Probability of Regulatory Approval Over Time: A Cohort Study of Cancer Therapies.

PURPOSE: New cancer therapies are frequently evaluated in multiple disease indications. We evaluated whether the probability of achieving US Food and Drug Administration (FDA) approval for a new cancer therapy changes with time. METHODS: We identified a cohort of anticancer drugs with a first registered efficacy trial from 2007 to 2011 on ClinicalTrials.gov. We downloaded all clinical trials for each included drug from the initiation of efficacy testing to January 11, 2021. Each trial was categorized by cancer indication and assigned to investigational trajectories on the basis of unique drug-indication pairings. We performed a univariate Cox's proportional hazards regression to assess the probability of a trajectory leading to regulatory approval over time since initiation of the first efficacy trial for a given drug. RESULTS: We included 213 drugs in our cohort, of which 37 (17.4%) received FDA approval in at least one oncology indication. In our primary analysis, we found a 15% decrease in the probability of approval for every year since initiation of the first efficacy trial (hazard ratio [HR], 0.85 [95% CI, 0.73 to 0.99]; P = .032). We found a 45% increase in the probability of approval for the first trajectory launched for a given drug in comparison with all others (HR, 0.55 [95% CI, 0.33 to 0.91]; P = .021). CONCLUSION: Drug-indication pairings pursued years after initial testing for efficacy have lowered probability of affecting care. Clinical trial investigators, sponsors, and regulatory bodies may benefit from awareness of this trend when considering both early and late trajectory trials in a drug's development.

Frequency of multiple changes to prespecified primary outcomes of clinical trials completed between 2009 and 2017 in German university medical centers: A meta-research study.

BACKGROUND: Clinical trial registries allow assessment of deviations of published trials from their protocol, which may indicate a considerable risk of bias. However, since entries in many registries can be updated at any time, deviations may go unnoticed. We aimed to assess the frequency of changes to primary outcomes in different historical versions of registry entries, and how often they would go unnoticed if only deviations between published trial reports and the most recent registry entry are assessed. METHODS AND FINDINGS: We analyzed the complete history of changes of registry entries in all 1746 randomized controlled trials completed at German university medical centers between 2009 and 2017, with published results up to 2022, that were registered in ClinicalTrials.gov or the German WHO primary registry (German Clinical Trials Register; DRKS). Data were retrieved on 24 January 2022. We assessed deviations between registry entries and publications in a random subsample of 292 trials. We determined changes of primary outcomes (1) between different versions of registry entries at key trial milestones, (2) between the latest registry entry version and the results publication, and (3) changes that occurred after trial start with no change between latest registry entry version and publication (so that assessing the full history of changes is required for detection of changes). We categorized changes as major if primary outcomes were added, dropped, changed to secondary outcomes, or secondary outcomes were turned into primary outcomes. We also assessed (4) the proportion of publications transparently reporting changes and (5) characteristics associated with changes. Of all 1746 trials, 23% (n = 393) had a primary outcome change between trial start and latest registry entry version, with 8% (n = 142) being major changes, that is, primary outcomes were added, dropped, changed to secondary outcomes, or secondary outcomes were turned into primary outcomes. Primary outcomes in publications were different from the latest registry entry version in 41% of trials (120 of the 292 sampled trials; 95% confidence interval (CI) [35%, 47%]), with major changes in 18% (54 of 292; 95% CI [14%, 23%]). Overall, 55% of trials (161 of 292; 95% CI [49%, 61%]) had primary outcome changes at any timepoint over the course of a trial, with 23% of trials (67 of 292; 95% CI [18%, 28%]) having major changes. Changes only within registry records, with no apparent discrepancy between latest registry entry version and publication, were observed in 14% of trials (41 of 292; 95% CI [10%, 19%]), with 4% (13 of 292; 95% CI [2%, 7%]) being major changes. One percent of trials with a change reported this in their publication (2 of 161 trials; 95% CI [0%, 4%]). An exploratory logistic regression analysis indicated that trials were less likely to have a discrepant registry entry if they were registered more recently (odds ratio (OR) 0.74; 95% CI [0.69, 0.80]; p<0.001), were not registered on ClinicalTrials.gov (OR 0.41; 95% CI [0.23, 0.70]; p = 0.002), or were not industry-sponsored (OR 0.29; 95% CI [0.21, 0.41]; p<0.001). Key limitations include some degree of subjectivity in the categorization of outcome changes and inclusion of a single geographic region. CONCLUSIONS: In this study, we observed that changes to primary outcomes occur in 55% of trials, with 23% trials having major changes. They are rarely transparently reported in the results publication and often not visible in the latest registry entry version. More transparency is needed, supported by deeper analysis of registry entries to make these changes more easily recognizable. Protocol registration: Open Science Framework (https://osf.io/t3qva; amendment in https://osf.io/qtd2b).

Artificial Intelligence for Clinical Decision Support in Acute Ischemic Stroke: A Systematic Review.

BACKGROUND: Established randomized trial-based parameters for acute ischemic stroke group patients into generic treatment groups, leading to attempts using various artificial intelligence (AI) methods to directly correlate patient characteristics to outcomes and thereby provide decision support to stroke clinicians. We review AI-based clinical decision support systems in the development stage, specifically regarding methodological robustness and constraints for clinical implementation. METHODS: Our systematic review included full-text English language publications proposing a clinical decision support system using AI techniques for direct decision support in acute ischemic stroke cases in adult patients. We (1) describe data and outcomes used in those systems, (2) estimate the systems' benefits compared with traditional stroke diagnosis and treatment, and (3) reported concordance with reporting standards for AI in healthcare. RESULTS: One hundred twenty-one studies met our inclusion criteria. Sixty-five were included for full extraction. In our sample, utilized data sources, methods, and reporting practices were highly heterogeneous. CONCLUSIONS: Our results suggest significant validity threats, dissonance in reporting practices, and challenges to clinical translation. We outline practical recommendations for the successful implementation of AI research in acute ischemic stroke treatment and diagnosis.

Competition for recruitment in SARS-CoV-2 Trials in the United States: a longitudinal cohort analysis.

OBJECTIVE: Competition among trials for patient enrollment can impede recruitment. We hypothesized that this occurred early in the COVID-19 pandemic, when an unprecedented number of clinical trials were launched. We performed a simple and multivariable regression analysis evaluating the relationship between the proportion of SARS-CoV-2 investigational trial sites within each USA state with unsuccessful patient-participant recruitment and: (i) the proportion of cases required to reach state recruitment goals; (ii) state population based on data from the US Census; and, (iii) number of trial sites per state. RESULTS: Our study included 151 clinical trials. The proportion of trials with successful recruitment was 72.19% (109 of 151 trials). We did not find a significant relationship between unsuccessful patient-participant recruitment, state recruitment goals, state population or the number of trial sites per state in both our simple and multivariable regression analyses. Our results do not suggest that early in the COVID-19 pandemic, competition for patient-participants impeded successful recruitment in SARS-CoV-2 trials. This may reflect the unique circumstances of the first few months of the pandemic in the United States, in which the number and location of SARS-CoV-2 cases was sufficient to meet trial recruitment requirements, despite the large number of trials launched.

How informative were early SARS-CoV-2 treatment and prevention trials? a longitudinal cohort analysis of trials registered on ClinicalTrials.gov.

BACKGROUND: Early in the SARS-CoV-2 pandemic, commentators warned that some COVID trials were inadequately conceived, designed and reported. Here, we retrospectively assess the prevalence of informative COVID trials launched in the first 6 months of the pandemic. METHODS: Based on prespecified eligibility criteria, we created a cohort of Phase 1/2, Phase 2, Phase 2/3 and Phase 3 SARS-CoV-2 treatment and prevention efficacy trials that were initiated from 2020-01-01 to 2020-06-30 using ClinicalTrials.gov registration records. We excluded trials evaluating behavioural interventions and natural products, which are not regulated by the U.S. Food and Drug Administration (FDA). We evaluated trials on 3 criteria of informativeness: potential redundancy (comparing trial phase, type, patient-participant characteristics, treatment regimen, comparator arms and primary outcome), trials design (according to the recommendations set-out in the May 2020 FDA guidance document on SARS-CoV-2 treatment and prevention trials) and feasibility of patient-participant recruitment (based on timeliness and success of recruitment). RESULTS: We included all 500 eligible trials in our cohort, 58% of which were Phase 2 and 84.8% were directed towards the treatment of SARS-CoV-2. Close to one third of trials met all three criteria and were deemed informative (29.9% (95% Confidence Interval 23.7-36.9)). The proportion of potentially redundant trials in our cohort was 4.1%. Over half of the trials in our cohort (56.2%) did not meet our criteria for high quality trial design. The proportion of trials with infeasible patient-participant recruitment was 22.6%. CONCLUSIONS: Less than one third of COVID-19 trials registered on ClinicalTrials.gov during the first six months met all three criteria for informativeness. Shortcomings in trial design, recruitment feasibility and redundancy reflect longstanding weaknesses in the clinical research enterprise that were likely amplified by the exceptional circumstances of a pandemic.