Creation and implementation of a historical controls database from randomized clinical trials

Abstract

Background: Ethical concerns about randomly assigning patients to suboptimal or placebo arms and the paucity of willing participants for randomization into control and experimental groups have renewed focus on the use of historical controls in clinical trials. Although databases of historical controls have been advocated, no published reports have described the technical and informatics issues involved in their creation.

Objective: To create a historical controls database by leveraging internal clinical trial data at Pfizer, focusing on patients who received only placebo in randomized controlled trials.

Methods: We transformed disparate clinical data sources by indexing, developing, and integrating clinical data within internal databases and archives. We focused primarily on trials mapped into a consistent standard and trials in the pain therapeutic area as a pilot.

Results: Of the more than 20,000 internal Pfizer clinical trials, 2404 completed placebo controlled studies with a parallel design were identified. Due to challenges with informed consent and data standards used in older clinical trials, studies completed before 2000 were excluded, yielding 1134 studies from which placebo subjects and associated clinical data were extracted.

Conclusions: It is technically feasible to pool portions of placebo populations through a stratification and segmentation approach for a historical placebo group database. A sufficiently large placebo controls database would enable previous distribution calculations on representative populations to supplement, not eliminate, the placebo arm of future clinical trials. Creation of an industry-wide placebo controls database, utilizing a universal standard, beyond the borders of Pfizer would add significant efficiencies to the clinical trial and drug development process.

Keywords: Historical Controls Database; ePlacebo; ePlacebo Database.

Figure 1

Road map and data access methods for ePlacebo database. Methods, systems, and algorithms employed to extract data along with decision algorithms and filters applied at each step are depicted. CCTR, corporate clinical trial registry; CDARS, clinical data analysis and reporting system; Demog, demography; OC, Oracle Clinical; PDS, Pfizer data standard; TA, therapeutic area.

Figure 2

Basic characteristics of subjects in ePlacebo database. (A) Histogram of subject counts in ePlacebo database from randomized controlled trials completed after 2000 for five therapeutic areas. (B–D) Counts of placebo subjects in pain by disease area (B) and snapshot of demographics for pain placebo patients by age (C), and race (D) with given gender breakdowns. Chronic, severe chronic pain; CvMeD, cardiovascular metabolic; Fibro, fibromyalgia; GenUr, genitourinary; Infl, inflammation; I. Pain, inflammatory pain; Neuro, neuroscience; Neurop, neuropathic pain; OA, osteoarthritis; OAP, osteoarthritis pain.

Figure 3

Scatter plot of laboratory values from ePlacebo database. Scatter plot depicts normal (green), greater than 1.5 times upper normal limit (blue), and greater than 3 times upper normal limit (red) values. Subjects are plotted on x-axis and normalized laboratory values are plotted on y-axis. Note multiple data points with same value can appear as one data point.