Longitudinal Methods for Pharmaceutical Policy Evaluation

Dennis Ross-Degnan, ScDHarvard Medical School and Harvard Pilgrim Health

Care WHO Collaborating Center in Pharmaceutical Policy

Boston, USA

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

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• Completene

ss• Consistency• Coding

Common Methodological Issues in Longitudinal Policy Evaluations

Time Study design Sample selection Data quality Data organization Statistical approach

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

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

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

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

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

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

Christine Lu, USA Market utilization or sales data (Abstract 878)

Sauwakon Ratanawijitrasin, 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

Listen, participate, enjoy…

Thank you!