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Comparing dynamic treatment strategies in patient-centered outcomes research
Investigator (PI): Zhang, Yi
Performing Organization (PO): (Current): Medical Technology and Practice Patterns Institute / (301) 652-4005
Supporting Agency (SA): Patient-Centered Outcomes Research Institute (PCORI)
Initial Year: 2012
Final Year: 2014
Record Source/Award ID: PCORI/1IP2PI000236
Funding: Total Award Amount: $593,472
Award Type: Grant
Award Information: PCORI: More information and project results (when completed)
Abstract: The most relevant questions in patient-centered outcomes research involve the interactions between providers and patients, decisions that result in changes to treatment strategies, and the influence these events have on real world patient outcomes. Researchers have termed these interactions "dynamic strategies" and defined them as a set of rules for choosing effective treatments for individual patients based on that individual's specific characteristics and treatment history, with the goal of optimizing the long-term clinical outcome. Two promising approaches for the comparison of dynamic strategy outcomes are inverse probability (IP) weighting and the parametric g-formula statistical techniques. Because the IP weighting method is applied and discussed in the literature, we will use this technique as our basis for comparison of how to best to implement the g-formula approach. The goal of this pilot project is to assess the feasibility of applying the g-formula and IP weighting to claims data by implementing and validating these two methods. We propose to use, as a case study, the comparison of dynamic strategies for epoetin to treat anemia among dialysis patients covered by Medicare. The issues in prescribing epoetin are similar to those found in many chronic conditions: a treatment whose duration and dose changes in response to time-varying prognostic factors that are themselves affected by prior treatment. To accomplish our goals, we will implement the g-formula and IP weighting using Medicare claims data to compare multiple dynamic strategies for the management of patients undergoing hemodialysis; refine and modify the implementation of the method, produce a step-by-step description of the application, and produce guidelines for the choice between both methods in PCOR; and validate the g-formula and IP weighting methods by emulating an existing clinical trial. We will compare the statistical efficiency and sensitivity of estimates to modeling assumptions with those in the randomized trial. Relevance: While randomized trials remain the gold standard in determining the effects of treatment interventions on patient outcomes, by necessity, they usually compare nondynamic treatment goals, and in many situations their results do not reflect the real world dynamic that occurs between physician and patients that results in alterations or adjustments to therapy. On the other hand, conventional statistical approaches of observational data are also not well-equipped to deal with the physician-patient dynamic and even introduce severe bias that comprises the usefulness of their results. In this project, we propose to apply advanced causal inference techniques for analysis of complex longitudinal observational data that addresses both unsolved issues with randomized trial and standard observational results. Achieving our research goals will help provide evidence-based answers to patients as articulated by core PCORI questions.
MeSH Terms:
  • Anemia /drug therapy
  • Hematinics /therapeutic use
  • Humans
  • Medicare
  • Models, Statistical
  • * Outcome Assessment, Health Care
  • Patient-Centered Care
  • Physician-Patient Relations
  • Pilot Projects
  • Probability
  • Program Development
  • Renal Insufficiency /complications
  • Research Design
  • Statistics as Topic
  • United States
Country: United States
State: Maryland
Zip Code: 20816
UI: 20133144
Project Status: Completed