Trial designs using real-world data: The changing landscape of the regulatory approval process.

PURPOSE: There is a need to develop hybrid trial methodology combining the best parts of traditional randomized controlled trials (RCTs) and observational study designs to produce real-world evidence (RWE) that provides adequate scientific evidence for regulatory decision-making. METHODS: This review explores how hybrid study designs that include features of RCTs and studies with real-world data (RWD) can combine the advantages of both to generate RWE that is fit for regulatory purposes. RESULTS: Some hybrid designs include randomization and use pragmatic outcomes; other designs use single-arm trial data supplemented with external comparators derived from RWD or leverage novel data collection approaches to capture long-term outcomes in a real-world setting. Some of these approaches have already been successfully used in regulatory decisions, raising the possibility that studies using RWD could increasingly be used to augment or replace traditional RCTs for the demonstration of drug effectiveness in certain contexts. These changes come against a background of long reliance on RCTs for regulatory decision-making, which are labor-intensive, costly, and produce data that can have limited applicability in real-world clinical practice. CONCLUSIONS: While RWE from observational studies is well accepted for satisfying postapproval safety monitoring requirements, it has not commonly been used to demonstrate drug effectiveness for regulatory purposes. However, this position is changing as regulatory opinions, guidance frameworks, and RWD methodologies are evolving, with growing recognition of the value of using RWE that is acceptable for regulatory decision-making.

Mapping track density changes in nigrostriatal and extranigral pathways in Parkinson's disease.

In Parkinson's disease (PD) the demonstration of neuropathological disturbances in nigrostriatal and extranigral brain pathways using magnetic resonance imaging remains a challenge. Here, we applied a novel diffusion-weighted imaging approach-track density imaging (TDI). Twenty-seven non-demented Parkinson's patients (mean disease duration: 5 years, mean score on the Hoehn & Yahr scale=1.5) were compared with 26 elderly controls matched for age, sex, and education level. Track density images were created by sampling each subject's spatially normalized fiber tracks in 1mm isotropic intervals and counting the fibers that passed through each voxel. Whole-brain voxel-based analysis was performed and significance was assessed with permutation testing. Statistically significant increases in track density were found in the Parkinson's patients, relative to controls. Clusters were distributed in disease-relevant areas including motor, cognitive, and limbic networks. From the lower medulla to the diencephalon and striatum, clusters encompassed the known location of the locus coeruleus and pedunculopontine nucleus in the pons, and from the substantia nigra up to medial aspects of the posterior putamen, bilaterally. The results identified in brainstem and nigrostriatal pathways show a large overlap with the known distribution of neuropathological changes in non-demented PD patients. Our results also support an early involvement of limbic and cognitive networks in Parkinson's disease.

A finite-element reciprocity solution for EEG forward modeling with realistic individual head models.

We present a finite element modeling (FEM) implementation for solving the forward problem in electroencephalography (EEG). The solution is based on Helmholtz's principle of reciprocity which allows for dramatically reduced computational time when constructing the leadfield matrix. The approach was validated using a 4-shell spherical model and shown to perform comparably with two current state-of-the-art alternatives (OpenMEEG for boundary element modeling and SimBio for finite element modeling). We applied the method to real human brain MRI data and created a model with five tissue types: white matter, gray matter, cerebrospinal fluid, skull, and scalp. By calculating conductivity tensors from diffusion-weighted MR images, we also demonstrate one of the main benefits of FEM: the ability to include anisotropic conductivities within the head model. Root-mean square deviation between the standard leadfield and the leadfield including white-matter anisotropy showed that ignoring the directional conductivity of white matter fiber tracts leads to orientation-specific errors in the forward model. Realistic head models are necessary for precise source localization in individuals. Our approach is fast, accurate, open-source and freely available online.

The Current Landscape and Emerging Applications for Real-World Data in Diagnostics and Clinical Decision Support and its Impact on Regulatory Decision Making.

Real-world data (RWD) and real-world evidence (RWE) are becoming essential tools for informing regulatory decision making in health care and offer an opportunity for all stakeholders in the healthcare ecosystem to evaluate medical products throughout their lifecycle. Although considerable interest has been given to regulatory decisions supported by RWE for treatment authorization, especially in rare diseases, less attention has been given to RWD/RWE related to in vitro diagnostic (IVD) products and clinical decision support systems (CDSS). This review examines current regulatory practices in relation to IVD product development and discusses the use of CDSS in assisting clinicians to retrieve, filter, and analyze patient data in support of complex decisions regarding diagnosis and treatment. The review then explores how utilizing RWD could augment regulatory body understanding of test performance, clinical outcomes, and benefit-risk profiles, and how RWD could be leveraged to augment CDSS and improve safety, quality, and efficiency of healthcare practices. Whereas we present examples of RWD assisting in the regulation of IVDs and CDSS, we also highlight key challenges within the current healthcare system which are impeding the potential of RWE to be fully realized. These challenges include issues such as data availability, reliability, accessibility, harmonization, and interoperability, often for reasons specific to diagnostics. Finally, we review ways that these challenges are actively being addressed and discuss how private-public collaborations and the implementation of standardized language and protocols are working toward producing more robust RWD and RWE to support regulatory decision making.

Application of Real-World Data to External Control Groups in Oncology Clinical Trial Drug Development.

Randomized controlled trials (RCTs) that assess overall survival are considered the "gold standard" when evaluating the efficacy and safety of a new oncology intervention. However, single-arm trials that use surrogate endpoints (e.g., objective response rate or duration of response) to evaluate clinical benefit have become the basis for accelerated or breakthrough regulatory approval of precision oncology drugs for cases where the target and research populations are relatively small. Interpretation of efficacy in single-arm trials can be challenging because such studies lack a standard-of-care comparator arm. Although an external control group can be based on data from other clinical trials, using an external control group based on data collected outside of a trial may not only offer an alternative to both RCTs and uncontrolled single-arm trials, but it may also help improve decision-making by study sponsors or regulatory authorities. Hence, leveraging real-world data (RWD) to construct external control arms in clinical trials that investigate the efficacy and safety of drug interventions in oncology has become a topic of interest. Herein, we review the benefits and challenges associated with the use of RWD to construct external control groups, and the relevance of RWD to early oncology drug development.

Use of Real-World Data to Emulate a Clinical Trial and Support Regulatory Decision Making: Assessing the Impact of Temporality, Comparator Choice, and Method of Adjustment.

External controls have been primarily used in the setting of single-arm trials of rare diseases; their use in common diseases has not been readily investigated, nor is there guidance on how to best select comparators. Thus, the objective of this study was to emulate a large cardiovascular outcome trial of type 2 diabetes to compare associations of effectiveness with different comparator groups to those reported in the trial. Using the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results (LEADER) trial, we investigated six comparator groups using three calendar time periods (Early: 1999-2003; Later: 2004-2008, and Contemporaneous: 2009-2013) and two comparators (sulfonylureas and other second-to-third-line antidiabetic drugs). Hazard ratios (HRs) of the three-point composite cardiovascular outcome were estimated using four variations of the propensity score (adjustment, stratification, fine stratification, and matching) and compared with the LEADER trial (HR, 0.87; 95% confidence interval, 0.78-0.97). When comparing users of liraglutide with users of sulfonylureas, the HRs ranged from 0.57 to 1.03, with estimates in the early period most closely reflecting the LEADER trial (HR, 0.57-0.88). In contrast, the HRs ranged from 0.73 to 0.97 when comparing liraglutide users with users of any second-to-third-line antidiabetic drugs, although the later period generated estimates closest to the LEADER trial (HR, 0.77-0.84). Different methods of adjustment led to generally consistent HRs, aside from the fine stratification in the early period. This study highlights the complex interplay between comparator, temporality, and method of adjustment when selecting comparators using real-word data. These design choices must be considered in the design of trial emulation studies.

Defining Clinically Relevant Target Populations Using Real-World Data to Guide the Design of Representative Antidiabetic Drug Trials.

The US Food and Drug Administration is considering replacing cardiovascular outcome trials of antidiabetic drugs with trials that better represent patients with type 2 diabetes. However, designing such representative trials requires understanding the underlying target populations (i.e., populations intended to receive the drug in the real-world setting). Thus, we used the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results (LEADER) trial as a motivating example to illustrate how different target populations impact trial representativeness. Using the United Kingdom Clinical Practice Research Datalink, we identified three target populations: (i) all patients with type 2 diabetes; (ii) patients prescribed liraglutide; and (iii) patients who would have been eligible to receive liraglutide based on treatment stage (i.e., patients with poorly controlled diabetes eligible to receive a second-to-fifth line antidiabetic drug). We then examined the representativeness of the LEADER trial by applying its eligibility criteria to each target population. The target populations of patients with type 2 diabetes (n = 279,763), those prescribed liraglutide (n = 14,421), and those eligible to receive liraglutide based on the treatment stage (n = 85,610) differed substantially in terms of hemoglobin A1c, body mass index, prevalence of heart failure, and chronic kidney disease. Applying the LEADER trial eligibility criteria to these target populations resulted in the inclusion of 19.1%, 20.7%, and 34.8% patients, respectively. This study highlights how real-world data can be used to define different target populations. Explicitly defining these target populations can help in the design of future trials of antidiabetic drugs.

Influence of noise correction on intra- and inter-subject variability of quantitative metrics in diffusion kurtosis imaging.

Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR-related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies making valuable inferences in group analysis and longitudinal studies.

Alterations in the motor neuron-renshaw cell circuit in the Sod1(G93A) mouse model.

Motor neurons become hyperexcitable during progression of amyotrophic lateral sclerosis (ALS). This abnormal firing behavior has been explained by changes in their membrane properties, but more recently it has been suggested that changes in premotor circuits may also contribute to this abnormal activity. The specific circuits that may be altered during development of ALS have not been investigated. Here we examined the Renshaw cell recurrent circuit that exerts inhibitory feedback control on motor neuron firing. Using two markers for Renshaw cells (calbindin and cholinergic nicotinic receptor subunit alpha2 [Chrna2]), two general markers for motor neurons (NeuN and vesicular acethylcholine transporter [VAChT]), and two markers for fast motor neurons (Chondrolectin and calcitonin-related polypeptide alpha [Calca]), we analyzed the survival and connectivity of these cells during disease progression in the Sod1(G93A) mouse model. Most calbindin-immunoreactive (IR) Renshaw cells survive to end stage but downregulate postsynaptic Chrna2 in presymptomatic animals. In motor neurons, some markers are downregulated early (NeuN, VAChT, Chondrolectin) and others at end stage (Calca). Early downregulation of presynaptic VAChT and Chrna2 was correlated with disconnection from Renshaw cells as well as major structural abnormalities of motor axon synapses inside the spinal cord. Renshaw cell synapses on motor neurons underwent more complex changes, including transitional sprouting preferentially over remaining NeuN-IR motor neurons. We conclude that the loss of presynaptic motor axon input on Renshaw cells occurs at early stages of ALS and disconnects the recurrent inhibitory circuit, presumably resulting in diminished control of motor neuron firing. J. Comp. Neurol. 521:1449-1469, 2013. © 2012 Wiley Periodicals, Inc.