Journal of Biopharmaceutical Statistics最新文献

{"title":"Borrowing using historical-bias power prior with empirical Bayes.","authors":"Hsin-Yu Lin, Elizabeth Slate","doi":"10.1080/10543406.2024.2429461","DOIUrl":"https://doi.org/10.1080/10543406.2024.2429461","url":null,"abstract":"<p><p>Adaptively incorporating historical information into analyses of current data can improve the precision of inference without requiring additional new observation. Unfortunately, not all borrowing methods are suitable when limited historical studies are available. When a single historical study is available, the power priors control the amount of information to borrow via specification of a weight parameter that discounts the contribution of the historical data in a likelihood combined with current data. We develop a new type of conditional power prior called the historical-bias power prior using an empirical Bayes approach. It relaxes the assumption of the traditional power priors to allow for historical bias. Moreover, our new weight function controls the amount of borrowing and only borrows when historical data satisfy the borrowing criteria. This is achieved by embedding the Frequentist test-then-pool approach in the weight function. Hence, the historical-bias power prior builds a bridge between the Frequentist test-then-pool and the Bayesian power prior. In the simulation, we examine the impact of historical bias on the operating characteristics for borrowing approaches, which has not been discussed in previous literature. The results show that the historical-bias power prior yields accurate estimation and robustly powerful tests for the experimental treatment effect with good type I error control, especially when historical bias exists.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-31"},"PeriodicalIF":1.2,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796481","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":"Investigating the impact of data monitoring committee recommendations on the probability of trial success.","authors":"Luca Rondano, Gaëlle Saint-Hilary, Mauro Gasparini, Stefano Vezzoli","doi":"10.1080/10543406.2024.2430308","DOIUrl":"https://doi.org/10.1080/10543406.2024.2430308","url":null,"abstract":"<p><p>Determining the probability of success of a clinical trial using a prior distribution on the treatment effect can significantly enhance decision-making by the sponsor. In a group sequential design, the probability of success calculated at the design stage can be updated to incorporate the information disclosed by the Data Monitoring Committee (DMC), usually consisting in a simple statement that advises to continue or to stop the trial, either for efficacy or futility, following pre-specified rules defined in the protocol. We define the \"probability of success post interim\" as the probability of success conditioned on the assumption that the DMC recommends continuing the trial after an interim analysis. A good assessment of this probability helps mitigate the tendency of the study team to express excessive optimism or unwarranted pessimism regarding the trial's ultimate outcome after the DMC recommendation. We explore the relationship between this \"probability of success post interim\" and the initial probability of success, and we provide an in-depth investigation of how interim boundaries impact these probabilities. This analysis offers valuable insights that can guide the selection of boundaries for both efficacy and futility interim analyses, leading to more informed clinical trial designs.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-17"},"PeriodicalIF":1.2,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796485","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":"DOD-BART: machine learning-based dose optimization design incorporating patient-level prognostic factors via Bayesian additive regression trees.","authors":"Yunqi Zhao, Rachael Liu, Jianchang Lin, Andy Chi, Simon Davies","doi":"10.1080/10543406.2024.2429463","DOIUrl":"https://doi.org/10.1080/10543406.2024.2429463","url":null,"abstract":"<p><p>Dose optimization is a critical stage of drug development in oncology and other disease areas. Early phase clinical trials are inherently heterogeneous due to their exploratory nature. The process of identifying an optimal dose involves careful considerations of the patient population, evaluation of therapeutic potential, and exploration of the dose-response and dose-toxicity relationships to ensure that it is safe and effective for the intended use. However, the complex mechanism of actions and uncertainties during dose optimization often introduce substantial gaps between those early phase trials and phase 3 randomized control trials. These gaps can indeed increase the chances of failure. To address these challenges, we propose a novel seamless phase I/II design, namely DOD-BART design, which utilizes machine learning technique, specifically Bayesian Additive Regression Trees (BART) to fully incorporate patient-level prognostic factors and outcomes. Our design provides a streamlined approach for dose exploration and optimization, automatically updated with emerging data to allocate patients to the most promising dose levels. DOD-BART elucidates disease relationships, analyzes and synthesizes emerging data, augments operational efficiency, and guides dose optimization for suitable population. Simulation studies demonstrate the robust performances of the DOD-BART design across a variety of realistic settings, with high probabilities of correctly identifying the optimal dose, allocating patients more to tolerable and efficacious dose levels, making less biased estimates, and efficiently utilizing patients' data.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752386","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":"Revolutionizing cardiovascular disease classification through machine learning and statistical methods.","authors":"Tapan Kumar Behera, Siddhartha Sathia, Sibarama Panigrahi, Pradeep Kumar Naik","doi":"10.1080/10543406.2024.2429524","DOIUrl":"https://doi.org/10.1080/10543406.2024.2429524","url":null,"abstract":"<p><strong>Background: </strong>Cardiovascular diseases (CVDs) include abnormal conditions of the heart, diseased blood vessels, structural problems of the heart, and blood clots. Traditionally, CVD has been diagnosed by clinical experts, physicians, and medical specialists, which is expensive, time-consuming, and requires expert intervention. On the other hand, cost-effective digital diagnosis of CVD is now possible because of the emergence of machine learning (ML) and statistical techniques.</p><p><strong>Method: </strong>In this research, extensive studies were carried out to classify CVD via 19 promising ML models. To evaluate the performance and rank the ML models for CVD classification, two benchmark CVD datasets are considered from well-known sources, such as Kaggle and the UCI repository. The results are analysed considering individual datasets and their combination to assess the efficiency and reliability of ML models on the basis of various performance measures, such as precision, kappa, accuracy, recall, and the F1 score. Since some of the ML models are stochastic, we repeated the simulation 50 times for each dataset using each model and applied nonparametric statistical tests to draw decisive conclusions.</p><p><strong>Results: </strong>The nonparametric Friedman - Nemenyi hypothesis test suggests that the Extra Tree Classifier provides statistically superior accuracy and precision compared with all other models. However, the Extreme Gradient Boost (XGBoost) classifier provides statistically superior recall, kappa, and F1 scores compared with those of all the other models. Additionally, the XGBRF classifier achieves a statistically second-best rank in terms of the recall measures.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-23"},"PeriodicalIF":1.2,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142711177","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":"The 2009 FDA PRO guidance, Potential Type I error, Descriptive Statistics and Pragmatic estimation of the number of interviews for item elicitation.","authors":"Josh Fleckner, Chris Barker","doi":"10.1080/10543406.2024.2420642","DOIUrl":"10.1080/10543406.2024.2420642","url":null,"abstract":"<p><p>A statistical methodology named \"capture recapture\", a Kaplan-Meier Summary Statistic, and an urn model framework are presented to describe the elicitation, then estimate both the number of interviews and the total number of items (\"codes\") that will be elicited during patient interviews, and present a summary graphical statistic that \"saturation\" has occurred. This methodology is developed to address a gap in the FDA 2009 PRO and 2012 PFDD guidance for determining the number of interviews (sample size). This estimate of the number of interviews (sample size) uses a two-step procedure. The estimate of the total number of items is then used to estimate the number of interviews to elicit all items. A framework called an urn model is a framework for describing the elicitation and demonstrate the algorithm for declaring saturation \"first interview with zero new codes\". A caveat emptor is that due to independence assumptions, the urn model is not used as a method for estimating probabilities. The URN model provides a framework to demonstrate that an algorithm such as \"first interview with zero new codes\" may establish that all codes have been elicited. The limitations of the Urn model, capture recapture, and Kaplan-Meier are summarized. The statistical methods and the estimates supplement but do not replace expert judgement and declaration of \"saturation.\" A graphical summary statistic is presented to summarize \"saturation,\" after expert declaration for two algorithms. An example of a capture-recapture estimate, using simulated data is provided. The example suggests that the estimate of total number of codes may be accurate when prepared as early as the second interview. A second simulation is presented with an URN model, under a strong assumption of independence that an algorithm such as 'first interview with zero new codes\" may fail to identify all codes. Potential errors in declaration of saturation are presented. Recommendations are presented for additional research and the use of the algorithm \"first interview with zero new codes.\"</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142711180","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":"BOP2-TE: Bayesian optimal phase 2 design for jointly monitoring efficacy and toxicity with application to dose optimization.","authors":"Kai Chen, Heng Zhou, J Jack Lee, Ying Yuan","doi":"10.1080/10543406.2024.2429481","DOIUrl":"10.1080/10543406.2024.2429481","url":null,"abstract":"<p><p>We propose a Bayesian optimal phase 2 design for jointly monitoring efficacy and toxicity, referred to as BOP2-TE, to improve the operating characteristics of the BOP2 design proposed by Zhou. BOP2-TE utilizes a Dirichlet-multinomial model to jointly model the distribution of toxicity and efficacy endpoints, making go/no-go decisions based on the posterior probability of toxicity and futility. In comparison to the original BOP2 and other existing designs, BOP2-TE offers the advantage of providing rigorous type I error control in cases where the treatment is toxic and futile, effective but toxic, or safe but futile, while optimizing power when the treatment is effective and safe. As a result, BOP2-TE enhances trial safety and efficacy. We also explore the incorporation of BOP2-TE into multiple-dose randomized trials for dose optimization, and consider a seamless design that integrates phase I dose finding with phase II randomized dose optimization. BOP2-TE is user-friendly, as its decision boundary can be determined prior to the trial's onset. Simulations demonstrate that BOP2-TE possesses desirable operating characteristics. We have developed a user-friendly web application as part of the BOP2 app, which is freely available at https://www.trialdesign.org.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142711249","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":"Latent class analysis of post-acute sequelae of SARS-CoV-2 infection.","authors":"Xiaowu Sun, Jonathan P DeShazo, Laura Anatale-Tardiff, Manuela Di Fusco, Kristen E Allen, Thomas M Porter, Henriette Coetzer, Santiago M C Lopez, Laura Puzniak, Joseph C Cappelleri","doi":"10.1080/10543406.2024.2424844","DOIUrl":"https://doi.org/10.1080/10543406.2024.2424844","url":null,"abstract":"<p><p>Symptoms post-SARS-CoV-2 infection may persist for months and cause significant impairment and impact to quality of life. Acute symptoms of SARS-CoV-2 infection are well studied, yet data on clusters of symptoms over time, or post-acute sequelae of SARS-CoV-2 infection (PASC), are limited. We aim to characterize PASC phenotypes by identifying symptom clusters over a six-month period following infection in individuals vaccinated (boosted and not) and those unvaccinated. Subjects with ≥1 self-reported symptom and positive RT-PCR for SARS-CoV-2 at CVS Health US test sites were recruited between January and April 2022. Patient-reported outcomes symptoms, health-related quality of life (HRQoL), work productivity and activity impairment (WPAI) were captured at 1 month, 3 months, and 6 months post-acute infection. Phenotypes of PASC were determined based on subject matter knowledge and balanced consideration of statistical criteria (lower AIC, lower BIC, and adequate entropy) and interpretability. Generalized estimation equation approach was used to investigate relationship between QoL, WPAI and number of symptoms and identified phenotypes, and relationship between phenotypes and vaccination status as well. LCA identified three phenotypes that are primarily differentiated by number of symptoms. These three phenotypes remained consistent across time periods. Subjects with more symptoms were associated with lower HRQoL, and worse WPAI scores. Vaccinated individuals were more likely to be in the low symptom burden latent classes at all time points compared to unvaccinated individuals.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645140","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":"Statistical operating characteristics of current early phase dose finding designs with toxicity and efficacy in oncology.","authors":"Hao Sun, Hsin-Yu Lin, Jieqi Tu, Revathi Ananthakrishnan, Eunhee Kim","doi":"10.1080/10543406.2024.2424845","DOIUrl":"https://doi.org/10.1080/10543406.2024.2424845","url":null,"abstract":"<p><p>Traditional phase I dose finding cancer clinical trial designs aim to determine the maximum tolerated dose (MTD) of the investigational cytotoxic agent based on a single toxicity outcome, assuming a monotone dose-response relationship. However, this assumption might not always hold for newly emerging therapies such as immuno-oncology therapies and molecularly targeted therapies, making conventional dose finding trial designs based on toxicity no longer appropriate. To tackle this issue, numerous early-phase dose finding clinical trial designs have been developed to identify the optimal biological dose (OBD), which takes both toxicity and efficacy outcomes into account. In this article, we review the current model-assisted dose finding designs, BOIN-ET, BOIN12, UBI, TEPI-2, PRINTE, STEIN, and uTPI to identify the OBD and compare their operating characteristics. Extensive simulation studies and a case study using a CAR T-cell therapy phase I trial have been conducted to compare the performance of the aforementioned designs under different possible dose-response relationship scenarios. The simulation results demonstrate that the performance of different designs varies depending on the particular dose-response relationship and the specific metric considered. Based on our simulation results and practical considerations, STEIN, PRINTE, and BOIN12 outperform the other designs from different perspectives.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-21"},"PeriodicalIF":1.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645142","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":"Machine learning approach for detection of MACE events within clinical trial data.","authors":"John A Spanias, Robbie Buderi, Pierre-Louis Bourlon, Christopher Tso, Caleb Strait, David Saunders, Kayleen Ports, Weixi Chen, Rahul Jain, Bhargav Koduru, Danielle Gerome, Eric Yang, Silvy Saltzmann, Aniketh Talwai, Tanmay Jain, Jacob Aptekar","doi":"10.1080/10543406.2024.2420640","DOIUrl":"https://doi.org/10.1080/10543406.2024.2420640","url":null,"abstract":"<p><p>Randomized controlled trials (RCTs) are the gold standard for clinical research but may not accurately reflect the impact of medicines in real-world settings. Supplementing RCTs with insights from real-world data (RWD) can address known limitations by including more diverse patient populations, additional types of sites-of-care, and practices more representative of the care most people receive. One current challenge in using RWD is the lack of an algorithmic approach to identifying outcomes. To address this, machine learning models for identifying a frequently used outcome, Major Adverse Cardiovascular Events (MACE), were developed in Clinical Trial Data (CTD). Anonymized CTD sourced from the Medidata Enterprise Data Store were used to develop model features on the condition that they would be useful for labelling MACE events and that they could also be found in RWD. These features were used to train three random forest models to identify each component of 3-point MACE in a patient's clinical trial journey. Performance metrics for the models are presented (recall = 0.72 [0.07], precision = 0.68 [0.12] - mean, [SD]) along with the top contributing features. We show that the models can be tuned specifically to replicate the adjudication panels' results and present a cost-benefit analysis for deploying such models in clinical trial settings. We demonstrate the viability of using advanced algorithms for identifying clinical outcomes in prospective clinical trials. Deployment of such models could reduce the resources required to conduct RCTs. Extending such models to RWD would facilitate approval of pragmatic clinical trials for regulatory submissions.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645141","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":"Defective regression models for cure rate data with competing risks.","authors":"K Silpa, E P Sreedevi, P G Sankaran","doi":"10.1080/10543406.2024.2424838","DOIUrl":"10.1080/10543406.2024.2424838","url":null,"abstract":"<p><p>In this paper, we propose a novel method for the analysis of cure rate data with competing risks using defective distributions. We develop two defective regression models for the analysis of competing risk data subjected to random right censoring. The proposed models enable us to estimate the cure fraction directly from the model. Simultaneously, we also estimate the regression parameters corresponding to each cause of failure using the method of maximum likelihood. We conduct a simulation study to evaluate the finite sample performance of the proposed estimators. The practical usefulness of the procedures is illustrated using two real-life data sets.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-17"},"PeriodicalIF":1.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632960","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}