functional connectivity assessed using MRI in patients with Alzheimer’s disease (AD) have been previously reported. However, factors contributing to the variability among patients have not been sufficiently investigated. Here, we investigated the association of patient-specific resting state parameters with CSF markers and clinical measures. Method We analyzed the baseline rs-fMRI of 158 patients (87 female, age 50-89) with mild AD collected in the Marguerite RoAD trial of gantenerumab in mild AD (NCT02051608) and 132 cognitively unimpaired adults (88 female, age 56-90) from ADNI. We investigated the within- and between-subjects’ variability in five brain networks (Sensory Motor, Cerebellar, Visual, Default Mode and Cognitive Control), inferred from the two groups using a Bayesian probabilistic model (PROFUMO, Harrison-Smith-2015; Harrison-Woolrich-2020). We computed spatial maps, BOLD amplitude changes, and spatial (temporal) functional connectivity between- and within-networks for each subject. Those metrics were compared between groups using T-tests. In AD patients we used linear models to investigate associations of the extracted metrics with CSF markers (Amyloid Beta 42, p-Tau217, pTAU/Abeta 1-42 ratio and Neurogranin) and cognitive scores (ADAS-Cog 13, MMSE and ADCS-ADL), while controlling for age, sex and MRI-scanner type. All results were corrected for multiplicity using FDR. Result On average the signal amplitude was lower in the Cognitive Control and the Sensory Motor (p < 0.0001) networks in AD patients compared to cognitively unimpaired adults. Interestingly, in AD patients the lower amplitude observed in the cognitive control network was associated with cognitive decline (β = 0.02, p = 0.01). Moreover, significant associations were observed in AD patients between lower signal amplitude and higher CSF levels of ABeta 42 (β = -0.02, p = 0.036) and cognitive decline (β = 0.02, p = 0.008) (Fig.2). AD group showed on average reduc

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Robust personalized pricing under uncertainty of purchase probabilities

arXiv

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arXiv 2024年

作者： Ikeda, Shunnosuke Nishimura, Naoki Sukegawa, Noriyoshi Takano, Yuichi Product Development Management Office Data Management & Planning Office Recruit Co. Ltd. 1–9–2 Chiyoda–ku Tokyo100–6640 Japan Department of Advanced Sciences Hosei University 3–7–2 Kajinocho Koganei–shi Tokyo184–8584 Japan Graduate School of Science and Technology University of Tsukuba 1–1–1 Tennodai Tsukuba–shi Ibaraki305–8573 Japan Institute of Systems and Information Engineering University of Tsukuba 1–1–1 Tennodai Tsukuba–shi Ibaraki305–8573 Japan

This paper is concerned with personalized pricing models aimed at maximizing the expected revenues or profits for a single item. While it is essential for personalized pricing to predict the purchase probabilities for each consumer, these predicted values are inherently subject to unavoidable errors that can negatively impact the realized revenues and profits. To address this issue, we focus on robust optimization techniques that yield reliable solutions to optimization problems under uncertainty. Specifically, we propose a robust optimization model for personalized pricing that accounts for the uncertainty of predicted purchase probabilities. This model can be formulated as a mixed-integer linear optimization problem, which can be solved exactly using mathematical optimization solvers. We also develop a Lagrangian decomposition algorithm combined with line search to efficiently find high-quality solutions for large-scale optimization problems. Experimental results demonstrate the effectiveness of our robust optimization model and highlight the utility of our Lagrangian decomposition algorithm in terms of both computational efficiency and solution quality. Copyright © 2024, The Authors. All rights reserved.

关键词： Profitability

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