Exploring the feasibility of using real-world data from a large clinical data research network to simulate clinical trials of Alzheimer\u2019s disease

Zhaoyi Chen,Jiang Bian,Yi Guo,Zhe He,William R Hogan,Elizabeth A Shenkman,Mattia Prosperi,Hansi Zhang,Fei Wang,Thomas J George

doi:10.1038/s41746-021-00452-1

Abstract

In this study, we explored the feasibility of using real-world data (RWD) from a large clinical research network to simulate real-world clinical trials of Alzheimer’s disease (AD). The target trial (i.e., NCT00478205) is a Phase III double-blind, parallel-group trial that compared the 23 mg donepezil sustained release with the 10 mg donepezil immediate release formulation in patients with moderate to severe AD. We followed the target trial’s study protocol to identify the study population, treatment regimen assignments and outcome assessments, and to set up a number of different simulation scenarios and parameters. We considered two main scenarios: (1) a one-arm simulation: simulating a standard-of-care (SOC) arm that can serve as an external control arm; and (2) a two-arm simulation: simulating both intervention and control arms with proper patient matching algorithms for comparative effectiveness analysis. In the two-arm simulation scenario, we used propensity score matching controlling for baseline characteristics to simulate the randomization process. In the two-arm simulation, higher serious adverse event (SAE) rates were observed in the simulated trials than the rates reported in original trial, and a higher SAE rate was observed in the 23 mg arm than in the 10 mg SOC arm. In the one-arm simulation scenario, similar estimates of SAE rates were observed when proportional sampling was used to control demographic variables. In conclusion, trial simulation using RWD is feasible in this example of AD trial in terms of safety evaluation. Trial simulation using RWD could be a valuable tool for post-market comparative effectiveness studies and for informing future trials’ design. Nevertheless, such an approach may be limited, for example, by the availability of RWD that matches the target trials of interest, and further investigations are warranted.

Highlights

Clinical trials, especially randomized controlled trials (RCTs), are critical in the drug discovery and development process to assess the efficacy and safety of the new treatment[1]
Similar to what we have found in our prior study[11], the common reasons for not computable criteria are (1) data elements needed for the criterion do not exist in the source database (e.g., “A cranial image is required, with no evidence of focal brain disease that would account for dementia.”), or (2) the criterion asked for subjective information either from the patient (e.g., “Patients who are unwilling or unable to fulfill the requirements of the study.”) or the investigator (e.g., “Clinical laboratory values must be within normal limits or, if abnormal, must be judged not clinically significant by the investigator.”)
We demonstrated that we could achieve similar estimate of serious adverse event (SAE) rates as the original trial when proportional sampling accounting for race distribution was used; and the statistics of the simulated control arm were stable across all bootstrap simulation runs, which suggests that using real-world data (RWD) we can robustly simulate the “standard of care” control arm

Summary

Introduction

Especially randomized controlled trials (RCTs), are critical in the drug discovery and development process to assess the efficacy and safety of the new treatment[1]. While the rigorously controlled conditions of clinical trials can reduce bias and improve the internal validity of the study results, they come with the drawbacks of high financial costs and long execution time[2]. The total cost of developing an Alzheimer’s disease (AD) drug was estimated at $5.6 billion with a timeline of 13 years from the preclinical studies to approval by the Food and Drug Administration (FDA)[3]. Strategies that can accelerate the drug development process and reduce costs will be of interest to pharmaceutical companies and benefit the patients.

Methods

Results

Conclusion