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Longitudinal Methods for Pharmaceutical Policy Evaluation. Dennis Ross-Degnan, ScD Harvard Medical School and Harvard Pilgrim Health Care WHO Collaborating Center in Pharmaceutical Policy Boston, USA. Session Objectives.

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longitudinal methods for pharmaceutical policy evaluation

Longitudinal Methods for Pharmaceutical Policy Evaluation

Dennis Ross-Degnan, ScD

Harvard Medical School and Harvard Pilgrim Health Care

WHO Collaborating Center in Pharmaceutical Policy

Boston, USA

session objectives
Session Objectives
  • Touch on key methodological issues in longitudinal studies to evaluate:
    • Pharmaceutical policy changes
    • Planned interventions
  • Hear experiences of researchers who have used longitudinal data in a range of settings
  • Introduce commonly-used statistical methods
    • Interrupted time series and survival analysis
  • Discuss
    • Other experiences and perspectives
    • Best practices and areas for methods development
using routine data for pharmaceutical policy research
Using Routine Data for Pharmaceutical Policy Research
  • Pharmacy procurement and sales
    • Public, mission, private sector
    • Centralized, supply chain, institutional
    • Volume, cost
  • Clinical care and pharmacy dispensing
    • Inpatient, outpatient, retail pharmacy
    • Electronic records
    • Manual systems
  • Insurance reimbursement
    • Claims, adjudicated payments
  • Critical Issues
    • Completeness
    • Consistency
    • Coding
common methodological issues in longitudinal policy evaluations
Common Methodological Issues in Longitudinal Policy Evaluations
  • Time
  • Study design
  • Sample selection
  • Data quality
  • Data organization
  • Statistical approach
issues related to time
Issues Related to Time
  • Key analytic variable for longitudinal research
    • Errors common: recording, coding
    • Importance of definitions (e.g., medication gaps)
  • Defining policy change point
    • Single point in time, instantaneous effects
    • Implementation spread over time
    • Co-interventions
  • Dynamics of policy impacts
    • Anticipatory changes, lagged response
    • Non-linear changes
  • Study period and unit of aggregation
    • Depends on data source and sample size
    • Optimal number of data points per policy period?
issues in study design
Issues in Study Design
  • Appropriate study units
    • Whose behavior will change?
    • External policy influences
  • Timing of implementation (prospective)
    • Opportunity for randomization?
    • Staggered implementation?
  • Comparisons and contrasts
    • Challenge of identifying similar groups or behaviors unaffected by intervention
    • Intended and unintended effects
    • High vs. low risk
issues in sample selection
Issues in Sample Selection
  • Facilities, prescribers, patients
    • Optimal sample structure?
    • Importance of denominators, continuity
    • Defining prevalent and incident diagnoses
  • Medications
    • Trade-offs among therapeutic alternatives
    • All vs. selected categories
  • How many is enough?
    • Representativeness
    • Need for precision
    • Problem of clustering
issues in data quality
Issues in Data Quality
  • Many challenges in using routine data
    • Usually not collected for research
    • Changes in data systems or routines
  • Common data quality issues
    • Combining data across facilities
    • Missingness
    • Unusual patterns, wild data points
  • Importance of diagnostics
    • Graphical display
    • Evaluating patterns of variability, missingness
    • Comparing baseline patterns in subgroups
issues in data organization
Issues in Data Organization
  • Choice of level of analysis
    • Aggregated across all units
    • Separately by logical units (facility, prescriber)
    • Patient-level analysis
  • Patient subgroups
    • Continuing vs. new patients
    • Clinical risk subgroups
  • Medication data
    • Therapeutic classification and organization
    • Policy-induced switching (market share analysis)
issues in statistical approach
Issues in Statistical Approach
  • Study design, sampling, and statistical approach must go hand in hand
    • Duration of available data is key factor
    • Level of analysis
  • Validity in longitudinal policy change models
    • Baseline serves as counterfactual
    • Co-intervention is the major confounder
    • Need to understand context and stability of system
  • Christine Lu, USA
    • Market utilization or sales data (Abstract 878)
  • SauwakonRatanawijitrasin, Thailand
    • Electronic clinical and pharmacy data (Abstract 811)
  • Ricardo Perez-Cuevas, Mexico
    • Electronic medical record data (Abstract 1118)
  • Joshua Kayiwa, Uganda
    • Routine data from manual systems (Abstract 505)
  • Mike Law, Canada
    • Overview of common analytic approaches