Joe Alexander Jr,1,* Roger A Edwards,2,* Luigi Manca,3 Roberto Grugni,3 Gianluca Bonfanti,3 Birol Emir,4 Ed Whalen,4 Steve Watt,1 Marina Brodsky,5 Bruce Parsons6
1Global Medical Affairs, Pfizer Inc, New York, NY 10017, USA; 2Health Services Consulting Corporation, Boxborough, MA 01719, USA; 3Fair Dynamics Consulting, SRL, Milan, Italy; 4Global Statistics, Pfizer Inc, New York, NY 10017, USA; 5Global Medical Affairs, Pfizer Inc, Groton, CT 06340, USA; 6Global Medical Product Evaluation, Pfizer Inc, New York, NY 10017, USA
*These authors contributed equally to this work
Correspondence: Roger A Edwards
Health Services Consulting Corporation, 169 Summer Road, Boxborough, MA 01719, USA
Tel +1 508472-0406 Fax +1 978264-0713
Purpose: Variability in patient treatment responses can be a barrier to effective care. Utilization of available patient databases may improve the prediction of treatment responses. We evaluated machine learning methods to predict novel, individual patient responses to pregabalin for painful diabetic peripheral neuropathy, utilizing an agent-based modeling and simulation platform that integrates real-world observational study (OS) data and randomized clinical trial (RCT) data.
Patients and methods: The best supervised machine learning methods were selected (through literature review) and combined in a novel way for aligning patients with relevant subgroups that best enable prediction of pregabalin responses. Data were derived from a German OS of pregabalin (N=2642) and nine international RCTs (N=1320). Coarsened exact matching of OS and RCT patients was used and a hierarchical cluster analysis was implemented. We tested which machine learning methods would best align candidate patients with specific clusters that predict their pain scores over time. Cluster alignments would trigger assignments of cluster-specific time-series regressions with lagged variables as inputs in order to simulate “virtual” patients and generate 1000 trajectory variations for given novel patients.
Results: Instance-based machine learning methods (k-nearest neighbor, supervised fuzzy c-means) were selected for quantitative analyses. Each method alone correctly classified 56.7% and 39.1% of patients, respectively. An “ensemble method” (combining both methods) correctly classified 98.4% and 95.9% of patients in the training and testing datasets, respectively.
Conclusion: An ensemble combination of two instance-based machine learning techniques best accommodated different data types (dichotomous, categorical, continuous) and performed better than either technique alone in assigning novel patients to subgroups for predicting treatment outcomes using microsimulation. Assignment of novel patients to a cluster of similar patients has the potential to improve prediction of patient outcomes for chronic conditions in which initial treatment response can be incorporated using microsimulation.
Clinical trial registries: www.clinicaltrials.gov: NCT00156078, NCT00159679, NCT00143156, NCT00553475.
Keywords: coarsened exact matching, hierarchical cluster analysis, time series regressions, agent-based modeling and simulation, machine learning
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