Journal of Biopharmaceutical Statistics最新文献

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."

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.

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.

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.

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 ($n$) and significance level ($\alpha$) 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.

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.

In oncology trials, health-related quality of life (HRQoL), specifically patient-reported symptom burden and functional status, can support the interpretation of survival endpoints, such as progression-free survival. However, applying time-to-event endpoints to patient-reported outcomes (PRO) data is challenging. For example, in time-to-deterioration analyses clinical events such as disease progression are common in many settings and are often handled through censoring the patient at the time of occurrence; however, disease progression and HRQoL are often related leading to informative censoring. Special consideration to the definition of events and intercurrent events (ICEs) is necessary. In this work, we demonstrate time-to-deterioration of PRO estimands and sensitivity analyses to answer research questions using composite, hypothetical, and treatment policy strategies applied to a single endpoint of disease-related symptoms. Multiple imputation methods under both the missing-at-random and missing-not-at-random assumptions are used as sensitivity analyses of primary estimands. Hazard ratios ranged from 0.52 to 0.66 over all the estimands and sensitivity analyses modeling a robust treatment effect favoring the treatment in time to disease symptom deterioration or death. Differences in the estimands include how people who experience disease progression or discontinue the randomized treatment due to AEs are accounted for in the analysis. We use the estimand framework to define interpretable and principled approaches for different time-to-deterioration research questions and provide practical recommendations. Reporting the proportions of patient events and patient censoring by reason helps understand the mechanisms that drive the results, allowing for optimal interpretation.