New statistical methods to compare the effectiveness of adaptive treatment plans
It is doubly robust in the sense that the average treatment effects are correctly estimated when either of the following conditions is satisfied: (1) The GP mean function correctly specifies the potential outcome model; and (2) the covariance function correctly specifies the matching structure. The...
| Main Author: | |
|---|---|
| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
[Washington, D.C.]
Patient-Centered Outcomes Research Institute (PCORI)
[2020], 2020
|
| Series: | Final research report
|
| Online Access: | |
| Collection: | National Center for Biotechnology Information - Collection details see MPG.ReNa |
| Summary: | It is doubly robust in the sense that the average treatment effects are correctly estimated when either of the following conditions is satisfied: (1) The GP mean function correctly specifies the potential outcome model; and (2) the covariance function correctly specifies the matching structure. The pcJIA CER study suggests that the early-combination plan is more effective in reducing disease activity 1 year later. We developed a user-friendly graphic interface online R Shiny application, "PCATS," which is easy to use, making GPMatch and BART methods accessible to general CER investigators. LIMITATIONS: The GPMatch method is computationally intensive and not yet extended to nonnormally distributed outcomes. The PCATS online app assumes no missing data and single time-dependent confounding. Missing data are an inherent feature of EMR data, and our CER study addressed missingness at the design, data management, and data analysis steps. By 12 months, the early-combination plan remained more effective than the step-up plan: The average improvement in cJADAS10 was 2.6 points (95% CI, 0.6-4.6 points) if the first-line treatment was continued or reduced and 2.2 points (95% CI, 0.3-4.14 points) if the treatment was escalated. Both CTPs were effective in improving the PedsQL score by 12 months, reporting improvements of 74.8 (+/-) 2.0 and 80.4 (+/-) 3.7 points for the step-up and early-combination CTPs, respectively. If treated on the early-combination plan, patients were expected to achieve an average of 5.61 (95% CI, −3.89 to 15.12) more points on the PedsQL than were patients treated on the step-up plan. CONCLUSIONS: The GPMatch method accomplishes matching and flexible modeling in the same step and has well-calibrated frequentist properties. Nevertheless, the study results may be limited by the missing data handling procedures. We assume that the EMR captures important treatment considerations from the clinician's perspective, but not from the patient's perspective. Sensitivity analyses were performed to account for missing potential confounders from the patient's perspective. Finally, the CER study only analyzed 2 of the 3 CTPs METHODS: We propose the GPMatch method, a nonparametric full bayesian doubly robust causal inference method that uses Gaussian process (GP) prior as a matching tool. We performed simulation studies to evaluate its performance compared with that of some widely used causal inference methods: propensity score subclassification, augmented inverse treatment probability weighting, regression adjustment, and bayesian additive regression trees (BART), under dual-misspecification settings. We extended both GPMatch and BART methods for ATS and applied them to electronic medical record (EMR) data to compare 2 consensus treatment plans (CTPs) that began with a disease-modifying antirheumatic drug (DMARD) at different times in treating children with pcJIA: the early-combination plan uses biologic and nonbiologic DMARDs (b+nbDMARD) soon after diagnosis, while the step-up plan starts with an nbDMARD first and then introduces bDMARDs later. The primary end points were Clinical Juvenile Arthritis Disease Activity Score (cJADAS10, with a cutoff at 10 for active joint count) results at 6 and 12 months, and the secondary end point was the Pediatric Quality of Life Inventory (PedsQL) score at 12 months. RESULTS: Simulation studies demonstrated that GPMatch, followed by BART, performed as well as or better than some commonly used non-bayesian causal inference methods for comparing both nonadaptive treatment strategies and ATS, as measured by the root mean square error (RMSE) and median absolute error (MAE). The pcJIA CER suggests that by 6 months, the early-combination plan reduced disease activity on average by 2.0 points (95% CI, 0.4-3.6 points) more than the step-up plan as measured by the cJADAS10. BACKGROUND: During routine clinical care, treatments are adaptive to patients' responses to previous treatment assignments. However, methods for comparative effectiveness research (CER) are predominately designed for nonadaptive treatments. This project aimed to evaluate the comparative effectiveness of patient-centered adaptive treatment strategies (PCATS) at the initiation of treatment and over the course of the disease progression. As a case in point, despite many medication options, polyarticular-course juvenile idiopathic arthritis (pcJIA) is often refractory and requires better adaptive treatment strategies (ATS). OBJECTIVES: Aim 1. To develop, refine, and disseminate bayesian causal inference methods for evaluating clinical effectiveness and for informing better PCATS. Aim 2. To evaluate the clinical effectiveness of the recommended ATS for patients with pcJIA using real-world data. |
|---|---|
| Physical Description: | 1 PDF file (286 pages) illustrations |