Journal of Biopharmaceutical Statistics最新文献

{"title":"Providing meaningful interpretation of performance outcome measures by co-calibration with patient-reported outcomes through the Rasch model: illustration with multiple sclerosis measures.","authors":"Antoine Regnault, Juliette Meunier, Anna Ciesluk, Wenting Cheng, Bing Zhu","doi":"10.1080/10543406.2023.2280557","DOIUrl":"10.1080/10543406.2023.2280557","url":null,"abstract":"<p><p>Performance outcome (PerfO) measures are based on tasks performed by patients in a controlled environment, making their meaningful interpretation challenging to establish. Co-calibrating PerfO and patient-reported outcome (PRO) measures of the same target concept allow for interpretation of the PerfO with the item content of the PRO. The Rasch model applied to the discretized PerfO measure together with the PRO items allows expressing parameters related to the PerfO measure in the PRO metric for it to be linked to the PRO responses. We applied this approach to two PerfO measures used in multiple sclerosis (MS) for walking and manual ability: the Timed 25-Foot Walk (T25FW) and the 9-Hole Peg Test (9HPT). To determine meaningful interpretation of these two PerfO measures, they were co-calibrated with two PRO measures of closely related concepts, the MS walking scale - 12 items (MSWS-12) and the ABILHAND, using the data of 2,043 subjects from five global clinical trials in MS. The probabilistic relationships between the PerfO measures and the PRO metrics were used to express the response pattern to the PRO items as a function of the unit of the PerfOs. This example illustrates the promises of the co-calibration approach for the interpretation of PerfO measures but also highlights the challenges associated with it, mostly related to the quality of the PRO metric in terms of coverage of the targeted concept. Co-calibration with PRO measures could also be an adequate solution for interpretation of digital sensor measures whose meaningfulness is also often questioned.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"851-871"},"PeriodicalIF":1.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138441687","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":"872-887"},"PeriodicalIF":1.2,"publicationDate":"2025-08-01","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":"Introduction to the special issue <i>Advances in statistical methods for the assessment of patient-centered outcomes</i>.","authors":"Jessica Roydhouse, Nunzio Camerlingo, Joseph C Cappelleri","doi":"10.1080/10543406.2025.2472801","DOIUrl":"10.1080/10543406.2025.2472801","url":null,"abstract":"","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"777-781"},"PeriodicalIF":1.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081629","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":"Weighted sum and order statistics methods for dynamic information borrowing in basket trials.","authors":"Cheng Huang, Chenghao Chu, Yimeng Lu, Bingming Yi, Ming-Hui Chen","doi":"10.1080/10543406.2025.2537088","DOIUrl":"https://doi.org/10.1080/10543406.2025.2537088","url":null,"abstract":"<p><p>In basket trials, the same investigational therapy is studied on multiple sub-populations simultaneously under a single protocol. The goal of basket trials is to identify the sub-populations in which the therapy is effective. Basket trials have become a popular and generally accepted study design in disease areas including but not limited to oncology and rare diseases, for their advantages in operation and ethical considerations. Extensive research work on information borrowing has been conducted to explore the statistical efficiency in basket trials. In this paper, two novel frequentist methods for basket trials are proposed. The first method borrows information to minimize the mean squared errors in the treatment effect estimation. The second method uses information across all baskets to optimize the multiple testing task in detecting the treatment effects in each basket. Extensive simulation studies show that the proposed methods substantially improved statistical efficiency in basket trials while limiting family-wise error rate inflation. Both methods can be implemented with common statistical models with or without adjustment for covariates.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-20"},"PeriodicalIF":1.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762352","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":"Analysis of Clinically Meaningful Change on Patient-Reported Outcomes: Renewed Insights About Covariate Adjustment.","authors":"Joseph C Cappelleri, Paul R Cislo","doi":"10.1080/10543406.2023.2237115","DOIUrl":"10.1080/10543406.2023.2237115","url":null,"abstract":"<p><p>Determining clinically meaningful change (CMC) in a patient-reported (PRO) measure is central to its existence in gauging how patients feel and function, especially for evaluating a treatment effect. Anchor-based approaches are recommended to estimate a CMC threshold on a PRO measure. Determination of CMC involves linking changes or differences in the target PRO measure to that in an external (anchor) measure that is easier to interpret than and appreciably associated with the PRO measure. One type of anchor-based approach for CMC is the \"mean change method\" where the mean change in score of the target PRO measure within a particular anchor transition level (e.g. one-category improvement) is subtracted from the mean change in score of within an adjacent anchor category (e.g. no change category). In the literature, the mean change method has been applied with and without an adjustment for the baseline scores for the PRO of interest. This article provides the analytic rationale and conceptual justification for keeping the analysis unadjusted and not controlling for baseline PRO scores. Two illustrative examples are highlighted. The current research is essentially a variation of Lord's paradox (where whether to adjust for a baseline variable depends on the research question) placed in a new context. Once the adjustment is made, the resulting CMC estimate reflects an artificial case where the anchor transition levels are forced to have the same average baseline PRO score. The unadjusted estimate acknowledges that the anchor transition levels are naturally occurring (not randomized) groups and thus maintains external validity.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"812-825"},"PeriodicalIF":1.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10269465","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":"Correction.","authors":"","doi":"10.1080/10543406.2025.2529762","DOIUrl":"10.1080/10543406.2025.2529762","url":null,"abstract":"","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"i"},"PeriodicalIF":1.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144592979","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":"Recurrent neural networks and attention scores for personalized prediction and interpretation of patient-reported outcomes.","authors":"Jinxiang Hu, Mohsen Nayebi Kerdabadi, Xiaohang Mei, Joseph Cappelleri, Richard Barohn, Zijun Yao","doi":"10.1080/10543406.2025.2469884","DOIUrl":"10.1080/10543406.2025.2469884","url":null,"abstract":"<p><p>We proposed an Interpretable Personalized Artificial Intelligence (AI) model for PRO measures via Recurrent Neural Networks (RNN) and attention scores, with data from an open label randomized clinical trial of pain in 402 participants with cryptogenic sensory polyneuropathy at 40 neurology care clinics. All patients were assigned to four treatment groups: nortriptyline, duloxetine, pregabalin, and mexiletine. Each patient had 4 PRO measures (quality of life SF-12; PROMIS: pain interference, fatigue, sleep disturbance) at 4 time points (baseline, week 4, week 8, and week 12). We included 201 patients without missing values. Participants were 30 years or older and 106 (52.7%) were men, majority were White (164, 81.6%). We fitted an RNN model with attention scores to the data to predict the PROMIS pain interference score. We evaluated the model performance with Mean Absolute Error (MAE) and R-square statistics. We also used attention scores to explain the global variable importance at model level, and at individual level for each patient. The best predictor of pain score was the SF-12 item (physical and emotional health interfere with social activities) and fatigue item (push yourself to get things done), the biggest drug-level contributor was mexiletine, the biggest time-level contributor was week 12. Overall, the model fit well (MAE = 3.7, R2 = 63%). Attention-RNN is a feasible and reliable model for predicting PRO outcomes utilizing longitudinal data, such as pain, and can provide personalized individual level interpretation.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"933-943"},"PeriodicalIF":1.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626027","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":"Use of Bayesian decision analysis to maximize value in patient-centered randomized clinical trials in Parkinson's disease.","authors":"Shomesh E Chaudhuri, Zied Ben Chaouch, Brett Hauber, Brennan Mange, Mo Zhou, Stephanie Christopher, Dawn Bardot, Margaret Sheehan, Anne Donnelly, Lauren McLaughlin, Brittany Caldwell, Heather L Benz, Martin Ho, Anindita Saha, Katrina Gwinn, Murray Sheldon, Andrew W Lo","doi":"10.1080/10543406.2023.2170400","DOIUrl":"10.1080/10543406.2023.2170400","url":null,"abstract":"<p><p>A fixed one-sided significance level of 5% is commonly used to interpret the statistical significance of randomized clinical trial (RCT) outcomes. While it is necessary to reduce the false positive rate, the threshold used could be chosen quantitatively and transparently to specifically reflect patient preferences regarding benefit-risk tradeoffs as well as other considerations. How can patient preferences be explicitly incorporated into RCTs in Parkinson's disease (PD), and what is the impact on statistical thresholds for device approval? In this analysis, we apply Bayesian decision analysis (BDA) to PD patient preference scores elicited from survey data. BDA allows us to choose a sample size (<math><mi>n</mi></math>) and significance level (<math><mi>α</mi></math>) that maximizes the overall expected value to patients of a balanced two-arm fixed-sample RCT, where the expected value is computed under both null and alternative hypotheses. For PD patients who had previously received deep brain stimulation (DBS) treatment, the BDA-optimal significance levels fell between 4.0% and 10.0%, similar to or greater than the traditional value of 5%. Conversely, for patients who had never received DBS, the optimal significance level ranged from 0.2% to 4.4%. In both of these populations, the optimal significance level increased with the severity of the patients' cognitive and motor function symptoms. By explicitly incorporating patient preferences into clinical trial designs and the regulatory decision-making process, BDA provides a quantitative and transparent approach to combine clinical and statistical significance. For PD patients who have never received DBS treatment, a 5% significance threshold may not be conservative enough to reflect their risk-aversion level. However, this study shows that patients who previously received DBS treatment present a higher tolerance to accept therapeutic risks in exchange for improved efficacy which is reflected in a higher statistical threshold.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"981-1000"},"PeriodicalIF":1.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10873547","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":"CORRECTION.","authors":"","doi":"10.1080/10543406.2023.2258646","DOIUrl":"10.1080/10543406.2023.2258646","url":null,"abstract":"","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1001-1002"},"PeriodicalIF":1.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10269100","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":"Reflections on estimands for patient-reported outcomes in cancer clinical trials.","authors":"Rachael Lawrance, Konstantina Skaltsa, Antoine Regnault, Lysbeth Floden","doi":"10.1080/10543406.2023.2280628","DOIUrl":"10.1080/10543406.2023.2280628","url":null,"abstract":"<p><p>It is common and important to include the patient's perspective of the impact of treatment on health-related quality of life (HRQoL) outcomes. In this commentary, we focus on applying the new addendum to ICH E9 guideline E9 (R1) relating to the estimand framework to Patient Reported Outcomes (PROs) collected in cancer clinical trials, from a statistician's viewpoint. Currently, common practice for statistical analysis of PRO endpoints of published cancer clinical trials demonstrates ambiguity, leaving critical questions unspecified, hindering conclusions about the effect of treatment on PRO endpoints as well as comparability between clinical trials. To avoid this scenario, we advocate the systematic use of the estimand framework which requires the prospective definition of clear PRO research questions. Among the five attributes of the estimands framework, the definition of the endpoint (what is the right PRO measure and timeframe to target and why?), the intercurrent event identification and management (what happens with PRO data post-disease progression, what is the impact of death?) and the population-level summary (what is an acceptable statistical summary for PRO data?) require the most attention for PRO estimands. We identify good practice and highlight discussion points including the challenges of statistical analysis in the presence of missing and/or unobservable data and in relation to death. Through this discussion we highlight that there is no \"statistical magic\", but that the estimand framework will help you find out what you really want to know when quantifying the benefit of treatments from the patients' perspective.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"782-792"},"PeriodicalIF":1.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048839","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}