Statistics in Biopharmaceutical Research最新文献

{"title":"Comment on” 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”","authors":"S. Vansteelandt","doi":"10.1080/19466315.2022.2128405","DOIUrl":"https://doi.org/10.1080/19466315.2022.2128405","url":null,"abstract":"I would like to thank the editor, Prof. Hamasaki, for the opportunity to comment on the thought-provoking work by the NISS working group on unplanned clinical trial disruptions (Van Lancker et al. 2022). The working group’s proposals focus on two basic problems relevant to clinical trials affected by the COVID19 pandemic. The first problem is that, due to the pandemic, the patient population may change systematically over the course of the trial. This raises questions over what is the relevant patient population for which the effect is of interest. The second problem, which receives the major focus in the paper, relates to problems of intercurrent events fueled by the pandemic. The solutions proposed by the working group are interesting and useful. In this commentary, I will nonetheless raise two conceptual shortcomings, which I will attempt to resolve by making more explicit use of methods from causal inference (as opposed to missing data analysis). First, the data collected in a randomized clinical trial are so precious that it is generally difficult to justify ignoring the data collected before or after the start of the pandemic. Those data will often still carry useful information about treatment efficacy, and should ideally be used. Second, whenever possible, analyses of randomized clinical trials should protect the null hypothesis of no treatment effect in the sense that rejection rates should be no larger than the nominal (5%) rate, even when the adopted assumptions fail. Intercurrent events 6 and 7 appear such that they will occur with equal rates in both arms of the trial. If this is so, then this suggests that standard analyses that target the treatment policy estimand, thus ignoring intercurrent events, will protect the null hypothesis of no treatment effect; indeed, the treatment policy estimand then reduces to the balanced estimand of Michiels et al. (2021), which expresses what the treatment effect had been had intercurrent events occurred at “equal rates” in both arms. In this light, analyses that invoke Missing At Random (MAR) assumptions must be taken with caution, as they may be biased whenever the MAR assumption fails. More importantly, analyses that explicitly combine biased and unbiased estimators, as in","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"112 - 115"},"PeriodicalIF":1.8,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48715465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"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","authors":"Kelly Van Lancker, S. Tarima, J. Bartlett, M. Bauer, Bharani Bharani-Dharan, F. Bretz, N. Flournoy, Hege Michiels, Camila Olarte Parra, J. L. Rosenberger, S. Cro","doi":"10.1080/19466315.2022.2094459","DOIUrl":"https://doi.org/10.1080/19466315.2022.2094459","url":null,"abstract":"Abstract 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 revisited 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.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"94 - 111"},"PeriodicalIF":1.8,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43711539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Good Data Science Practice: Moving toward a Code of Practice for Drug Development (Rejoinder)","authors":"Mark Baillie, Conor Moloney, Carsten Philipp Mueller, Jonas Dorn, J. Branson, D. Ohlssen","doi":"10.1080/19466315.2022.2128402","DOIUrl":"https://doi.org/10.1080/19466315.2022.2128402","url":null,"abstract":"Fang and He ask why we focus on exploratory (cite “[...] 26 times exploratory [...] only 3 times confirmatory [/.]”) over confirmatory activities and if as a consequence our data science definition is limited in scope. They also ask if the definition of data science should be more specific, with a focus on treatment effectiveness: “exploratory activities are insufficient for the purpose of establishing the existence and estimating the magnitude of treatment effects, which is confirmatory in nature.”","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"89 - 91"},"PeriodicalIF":1.8,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42842820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editor’s Note: Special Section on a Collection of Articles on Opportunities and Challenges in Utilizing Real-World Data for Clinical Trials and Medical Product Development","authors":"T. Hamasaki","doi":"10.1080/19466315.2022.2162291","DOIUrl":"https://doi.org/10.1080/19466315.2022.2162291","url":null,"abstract":"There have been increasing discussions on how real-world data (RWD) and real-world evidence (RWE) can play a role in health care decisions, particularly in medical product regulation, where RWD are the data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources (e.g., observational studies, electronic health records, product, and disease registries, etc.), and RWE is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD (Food and Drug Administration (FDA) 2017). Unitizing external data sources in the design and analysis of clinical trials or medical product development is not a new idea. In assessing clinical trial feasibility of a medical product, external data sources have often been used to find new hypotheses/findings, characterizing relevant patient populations and subpopulations, understanding unmet need, identifying important assumptions about the impact of potential eligibility criteria on trial feasibility. At the protocol development of the clinical trials, they have been used to estimate the expected effect size of the medical products, to calculate the sample size, and to support patient recruitment, and during the trial conduct, they might be used to change or modify the trial protocol or designs, or sometimes to stop the trial. At the end of the development of the medical product, in general, comprehensive integrated analysis of the efficacy and safety has been conducted, including other sources of information relevant to efficacy and safety of the product. Furthermore, in Japan, there is a very unique regulatory decision-making framework for evaluating off-label use of unapproved medical products, so called “Public KnowledgeBased Applications” (“Kochi Shinsei” in Japanese) (Ministry of Health and Welfare (MHLW) 1980). A sponsor is able to submit an application without conducting (additional) clinical trials, if efficacy and safety for a new indication of the medical product are recognized to be well known in the medical and pharmacological field through publications. This framework is a great practice of regulatory decision-making based on RWD/RWE. What is happening right now? What is different from current practice? Due to the latest advanced technologies, it is much easier to gather and store huge amounts of health-related data in “real time.” It is expected that RWD/RWE can be used into","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"1 - 2"},"PeriodicalIF":1.8,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41767006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment on “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”","authors":"M. Akacha, Tianmeng Lyu","doi":"10.1080/19466315.2022.2151507","DOIUrl":"https://doi.org/10.1080/19466315.2022.2151507","url":null,"abstract":"We","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"116 - 118"},"PeriodicalIF":1.8,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43052969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Effectiveness Research using Bayesian Adaptive Designs for Rare Diseases: Response Adaptive Randomization Reusing Participants.","authors":"Fengming Tang, Byron J Gajewski","doi":"10.1080/19466315.2021.1961854","DOIUrl":"10.1080/19466315.2021.1961854","url":null,"abstract":"<p><p>Slow accrual rate is a major challenge in clinical trials for rare diseases and is identified as the most frequent reason for clinical trials to fail. This challenge is amplified in comparative effectiveness research where multiple treatments are compared to identify the best treatment. Novel efficient clinical trial designs are in urgent need in these areas. Our proposed response adaptive randomization (RAR) reusing participants trial design mimics the real-world clinical practice that allows patients to switch treatments when desired outcome is not achieved. The proposed design increases efficiency by two strategies: 1) Allowing participants to switch treatments so that each participant can have more than one observation and hence it is possible to control for participant specific variability to increase statistical power; and 2) Utilizing RAR to allocate more participants to the promising arms such that ethical and efficient studies will be achieved. Extensive simulations were conducted and showed that, compared with trials where each participant receives one treatment, the proposed participants reusing RAR design can achieve comparable power with a smaller sample size and a shorter trial duration, especially when the accrual rate is low. The efficiency gain decreases as the accrual rate increases.</p>","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 1","pages":"154-163"},"PeriodicalIF":1.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9979780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10845588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power and Sample Size Calculations for the Restricted Mean Time Analysis of Prioritized Composite Endpoints.","authors":"Lu Mao","doi":"10.1080/19466315.2022.2110936","DOIUrl":"https://doi.org/10.1080/19466315.2022.2110936","url":null,"abstract":"<p><p>As a new way of reporting treatment effect, the restricted mean time in favor (RMT-IF) of treatment measures the net average time the treated have had a less serious outcome than the untreated over a specified time window. With multiple outcomes of differing severity, this offers a more interpretable and data-efficient alternative to the prototypical restricted mean (event-free) survival time. To facilitate its adoption in actual trials, we develop simple approaches to power and sample size calculations and implement them in user-friendly R programs. In doing so we model the bivariate outcomes of death and a nonfatal event using a Gumbel-Hougaard copula with component-wise proportional hazards structures, under which the RMT-IF estimand is derived in closed form. In a standard set-up for censoring, the variance of the nonparametric effect-size estimator is simplified and computed via a hybrid of numerical and Monte Carlo integrations, allowing us to compute the power and sample size as functions of component-wise hazard ratios. Simulation studies show that these formulas provide accurate approximations in realistic settings. To illustrate our methods, we consider designing a new trial to evaluate treatment effect on the composite outcomes of death and cancer relapse in lymph node-positive breast cancer patients, with baseline parameters calculated from a previous study.</p>","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 3","pages":"540-548"},"PeriodicalIF":1.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/4f/ea/nihms-1846183.PMC10473860.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10143350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods.","authors":"Camila Olarte Parra,&nbsp;Rhian M Daniel,&nbsp;Jonathan W Bartlett","doi":"10.1080/19466315.2022.2081599","DOIUrl":"https://doi.org/10.1080/19466315.2022.2081599","url":null,"abstract":"<p><p>The ICH E9 addendum introduces the term intercurrent event to refer to events that happen after treatment initiation and that can either preclude observation of the outcome of interest or affect its interpretation. It proposes five strategies for handling intercurrent events to form an estimand but does not suggest statistical methods for estimation. In this article we focus on the hypothetical strategy, where the treatment effect is defined under the hypothetical scenario in which the intercurrent event is prevented. For its estimation, we consider causal inference and missing data methods. We establish that certain \"causal inference estimators\" are identical to certain \"missing data estimators.\" These links may help those familiar with one set of methods but not the other. Moreover, using potential outcome notation allows us to state more clearly the assumptions on which missing data methods rely to estimate hypothetical estimands. This helps to indicate whether estimating a hypothetical estimand is reasonable, and what data should be used in the analysis. We show that hypothetical estimands can be estimated by exploiting data after intercurrent event occurrence, which is typically not used. Supplementary materials for this article are available online.</p>","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"15 2","pages":"421-432"},"PeriodicalIF":1.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228513/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9620322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A flexible analytical framework for reference-based imputation, delta adjustment and tipping-point stress-testing","authors":"Alberto García-Hernandez, T. Pérez, M. Pardo, D. Rizopoulos","doi":"10.1080/19466315.2022.2151506","DOIUrl":"https://doi.org/10.1080/19466315.2022.2151506","url":null,"abstract":"Abstract This article addresses the challenge of implementing the treatment policy strategy when subjects are not followed up after treatment discontinuation. This problem can be addressed using reference-based imputation, delta adjustment, and tipping-point analysis. Our new framework tackles this problem analytically. We characterize the process that measures the response regardless of drug discontinuation, Z(t), using its association with two observable processes: time to drug dropout , and the variable representing the response in a hypothetical world without drug discontinuation Y(t). We define the intervention discontinuation effect (IDE) as the unobservable process that quantifies the difference between Y(t) and Z(t) after . We express various well-known imputation rules as forms of the IDE. We model Y using mixed models and with the Royston-Parmar model. We build estimators for the marginal mean of Z given the estimated parameters for Y and T . We demonstrate that this simple estimator building suits all studied rules and provide guidance to extend this methodology. With the proposed framework, we can analytically resolve a broad range of imputation rules and have right-censored treatment discontinuation. This methodology is more efficient and computationally faster than multiple imputation and, unlike Rubin’s variance estimator, presents no standard error over-estimation.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45504490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A closer look at the kernels generated by the decision and regression tree ensembles","authors":"Dai Feng, R. Baumgartner","doi":"10.1080/19466315.2022.2150680","DOIUrl":"https://doi.org/10.1080/19466315.2022.2150680","url":null,"abstract":"Abstract Tree ensembles can be interpreted as implicit kernel generators, where the ensuing proximity matrix represents the data-driven tree ensemble kernel. Focus of our work is the utility of tree based ensembles as kernel generators that (in conjunction with a regularized linear model) enable kernel learning. We elucidate the performance of the tree based random forest (RF) and gradient boosted tree (GBT) kernels in a comprehensive simulation study comprising of continuous and binary targets. We show that for continuous targets (regression), this kernel learning approach is competitive to the respective tree ensemble in higher dimensional scenarios, particularly in cases with larger number of noisy features. For the binary target (classification), the tree ensemble based kernels and their respective ensembles exhibit comparable performance. We provide the results from several real life datasets for regression and classification relevant for biopharmaceutical and biomedical applications, that are in line with the simulations to show how these insights may be leveraged in practice. We discuss general applicability and extensions of the tree ensemble based kernels for survival targets and interpretable landmarking in classification and regression. Finally, we outline future research for kernel learning due to feature space partitionings.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45161164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}