Good Research Practices for Retrospective Database Analysis Task Force

ISPOR 13th Annual International Meeting - Forum Presentation - Tues, 6 May 2008
Design and Analysis of Non-Randomized Studies of Treatment Effects Using Secondary Databases
 Presentation

Task Force Co-Chair:
Michael Johnson PhD, Associate Professor University of Houston, College of Pharmacy, Houston, TX, USA;
Sebastian Schneeweiss MD, ScD, Director of Drug Evaluation and Outcomes Research, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA

Task Force Members:
David Atkins, MD, Assoc Director, HSR & D, Dept. of Veterans Affairs Health Services, Washington, DC , USA
Marc Berger MD, Vice President, Global Health Outcome, Eli Lilly and Company, Indianapolis, IN, USA
Emily Cox, PhD, Sr. Director of Research, Express Scripts, St. Louis, MO, USA
William Crown PhD, President, i3 Innovus, Waltham, MA, USA
Colin Dormuth, ScD, Assistant Professor, Dept. of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia; Chair of Pharmacoepidemiology Group, Therapeutics Initiative, Vancouver, BC, Canada
Edeltraut Garbe, MD, PhD, Head of the Department of Clinical Epidemiology, Bremen Institute for Prevention Research and Social Medicine, Bremen, Germany
Muhammad Mamdani, PharmD, MA, MPH, Director of the Applied Health Research Centre of the Li Ka Shing Knowledge Institute of St. Michael's Hospital and Associate Professor at the University of Toronto, Toronto, Ontario, Canada
Bradley Martin PhD, RPh, PharmD Assoc. Prof. and Division Chair, College of Pharmacy, University of Arkansas for Medical Sciences, Department of Pharmacy Practice, Little Rock, AR, USA
Uwe Siebert MD, MPH, MSc, ScD, Professor of Public Health, University of Health Sciences, Hall, Austria
Tjeerd Van Staa PhD, MD, MSc, MA, Head of Research, GPRD, London, UK

Background
With the advent of the Medicare Prescription Drug, Improvement and Modernization Act of 2003 (Medicare Modernization Act or MMA), extending pharmaceutical coverage to 42 million Medicare beneficiaries, the need for more information on the safety, effectiveness, and cost-effectiveness of drugs in everyday use has never been more critically needed. Indeed, the Medicare Modernization Act included in Section 1013 states a specific statement of need for more comparative clinical effectiveness studies.

Observational data provide a wealth of information on drug treatment in everyday practice, where long-term safety and effectiveness of drugs could be estimated. The major difficulty to overcome with estimation of true treatment effects (causal effects) from observational data is the presence of confounding factors affecting both treatment and outcome. Standard statistical approaches such as logistic regression for binary outcomes and proportional hazards regression for time-to-event outcomes are familiar and adequate methods to adjust for traditional confounding. With the proliferation of longitudinal data, with time-varying measurement of exposure and disease outcome, these methods have been shown to be biased in the presence of time-varying confounding. Methods such as marginal structural models using inverse probability of treatment weighting have been developed, but are not as well understood or in wide application as now more traditional methods such as propensity scoring.

Observational studies using retrospective data from large administrative and clinical ‘claims type’ databases contain a wealth of information which could be used to supplement the findings from randomized trials on the safety and effectiveness of drugs in routine clinical practice. The key feature of retrospective database studies that limits their usefulness and adoption of findings from these studies into policy and practice is that the observational design and resulting statistical control of confounding provides a weaker framework for internal validity and especially causal inference of exposure-disease associations than experimental designs. A full exploration of good research practices for retrospective databases will include an over-arching view toward methods that address this concern and that attempt to guard against threats to internal validity and improve causal inference.

Recommended Action
The Good Research Practices for Retrospective Database Analysis will gather state of the art good research practices on the use of retrospective databases in observational studies of clinical effectiveness. The Task Force will produce a report summarizing our findings and make recommendations on best practices.

Goal
To determine state of the art standards in the use of retrospective databases for research purposes.

1. The Task Force on Good Research Practices for Retrospective Databases will keep an overarching view toward the need to ensure internal validity and improve causal inference from observational studies.
2. The Task Force will review prior work from past and ongoing ISPOR task forces and other initiatives to establish baseline standards from which to set an agenda for work.
3. The appropriate use of Medicare Part D claims, when available, will be of special interest to the task force.
4. Methods to handle longitudinal data analysis with time-varying measures including time-dependent confounding will be of considerable interest.