Clinical Trials最新文献

Background: Issues with specification of margins, adherence, and analytic population can potentially bias results toward the alternative in randomized noninferiority pragmatic trials. To investigate this potential for bias, we conducted a targeted search of the medical literature to examine how noninferiority pragmatic trials address these issues.

Methods: An Ovid MEDLINE database search was performed identifying publications in New England Journal of Medicine, Journal of the American Medical Association, Lancet, or British Medical Journal published between 2015 and 2021 that included the words "pragmatic" or "comparative effectiveness" and "noninferiority" or "non-inferiority." Our search identified 14 potential trials, 12 meeting our inclusion criteria (11 individually randomized, 1 cluster-randomized).

Results: Eleven trials had results that met the criteria established for noninferiority. Noninferiority margins were prespecified for all trials; all but two trials provided justification of the margin. Most trials did some monitoring of treatment adherence. All trials conducted intent-to-treat or modified intent-to-treat analyses along with per-protocol analyses and these analyses reached similar conclusions. Only two trials included all randomized participants in the primary analysis, one used multiple imputation for missing data. The percentage excluded from primary analyses ranged from ∼2% to 30%. Reasons for exclusion included randomization in error, nonadherence, not receiving assigned treatment, death, withdrawal, lost to follow-up, and incomplete data.

Conclusion: Specification of margins, adherence, and analytic population require careful consideration to prevent bias toward the alternative in noninferiority pragmatic trials. Although separate guidance has been developed for noninferiority and pragmatic trials, it is not compatible with conducting a noninferiority pragmatic trial. Hence, these trials should probably not be done in their current format without developing new guidelines.

Background/aims: Protecting patient safety is an essential component of the conduct of clinical trials. Rigorous safety monitoring schemes are implemented for these studies to guard against excess toxicity risk from study therapies. They often include protocol-specified stopping rules dictating that an excessive number of safety events will trigger a halt of the study. Statistical methods are useful for constructing rules that protect patients from exposure to excessive toxicity while also maintaining the chance of a false safety signal at a low level. Several statistical techniques have been proposed for this purpose, but the current literature lacks a rigorous comparison to determine which method may be best suitable for a given trial design. The aims of this article are (1) to describe a general framework for repeated monitoring of safety events in clinical trials; (2) to survey common statistical techniques for creating safety stopping criteria; and (3) to provide investigators with a software tool for constructing and assessing these stopping rules.

Methods: The properties and operating characteristics of stopping rules produced by Pocock and O'Brien-Fleming tests, Bayesian Beta-Binomial models, and sequential probability ratio tests (SPRTs) are studied and compared for common scenarios that may arise in phase II and III trials. We developed the R package "stoppingrule" for constructing and evaluating stopping rules from these methods. Its usage is demonstrated through a redesign of a stopping rule for BMT CTN 0601 (registered at Clinicaltrials.gov as NCT00745420), a phase II, single-arm clinical trial that evaluated outcomes in pediatric sickle cell disease patients treated by bone marrow transplant.

Results: Methods with aggressive stopping criteria early in the trial, such as the Pocock test and Bayesian Beta-Binomial models with weak priors, have permissive stopping criteria at late stages. This results in a trade-off where rules with aggressive early monitoring generally will have a smaller number of expected toxicities but also lower power than rules with more conservative early stopping, such as the O-Brien-Fleming test and Beta-Binomial models with strong priors. The modified SPRT method is sensitive to the choice of alternative toxicity rate. The maximized SPRT generally has a higher number of expected toxicities and/or worse power than other methods.

Conclusions: Because the goal is to minimize the number of patients exposed to and experiencing toxicities from an unsafe therapy, we recommend using the Pocock or Beta-Binomial, weak prior methods for constructing safety stopping rules. At the design stage, the operating characteristics of candidate rules should be evaluated under various possible toxicity rates in order to guide the choice of rule(s) for a given trial; our R package facilitates this evaluation.

Background: High-quality decision-making in the pharmaceutical industry requires accurate assessments of the Probability of Technical Success of clinical trials. Failure to do so will lead to lost opportunities for both patients and investors. Pharmaceutical companies employ different methodologies to determine Probability of Technical Success values. Some companies use power and assurance calculations; others prefer to use industry benchmarks with or without the overlay of subjective modulations. At AstraZeneca, both assurance calculations and industry benchmarks are used, and both methods are combined with modulations.

Methods: AstraZeneca has recently implemented a simple algorithm that allows for modulation of a Probability of Technical Success value. The algorithm is based on a set of multiple-choice questions. These questions cover a comprehensive set of issues that have historically been considered by AstraZeneca when subjective modulations to Probability of Technical Success values were made but do so in a much more structured way.

Results: A set of 57 phase 3 Probability of Technical Success assessments suggests that AstraZeneca's historical estimation of Probability of Technical Success has been reasonably accurate. A good correlation between the subjective modulation and the modulation algorithm was found. This latter observation, combined with the finding that historically AstraZeneca has been reasonably accurate in its estimation of Probability of Technical Success, gives confidence in the validity of the novel method.

Discussion: Although it is too early to demonstrate whether the method has improved the accuracy of company's Probability of Technical Success assessments, we present our data and analysis here in the hope that it may assist the pharmaceutical industry in addressing this key challenge. This new methodology, developed for pivotal studies, enables AstraZeneca to develop more consistent Probability of Technical Success assessments with less effort and can be used to adjust benchmarks as well as assurance calculations.

Conclusion: The Probability of Technical Success modulation algorithm addresses several concerns generally associated with assurance calculations or benchmark without modulation: selection biases, situations where little relevant prior data are available and the difficulty to model many factors affecting study outcomes. As opposed to using industry benchmarks, the Probability of Technical Success modulation algorithm allows to accommodate project-specific considerations.

Background: Comparative effectiveness research is meant to determine which commonly employed medical interventions are most beneficial, least harmful, and/or most costly in a real-world setting. While the objectives for comparative effectiveness research are clear, the field has failed to develop either a uniform definition of comparative effectiveness research or an appropriate set of recommendations to provide standards for the design of critical care comparative effectiveness research trials, spurring controversy in recent years. The insertion of non-representative control and/or comparator arm subjects into critical care comparative effectiveness research trials can threaten trial subjects' safety. Nonetheless, the broader scientific community does not always appreciate the importance of defining and maintaining critical care practices during a trial, especially when vulnerable, critically ill populations are studied. Consequently, critical care comparative effectiveness research trials sometimes lack properly constructed control or active comparator arms altogether and/or suffer from the inclusion of "unusual critical care" that may adversely affect groups enrolled in one or more arms. This oversight has led to critical care comparative effectiveness research trial designs that impair informed consent, confound interpretation of trial results, and increase the risk of harm for trial participants.

Methods/examples: We propose a novel approach to performing critical care comparative effectiveness research trials that mandates the documentation of critical care practices prior to trial initiation. We also classify the most common types of critical care comparative effectiveness research trials, as well as the most frequent errors in trial design. We present examples of these design flaws drawn from past and recently published trials as well as examples of trials that avoided those errors. Finally, we summarize strategies employed successfully in well-designed trials, in hopes of suggesting a comprehensive standard for the field.

Conclusion: Flawed critical care comparative effectiveness research trial designs can lead to unsound trial conclusions, compromise informed consent, and increase risks to research subjects, undermining the major goal of comparative effectiveness research: to inform current practice. Well-constructed control and comparator arms comprise indispensable elements of critical care comparative effectiveness research trials, key to improving the trials' safety and to generating trial results likely to improve patient outcomes in clinical practice.

Background: The contribution of the statistician to the design and analysis of a clinical trial is acknowledged as essential. Ability to reconstruct the statistical contribution to a trial requires rigorous and transparent documentation as evidenced by the reproducibility of results. The process of validating statistical programmes is a key requirement. While guidance relating to software development and life cycle methodologies details steps for validation by information systems developers, there is no guidance applicable to programmes written by statisticians. We aimed to develop a risk-based approach to the validation of statistical programming that would support scientific integrity and efficient resource use within clinical trials units.

Methods: The project was embedded within the Information Systems Operational Group and the Statistics Operational Group of the UK Clinical Research Collaboration Registered Clinical Trials Unit network. Members were asked to share materials relevant to validation of statistical programming. A review of the published literature, regulatory guidance and knowledge of relevant working groups was undertaken. Surveys targeting the Information Systems Operational Group and Statistics Operational Group were developed to determine current practices across the Registered Clinical Trials Unit network. A risk-based approach was drafted and used as a basis for a workshop with representation from statisticians, information systems developers and quality assurance managers (n = 15). The approach was subsequently modified and presented at a second, larger scale workshop (n = 47) to gain a wider perspective, with discussion of content and implications for delivery. The approach was revised based on the discussions and suggestions made. The workshop was attended by a member of the Medicines for Healthcare products Regulatory Agency Inspectorate who also provided comments on the revised draft.

Results: Types of statistical programming were identified and categorised into six areas: generation of randomisation lists; programmes to explore/understand the data; data cleaning, including complex checks; derivations including data transformations; data monitoring; or interim and final analysis. The risk-based approach considers each category of statistical programme against the impact of an error and its likelihood, whether the programming can be fully prespecified, the need for repeated use and the need for reproducibility. Approaches to the validation of programming within each category are proposed.

Conclusion: We have developed a risk-based approach to the validation of statistical programming. It endeavours to facilitate the implementation of targeted quality assurance measures while making efficient use of limited resources.