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Purpose: In the context of clinical research, there is an increasing need for new study designs that help to incorporate already available data. With the help of historical controls, the existing information can be utilized to support the new study design, but of course, inclusion also carries the risk of bias in the study results. Methods: To combine historical and randomized controls we investigate the Fill-it-up-design, which in the first step checks the comparability of the historical and randomized controls performing an equivalence pre-test. If equivalence is confirmed, the historical control data will be included in the new RCT. If equivalence cannot be confirmed, the historical controls will not be considered at all and the randomization of the original study will be extended. We are investigating the performance of this study design in terms of type I error rate and power. Results: We demonstrate how many patients need to be recruited in each of the two steps in the Fill-it-up-design and show that the family wise error rate of the design is kept at 5 % . The maximum sample size of the Fill-it-up-design is larger than that of the single-stage design without historical controls and increases as the heterogeneity between the historical controls and the concurrent controls increases. Conclusion: The two-stage Fill-it-up-design represents a frequentist method for including historical control data for various study designs. As the maximum sample size of the design is larger, a robust prior belief is essential for its use. The design should therefore be seen as a way out in exceptional situations where a hybrid design is considered necessary. [ABSTRACT FROM AUTHOR]

Abstract Purpose In the context of clinical research, there is an increasing need for new study designs that help to incorporate already available data. With the help of historical controls, the existing information can be utilized to support the new study design, but of course, inclusion also carries the risk of bias in the study results. Methods To combine historical and randomized controls we investigate the Fill-it-up-design, which in the first step checks the comparability of the historical and randomized controls performing an equivalence pre-test. If equivalence is confirmed, the historical control data will be included in the new RCT. If equivalence cannot be confirmed, the historical controls will not be considered at all and the randomization of the original study will be extended. We are investigating the performance of this study design in terms of type I error rate and power. Results We demonstrate how many patients need to be recruited in each of the two steps in the Fill-it-up-design and show that the family wise error rate of the design is kept at 5 $$\%$$ % . The maximum sample size of the Fill-it-up-design is larger than that of the single-stage design without historical controls and increases as the heterogeneity between the historical controls and the concurrent controls increases. Conclusion The two-stage Fill-it-up-design represents a frequentist method for including historical control data for various study designs. As the maximum sample size of the design is larger, a robust prior belief is essential for its use. The design should therefore be seen as a way out in exceptional situations where a hybrid design is considered necessary.

Purpose: Use of real‐world data (RWD) for external controls added to single‐arm trials (SAT) is increasingly prevalent in regulatory submissions. Due to inherent differences in the data‐generating mechanisms, biases can arise. This paper aims to illustrate how to use quantitative bias analysis (QBA). Methods: Advanced non‐small cell lung cancer (NSCLC) serves as an example, where many small subsets of patients with molecular tumor subtypes exist. First, some sources of bias that may occur in oncology when comparing RWD to SAT are described. Second, using a hypothetical immunotherapy agent, a dataset is simulated based on expert input for survival analysis of advanced NSCLC. Finally, we illustrate the impact of three biases: missing confounder, misclassification of exposure, and outcome evaluation. Results: For each simulated scenario, bias was induced by removing or adding data; hazard ratios (HRs) were estimated applying conventional analyses. Estimating the bias‐adjusted treatment effect and uncertainty required carefully selecting the bias model and bias factors. Although the magnitude of each biased and bias‐adjusted HR appeared moderate in all three hypothetical scenarios, the direction of bias was variable. Conclusion: These findings suggest that QBA can provide an intuitive framework for bias analysis, providing a key means of challenging assumptions about the evidence. However, the accuracy of bias analysis is itself dependent on correct specification of the bias model and bias factors. Ultimately, study design should reduce bias, but QBA allows us to evaluate the impact of unavoidable bias to assess the quality of the evidence. [ABSTRACT FROM AUTHOR]

In clinical trials, it is common to design a study that permits the administration of an experimental treatment to participants in the placebo or standard of care group post primary endpoint. This is often seen in the open-label extension phase of a phase III, pivotal study of the new medicine, where the focus is on assessing long-term safety and efficacy. With the availability of external controls, proper estimation and inference of long-term treatment effect during the open-label extension phase in the absence of placebo-controlled patients are now feasible. Within the framework of causal inference, we propose several difference-in-differences (DID) type methods and a synthetic control method (SCM) for the combination of randomized controlled trials and external controls. Our realistic simulation studies demonstrate the desirable performance of the proposed estimators in a variety of practical scenarios. In particular, DID methods outperform SCM and are the recommended methods of choice. An empirical application of the methods is demonstrated through a phase III clinical trial in rare disease. [ABSTRACT FROM AUTHOR]

Incorporation of external information is becoming increasingly common when designing clinical trials. Availability of multiple sources of information has inspired the development of methodologies that account for potential heterogeneity not only between the prospective trial and the pooled external data sources but also between the different external data sources themselves. Our approach proposes an intuitive way of handling such a scenario for the continuous outcomes setting by using propensity score-based stratification and then utilizing robust meta-analytic predictive priors for each stratum to incorporate the prior data to distinguish among different external data sources in each stratum. Through extensive simulations, our approach proves to be more efficient and less biased than the currently available methods. A real case study using clinical trials that study schizophrenia from multiple different sources is also included. [ABSTRACT FROM AUTHOR]

The development process of medical devices can be streamlined by combining different study phases. Here, for a diagnostic medical device, we present the combination of confirmation of diagnostic accuracy (phase III) and evaluation of clinical effectiveness regarding patient-relevant endpoints (phase IV) using a seamless design. This approach is used in the Thyroid HEmorrhage DetectOr Study (HEDOS & HEDOS II) investigating a post-operative hemorrhage detector named ISAR-M THYRO® in patients after thyroid surgery. Data from the phase III trial are reused as external controls in the control group of the phase IV trial. An unblinded interim analysis is planned between the two study stages which includes a recalculation of the sample size for the phase IV part after completion of the first stage of the seamless design. The study concept presented here is the first seamless design proposed in the field of diagnostic studies. Hence, the aim of this work is to emphasize the statistical methodology as well as feasibility of the proposed design in relation to the planning and implementation of the seamless design. Seamless designs can accelerate the overall trial duration and increase its efficiency in terms of sample size and recruitment. However, careful planning addressing numerous methodological and procedural challenges is necessary for successful implementation as well as agreement with regulatory bodies. [ABSTRACT FROM AUTHOR]

AbstractThe article argues that government organizations and the discipline of public administration should embrace a New Public Integrity Management (NPIM) to bridge the gap between advocates of external and internal public integrity controls on the behavior of public employees and contract government employees. The article argues that external and internal controls are important in maintaining public trust in government. This includes the management of the acceptance of gifts and private hospitality by government and contract government employees. The article traces the evolution of federal executive branch public integrity management and argues that the discipline of public administration has demonstrated a long-standing hostility towards external ethics controls due to early 20th-century divorce between law and administration. Of particular importance, the NPIM requires the adoption of a much broader definition of public corruption, which includes the conduct of private sector partners of government agencies responsible for the delivery of public goods and services paid for with public funds. Finally, the article argues that government ethics programs need to integrate moral reasoning education into their public integrity training programs and discuss criminal and administrative rules of conduct directed at protecting public confidence in the impartiality of bureaucratic decision-making. [ABSTRACT FROM AUTHOR]

A fully powered randomized controlled cancer trial can be challenging to conduct in children because of difficulties in enrollment of pediatric patients due to low disease incidence. One way to improve the feasibility of trials in pediatric patients, when clinically appropriate, is through borrowing information from comparable external adult trials in the same disease. Bayesian analysis of a pediatric trial provides a way of seamlessly augmenting pediatric trial efficacy data with data from external adult trials. However, not all external adult trial subjects may be equally clinically relevant with respect to the baseline disease severity, prognostic factors, co-morbidities, and prior therapy observed in the pediatric trial of interest. The propensity score matching method provides a way of matching the external adult subjects to the pediatric trial subjects on a set of clinically determined baseline covariates, such as baseline disease severity, prognostic factors and prior therapy. The matching then allows Bayesian information borrowing from only the most clinically relevant external adult subjects. Through a case study in pediatric acute lymphoblastic leukemia (ALL), we examine the utility of propensity score matched mixture and power priors in bringing appropriate external adult efficacy information into pediatric trial efficacy assessment, and present considerations for scaling fixed borrowing from external adult data. [ABSTRACT FROM AUTHOR]

In single-arm clinical trials with external controls, usually the estimand of interest is defined as the average treatment effect on the treated (ATT), and external controls are leveraged to provide an estimator of the estimand. Recently, virtual control approaches have been proposed to predict the outcomes for experimental study subjects as if they were receiving the control treatment, resulting in so-called “virtual controls” for comparison with their observed outcomes under the experimental intervention. We consider the virtual control approaches within the causal inference framework, discussing the properties of the vanilla virtual control approach and the targeted virtual control approach. We illustrate via simulation that the targeted virtual control approach is doubly robust, whereas the vanilla virtual control approach is not. We demonstrate the targeted virtual control approach is the same as the targeted maximum likelihood estimator (TMLE) when targeted at the ATT estimand. Therefore, through the notion of virtual controls, we offer a tangible way to understand and interpret TMLE when targeted at the ATT estimand. [ABSTRACT FROM AUTHOR]

Background: The drive to expedite patient access for diseases with high unmet treatment needs has come with an increasing use of single-arm trials (SATs), especially in oncology. However, the lack of control arms in such trials creates challenges to assess and demonstrate comparative efficacy. External control (EC) arms can be used to bridge this gap, with various types of sources available to obtain relevant data. Objective: To examine the source of ECs in single-arm oncology health technology assessment (HTA) submissions to the National Institute for Health and Care Excellence (NICE) and the Pharmaceutical Benefits Advisory Committee (PBAC) and how this selection was justified by manufacturers and assessed by the respective HTA body. Methods: Single-arm oncology HTA submission reports published by NICE (England) and PBAC (Australia) from January 2011 to August 2021 were reviewed, with data qualitatively synthesized to identify themes. Results: Forty-eight oncology submissions using EC arms between 2011 and 2021 were identified, with most submissions encompassing blood and bone marrow cancers (52%). In HTA submissions to NICE and PBAC, the EC arm was typically constructed from a combination of data sources, with the company’s justification in data source selection infrequently provided (PBAC [2 out of 19]; NICE [6 out of 29]), although this lack of justification was not heavily criticized by either HTA body. Conclusion: Although HTA bodies such as NICE and PBAC encourage that EC source justification should be provided in submissions, this review found that this is not typically implemented in practice. Guidance is needed to establish best practices as to how EC selection should be documented in HTA submissions.

Rare-disease registries can be useful for studying the associations between environmental exposures and disease severity, but often require the addition of a healthy comparison control group. Defining a surrogate control group, matched and balanced on potentially confounding variables, would allow for the comparison of exposure distributions with cases from a registry. In the present study, we assess whether controls selected externally, using stratification score (SS) matching, can serve as effective proxies for internal controls. In addition, we use methyl paraben (MEPB) to compare the estimated associations between an externally matched sample and case-control frequencies in a cohort with internally matched controls. We started with 225 eligible cases of autism spectrum disorder (ASD) from Childhood Autism Risks from Genetics and the Environment (CHARGE), 241 internal controls from CHARGE, and 265 external controls from the National Health and Nutrition Examination Survey (NHANES) cycles 2005-2016. We calculated the SSs using demographic covariates and matched 1:1 using a caliper method without a replacement. The distribution of the covariates and the mean squared error of the paired differences (MSEpaired) in the SSs between the internal and external group were similar (MSEpaired = 0.007 and 0.011, respectively). The association between MEPB and ASD compared to the controls was similar between the externally matched SS pairs and the original frequency matched cohort. Controls selected externally, via SS matching, can provide a comparable bias reduction to that provided by the internal controls, and therefore may be a useful strategy in situations when the internal controls are not available.

Abstract Background Platform trials gained popularity during the last few years as they increase flexibility compared to multi-arm trials by allowing new experimental arms entering when the trial already started. Using a shared control group in platform trials increases the trial efficiency compared to separate trials. Because of the later entry of some of the experimental treatment arms, the shared control group includes concurrent and non-concurrent control data. For a given experimental arm, non-concurrent controls refer to patients allocated to the control arm before the arm enters the trial, while concurrent controls refer to control patients that are randomised concurrently to the experimental arm. Using non-concurrent controls can result in bias in the estimate in case of time trends if the appropriate methodology is not used and the assumptions are not met. Methods We conducted two reviews on the use of non-concurrent controls in platform trials: one on statistical methodology and one on regulatory guidance. We broadened our searches to the use of external and historical control data. We conducted our review on the statistical methodology in 43 articles identified through a systematic search in PubMed and performed a review on regulatory guidance on the use of non-concurrent controls in 37 guidelines published on the EMA and FDA websites. Results Only 7/43 of the methodological articles and 4/37 guidelines focused on platform trials. With respect to the statistical methodology, in 28/43 articles, a Bayesian approach was used to incorporate external/non-concurrent controls while 7/43 used a frequentist approach and 8/43 considered both. The majority of the articles considered a method that downweights the non-concurrent control in favour of concurrent control data (34/43), using for instance meta-analytic or propensity score approaches, and 11/43 considered a modelling-based approach, using regression models to incorporate non-concurrent control data. In regulatory guidelines, the use of non-concurrent control data was considered critical but was deemed acceptable for rare diseases in 12/37 guidelines or was accepted in specific indications (12/37). Non-comparability (30/37) and bias (16/37) were raised most often as the general concerns with non-concurrent controls. Indication specific guidelines were found to be most instructive. Conclusions Statistical methods for incorporating non-concurrent controls are available in the literature, either by means of methods originally proposed for the incorporation of external controls or non-concurrent controls in platform trials. Methods mainly differ with respect to how the concurrent and non-concurrent data are combined and temporary changes handled. Regulatory guidance for non-concurrent controls in platform trials are currently still limited.

While randomized controlled trials (RCTs) are the gold standard for estimating treatment effects in medical research, there is increasing use of and interest in using real‐world data for drug development. One such use case is the construction of external control arms for evaluation of efficacy in single‐arm trials, particularly in cases where randomization is either infeasible or unethical. However, it is well known that treated patients in non‐randomized studies may not be comparable to control patients—on either measured or unmeasured variables—and that the underlying population differences between the two groups may result in biased treatment effect estimates as well as increased variability in estimation. To address these challenges for analyses of time‐to‐event outcomes, we developed a meta‐analytic framework that uses historical reference studies to adjust a log hazard ratio estimate in a new external control study for its additional bias and variability. The set of historical studies is formed by constructing external control arms for historical RCTs, and a meta‐analysis compares the trial controls to the external control arms. Importantly, a prospective external control study can be performed independently of the meta‐analysis using standard causal inference techniques for observational data. We illustrate our approach with a simulation study and an empirical example based on reference studies for advanced non‐small cell lung cancer. In our empirical analysis, external control patients had lower survival than trial controls (hazard ratio: 0.907), but our methodology is able to correct for this bias. An implementation of our approach is available in the R package ecmeta. [ABSTRACT FROM AUTHOR]

Causal variants for rare genetic diseases are often rare in the general population. Rare variants may also contribute to common complex traits and can have much larger per-allele effect sizes than common variants, although power to detect these associations can be limited. Sequencing costs have steadily declined with technological advancements, making it feasible to adopt whole-exome and whole-genome profiling for large biobank-scale sample sizes. These large amounts of sequencing data provide both opportunities and challenges for rare-variant association analysis. Herein, we review the basic concepts of rare-variant analysis methods, the current state-of-the-art methods in utilizing variant annotations or external controls to improve the statistical power, and particular challenges facing rare variant analysis such as accounting for population structure, extremely unbalanced case-control design. We also review recent advances and challenges in rare variant analysis for familial sequencing data and for more complex phenotypes such as survival data. Finally, we discuss other potential directions for further methodology investigation. [ABSTRACT FROM AUTHOR]

Causal variants for rare genetic diseases are often rare in the general population. Rare variants may also contribute to common complex traits and can have much larger per-allele effect sizes than common variants, although power to detect these associations can be limited. Sequencing costs have steadily declined with technological advancements, making it feasible to adopt whole-exome and whole-genome profiling for large biobank-scale sample sizes. These large amounts of sequencing data provide both opportunities and challenges for rare-variant association analysis. Herein, we review the basic concepts of rare-variant analysis methods, the current state-of-the-art methods in utilizing variant annotations or external controls to improve the statistical power, and particular challenges facing rare variant analysis such as accounting for population structure, extremely unbalanced case-control design. We also review recent advances and challenges in rare variant analysis for familial sequencing data and for more complex phenotypes such as survival data. Finally, we discuss other potential directions for further methodology investigation.

Objectives: To provide an overview of analytical methods in scientific literature for comparing uncontrolled medicine trials to external controls from individual patient-level real-world data (IPD-RWD). In addition, to compare these methods with recommendations made in guidelines from European regulatory and health technology assessment (HTA) organizations and with their evaluations described in assessment reports., Methods: A systematic literature review (until March 1 st 2023) in PubMed and Connected Papers was performed to identify analytical methods for comparing uncontrolled trials with external controls from IPD-RWD. These methods were compared descriptively to methods recommended in method guidelines and encountered in assessment reports of the European Medicines Agency (2015-2020) and four European HTA organizations (2015-2023)., Results: Thirty-four identified scientific articles described analytical methods for comparing uncontrolled trial data to IPD-RWD-based external controls. The various methods covered controlling for confounding and/or dependent censoring, correction for missing data; and analytical comparative modelling methods. Seven guidelines also focused on research design, RWD quality and transparency aspects, and four of those recommended analytical methods for comparisons with IPD-RWD. The methods discussed in regulatory (n=15) and HTA (n=35) assessment reports were often based on aggregate data and lacked transparency due to the few details provided., Conclusion: Literature and guidelines suggest a methodological approach to comparing uncontrolled trials with external controls from IPD-RWD similar to target trial emulation, using state-of-the-art methods. External controls supporting regulatory and HTA decision-making were rarely in line with this approach. Twelve recommendations are proposed to improve the quality and acceptability of these methods., (Copyright © 2024. Published by Elsevier Inc.)

• We assessed suitability of external controls for drug evaluations in ambulatory DMD. • NSAA outcomes were compared across placebo arms and real-world data/natural history data. • 48-week changes in NSAA were very similar across these data sources. • Adjustment for baseline prognostic factors further reduced these small differences. • Careful use of well-designed external controls can advance DMD drug development. Using external controls based on real-world or natural history data (RWD/NHD) for drug evaluations in Duchenne muscular dystrophy (DMD) is appealing given the challenges of enrolling placebo-controlled trials, especially for multi-year trials. Comparisons to external controls, however, face risks of bias due to differences in outcomes between trial and RWD/NHD settings. To assess this bias empirically, we conducted a multi-institution study comparing mean 48-week changes in North Star Ambulatory Assessment (NSAA) total score between trial placebo arms and RWD/NHD sources, with and without adjustment for baseline prognostic factors. Analyses used data from three placebo arms (235 48-week intervals, N = 235 patients) and three RWD/NHD sources (348 intervals, N = 202 patients). Differences in mean ΔNSAA between placebo arms and RWD/NHD sources were small before adjustment (-1.2 units, 95% CI: [-2.0 -0.5]) and were attenuated and no longer statistically significant after adjustment (0.1 units (95% CI: [-0.6, 0.8]). Results were similar whether adjusting using multivariable regression or propensity score matching. This consistency in ΔNSAA between trial placebo arms and RWD/NHD sources accords with prior findings for the six-minute walk distance, provides a well-validated framework for baseline adjustment of prognostic factors, and supports the suitability of RWD/NHD external controls for drug evaluations in ambulatory DMD. [ABSTRACT FROM AUTHOR]

Although randomized control trials allow for a comparison of treatment arms with minimal concern for confounding by known and unknown factors, a randomized study is not feasible in certain disease settings. When a randomized design is not possible, incorporating external control data into the study design can be an effective way to expand the interpretability of the results of an experimental arm by introducing the ability to carry out a formal or an informal comparative analysis. This paper provides an introduction to the concepts of external controls in oncology trials, followed by a review of relevant and current research on this topic. The paper also focuses on general considerations for designing a trial that may incorporate external control data, followed by case studies of the marketing applications submitted to the Food and Drug Administration that included external control data. • Considerations for the use of external controls in oncology clinical trials are multifaceted and depend on the intended use. • Review of recent research and some marketing applications describe current usage of external control data in oncology. • Including external controls for primary evidence of efficacy requires careful pre-specification of study design elements. [ABSTRACT FROM AUTHOR]

• With increasing access to modulators, placebo-controlled trials are less feasible. • Real-world evidence may provide a feasible comparison for evaluating novel therapies. • We provide an example of including controls from a historical trial into a later trial. • Accounting for study differences, estimates of treatment efficacy match the original. Given future challenges in conducting large randomized, placebo controlled trials for future CF therapeutics development, we evaluated the potential for using external historical controls to either enrich or replace traditional concurrent placebo groups in CF trials. The study included data from sequentially completed, randomized, controlled clinical trials, EPIC and OPTIMIZE respectively, evaluating optimal antibiotic therapy to reduce the risk of pulmonary exacerbation in children with early Pseudomonas aeruginosa infection. The primary treatment effect in OPTIMIZE, the risk of pulmonary exacerbation associated with azithromycin, was re-estimated in alternative designs incorporating varying numbers of participants from the earlier trial (EPIC) as historical controls. Bias and precision of these estimates were characterized. Propensity scores were derived to adjust for baseline differences across study populations, and both Poisson and Cox regression were used to estimate treatment efficacy. Replacing 86 OPTIMIZE placebo participants with 304 controls from EPIC to mimic a fully historically controlled trial resulted an 8% reduction in risk of pulmonary exacerbations (Hazard ratio (HR):0.92 95% CI 0.61, 1.34) when not adjusting for key baseline differences between study populations. After adjustment, a 37% decrease in risk of exacerbation (HR:0.63, 95% CI 0.50, 0.80) was estimated, comparable to the estimate from the original trial comparing the 86 placebo participants to 77 azithromycin participants on azithromycin (45%, HR:0.55, 95% CI: 0.34, 0.86). Other adjusted approaches provided similar estimates for the efficacy of azithromycin in reducing exacerbation risk: pooling all controls from both studies provided a HR of 0.60 (95% x'CI 0.46, 0.77) and augmenting half the OPTIMIZE placebo participants with EPIC controls gave a HR 0.63 (95% CI 0.48, 0.82). The potential exists for future CF trials to utilize historical control data. Careful consideration of both the comparability of controls and of optimal methods can reduce the potential for biased estimation of treatment effects. [ABSTRACT FROM AUTHOR]

Development of new therapeutics for a rare disease such as cystic fibrosis (CF) is hindered by challenges in accruing enough patients for clinical trials. Using external controls from well-matched historical trials can reduce prospective trial sizes, and this approach has supported regulatory approval of new interventions for other rare diseases. We consider three statistical methods that incorporate external controls into a hypothetical clinical trial of a new treatment to reduce pulmonary exacerbations in CF patients: 1) inverse probability weighting, 2) Bayesian modeling with propensity score-based power priors, and 3) hierarchical Bayesian modeling with commensurate priors. We compare the methods via simulation study and in a real clinical trial data setting. Simulations showed that bias in the treatment effect was <4% using any of the methods, with type 1 error (or in the Bayesian cases, posterior probability of the null hypothesis) usually <5%. Inverse probability weighting was sensitive to similarity in prevalence of the covariates between historical and prospective trial populations. The commensurate prior method performed best with real clinical trial data. Using external controls to reduce trial size in future clinical trials holds promise and can advance the therapeutic pipeline for rare diseases., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The regulatory standards of the United States Food and Drug Administration (FDA) require substantial evidence of effectiveness from adequate and well-controlled trials that typically use a valid comparison to an internal concurrent control. However, when it is not feasible or ethical to use an internal control, particularly in rare disease populations, relying on external controls may be acceptable. To better understand the use of external controls to support product development and approval, we reviewed FDA regulatory approval decisions between 2000 and 2019 for drug and biologic products to identify pivotal studies that leveraged external controls, with a focus on select therapeutic areas. Forty-five approvals were identified where FDA accepted external control data in their benefit/risk assessment; they did so for many reasons including the rare nature of the disease, ethical concerns regarding use of a placebo or no-treatment arm, the seriousness of the condition, and the high unmet medical need. Retrospective natural history data, including retrospective reviews of patient records, was the most common source of external control (44%). Other types of external control were baseline control (33%); published data (11%); and data from a previous clinical study (11%). To gain further insights, a comprehensive evaluation of selected approvals utilizing different types of external control is provided to highlight the variety of approaches used by sponsors and the challenges encountered in supporting product development and FDA decision making; particularly, the value and use of retrospective natural history in the development of products for rare diseases. Education on the use of external controls based on FDA regulatory precedent will allow for continued use and broader application of innovative approaches to clinical trial design, while avoiding delays in product development for rare diseases. Learnings from this review also highlight the need to update regulatory guidance to acknowledge the utility of external controls, particularly retrospective natural history data. [ABSTRACT FROM AUTHOR]

Publicly available genetic summary data have high utility in research and the clinic, including prioritizing putative causal variants, polygenic scoring, and leveraging common controls. However, summarizing individual-level data can mask population structure, resulting in confounding, reduced power, and incorrect prioritization of putative causal variants. This limits the utility of publicly available data, especially for understudied or admixed populations where additional research and resources are most needed. Although several methods exist to estimate ancestry in individual-level data, methods to estimate ancestry proportions in summary data are lacking. Here, we present Summix, a method to efficiently deconvolute ancestry and provide ancestry-adjusted allele frequencies (AFs) from summary data. Using continental reference ancestry, African (AFR), non-Finnish European (EUR), East Asian (EAS), Indigenous American (IAM), South Asian (SAS), we obtain accurate and precise estimates (within 0.1%) for all simulation scenarios. We apply Summix to gnomAD v.2.1 exome and genome groups and subgroups, finding heterogeneous continental ancestry for several groups, including African/African American (∼84% AFR, ∼14% EUR) and American/Latinx (∼4% AFR, ∼5% EAS, ∼43% EUR, ∼46% IAM). Compared to the unadjusted gnomAD AFs, Summix's ancestry-adjusted AFs more closely match respective African and Latinx reference samples. Even on modern, dense panels of summary statistics, Summix yields results in seconds, allowing for estimation of confidence intervals via block bootstrap. Given an accompanying R package, Summix increases the utility and equity of public genetic resources, empowering novel research opportunities. [ABSTRACT FROM AUTHOR]

Background: Regulatory agencies now recognize single-arm trials with external historical controls, particularly common in oncology, to assess promising treatments for rare or specific indications. When a new treatment indication depends on events over time, such as treatment failures, this design can introduce time-related biases in comparisons with external controls.Methods: We describe two potential biases resulting from calendar time and choice of time zero. We illustrate these biases using simulated data, emulating those from a single-arm trial of the effectiveness of blinatumomab in treating relapsed or refractory acute lymphoblastic leukemia on the outcome of mortality.Results: The trial compared 189 patients treated with blinatumomab with 1112 external historical control patients. First, calendar time was not concurrent, with the blinatumomab arm diagnosed during 2010-2014 and the control cohort during 1990-2013. The median survival under blinatumomab was 6.1 months compared with 3.3 months in the control arm, though for the latter it increased from 2.4 to 4.2 months over the 24-year period. Second, using the latest line of salvage treatment as cohort, entry for the control cohort introduces selection bias. The corresponding hazard ratio of death with blinatumomab compared with control was 0.56 (95% CI = 0.47, 0.67) but became 0.98 (95% CI = 0.83, 1.15) after redefining cohort entry by the matched line of salvage treatment rather than the latest line.Conclusion: While single-arm trials with external historical controls are gaining recognition, a proper understanding of time-related sources of bias is essential if such trials will be used to provide valid evidence for drug approval from regulatory agencies. [ABSTRACT FROM AUTHOR]

To address the challenges of assessing the impact of a reasonably likely surrogate endpoint on long-term graft survival in prospective kidney transplant clinical trials, the Transplant Therapeutics Consortium established a real-world evidence workgroup evaluating the scientific value of using transplant registry data as an external control to supplement the internal control group. The United Network for Organ Sharing retrospectively simulated the use of several distinct contemporaneous external control groups, applied multiple cause inference methods, and compared treatment effects to those observed in the BENEFIT study. Applying BENEFIT study enrollment criteria produced a smaller historical cyclosporine control arm (n = 153) and a larger, alternative (tacrolimus) historical control arm (n = 1069). Following covariate-balanced propensity scoring, Kaplan-Meier 5-year all-cause graft survivals were 81.3% and 81.7% in the Organ Procurement and Transplantation Network (OPTN) tacrolimus and cyclosporine external control arms, similar to 80.3% observed in the BENEFIT cyclosporine treatment arm. Five-year graft survival in the belatacept-less intensive arm was significantly higher than the OPTN controls using propensity scoring for comparing cyclosporine and tacrolimus. Propensity weighting using OPTN controls closely mirrored the BENEFIT study's long-term control (cyclosporine) arm's survival rate and the less intensive arm's treatment effect (significantly higher survival vs control). This study supports the feasibility and validity of using supplemental external registry controls for long-term survival in kidney transplant clinical trials., (Copyright © 2023 American Society of Transplantation & American Society of Transplant Surgeons. Published by Elsevier Inc. All rights reserved.)

Background: The drive to expedite patient access for diseases with high unmet treatment needs has come with an increasing use of single-arm trials (SATs), especially in oncology. However, the lack of control arms in such trials creates challenges to assess and demonstrate comparative efficacy. External control (EC) arms can be used to bridge this gap, with various types of sources available to obtain relevant data. Objective: To examine the source of ECs in single-arm oncology health technology assessment (HTA) submissions to the National Institute for Health and Care Excellence (NICE) and the Pharmaceutical Benefits Advisory Committee (PBAC) and how this selection was justified by manufacturers and assessed by the respective HTA body. Methods: Single-arm oncology HTA submission reports published by NICE (England) and PBAC (Australia) from January 2011 to August 2021 were reviewed, with data qualitatively synthesized to identify themes. Results: Forty-eight oncology submissions using EC arms between 2011 and 2021 were identified, with most submissions encompassing blood and bone marrow cancers (52%). In HTA submissions to NICE and PBAC, the EC arm was typically constructed from a combination of data sources, with the company's justification in data source selection infrequently provided (PBAC [2 out of 19]; NICE [6 out of 29]), although this lack of justification was not heavily criticized by either HTA body. Conclusion: Although HTA bodies such as NICE and PBAC encourage that EC source justification should be provided in submissions, this review found that this is not typically implemented in practice. Guidance is needed to establish best practices as to how EC selection should be documented in HTA submissions.

Background: First-line use of bevacizumab for glioblastoma (GBM) was evaluated in 2 phase 3 randomized controlled trials (RCT), demonstrating an impact on progression-free survival but not overall survival (OS). However, the crossover events of these trials raised concerns regarding the reliability of this latter analysis. In this study, we conducted an external control-based reassessment of the bevacizumab efficacy in newly diagnosed GBM (ndGBM) against the standard Stupp protocol., Methods: A systematic review of the literature was conducted to identify the phase 3 RCTs in ndGBM incorporating the Stupp protocol as an arm. For the selected studies, we extracted individual patient survival pseudodata of the Stupp protocol arm by digitizing the Kaplan-Meier plots. A comprehensive pipeline was established to select suitable control studies as external benchmarks., Results: Among the 13 identified studies identified in our systematic review, 4 studies resulted as comparable with the AVAglio trial and 2 with the RTOG 0825. Pooled individual patient pseudodata analysis showed no differences in terms of OS when bevacizumab was added to the Stupp protocol., Conclusions: The external-controlled-based reassessment of the bevacizumab treatment in ndGBM confirmed its lack of efficacy in extending OS. Our study includes a summary table of individual patient survival pseudodata from all phase 3 RCTs in ndGBM employing the Stupp protocol and provides a pipeline that offers comprehensive guidance for conducting external control-based assessments in ndGBM., Competing Interests: To the best of our knowledge, none of the authors have conflicts of interest related to the findings or materials presented in this study., (© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Pediatric cancer consists of a diverse group of rare diseases. Due to limited patient populations, standard randomized and controlled trials are often infeasible. As a result, single-arm trials are common in pediatric oncology and the use of external controls is often desirable or necessary to help generate actionable evidence and contextualize trial results. In this paper, we illustrate unique features in pediatric oncology clinical trials and describe their impact on the use of external controls. Various types of relevant external control data sources are described in terms of their utility and drawbacks. Statistical methodologies and design implications with external control are discussed. Two recent case studies using external controls to support pediatric oncology drug development are described in detail., Competing Interests: Conflicts of Interest Statement Yuan Ji: Co-Founder of Bayesoft Inc., a statistical company providing consultation and software service for pharmaceutical and biotech industry. IDMC member for Astellas, Boehringer Ingelheim and Lyell. Executive Advisor for Cytel Inc. Haitao Pan was partially supported by American Lebanese Syrian Associated Charities (ALSAC).

Randomized clinical trials (RCTs) are the gold standard in producing clinical evidence of efficacy and safety of medical interventions. More recently, a new paradigm is emerging—specifically within the context of preauthorization regulatory decision‐making—for some novel uses of real‐world evidence (RWE) from a variety of real‐world data (RWD) sources to answer certain clinical questions. Traditionally reserved for rare diseases and other special circumstances, external controls (eg, historical controls) are recognized as a possible type of control arm for single‐arm trials. However, creating and analyzing an external control arm using RWD can be challenging since design and analytics may not fully control for all systematic differences (biases). Nonetheless, certain biases can be attenuated using appropriate design and analytical approaches. The main objective of this paper is to improve the scientific rigor in the generation of external control arms using RWD. Here we (a) discuss the rationale and regulatory circumstances appropriate for external control arms, (b) define different types of external control arms, and (c) describe study design elements and approaches to mitigate certain biases in external control arms. This manuscript received endorsement from the International Society for Pharmacoepidemiology (ISPE). [ABSTRACT FROM AUTHOR]

Prior research has found that under certain conditions, small packages can paradoxically increase consumption. The authors build on this work by suggesting that people low in appearance self-esteem (ASE) are particularly sensitive to external control properties (i.e., packaging-related factors that signal the ability of packaging to regulate food intake) and, as a result, increase consumption levels when packages are small (vs. large or absent). Factors that highlight the external control properties of small packages, such as the visibility of product quantity, location of the caloric content, and communicated caloric content, further increase consumption, particularly among people with low ASE. The underlying process appears to be, at least in part, cognitively driven. The effects are mitigated when participants are under cognitive load, and the findings are mediated by cognitions regarding the ability of small packages to regulate food intake. The results have important practical implications suggesting that to quell the effects of small packages on overconsumption, emphasis on the external control properties of small packages should be minimized. [ABSTRACT FROM AUTHOR]

Designing and conducting active-controlled trials of HIV pre-exposure prophylaxis (PrEP) therapeutics are becoming challenging due to inconsistency of adherence across trials and the need for increasingly larger trials to assess efficacy. In the United States (US), trials evaluating oral contraceptives (OC) use the Pearl Index to assess efficacy in single-arm trials. This article explores the possibility of using an index ("HIV Incidence Index") analogous to the Pearl Index to assess preventive efficacy in active-controlled HIV prevention trials in sexually-acquired HIV. One proposal for constructing an HIV Incidence Index is to use the incidence of sexually transmitted infections (STIs) during a trial to serve as a marker for HIV risk behavior and to estimate what the HIV seroconversion rate would have been had the participants not been on efficacious PrEP. In addition, a consensus of expert opinion will be needed to define clinically acceptable HIV seroconversion rates for populations at risk of HIV-infection receiving active PrEP. [ABSTRACT FROM AUTHOR]

Decreased efficacy of antibiotics due to resistant pathogens has created a need for the development of more effective medical interventions. Despite the increasing prevalence of pathogens resistant to one or more drugs, identifying and enrolling participants into clinical trials that evaluate new interventions for the treatment of some diseases can be challenging given the low prevalence of disease in which there are no effective treatments. Thus researchers might be tempted to consider externally-controlled trials that may allow for a reduction of the necessary number of prospectively-identified trial participants, thus easing recruitment burden and resulting in more timely trial completion relative to randomized controlled trials. We discuss advantages and disadvantages in externally controlled trials and review requirements for a valid externally-controlled trial. As ECTs are subject to the bias of observational studies, the criteria for a valid ECT should be carefully evaluated before these designs are implemented. Given considerable variation in study results in the resistant pathogen setting, the lack of information on important patient characteristics that may confound estimates of treatment effects, as well as the improvements in medical practice and evolving antibiotic resistance, the use of ECTs in the resistant pathogen setting, is not recommended. ECTs should be should be limited to specific situations where superiority of the effect of the new intervention is dramatic, the usual course of the disease highly predictable, the endpoints are objective (e. g., all-cause mortality) and the impact of baseline and treatment variables on outcomes is well characterized. Given that the resistant pathogen setting does not satisfy these criteria, we conclude that that randomized clinical trials are needed to evaluate new treatments for resistant pathogens. Innovative approaches to trial design that may ease recruitment burden while evaluating the benefits and harms of new treatments are being developed and utilized. [ABSTRACT FROM AUTHOR]

Background: Platform trials gained popularity during the last few years as they increase flexibility compared to multi-arm trials by allowing new experimental arms entering when the trial already started. Using a shared control group in platform trials increases the trial efficiency compared to separate trials. Because of the later entry of some of the experimental treatment arms, the shared control group includes concurrent and non-concurrent control data. For a given experimental arm, non-concurrent controls refer to patients allocated to the control arm before the arm enters the trial, while concurrent controls refer to control patients that are randomised concurrently to the experimental arm. Using non-concurrent controls can result in bias in the estimate in case of time trends if the appropriate methodology is not used and the assumptions are not met., Methods: We conducted two reviews on the use of non-concurrent controls in platform trials: one on statistical methodology and one on regulatory guidance. We broadened our searches to the use of external and historical control data. We conducted our review on the statistical methodology in 43 articles identified through a systematic search in PubMed and performed a review on regulatory guidance on the use of non-concurrent controls in 37 guidelines published on the EMA and FDA websites., Results: Only 7/43 of the methodological articles and 4/37 guidelines focused on platform trials. With respect to the statistical methodology, in 28/43 articles, a Bayesian approach was used to incorporate external/non-concurrent controls while 7/43 used a frequentist approach and 8/43 considered both. The majority of the articles considered a method that downweights the non-concurrent control in favour of concurrent control data (34/43), using for instance meta-analytic or propensity score approaches, and 11/43 considered a modelling-based approach, using regression models to incorporate non-concurrent control data. In regulatory guidelines, the use of non-concurrent control data was considered critical but was deemed acceptable for rare diseases in 12/37 guidelines or was accepted in specific indications (12/37). Non-comparability (30/37) and bias (16/37) were raised most often as the general concerns with non-concurrent controls. Indication specific guidelines were found to be most instructive., Conclusions: Statistical methods for incorporating non-concurrent controls are available in the literature, either by means of methods originally proposed for the incorporation of external controls or non-concurrent controls in platform trials. Methods mainly differ with respect to how the concurrent and non-concurrent data are combined and temporary changes handled. Regulatory guidance for non-concurrent controls in platform trials are currently still limited., (© 2023. The Author(s).)

The article focuses on the research conducted on the white-collar crime of executives and its prevention in the largest business organisations in Norway. The white-collar crime refers to financial crime committed by white-collar criminals. It includes crimes against property, fraud, bribery, corruption, money laundering, embezzlement, insider trading, tax violations and cyber attacks.

Recent advances in genotyping and sequencing technologies have enabled genetic association studies to leverage high-quality genotyped data to identify variants accounting for a substantial portion of disease risk. The usage of external controls, whose genomes have already been genotyped and are publicly available, could be a cost-effective approach to increase the power of association testing. There has been recent effort to integrate external controls while adjusting for possible batch effects, such as the integrating External Controls into Association Test (iECAT). The original iECAT test, however, cannot adjust for covariates such as age, gender, and so forth. Hence, based on the insight of iECAT, we propose a novel score-based test that allows for covariate adjustment and constructs a shrinkage score statistic that is a weighted sum of the score statistics using exclusively internal samples and uses both internal and external control samples. We assess the existence of batch effect at a variant by comparing control samples of internal and external sources. We show by simulation studies that our method has increased power over the original iECAT while controlling for type I error rates. We present the application of our method to the association studies of age-related macular degeneration (AMD) utilizing data from the International AMD Genomics Consortium and Michigan Genomics Initiative. Through the incorporation of the score test approach, we extend the use of iECAT to adjust for covariates and improve power, further honing the statistical methods needed to identify disease-causing variants within the human genome., (© 2020 Wiley Periodicals LLC.)