Longitudinal methods for pharmaceutical policy evaluation
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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 Evaluations

  • 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 Evaluations

  • 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 Evaluations

  • 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 Evaluations

  • 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 Evaluations

  • 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 Evaluations

  • 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

Presenters Evaluations

  • 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

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