Determining the extent and frequency of on-site monitoring: a bayesian risk-based approach.

BACKGROUND: On-site monitoring is a crucial component of quality control in clinical trials. However, many cast doubt on its cost-effectiveness due to various issues, such as a lack of monitoring focus that could assist in prioritizing limited resources during a site visit. Consequently, an increasing number of trial sponsors are implementing a hybrid monitoring strategy that combines on-site monitoring with centralised monitoring. One of the primary objectives of centralised monitoring, as stated in the clinical trial guidelines, is to guide and adjust the extent and frequency of on-site monitoring. Quality tolerance limits (QTLs) introduced in ICH E6(R2) and thresholds proposed by TransCelerate Biopharma are two existing approaches for achieving this objective at the trial- and site-levels, respectively. The funnel plot, as another threshold-based site-level method, overcomes the limitation of TransCelerate's method by adjusting thresholds flexibly based on site sizes. Nonetheless, both methods do not transparently explain the reason for choosing the thresholds that they used or whether their choices are optimal in any certain sense. Additionally, related Bayesian monitoring methods are also lacking. METHODS: We propose a simple, transparent, and user-friendly Bayesian-based risk boundary for determining the extent and frequency of on-site monitoring both at the trial- and site-levels. We developed a four-step approach, including: 1) establishing risk levels for key risk indicators (KRIs) along with their corresponding monitoring actions and estimates; 2) calculating the optimal risk boundaries; 3) comparing the outcomes of KRIs against the optimal risk boundaries; and 4) providing recommendations based on the comparison results. Our method can be used to identify the optimal risk boundaries within an established risk level range and is applicable to continuous, discrete, and time-to-event endpoints. RESULTS: We evaluate the performance of the proposed risk boundaries via simulations that mimic various realistic clinical trial scenarios. The performance of the proposed risk boundaries is compared against the funnel plot using real clinical trial data. The results demonstrate the applicability and flexibility of the proposed method for clinical trial monitoring. Moreover, we identify key factors that affect the optimality and performance of the proposed risk boundaries, respectively. CONCLUSION: Given the aforementioned advantages of the proposed risk boundaries, we expect that they will benefit the clinical trial community at large, in particular in the realm of risk-based monitoring.

Lipoprotein(a): Are we ready for large-scale clinical trials?

Cardiovascular diseases (CVD) are currently the most important disease threatening human health, which may be due to the high incidence of risk factors including hyperlipidemia. With the deepening of research on lipoprotein, lipoprotein (a) [Lp(a)] has been shown to be an independent risk factor for atherosclerotic cardiovascular diseases and calcified aortic valve stenosis and is now an unaddressed "residual risk" in current CVD management. Accurate measurement of Lp(a) concentration is the basis for diagnosis and treatment of high Lp(a). This review summarized the Lp(a) structure, discussed the current problems in clinical measurement of plasma Lp(a) concentration and the effects of existing lipid-lowering therapies on Lp(a).

Six-minute walk test as clinical end point in cardiomyopathy clinical trials, including ATTR-CM: a systematic literature review.

Aim: The six-minute walk test (6MWT) is a common measure of functional capacity in patients with heart failure (HF). Primary clinical study end points in cardiomyopathy (CM) trials, including transthyretin-mediated amyloidosis with CM (ATTR-CM), are often limited to hospitalization and mortality. Objective: To investigate the relationship between the 6MWT and hospitalization or mortality in CM, including ATTR-CM. Method: A PRISMA-guided systematic literature review was conducted using search terms for CM, 6MWT, hospitalization and mortality. Results: Forty-one studies were identified that reported 6MWT data and hospitalization or mortality data for patients with CM. The data suggest that a greater 6MWT distance is associated with a reduced risk of hospitalization or mortality in CM. Conclusion: The 6MWT is an accepted alternative end point in CM trials, including ATTR-CM.

Evaluating the robustness of an AI pathfinder application on eligibility criteria in multiple myeloma trials using real-world data and historical trials.

Background: Eligibility criteria are pivotal in achieving clinical trial success, enabling targeted patient enrollment while ensuring the trial safety. However, overly restrictive criteria hinder enrollment and study result generalizability. Broadening eligibility criteria enhances the trial inclusivity, diversity and enrollment pace. Liu et al. proposed an AI pathfinder method leveraging real-world data to broaden criteria without compromising efficacy and safety outcomes, demonstrating promise in non-small cell lung cancer trials. Aim: To assess the robustness of the methodology, considering diverse qualities of real-world data and to promote its application. Materials/Methods: We revised the AI pathfinder method, applied it to relapsed and refractory multiple myeloma trials and compared it using two real-world data sources. We modified the assessment and considered a bootstrap confidence interval of the AI pathfinder to enhance the decision robustness. Results & conclusion: Our findings confirmed the AI pathfinder's potential in identifying certain eligibility criteria, in other words, prior complications and laboratory tests for relaxation or removal. However, a robust quantitative assessment, accounting for trial variability and real-world data quality, is crucial for confident decision-making and prioritizing safety alongside efficacy.

Doses Evaluated in Clinical Pharmacology Studies Investigating the Effect of Intrinsic and Extrinsic Factors on PK and Safety: Case Examples from Approved Drug Development Programs.

Dose selection for investigations of intrinsic and extrinsic factors of pharmacokinetic variability as well as safety is a challenging question in the early clinical stage of drug development. The dose of an investigational product is chosen considering the compound information available to date, feasibility of the assessments, regulatory requirements, and the intent to maximize information for later regulatory submission. This review selected 37 programs as case examples of recently approved drugs to explore the doses selected with focus on studies of drug interaction, renal and hepatic impairment, food effect and concentration-QTc assessment.The review found that regulatory agencies may consider alternative approaches if justified and safe as illustrated in these examples. It is thus recommendable to use the first in human trial as an opportunity to assess QT-prolongation and drug interactions using probes or endogenous markers while maximizing the DDI potential, increasing sensitivity and ensuring safety. Early understanding of dose proportionality assists dose finding and simple and fast to conduct DDI study designs are advantageous. Single dose impairment studies despite non-proportional/time-dependent PK are often acceptability.Overall, the early understanding of the drug's safety profile is essential to ensure the safety of doses selected while preventing clinical trials with unnecessary exposure when using high doses or multiple doses. The information collected in this retrospective survey is a good reminder to tailor the early clinical program to the profile and needs of the molecule and consider regulatory opportunities to streamline the development path.

Trial participants' self-reported understanding of randomisation phrases in participation information leaflets can be high, but acceptability of some descriptions is low, especially those linked to gambling and luck.

BACKGROUND: Evidence indicates that trial participants often struggle to understand participant information leaflets (PILs) for clinical trials, including the concept of randomisation. We analysed the language used to describe randomisation in PILs and determine the most understandable and acceptable description through public and participant feedback. METHODS: We collected 280 PILs/informed consent forms and one video animation from clinical research facilities/clinical trial units in Ireland and the UK. We extracted text on how randomisation was described, plus trial characteristics. We conducted content analysis to group the randomisation phrases inductively. We then excluded phrases that appeared more than once or were very similar to others. The final list of randomisation phrases was then presented to an online panel of participants and the public. Panel members were asked to rate each phrase on a 5-point Likert scale in terms of their understanding of the phrase, confidence in their understanding and acceptability of the phrase. RESULTS: Two hundred and eighty PILs and the transcribed text from one video animation represented 229 ongoing or concluded trials. The pragmatic content analysis generated five inductive categories: (1) explanation of why randomisation is required in trials; (2) synonyms for randomisation; (3) comparative randomisation phrases; (4) elaborative phrases for randomisation (5) and phrases that describe the process of randomisation. We had 48 unique phrases, which were shared with 73 participants and members of the public. Phrases that were well understood were not necessarily acceptable. Participants understood, but disliked, comparative phrases that referenced gambling, e.g. toss of a coin, like a lottery, roll of a die. They also disliked phrases that attributed decision-making to computers or automated systems. Participants liked plain language descriptions of what randomisation is and those that did not use comparative phrases. CONCLUSIONS: Potential trial participants are clear on their likes and dislikes when it comes to describing randomisation in PILs. We make five recommendations for practice.

Alternatives to the P value: connotations of significance.

The statistical significance of a clinical trial analysis result is determined by a mathematical calculation and probability based on null hypothesis significance testing. However, statistical significance does not always align with meaningful clinical effects; thus, assigning clinical relevance to statistical significance is unreasonable. A statistical result incorporating a clinically meaningful difference is a better approach to present statistical significance. Thus, the minimal clinically important difference (MCID), which requires integrating minimum clinically relevant changes from the early stages of research design, has been introduced. As a follow-up to the previous statistical round article on P values, confidence intervals, and effect sizes, in this article, we present hands-on examples of MCID and various effect sizes and discuss the terms statistical significance and clinical relevance, including cautions regarding their use.

Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review.

Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare companies, regulators, and stakeholders to assess the safety and efficacy of new treatments, interventions, and medical procedures. The Bayesian framework offers a unique advantage over the classical framework, especially when incorporating prior information into a new trial with quality external data, such as historical data or another source of co-data. In recent years, there has been a significant increase in regulatory submissions using Bayesian statistics due to its flexibility and ability to provide valuable insights for decision-making, addressing the modern complexity of clinical trials where frequentist trials are inadequate. For regulatory submissions, companies often need to consider the frequentist operating characteristics of the Bayesian analysis strategy, regardless of the design complexity. In particular, the focus is on the frequentist type I error rate and power for all realistic alternatives. This tutorial review aims to provide a comprehensive overview of the use of Bayesian statistics in sample size determination, control of type I error rate, multiplicity adjustments, external data borrowing, etc., in the regulatory environment of clinical trials. Fundamental concepts of Bayesian sample size determination and illustrative examples are provided to serve as a valuable resource for researchers, clinicians, and statisticians seeking to develop more complex and innovative designs.

Factors Influencing Willingness to Participate in Clinical Studies in Pediatric Anesthesia (FILIPPA): A vignette-based, structured interview study.

BACKGROUND: Informed consent is a relevant backdrop for conducting clinical trials, particularly those involving children. While several factors are known to influence the willingness to consent to pediatric anesthesia studies, the influence of study design on consenting behavior is unknown. AIMS: To quantify the impact of study complexity on willingness to consent to pediatric anesthesia studies. METHODS: We conducted a vignette-based interview study by presenting three hypothetical studies to 106 parents or legal guardians whose children were scheduled to undergo anesthesia. These studies differed in level of complexity and included an example of a prospective observational study, a randomized controlled trial, and a phase-II-pharmacological study. Primary outcome was the willingness to consent, using a 5-point Likert scale ranging from "absolutely consent" to "absolutely decline". Secondary outcomes were the effects of child-related (such as sex, age, previous anesthesia, research exposure) and proxy-related factors. RESULTS: Response probabilities for "absolute consent" were 90.9% [95% CI 85.3-96.5] for the observational study, 48.6% [95% CI 38.3-58.9] for the randomized controlled trial, and 32.7% [95% CI 23.9-41.6] for the phase-II-pharmacological study. Response probabilities for "absolutely decline" were 1.6% [95% CI 0.3-2.8], 14.4% [95% CI 8.3-20.5], and 24.7% [95% CI 16.6-32.7], respectively. Significant effects were found for previous research exposure (OR = 0.486 [95% CI 0.256-0.923], p = .027), older age (OR = 0.963 [95% CI 0.927-0.999], p = .045) and the gender of the parent or legal guardian, as mothers were less willing to consent (OR = 0.234 [95% CI 0.107-0.512], p < .001). CONCLUSIONS: Willingness to consent decreased with increasing level of study complexity. When conducting more complex studies, greater efforts need to be made to increase the enrollment of pediatric patients.

Comparing prospectively assigned trial and real-world lung cancer patients.

Aim: To evaluate the comparability of a probable clinical trial (CT) cohort derived from electronic medical records (EMR) data with a real-world cohort treated with the same therapy and identified using the same inclusion and exclusion criteria to emulate an external control. Methods: We utilized de-identified patient-level structured data sourced from EMRs. We then compared patterns of overall survival (OS) between probable CT patients with those drawn from non-contemporaneous real-world data (RWD) using a two-sided log-rank test, hazard ratios (HRs) using a Cox proportional-hazards model and Kaplan-Meier (KM) survival curves. Each regression estimate was calculated with a corresponding 95% confidence interval. We additionally conducted multiple matching methods to assess their relative performance. Results: Median (standard deviation) OS was 10.2 (0.7) months for the RWD arm and 11.3 (1.3) for the probable CT arm with a Log rank p-value equal to 0.4771. OS in both cohorts is longer than the reported CT median OS of 9.2 (0.6). The HRs generated under all five assessed matching methods (including without adjustment) were not statistically significant at the 95% confidence level. Conclusion: Our results suggest, with caveats noted, that survival patterns between real-world and CT cohorts in this NSCLC setting are not statistically significantly different.

Novel non-linear models for clinical trial analysis with longitudinal data: A tutorial using SAS for both frequentist and Bayesian methods.

Longitudinal data from clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) when the time variable is categorical or linear mixed-effects models (ie, random effects model) when the time variable is continuous. In these models, statistical inference is typically based on the absolute difference in the adjusted mean change (for categorical time) or the rate of change (for continuous time). Previously, we proposed a novel approach: modeling the percentage reduction in disease progression associated with the treatment relative to the placebo decline using proportional models. This concept of proportionality provides an innovative and flexible method for simultaneously modeling different cohorts, multivariate endpoints, and jointly modeling continuous and survival endpoints. Through simulated data, we demonstrate the implementation of these models using SAS procedures in both frequentist and Bayesian approaches. Additionally, we introduce a novel method for implementing MMRM models (ie, analysis of response profile) using the nlmixed procedure.

Application of the Estimand Framework to Anesthesia Trials.

SUMMARY: Events occurring after randomization, such as use of rescue medication, treatment discontinuation, or death, are common in randomized trials. These events can change either the existence or interpretation of the outcome of interest. However, appropriate handling of these intercurrent events is often unclear. The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) E9(R1) addendum introduced the estimand framework, which aligns trial objectives with the design, conduct, statistical analysis, and interpretation of results. This article describes how the estimand framework can be used in anesthesia trials to precisely define the treatment effect to be estimated, key attributes of an estimand, common intercurrent events in anesthesia trials with strategies for handling them, and use of the estimand framework in a hypothetical anesthesia trial on postoperative delirium. When planning anesthesia trials, clearly defining the estimand is vital to ensure that what is being estimated is clearly understood, is clinically relevant, and helps answer the clinical questions of interest.

Insights into drug development with quantitative systems pharmacology: A prospective case study of uncovering hyperkalemia risk in diabetic nephropathy with virtual clinical trials.

The quantitative systems pharmacology (QSP) approach is widely applied to address various essential questions in drug discovery and development, such as identification of the mechanism of action of a therapeutic agent, patient stratification, and the mechanistic understanding of the progression of disease. In this review article, we show the current landscape of the application of QSP modeling using a survey of QSP publications over 10 years from 2013 to 2022. We also present a use case for the risk assessment of hyperkalemia in patients with diabetic nephropathy treated with mineralocorticoid receptor antagonists (MRAs, renin-angiotensin-aldosterone system inhibitors), as a prospective simulation of late clinical development. A QSP model for generating virtual patients with diabetic nephropathy was used to quantitatively assess that the nonsteroidal MRAs, finerenone and apararenone, have a lower risk of hyperkalemia than the steroidal MRA, eplerenone. Prospective simulation studies using a QSP model are useful to prioritize pharmaceutical candidates in clinical development and validate mechanism-based pharmacological concepts related to the risk-benefit, before conducting large-scale clinical trials.

Assessing readiness to use electronic health record data for outcome ascertainment in clinical trials - A case study.

BACKGROUND: Variable data quality poses a challenge to using electronic health record (EHR) data to ascertain acute clinical outcomes in multi-site clinical trials. Differing EHR platforms and data comprehensiveness across clinical trial sites, especially if patients received care outside of the clinical site's network, can also affect validity of results. Overcoming these challenges requires a structured approach. METHODS: We propose a framework and create a checklist to assess the readiness of clinical sites to contribute EHR data to a clinical trial for the purpose of outcome ascertainment, based on our experience with the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) study, which enrolled 5451 participants in 86 primary care practices across 10 healthcare systems (sites). RESULTS: The site readiness checklist includes assessment of the infrastructure (i.e., size and structure of the site's healthcare system or clinical network), data procurement (i.e., quality of the data), and cost of obtaining study data. The checklist emphasizes the importance of understanding how data are captured and integrated across a site's catchment area and having a protocol in place for data procurement to ensure consistent and uniform extraction across each site. CONCLUSIONS: We suggest rigorous, prospective vetting of the data quality and infrastructure of each clinical site before launching a multi-site trial dependent on EHR data. The proposed checklist serves as a guiding tool to help investigators ensure robust and unbiased data capture for their clinical trials. ORIGINAL TRIAL REGISTRATION NUMBER: NCT02475850.

Developing an international consensus Reporting guideline for intervention Fidelity in Non-Drug, non-surgical trials: The ReFiND protocol.

BACKGROUND: Inadequate reporting of fidelity to interventions in trials limits the transparency and interpretation of trial findings. Despite this, most trials of non-drug, non-surgical interventions lack comprehensive reporting of fidelity. If fidelity is poorly reported, it is unclear which intervention components were tested or implemented within the trial, which also hinders research reproducibility. This protocol describes the development process of a reporting guideline for fidelity of non-drug, non-surgical interventions (ReFiND) in the context of trials. METHODS: The ReFiND guideline will be developed in six stages. Stage one: a guideline development group has been formed to oversee the guideline methodology. Stage two: a scoping review will be conducted to identify and summarize existing guidance documents on the fidelity of non-drug, non-surgical interventions. Stage three: a Delphi study will be conducted to reach consensus on reporting items. Stage four: a consensus meeting will be held to consolidate the reporting items and discuss the wording and structure of the guideline. Stage five: a guidance statement, an elaboration and explanation document, and a reporting checklist will be developed. Stage six: different strategies will be used to disseminate and implement the ReFiND guideline. DISCUSSION: The ReFiND guideline will provide a set of items developed through international consensus to improve the reporting of intervention fidelity in trials of non-drug, non-surgical interventions. This reporting guideline will enhance transparency and reproducibility in future non-drug, non-surgical intervention research.

"Reality is frequently inaccurate" A case study examining the whens and whys of post-live database changes in a UK clinical trials unit *Douglas Adams.

INTRODUCTION: Accurately estimating the costs of clinical trials is challenging. There is currently no reference class data to allow researchers to understand the potential costs associated with database change management in clinical trials. METHODS: We used a case-based approach, summarising post-live changes in eleven clinical trial databases managed by Sheffield Clinical Trials Research Unit. We reviewed the database specifications for each trial and summarised the number of changes, change type, change category, and timing of changes. We pooled our experiences and made observations in relation to key themes. RESULTS: Median total number of changes across the eleven trials was 71 (range 40-155) and median number of changes per study week was 0.48 (range 0.32-1.34). The most common change type was modification (median 39, range 20-90), followed by additions (median 32, range 18-55), then deletions (median 7, range 1-12). In our sample, changes were more common in the first half of the trial's lifespan, regardless of its overall duration. Trials which saw continuous changes seemed more likely to be external pilots or trials in areas where the trial team was either less experienced overall or within the particular therapeutic area. CONCLUSIONS: Researchers should plan trials with the expectation that clinical trial databases will require changes within the life of the trial, particularly in the early stages or with a less experienced trial team. More research is required to understand potential differences between clinical trial units and database types.

Should You Run a Dedicated TQT Study? Sponsor and Regulatory Considerations on Substitution Pathways to Assess QT Liability.

Cardiac safety regulatory guidance for drug development has undergone several monumental shifts over the past decade as technological advancements, analysis models and study best practices have transformed this landscape. Once, clinical proarrhythmic risk assessment of a new chemical entity (NCE) was nearly exclusively evaluated in a dedicated thorough QT (TQT) study. However, since the introduction of the International Council for Harmonisation (ICH) E14/S7B Q&A 5.1 and 6.1 TQT substitutions, drug developers are offered an alternative pathway to evaluate proarrhythmic risk during an ascending dose study in healthy volunteers or during a powered patient study, respectively. In addition, the findings as well as the manner in which nonclinical studies are conducted (i.e., utilizing best practices) can dictate the need for a positive control in the clinical study and/or affect the labeling outcome. Drug sponsors are now faced with the option of pursuing a dedicated TQT study or requesting a TQT substitution. Potential factors influencing the choice of pathway include the NCE mechanism of action, pharmacokinetic properties, and safety profile, as well as business considerations. This tutorial will highlight the regulatory framework for integrated arrhythmia risk prediction models to outline drug safety, delineate potential reasons why a TQT substitution request may be rejected and discuss when a standalone TQT is recommended.

Categorization of the amyotrophic lateral sclerosis population via the clinical determinant of post-onset ΔFS for study design and medical practice.

The amyotrophic lateral sclerosis (ALS) functional rating scale-revised (ALSFRS-R) has become the most widely utilized measure of disease severity in patients with ALS, with change in ALSFRS-R from baseline being a trusted primary outcome measure in ALS clinical trials. This is despite the scale having several established limitations, and although alternative scales have been proposed, it is unlikely that these will displace ALSFRS-R in the foreseeable future. Here, we discuss the merits of delta FS (ΔFS), the slope or rate of ALSFRS-R decline over time, as a relevant tool for innovative ALS study design, with an as yet untapped potential for optimization of drug effectiveness and patient management. In our view, categorization of the ALS population via the clinical determinant of post-onset ΔFS is an important study design consideration. It serves not only as a critical stratification factor and basis for patient enrichment but also as a tool to explore differences in treatment response across the overall population; thereby, facilitating identification of responder subgroups. Moreover, because post-onset ΔFS is derived from information routinely collected as part of standard patient care and monitoring, it provides a suitable patient selection tool for treating physicians. Overall, post-onset ΔFS is a very attractive enrichment tool that is, can and should be regularly incorporated into ALS trial design.