Health Locus of Control and Medical Behavioral Interventions: Systematic Review and Recommendations.

BACKGROUND: Health locus of control (HLOC) is a theory that describes how individuals perceive different forces that influence their lives. The concept of a locus of control can affect an individual's likelihood to commit to behaviors related to their health. This study explores the literature on the relationships between HLOC and medical behavioral interventions. OBJECTIVE: This study aims to better understand how HLOC constructs can potentially affect patient responses to health behavioral interventions and to propose a series of guidelines for individuals interested in designing medical behavioral interventions related to HLOC. METHODS: We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology and performed an analysis of 50 papers related to the topic of HLOC and medical behavioral interventions. Inclusion criteria were studies that had a behavioral intervention involving patients and contained a metric of at least 1 of the constructs related to HLOC. The initial screening and search were conducted by 2 researchers (AY and SM) separately. The results were then combined and compared. RESULTS: Our findings explore the influence of different levels of HLOC along with the importance of both patient- and health-related context when assessing the relationships between HLOC constructs and the likelihood of health behavior change. The findings show that different constructs related to HLOC can act as reliable predictors for patient responses to medical behavioral interventions. Patients who score higher on internal HLOC measures are more likely to exhibit behaviors that are consistent with positive health outcomes. Patients who score higher on chance HLOC are more likely to exhibit behaviors that may lead to adverse health outcomes. These conclusions are supported by most of the 50 studies surveyed. CONCLUSIONS: We propose guidelines for individuals designing medical behavioral interventions so that they can make use of these relationships linked to HLOC. The three guidelines suggested are as follows: (1) in most situations, improving internal HLOC will improve health outcomes for patients; (2) patients with high external HLOC should be further studied to determine the source of the external HLOC; and (3) patients with a high chance HLOC are less likely to follow preventative behaviors or be responsive to interventions. Limitations of the study are that the primary search and analysis were conducted by 2 principal researchers (AY and SM). Interpretation and development of the guidelines are subject to individual interpretation of results and may not be applicable to all contexts.

A Framework for the Use and Likelihood of Regulatory Acceptance of Single-Arm Trials.

BACKGROUND: Single-arm clinical trials (SAT) are common in drug and biologic submissions for rare or life-threatening conditions, especially when no therapeutic options exist. External control arms (ECAs) improve interpretation of SATs but pose methodological and regulatory challenges. OBJECTIVE: Through narrative reviews and expert input, we developed a framework for considerations that might influence regulatory use and likelihood of regulatory acceptance of an SAT, identifying non-oncology first indication approvals as an area of interest. We systematically analyzed FDA and EMA approvals using SATs as pivotal evidence. The framework guided outcome abstraction on regulatory responses. METHODS: We examined all non-oncology FDA and EMA drug and biologic approvals for first indications from 2019 to 2022 to identify those with SAT as pivotal safety or efficacy evidence. We abstracted outcomes, key study design features, regulator responses to SAT and (where applicable) ECA design, and product label content. RESULTS: Among 20 SAT-based FDA approvals and 17 SAT-based EMA approvals, most common indications were progressive rare diseases with high unmet need/limited therapeutic options and a natural history without spontaneous improvement. Of the types of comparators, most were natural history cohorts (45% FDA; 47% EMA) and baseline controls (40% FDA; 47% EMA). Common critiques were of non-contemporaneous ECAs, subjective endpoints, and baseline covariate imbalance between arms. CONCLUSION: Based on recent FDA and EMA approvals, the likelihood of regulatory success for SATs with ECAs depends on many design, analytic, and data quality considerations. Our framework is useful in early drug development when considering SAT strategies for evidence generation.

Exploring the Relationship Between Psychological Constructs and Decision-Making Preferences in Psychiatric Outpatients.

Objective: This study aimed to elucidate the relationships among health locus of control, psychological reactance, attitudes toward psychiatric treatment, and patient decision-making preferences within a psychiatric outpatient population. Methods: A total of 200 consecutive psychiatric outpatients from a community mental health center in Tenerife, Spain, were approached for participation between September 2023 and March 2024. Of these, 151 patients consented to participate in this cross-sectional study. Participants were selected based on their willingness to participate and were provided with informed consent forms. Data were collected using the Patient's Health Belief Questionnaire on Psychiatric Treatment (PHBQPT) and the Control Preferences Scale (CPS). The PHBQPT evaluates health beliefs impacting adherence to psychiatric treatment, while the CPS assesses the preferred level of involvement in medical decision-making. Sociodemographic data were also collected to contextualize the findings. Results: Significant correlations were found between patients' control preferences and their attitudes towards medication, compliance with psychiatric advice, and perceptions of treatment control. A collaborative control preference was notably associated with positive attitudes toward medication and trust in the psychiatrist. These findings suggest that tailored treatment approaches prioritizing patient involvement could enhance adherence and outcomes. Conclusion:  The study underscores the importance of considering psychological constructs in psychiatric care to foster a holistic, patient-centered approach. Recognizing and integrating patients' control preferences, attitudes towards medication, and psychological reactance can improve the therapeutic relationship and treatment adherence. Future research should explore longitudinal and interventional studies to further understand the impact of aligning treatment approaches with patient preferences and psychological profiles.

In today's fast-paced world, understanding how we can better cater to the needs of psychiatric patients is more important than ever. This study, conducted in Tenerife, Spain, with 151 participants, shines a light on the intricate relationship between a patient's psychological mindset and their involvement in psychiatric care. Researchers explored how patients' beliefs about health control, their resistance or openness to psychiatric advice, and their preferences in treatment decisions intertwine to affect their approach to psychiatric treatment. The core findings reveal a fascinating tapestry of patient attitudes and behaviors. For example, patients who prefer a joint approach with their psychiatrists toward managing their treatment tend to have a more positive outlook on medication and a deeper trust in their doctors. This suggests that when patients feel they are part of the decision-making process, they are more likely to follow through with treatment plans, leading to better outcomes. What does this mean in everyday terms? Essentially, the study highlights the power of listening and integrating patients' viewpoints into their care plans. When patients see their insights and preferences reflected in their treatment, their engagement and adherence to medication improve. This not only fosters a more supportive environment for healing but also paves the way for more personalized, effective psychiatric care. In essence, this research is a call to action for healthcare providers to delve deeper into understanding each patient's unique psychological makeup. By doing so, they can tailor treatments that resonate more closely with the patient's own beliefs and preferences, ultimately leading to a more positive healthcare experience for everyone involved.

Examining external control arms in oncology: A scoping review of applications to date.

OBJECTIVES: Randomized controlled trials (RCTs) are the gold standard for evaluating the comparative efficacy and safety of new cancer therapies. However, enrolling patients in control arms of clinical trials can be challenging for rare cancers, particularly in the context of precision oncology and targeted therapies. External Control Arms (ECAs) are a potential solution to address these challenges in clinical research design. We conducted a scoping review to explore the use of ECAs in oncology. METHODS: We systematically searched four databases, namely MEDLINE, EMBASE, Web of Science, and Scopus. We screened titles, abstracts, and full texts for eligible articles focusing on patients undergoing therapy for cancer, employing ECAs, and reporting clinical outcomes. RESULTS: Of the 629 articles screened, 23 were included in this review. The earliest included studies were published in 1996, while most studies were published in the past 5 years. 44% (10/23) of ECAs were employed in blood-related cancer studies. Geographically, 30% (7/23) of studies were conducted in the United States, 22% (5/23) in Japan, and 9% (2/23) in South Korea. The primary data sources used to construct the ECAs involved pooled data from previous trials (35%, 8/23), administrative health databases (17%, 4/23) and electronic medical records (17%, 4/23). While 52% (12/23) of the studies employed methods to align treatment and ECAs characteristics, 48% (11/23) lacked explicit strategies. CONCLUSION: ECAs offer a valuable approach in oncology research, particularly when alternative designs are not feasible. However, careful methodological planning and detailed reporting are essential for meaningful and reliable results.

Comparative effectiveness of lazertinib in patients with EGFR T790M-positive non-small-cell lung cancer using a real-world external control.

Lazertinib is a recently developed third-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors used for patients with advanced EGFR T790M-positive non-small-cell lung cancer. We evaluated the effectiveness of lazertinib compared with osimertinib using an external control. We obtained individual patient data for the lazertinib arm from the LASER201 trial and the osimertinib arm from registry data at the Samsung Medical Center. In total, 75 and 110 patients were included in the lazertinib and osimertinib groups, respectively. After propensity score matching, each group had 60 patients and all baseline characteristics were balanced. The median follow-up duration was 22.0 and 29.6 months in the lazertinib and osimertinib group, respectively. The objective response rate (ORR) were 76.7% and 86.7% for lazertinib and osimertinib, respectively (p = 0.08). The median progression-free survival (PFS) was 12.3 months (95% confidence interval [CI] 9.5-19.1) and 14.4 months (95% CI 11.8-18.1) for the lazertinib and osimertinib group, respectively (hazard ratio [HR] 0.97; 95% CI 0.64-1.45, p = 0.86). The median overall survival with lazertinib was not reached and that with osimertinib was 29.8 months (HR 0.44; 95% CI 0.25-0.77, p = 0.005). Our study suggests that lazertinib has an ORR and PFS comparable to those of osimertinib and has the potential for superior survival benefits.

Cytarabine dose intensification improves survival in older patients with secondary/high-risk acute myeloid leukemia in matched real-world versus clinical trial data.

Since 1980's, the established/standard treatment of acute myeloid leukemia (AML) is cytarabine infusion with anthracycline (7 + 3 regimen). We compared the 7 + 3 regimen in older secondary/high-risk AML patients from a clinical trial with a matched population from the Swedish AML Registry treated with an increased cytarabine dose in induction and consolidation as recommended in the Swedish National Guidelines since 2005. After successful propensity score matching, 104 patients per group were included. The primary outcome was overall survival (OS), and standard dosed patients had a median OS of 6.4 versus 10.7 months with increased dose intensity (hazard ratio: 0.69, p = 0.012), with 5-year OS of 8.7% and 18.1%, and remission rates of 36% and 60%, respectively (p < 0.001). Median OS after allogeneic hematopoietic cell transplantation (in 27.9% per group) was 10.4 and 20.7 months, respectively. We conclude that the more intensive cytarabine schedule seems to provide improved outcomes inthe investigated AML patient group.

Development of the Food-Related Control Scale for Long-Term Care.

OBJECTIVE: Develop and test a Food-Related Control Scale (FRCS) measuring resident-perceived control in long-term care food service. DESIGN: A bank of 15 initial items based on a multidimensional locus of control construct was developed initially. Expert review, cognitive interviews, a pilot study, and factor analysis were used to validate the instrument and assess reliability. SETTING: Individual phone-based cognitive interviews and 16 skilled nursing facilities in the US. PARTICIPANTS: Cognitive interviews included a convenience sample of independently living adults aged ≥ 65 (n = 13), whereas the pilot study included skilled nursing facility-residing adults (n = 166). VARIABLES MEASURED: Perception of food-related control in a long-term care setting. ANALYSIS: Cognitive interviews were analyzed to develop items. Quantitative data from skilled nursing facility residents were analyzed using SAS software for structural equation modeling and factor analysis. RESULTS: A 2-dimensional construct (9 items) of the FRCS demonstrated reliability with factor analysis. Concurrent validity within the locus of control construct was demonstrated with the Multidimensional Health Locus of Control Scale (standardized estimate of 0.430; P < 0.1). CONCLUSIONS AND IMPLICATIONS: The FRCS may be used to determine how residents in long-term care perceive control over their food experiences. Further testing is necessary to determine the appropriateness of the FRCS for different population uses.

Statistical methods to control for confounders in rare disease settings that use external control.

In the drug development for rare disease, the number of treated subjects in the clinical trial is often very small, whereas the number of external controls can be relatively large. There is no clear guidance on choosing an appropriate statistical method to control baseline confounding in this situation. To fill this gap, we conduct extensive simulations to evaluate the performance of commonly used matching and weighting methods as well as the more recently developed targeted maximum likelihood estimation (TMLE) and cardinality matching in small sample settings, mimicking the motivating data from a pediatric rare disease. Among the methods examined, the performance of coarsened exact matching (CEM) and TMLE are relatively robust under various model specifications. CEM is only feasible when the number of controls far exceeds the number of treated, whereas TMLE has better performance with less extreme treatment allocation ratios. Our simulations suggest bootstrap is useful for variance estimation in small samples after matching.

Self-directed learning in dental hygiene students: Impact of locus of control and personality traits.

OBJECTIVES: The purpose of this study was to identify demographic variables, the level of self-directed learning (SDL), locus of control (LOC), and personality traits in dental hygiene (DH) students and to identify predictive variables for SDL. METHODS: Cross-sectional survey research was conducted with a convenience sample of DH students (n = 229) in the United States using a web-based survey. The validated instruments used were the Self-Directed Learning Instrument (SDLI) to determine level of SDL, the Academic Locus of Control (ALC) scale for college students to evaluate LOC, and the Big Five Inventory-2-Extra Short Form (BFI-2-XS) to identify personality traits. Descriptive, correlation, and regression analyses were performed. RESULTS: The completion rate was 68.9% (n = 159). The average SDLI score of the sample was 82.59, indicating a high overall level of SDL. The average ALC score was 10.34, indicating an overall internal LOC. Internal LOC (B = -0.319, SE = 0.082, ß = -0.330, p < 0.001) and an open-minded personality trait (B = 1.156, SE = 0.363, ß = 0.233, p = 0.002) emerged as significant predictors of learning motivation (B = -0.138, SE = 0.057, ß = -0.214, p < 0.017) and self-monitoring (B = 0.553, SE = 0.253, ß = 0.167, p = 0.030) SDLI constructs. CONCLUSION: SDL among DH students can be cultivated by enhancing their learning motivation and self-monitoring skills. This may be achieved by helping learners increase their internal locus of control and open-mindedness. Future studies should explore exercises to help promote these traits.

Effectiveness of simulation-based clinical research curriculum for undergraduate medical students - a pre-post intervention study with external control.

BACKGROUND: Simulation is widely utilized in medical education. Exploring the effectiveness of high-fidelity simulation of clinical research within medical education may inform its integration into clinical research training curricula, finally cultivating physician-scientist development. METHODS: Standard teaching scripts for both clinical trial and cross-sectional study simulation were designed. We recruited undergraduates majoring in clinical medicine at 3th grade into a pre-post intervention study. Additionally, a cross-sectional survey randomly selected medical undergraduates at 4th or 5th grade, medical students in master and doctor degree as external controls. Self-assessment scores of knowledge and practice were collected using a 5-point Likert scale. Changes in scores were tested by Wilcoxon signed-rank test and group comparisons were conducted by Dunn's tests with multiple corrections. Multivariable quantile regressions were used to explore factors influencing the changes from baseline. RESULTS: Seventy-eight undergraduates involved the clinical trial simulation and reported improvement of 1.60 (95% CI, 1.48, 1.80, P < 0.001) in knowledge and 1.82 (95% CI, 1.64, 2.00, P < 0.001) in practice score. 83 undergraduates involved in the observational study simulation and reported improvement of 0.96 (95% CI, 0.79, 1.18, P < 0.001) in knowledge and 1.00 (95% CI, 0.79, 1.21, P < 0.001) in practice. All post-intervention scores were significantly higher than those of the three external control groups, P < 0.001. Higher agreement on the importance of clinical research were correlated with greater improvements in scores. Undergraduates in pre-post study showed high confidence in doing a future clinical research. CONCLUSION: Our study provides evidence supporting the integration of simulation into clinical research curriculum for medical students. The importance of clinical research can be emphasized during training to enhance learning effect.

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

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

Augmenting external control arms using Bayesian borrowing: a case study in first-line non-small cell lung cancer.

Aim: This study aimed to improve comparative effectiveness estimates and discuss challenges encountered through the application of Bayesian borrowing (BB) methods to augment an external control arm (ECA) constructed from real-world data (RWD) using historical clinical trial data in first-line non-small-cell lung cancer (NSCLC). Materials & methods: An ECA for a randomized controlled trial (RCT) in first-line NSCLC was constructed using ConcertAI Patient360™ to assess chemotherapy with or without cetuximab, in the bevacizumab-inappropriate subpopulation. Cardinality matching was used to match patient characteristics between the treatment arm (cetuximab + chemotherapy) and ECA. Overall survival (OS) was assessed as the primary outcome using Cox proportional hazards (PH). BB was conducted using a static power prior under a Weibull PH parameterization with borrowing weights from 0.0 to 1.0 and augmentation of the ECA from a historical control trial. Results: The constructed ECA yielded a higher overall survival (OS) hazard ratio (HR) (HR = 1.53; 95% CI: 1.21-1.93) than observed in the matched population of the RCT (HR = 0.91; 95% CI: 0.73-1.13). The OS HR decreased through the incorporation of BB (HR = 1.30; 95% CI: 1.08-1.54, borrowing weight = 1.0). BB was applied to augment the RCT control arm via a historical control which improved the precision of the observed HR estimate (1.03; 95% CI: 0.86-1.22, borrowing weight = 1.0), in comparison to the matched population of the RCT alone. Conclusion: In this study, the RWD ECA was unable to successfully replicate the OS estimates from the matched population of the selected RCT. The inability to replicate could be due to unmeasured confounding and variations in time-periods, follow-up and subsequent therapy. Despite these findings, we demonstrate how BB can improve precision of comparative effectiveness estimates, potentially aid as a bias assessment tool and mitigate challenges of traditional methods when appropriate external data sources are available.

Appropriate Relevancy and Reliability of Real-World Data for the Utilization of Regulatory Submission.

The extraction of data that contribute to regulatory approval from real-world data (RWD) is difficult because of the lack of a standardized data format and extraction methodology. Additionally, when real-world evidence (RWE) is used as an external control group, the similarity between internal and external control data is not evaluated. To investigate the data extraction methodology for the external control data of rare molecular subtypes, we have initiated the "REALISE" study. In this study, we aim to elucidate the "relevance" and "reliability" of RWD/RWE necessary for regulatory approval. As most databases are not designed for regulatory use in the creation phase, we will investigate retrospective methodologies to ensure RWD/RWE reliability. This study will compare the "relevance" and "reliability" of the ARCAD global database, SCRUM-Japan Registry, SCRUM-Japan observational study, and Flatiron Health RWD, and statistically analyze the differences and similarities among the four databases. We will also examine the methodology for extracting sufficiently relevant data from the SCRUM-Japan observational study. Additionally, if the reliability of the RWD/RWE does not reach the required level for regulatory approval, we will examine the methodologies to ensure the "reliability" of the SCRUM-Japan observational study for regulatory approval. The obtained results will be submitted to the "Consultation for Development of Registry" in the Pharmaceuticals and Medical Devices Agency, and we will discuss the standard methodology. The procedures and findings identified in the REALISE study will be organized from the perspectives of "database construction," "data analysis," and "outcome evaluation" and will be issued as "the draft guidelines."

The Next Horizon of Drug Development: External Control Arms and Innovative Tools to Enrich Clinical Trial Data.

Conducting clinical trials (CTs) has become increasingly costly and complex in terms of designing and operationalizing. These challenges exist in running CTs on novel therapies, particularly in oncology and rare diseases, where CTs increasingly target narrower patient groups. In this study, we describe external control arms (ECA) and other relevant tools, such as virtualization and decentralized clinical trials (DCTs), and the ability to follow the clinical trial subjects in the real world using tokenization. ECAs are typically constructed by identifying appropriate external sources of data, then by cleaning and standardizing it to create an analysis-ready data file, and finally, by matching subjects in the external data with the subjects in the CT of interest. In addition, ECA tools also include subject-level meta-analysis and simulated subjects' data for analyses. By implementing the recent advances in digital health technologies and devices, virtualization, and DCTs, realigning of CTs from site-centric designs to virtual, decentralized, and patient-centric designs can be done, which reduces the patient burden to participate in the CTs and encourages diversity. Tokenization technology allows linking the CT data with real-world data (RWD), creating more comprehensive and longitudinal outcome measures. These tools provide robust ways to enrich the CT data for informed decision-making, reduce the burden on subjects and costs of trial operations, and augment the insights gained for the CT data.

Accelerating drug development at Bristol Myers Squibb through innovation.

This paper focuses on the use of novel technologies and innovative trial designs to accelerate evidence generation and increase pharmaceutical Research and Development (R&D) productivity, at Bristol Myers Squibb. We summarize learnings with case examples, on how we prepared and continuously evolved to address the increasing cost, complexities, and external pressures in drug development, to bring innovative medicines to patients much faster. These learnings were based on review of internal efforts toward accelerating R&D focusing on four key areas: adopting innovative trial designs, optimizing trial designs, leveraging external control data, and implementing novel methods using artificial intelligence and machine learning.

Considerations for master protocols using external controls.

There has been an increasing use of master protocols in oncology clinical trials because of its efficiency to accelerate cancer drug development and flexibility to accommodate multiple substudies. Depending on the study objective and design, a master protocol trial can be a basket trial, an umbrella trial, a platform trial, or any other form of trials in which multiple investigational products and/or subpopulations are studied under a single protocol. Master protocols can use external data and evidence (e.g. external controls) for treatment effect estimation, which can further improve efficiency of master protocol trials. This paper provides an overview of different types of external controls and their unique features when used in master protocols. Some key considerations in master protocols with external controls are discussed including construction of estimands, assessment of fit-for-use real-world data, and considerations for different types of master protocols. Similarities and differences between regular randomized controlled trials and master protocols when using external controls are discussed. A targeted learning-based causal roadmap is presented which constitutes three key steps: (1) define a target statistical estimand that aligns with the causal estimand for the study objective, (2) use an efficient estimator to estimate the target statistical estimand and its uncertainty, and (3) evaluate the impact of causal assumptions on the study conclusion by performing sensitivity analyses. Two illustrative examples for master protocols using external controls are discussed for their merits and possible improvement in causal effect estimation.

Applying the estimand and target trial frameworks to external control analyses using observational data: a case study in the solid tumor setting.

Introduction: In causal inference, the correct formulation of the scientific question of interest is a crucial step. The purpose of this study was to apply causal inference principles to external control analysis using observational data and illustrate the process to define the estimand attributes. Methods: This study compared long-term survival outcomes of a pooled set of three previously reported randomized phase 3 trials studying patients with metastatic non-small cell lung cancer receiving front-line chemotherapy and similar patients treated with front-line chemotherapy as part of routine clinical care. Causal inference frameworks were applied to define the estimand aligned with the research question and select the estimator to estimate the estimand of interest. Results: The estimand attributes of the ideal trial were defined using the estimand framework. The target trial framework was used to address specific issues in defining the estimand attributes using observational data from a nationwide electronic health record-derived de-identified database. The two frameworks combined allow to clearly define the estimand and the aligned estimator while accounting for key baseline confounders, index date, and receipt of subsequent therapies. The hazard ratio estimate (point estimate with 95% confidence interval) comparing the randomized clinical trial pooled control arm with the external control was close to 1, which is indicative of similar survival between the two arms. Discussion: The proposed combined framework provides clarity on the causal contrast of interest and the estimator to adopt, and thus facilitates design and interpretation of the analyses.

Quantitative bias analysis for external control arms using real-world data in clinical trials: a primer for clinical researchers.

Development of medicines in rare oncologic patient populations are growing, but well-powered randomized controlled trials are typically extremely challenging or unethical to conduct in such settings. External control arms using real-world data are increasingly used to supplement clinical trial evidence where no or little control arm data exists. The construction of an external control arm should always aim to match the population, treatment settings and outcome measurements of the corresponding treatment arm. Yet, external real-world data is typically fraught with limitations including missing data, measurement error and the potential for unmeasured confounding given a nonrandomized comparison. Quantitative bias analysis (QBA) comprises a collection of approaches for modelling the magnitude of systematic errors in data which cannot be addressed with conventional statistical adjustment. Their applications can range from simple deterministic equations to complex hierarchical models. QBA applied to external control arm represent an opportunity for evaluating the validity of the corresponding comparative efficacy estimates. We provide a brief overview of available QBA approaches and explore their application in practice. Using a motivating example of a comparison between pralsetinib single-arm trial data versus pembrolizumab alone or combined with chemotherapy real-world data for RET fusion-positive advanced non-small cell lung cancer (aNSCLC) patients (1-2% among all NSCLC), we illustrate how QBA can be applied to external control arms. We illustrate how QBA is used to ascertain robustness of results despite a large proportion of missing data on baseline ECOG performance status and suspicion of unknown confounding. The robustness of findings is illustrated by showing that no meaningful change to the comparative effect was observed across several 'tipping-point' scenario analyses, and by showing that suspicion of unknown confounding was ruled out by use of E-values. Full R code is also provided.

Leveraging external control data in the design and analysis of neuro-oncology trials: Pearls and perils.

BACKGROUND: Randomized controlled trials have been the gold standard for evaluating medical treatments for many decades but they are often criticized for requiring large sample sizes. Given the urgent need for better therapies for glioblastoma, it has been argued that data collected from patients treated with the standard regimen can provide high-quality external control data to supplement or replace concurrent control arm in future glioblastoma trials. METHODS: In this article, we provide an in-depth appraisal of the use of external control data in the context of neuro-oncology trials. We describe several clinical trial designs with particular attention to how external information is utilized and address common fallacies that may lead to inappropriate adoptions of external control data. RESULTS: Using 2 completed glioblastoma trials, we illustrate the use of an assessment tool that lays out a blueprint for assembling a high-quality external control data set. Using statistical simulations, we draw caution from scenarios where these approaches can fall short on controlling the type I error rate. CONCLUSIONS: While this approach may hold promise in generating informative data in certain settings, this sense of optimism should be tampered with a healthy dose of skepticism due to a myriad of design and analysis challenges articulated in this review. Importantly, careful planning is key to its successful implementation.

An evolutionary algorithm for the direct optimization of covariate balance between nonrandomized populations.

Matching reduces confounding bias in comparing the outcomes of nonrandomized patient populations by removing systematic differences between them. Under very basic assumptions, propensity score (PS) matching can be shown to eliminate bias entirely in estimating the average treatment effect on the treated. In practice, misspecification of the PS model leads to deviations from theory and matching quality is ultimately judged by the observed post-matching balance in baseline covariates. Since covariate balance is the ultimate arbiter of successful matching, we argue for an approach to matching in which the success criterion is explicitly specified and describe an evolutionary algorithm to directly optimize an arbitrary metric of covariate balance. We demonstrate the performance of the proposed method using a simulated dataset of 275,000 patients and 10 matching covariates. We further apply the method to match 250 patients from a recently completed clinical trial to a pool of more than 160,000 patients identified from electronic health records on 101 covariates. In all cases, we find that the proposed method outperforms PS matching as measured by the specified balance criterion. We additionally find that the evolutionary approach can perform comparably to another popular direct optimization technique based on linear integer programming, while having the additional advantage of supporting arbitrary balance metrics. We demonstrate how the chosen balance metric impacts the statistical properties of the resulting matched populations, emphasizing the potential impact of using nonlinear balance functions in constructing an external control arm. We release our implementation of the considered algorithms in Python.