Clinical Trials最新文献

Background: Performing large randomized trials in anesthesiology is often challenging and costly. The clinically integrated randomized trial is characterized by simplified logistics embedded into routine clinical practice, enabling ease and efficiency of recruitment, offering an opportunity for clinicians to conduct large, high-quality randomized trials under low cost. Our aims were to (1) demonstrate the feasibility of the clinically integrated trial design in a high-volume anesthesiology practice and (2) assess whether trial quality improvement interventions led to more balanced accrual among study arms and improved trial compliance over time.

Methods: This is an interim analysis of recruitment to a cluster-randomized trial investigating three nerve block approaches for mastectomy with immediate implant-based reconstruction: paravertebral block (arm 1), paravertebral plus interpectoral plane blocks (arm 2), and serratus anterior plane plus interpectoral plane blocks (arm 3). We monitored accrual and consent rates, clinician compliance with the randomized treatment, and availability of outcome data. Assessment after the initial year of implementation showed a slight imbalance in study arms suggesting areas for improvement in trial compliance. Specific improvement interventions included increasing the frequency of communication with the consenting staff and providing direct feedback to clinician investigators about their individual recruitment patterns. We assessed overall accrual rates and tested for differences in accrual, consent, and compliance rates pre- and post-improvement interventions.

Results: Overall recruitment was extremely high, accruing close to 90% of the eligible population. In the pre-intervention period, there was evidence of bias in the proportion of patients being accrued and receiving the monthly block, with higher rates in arm 3 (90%) compared to arms 1 (81%) and 2 (79%, p = 0.021). In contrast, in the post-intervention period, there was no statistically significant difference between groups (p = 0.8). Eligible for randomization rate increased from 89% in the pre-intervention period to 95% in the post-intervention period (difference 5.7%; 95% confidence interval = 2.2%-9.4%, p = 0.002). Consent rate increased from 95% to 98% (difference of 3.7%; 95% confidence interval = 1.1%-6.3%; p = 0.004). Compliance with the randomized nerve block approach was maintained at close to 100% and availability of primary outcome data was 100%.

Conclusion: The clinically integrated randomized trial design enables rapid trial accrual with a high participant compliance rate in a high-volume anesthesiology practice. Continuous monitoring of accrual, consent, and compliance rates is necessary to maintain and improve trial conduct and reduce potential biases. This trial methodology serves as a template for the implementation of other large, low-cost randomized

Background: Restricted mean survival time is the expected duration of survival up to a chosen time of restriction $\tau$. For comparison studies, the difference in restricted mean survival times between two groups provides a summary measure of the treatment effect that is free of assumptions regarding the relative shape of the two survival curves, such as proportional hazards. However, it can be difficult to judge the magnitude of the effect from a comparison of restricted means due to the truncation of observation at time $\tau$.

Methods: In this article, we describe additional ways of expressing the treatment effect based on restricted means that can be helpful in this regard. These include the ratio of restricted means, the ratio of life-years (or time) lost, and the average integrated difference between the survival curves, equal to the difference in restricted means divided by $\tau .$ These alternative metrics are straightforward to calculate and provide a means for scaling the effect size as an aid to interpretation. Examples from two randomized, multicenter clinical trials in prostate cancer, NRG/RTOG 0521 and NRG/RTOG 0534, with primary endpoints of overall survival and biochemical/radiological progression-free survival, respectively, are presented to illustrate the ideas.

Results: The four effect measures (restricted mean survival time difference, restricted mean survival time ratio, time lost ratio, and average survival rate difference) were 0.45 years, 1.05, 0.81, and 0.038 for RTOG 0521 and 1.36 years, 1.17, 0.56, and 0.12 for RTOG 0534 with $\tau$ = 12 and 11 years, respectively. Thus, for example, the 0.45-year difference in the first trial translates into a 19% reduction in time lost and a 3.8% average absolute difference between the survival curves over the 12-year horizon, a modest effect size, whereas the 1.36-year difference in the second trial corresponds to a 44% reduction in time lost and a 12% absolute survival difference, a rather large effect.

Conclusions: In addition to the difference in restricted mean survival times, these alternative measures can be helpful in determining whether the magnitude of the treatment effect is clinically meaningful.

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.

Background: After an initial recommendation from the World Health Organisation, trials of patients hospitalised with COVID-19 often include an ordinal clinical status outcome, which comprises a series of ordered categorical variables, typically ranging from 'Alive and discharged from hospital' to 'Dead'. These ordinal outcomes are often analysed using a proportional odds model, which provides a common odds ratio as an overall measure of effect, which is generally interpreted as the odds ratio for being in a higher category. The common odds ratio relies on the assumption of proportional odds, which implies an identical odds ratio across all ordinal categories; however, there is generally no statistical or biological basis for which this assumption should hold; and when violated, the common odds ratio may be a biased representation of the odds ratios for particular categories within the ordinal outcome. In this study, we aimed to evaluate to what extent the common odds ratio in published COVID-19 trials differed to simple binary odds ratios for clinically important outcomes.

Methods: We conducted a systematic review of randomised trials evaluating interventions for patients hospitalised with COVID-19, which used a proportional odds model to analyse an ordinal clinical status outcome, published between January 2020 and May 2021. We assessed agreement between the common odds ratio and the odds ratio from a standard logistic regression model for three clinically important binary outcomes: 'Alive', 'Alive without mechanical ventilation', and 'Alive and discharged from hospital'.

Results: Sixteen randomised clinical trials, comprising 38 individual comparisons, were included in this study; of these, only 6 trials (38%) formally assessed the proportional odds assumption. The common odds ratio differed by more than 25% compared to the binary odds ratios in 55% of comparisons for the outcome 'Alive', 37% for 'Alive without mechanical ventilation', and 24% for 'Alive and discharged from hospital'. In addition, the common odds ratio systematically underestimated the odds ratio for the outcome 'Alive' by -16.8% (95% confidence interval: -28.7% to -2.9%, p = 0.02), though differences for the other outcomes were smaller and not statistically significant (-8.4% for 'Alive without mechanical ventilation' and 3.6% for 'Alive and discharged from hospital'). The common odds ratio was statistically significant for 18% of comparisons, while the binary odds ratio was significant in 5%, 16%, and 3% of comparisons for the outcomes 'Alive', 'Alive without mechanical ventilation', and 'Alive and discharged from hospital', respectively.

Conclusion: The common odds ratio from proportional odds models often differs substantially to odds ratios from clinically important binary outcomes, and similar to composite outcomes, a beneficial common OR from a proportional odds model does not

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.

Background: Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life.

Purpose: While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups.

Methods and conclusions: Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.

The U.S. Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection toward identifying the optimal biological dose that offers the best balance between benefit and risk, rather than the maximum tolerated dose. However, achieving dose optimization is a challenging task that involves a variety of factors and is considerably more complicated than identifying the maximum tolerated dose, both in terms of design and implementation. This article provides a comprehensive review of various design strategies for dose-optimization trials, including phase 1/2 and 2/3 designs, and highlights their respective advantages and disadvantages. In addition, practical considerations for selecting an appropriate design and planning and executing the trial are discussed. The article also presents freely available software tools that can be utilized for designing and implementing dose-optimization trials. The approaches and their implementation are illustrated through real-world examples.