Clinical Trials最新文献

Background/aims: Regulatory guidelines recommend that sponsors develop a risk-based approach to monitoring clinical trials. However, there is a lack of evidence to guide the effective implementation of monitoring activities encompassed in this approach. The aim of this study was to assess the efficiency and impact of the risk-based monitoring approach used for a multicentre randomised controlled trial comparing treatments in paediatric patients undergoing cardiac bypass surgery.

Methods: This is a secondary analysis of data from a randomised controlled trial that implemented targeted source data verification as part of the risk-based monitoring approach. Monitoring duration and source to database error rates were calculated across the monitored trial dataset. The monitored and unmonitored trial dataset, and simulated trial datasets with differing degrees of source data verification and cohort sizes were compared for their effect on trial outcomes.

Results: In total, 106,749 critical data points across 1,282 participants were verified from source data either remotely or on-site during the trial. The total time spent monitoring was 365 hours, with a median (interquartile range) of 10 (7, 16) minutes per participant. An overall source to database error rate of 3.1% was found, and this did not differ between treatment groups. A low rate of error was found for all outcomes undergoing 100% source data verification, with the exception of two secondary outcomes with error rates >10%. Minimal variation in trial outcomes were found between the unmonitored and monitored datasets. Reduced degrees of source data verification and reduced cohort sizes assessed using simulated trial datasets had minimal impact on trial outcomes.

Conclusions: Targeted source data verification of data critical to trial outcomes, which carried with it a substantial time investment, did not have an impact on study outcomes in this trial. This evaluation of the cost-effectiveness of targeted source data verification contributes to the evidence-base regarding the context where reduced emphasis should be placed on source data verification as the foremost monitoring activity.

Background: The Bayesian group sequential design has been applied widely in clinical studies, especially in Phase II and III studies. It allows early termination based on accumulating interim data. However, to date, there lacks development in its application to stepped-wedge cluster randomized trials, which are gaining popularity in pragmatic trials conducted by clinical and health care delivery researchers.

Methods: We propose a Bayesian adaptive design approach for stepped-wedge cluster randomized trials, which makes adaptive decisions based on the predictive probability of declaring the intervention effective at the end of study given interim data. The Bayesian models and the algorithms for posterior inference and trial conduct are presented.

Results: We present how to determine design parameters through extensive simulations to achieve desired operational characteristics. We further evaluate how various design factors, such as the number of steps, cluster size, random variability in cluster size, and correlation structures, impact trial properties, including power, type I error, and the probability of early stopping. An application example is presented.

Conclusion: This study presents the incorporation of Bayesian adaptive strategies into stepped-wedge cluster randomized trials design. The proposed approach provides the flexibility to stop the trial early if substantial evidence of efficacy or futility is observed, improving the flexibility and efficiency of stepped-wedge cluster randomized trials.

Background: Knowing the predictive factors of the variation in a center-level continuous outcome of interest is valuable in the design and analysis of parallel-arm cluster randomized trials. The symbolic two-step method for sample size planning that we present incorporates this knowledge while simultaneously accounting for patient-level characteristics. Our approach is illustrated through application to cluster randomized trials in cancer care delivery research. The required number of centers (clusters) depends on the between- and within-center variance; the within-center variance is a function of estimates obtained by regressing the log within-center variance on predictive factors. Obtaining accurate estimates of the components needed to characterize the within-center variation is challenging.

Methods: Using our previously derived sample size formula, our objective in the current research is to directly account for the imprecision in these estimates, using a Bayesian approach, to safeguard against designing an underpowered study when using the symbolic two-step method. Using estimates of the required components, including the number of centers that contribute to those estimates, we make formal allowance for the imprecision in these estimates on which a sample size will be based.

Results: The mean of the distribution for power is consistently smaller than the single point estimate that the sample size formula yields. The reduction in power is more pronounced in the presence of increased uncertainty about the estimates with the reduction becoming more attenuated with increased numbers of centers that contribute to the estimates.

Conclusions: Accounting for imprecision in the estimates of the components required for sample size estimation using the symbolic two-step method in the design of a cluster randomized trial yields conservative estimates of power.

Background/aims: Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, 103.4 million cases and 1.1 million deaths have occurred nationally as of November 2023. Despite the benefit of mitigating measures, the pandemic's effect on participant safety is rarely documented.

Methods: This study assessed noncompliance occurring from July 2019 to August 2021 that were stratified by the date of noncompliance (before or after restrictions). Events were described by size, site, noncompliance type, primary category, subcategory, and cause. In addition, noncompliance associated with COVID-19 was analyzed to determine characteristics.

Results: In total, 323 noncompliance events occurred across 21,146 participants at risk in 35 protocols. The overall rate of noncompliance increased from 0.008 events per participant to 0.022 events per participant after the COVID-19 restrictions (p < 0.001). For onsite protocols, the median within protocol change in rates was 0.001 (interquartile range = 0.141) after the onset of COVID-19 restrictions (p = 0.54). For large-sized protocols (n ≥ 100), the median within protocol change in rates was also 0.001 (interquartile range = 0.017) after COVID-19 restrictions (p = 0.15). For events related to COVID-19 restrictions, 160/162 (99%) were minor deviations, 161/162 (99%) were procedural noncompliance, and 124/162 (77%) were an incomplete study visit.

Conclusion: These noncompliance events have implications for clinical trial methodology because nonadherence to trial design can lead to participant safety concerns and loss of trial data validity. Protocols should be written to better facilitate the capture of all safety and efficacy data. This recommendation should be considered when changes occur to the protocol environment that are outside of the study team's control.

In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.

Background/aims: The SARS-CoV-2 pandemic disproportionately impacted communities with lower access to health care in the United States, particularly before vaccines were widely available. These same communities are often underrepresented in clinical trials. Efforts to ensure equitable enrollment of participants in trials related to treatment and prevention of Covid-19 can raise concerns about exploitation if communities with lower access to health care are targeted for recruitment.

Methods: To enhance equity while avoiding exploitation, our site developed and implemented a three-part recruitment strategy for pediatric Covid-19 vaccine studies. First, we publicized a registry for potentially interested participants. Next, we applied public health community and social vulnerability indices to categorize the residence of families who had signed up for the registry into three levels to reflect the relative impact of the pandemic on their community: high, medium, and low. Finally, we preferentially offered study participation to interested families living in areas categorized by these indices as having high impact of the Covid-19 pandemic on their community.

Results: This approach allowed us to meet goals for study recruitment based on public health metrics related to disease burden, which contributed to a racially diverse study population that mirrored the surrounding community demographics. While this three-part recruitment strategy improved representation of minoritized groups from areas heavily impacted by the Covid-19 pandemic, important limitations were identified that would benefit from further study.

Conclusion: Future use of this approach to enhance equitable access to research while avoiding exploitation should test different methods to build trust and communicate with underserved communities more effectively.

Since the middle of the 20th century, oncology's dose-finding paradigm has been oriented toward identifying a drug's maximum tolerated dose, which is then carried forward into phase 2 and 3 trials and clinical practice. For most modern precision medicines, however, maximum tolerated dose is far greater than the minimum dose needed to achieve maximal benefit, leading to unnecessary side effects. Regulatory change may decrease maximum tolerated dose's predominance by enforcing dose optimization of new drugs. Dozens of already approved cancer drugs require re-evaluation, however, introducing a new methodologic and ethical challenge in cancer clinical trials. In this article, we assess the history and current landscape of cancer drug dose finding. We provide a set of strategic priorities for postapproval dose optimization trials of the future. We discuss ethical considerations for postapproval dose optimization trial design and review three major design strategies for these unique trials that would both adhere to ethical standards and benefit patients and funders. We close with a discussion of financial and reporting considerations in the realm of dose optimization. Taken together, we provide a comprehensive, bird's eye view of the postapproval dose optimization trial landscape and offer our thoughts on the next steps required of methodologies and regulatory and funding regimes.

The Patient-Centered Dosing Initiative, a patient-led effort advocating for a paradigm shift in determining cancer drug dosing strategies, pioneers a departure from traditional oncology drug dosing practices. Historically, oncology drug dosing relies on identifying the maximum tolerated dose through phase 1 dose escalation methodology, favoring higher dosing for greater efficacy, often leading to higher toxicity. However, this approach is not universally applicable, especially for newer treatments like targeted therapies and immunotherapies. Patient-Centered Dosing Initiative challenges this "more is better" ethos, particularly as metastatic breast cancer patients themselves, as they not only seek longevity but also a high quality of life since most metastatic breast cancer patients stay on treatment for the rest of their lives. Surveying 1221 metastatic breast cancer patients and 119 oncologists revealed an evident need for flexible dosing strategies, advocating personalized care discussions based on patient attributes. The survey results also demonstrated an openness toward flexible dosing and a willingness from both patients and clinicians to discuss dosing as part of their care. Patient-centered dosing emphasizes dialogue between clinicians and patients, delving into treatment efficacy-toxicity trade-offs. Similarly, clinical trial advocacy for multiple dosing regimens encourages adaptive strategies, moving away from strict adherence to maximum tolerated dose, supported by recent research in optimizing drug dosages. Recognizing the efficacy-effectiveness gap between clinical trials and real-world practice, Patient-Centered Dosing Initiative underscores the necessity for patient-centered dosing strategies. A focus on individual patient attributes aligns with initiatives like Project Optimus and Project Renewal, aiming to optimize drug dosages for improved treatment outcomes at both the pre- and post-approval phases. Patient-Centered Dosing Initiative's efforts extend to patient education, providing tools to initiate dosage-related conversations with physicians. In addition, it emphasizes physician-patient dialogues and post-marketing studies as essential in determining optimal dosing and refining drug regimens. A dose-finding paradigm prioritizing drug safety, tolerability, and efficacy benefits all stakeholders, reducing emergency care needs and missed treatments for patients, aligning with oncologists' and patients' shared goals. Importantly, it represents a win-win scenario across healthcare sectors. In summary, the Patient-Centered Dosing Initiative drives transformative changes in cancer drug dosing, emphasizing patient well-being and personalized care, aiming to enhance treatment outcomes and optimize oncology drug delivery.

Targeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of "more is better" is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I-II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I-II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I-II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose-outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I-II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.