Assessing the Value of Time Series Real-World and Clinical Trial Data vs. Baseline-Only Data in Predicting Responses to Pregabalin Therapy for Patients with Painful Diabetic Peripheral Neuropathy
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Background and Objective
Treatment challenges necessitate new approaches to customize care to individual patient needs. Integrating data from randomized controlled trials and observational studies may reduce potential covariate biases, yielding information to improve treatment outcomes. The objective of this study was to predict pregabalin responses, in individuals with painful diabetic peripheral neuropathy, by examining time series data (lagged inputs) collected after treatment initiation vs. baseline using microsimulation.
The platform simulated pregabalin-treated patients to estimate hypothetical future pain responses over 6 weeks based on six distinct time series regressions with lagged variables as inputs (hereafter termed “time series regressions”). Data were from three randomized controlled trials (N = 398) and an observational study (N = 3159). Regressions were derived after performing a hierarchical cluster analysis with a matched patient dataset from coarsened exact matching. Regressions were validated using unmatched (observational study vs. randomized controlled trial) patients. Predictive implications (of 6-week outcomes) were compared using only baseline vs. 1- to 2-week prior data.
Time series regressions for pain performed well (adjusted R2 0.85–0.91; root mean square error 0.53–0.57); those with only baseline data performed less well (adjusted R2 0.13–0.44; root mean square error 1.11–1.40). Simulated patient distributions yielded positive predictive values for > 50% pain score improvements from baseline for the six clusters (287–777 patients each; range 0.87–0.98).
Effective prediction of pregabalin response for painful diabetic peripheral neuropathy was accomplished through combining cluster analyses, coarsened exact matching, and time series regressions, reflecting distinct patterns of baseline and “on-treatment” variables. These results advance the understanding of microsimulation to predict patient treatment responses through integration and inter-relationships of multiple, complex, and time-dependent characteristics.
Plain Language Summary
Why Combine Different Data Sources?
Analyzing the tremendous amount of patient data can provide meaningful insights to improve healthcare quality. Using statistical methods to combine data from clinical trials with real-world studies can improve overall data quality (e.g., reducing biases related to real-world patient variability).
Why Consider a Time Series Analysis?
The best predictor of future outcomes is past outcomes. A “time series” collects data at regular intervals over time. Statistical analyses of time series data allow us to discern time-dependent patterns to predict future clinical outcomes. Modeling and simulation make it possible to combine enormous amounts of data from clinical trial databases to predict a patient’s clinical response based on data from similar patients. This approach improves selecting the right drug/dose for the right patient at the right time (i.e., personalized medicine). Using modeling and simulation, we predicted which patients would show a positive response to pregabalin (a neuropathic pain drug) for painful diabetic peripheral neuropathy.
What are the Major Findings and Implications?
For pregabalin-treated patients, a time series analysis had substantially more predictive value vs. analysis only of baseline data (i.e., data collected at treatment initiation). The ability to best predict which patients will respond to therapy has the overall implication of better informing drug treatment decisions. For example, an appropriate modeling and simulation platform complete with relevant historical clinical data could be integrated into a stand-alone device used to monitor and also predict a patient’s response to therapy based on daily outcome measures (e.g., smartphone apps, wearable technologies).
Editorial support in the form of copy editing and formatting was provided by Ray Beck, Jr, PhD, of Engage Scientific Solutions and was funded by Pfizer. These analyses were funded by Pfizer.
RAE and JA conceived, designed, and led all aspects of the analyses and related article. LM, AS, and RG performed all statistical analyses and/or simulation/analytics related to this study. EW and BE performed the statistical analyses for the original randomized controlled trials and observational study and offered insights related to those studies and analyses. BP, MB, and SW provided interpretations of the data related to clinical relevance and unmet medical needs. All authors participated in the drafting of the article and final approval of its content.
Compliance with Ethical Standards
These analyses were funded by Pfizer.
Conflict of interest
BE, BP, SW, and EW are employees of Pfizer. JA and MB were employed by Pfizer at the time the study was conducted. RE is an employee of Health Services Consulting Corporation who was a paid consultant by Pfizer in connection with this study and development of this article. LM, RG, and AS are employees of Fair Dynamics Consulting who were paid subcontractors to Health Services Consulting Corporation in connection with this study and the development of this article.
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