31st Annual Meeting of the Society of Medical Decision Making Abstracts.

Abstract

TRA-1 FROM TRIALS TO OBSERVATIONAL DATA: MODELING NATURAL AND “UNNATURAL” HISTORY Katia Noyes, PhD, Alina Bajorska, MS, Andre R. Chappel, BA, Steven Schwid, MD, Lahar R. Mehta, MD, BS, Robert G. Holloway, MD, MPH, and Andrew W. Dick, PhD (1)University of Rochester, Rochester, NY, (2)RAND Co., Pittsburgh, PA Purpose: Cost-effectiveness analysis requires comparison of outcomes in treated and untreated populations. Data from randomized clinical trials (RCT) do not provide progression rates representative of the general population, while treatment effects in observational data may be biased due to non-randomization. We developed a novel approach for estimating untreated progression rates by using data from a population-based longitudinal survey, correcting for the effects of patients’ treatments as reported by pivotal trials. Method: We used data from the 2000-2005 Sonya Slifka nationally representative MS cohort. Disease progression was characterized by disability-based disease states and relapses. We modeled probabilities of disease state transitions using a firstorder annual Markov model that adjusted for demographics, disease duration, recent relapse rates, prior states, and the specific disease-modifying therapy (DMT). To estimate transitional probabilities, we developed an iterative multinomial logistic regression algorithm, constraining the effects of DMT to match those reported by RCTs as follows. We selected initial annual treatment factors and estimated first progression probabilities for controls. For those probabilities, using a numerical algorithm, we found new treatment factors that resulted in the same risk ratios of progression as reported by the trials. The new factors were used in the regression model to adjust for DMT effects and to reestimate the probabilities for controls. We continued this process iteratively, until the identified factors for the final control probabilities matched published DMT effects from RCTs. Result: After correcting for the DMT treatment effects and other observable risk factors, the probability of disability progression was greater for estimates based on all MS patients compared to the estimates based on untreated individuals only. The 95% confidence intervals using the entire cohort (including treated and untreated individuals) were narrower than the intervals based on the subsample of untreated patients. Conclusion: Our results indicate that the untreated patients in our study had lower estimates of disease progression than the treated patients would have had if they remained untreated. This suggests that patients who forgo treatment are likely to have milder, slower progressing forms of MS. Correcting for treatment effects in a more inclusive group of patients likely provides a more realistic estimate of disease progression than simply characterizing progression in an untreated cohort. The use of a population-based cohort also improves the precision of disease progression estimates. TRA-2 ESTIMATING PREFERENCE AND SELECTION EFFECTS, HOW TO UNTANGLE THE EFFECT OF INFORMED CHOICE Robin Turner, PhD, Kirsten McCaffery, PhD, Petra Macaskill, PhD, Siew Foong Chan, MAppStat, Stephen Walter, PhD, and Les Irwig, PhD (1)University of Sydney, Sydney, Australia, (2)McMaster University, Hamilton, ON, Canada Purpose: Randomised controlled trials traditionally investigate treatment effects but can also be used to estimate selection effects (the self-selection of one treatment over another) and preference effects (the effect of receiving the preferred treatment). This study illustrates a method (Rucker 1989 Statist. Med.) to estimate treatment, preference and selection effects to investigate whether informed choice supported by a decision aid is beneficial compared to policy directed management (limited patient choice). Method: The method is illustrated using data from the IMAP trial, which was designed to investigate the psychosocial outcomes over 1 year of an informed choice between HPV triage or usual care by repeat Pap smear compared to policy directed management of each option. We used a 3-arm trial design with patients randomised to either one of two treatments (limited choice) or to an informed choice arm. The method is unique in that it allows the effects of treatment, preference (i.e. choice) and selection (selection bias) to be estimated separately. Information from the choice arm is used to estimate effects within the randomised arms for those who did and did not receive their preferred treatment. Results: With traditional analysis those in the HPV arm were more satisfied than those in the Pap arm, with little difference between informed choice and HPV. There was little difference in quality of life (SF36) scores between the three arms. The Rucker analysis showed weak evidence for an effect of preference on the SF36 scores: mental health score (6.0, 95% CI –0.6 to 12.9, P = 0.07) with choice associated with improved quality of life. There was evidence of a selection effect for the satisfaction of women with their health care in general and with the care of their abnormal Pap, with women who selected or would have selected HPV being less satisfied than those who selected or would have selected Pap triage (–2.1 95% CI –4.0 to –0.3, P = 0.02 and –1.2, 95%CI –2.5 to –0.2, P = 0.03). Conclusions: The Rucker method should be used to estimate the effect of informed choice compared to policy or clinician directed management (ie. limited patient choice) as it brings important additional information to the interpretation of trial data. TRA-3 HEALTH OUTCOMES AND COSTS OF COMMUNITY MITIGATION STRATEGIES FOR PANDEMIC INFLUENZA IN THE U.S Daniella J. Perlroth, MD, Robert J. Glass, PhD, Vickey J. Davey, RN, MPH, Alan Garber, MD, PhD, and Douglas K. Owens, MD, MS (1)Veterans Affairs Palo Alto Health Care System and Stanford University, Stanford, CA, (2)Sandia National Laboratories, Albuquerque, NM, (3)Veterans Health Administration, Department of Veterans Affairs, Bethesda, MD Purpose: The optimal community-level approach to control pandemic influenza is unknown. Method: We estimated the health outcomes and costs of combinations of 4 social distancing strategies (adult social distancing, child social distancing, school closure and household quarantine) and 2 antiviral medication strategies (treatment alone or treatment and prophylaxis) to mitigate an influenza pandemic for a demographically “typical” U.S. community. We used a social network, agent-based model to estimate strategy effectiveness. We used data from the literature to estimate clinical outcomes and health care utilization. Outcomes included cases averted, total SEMDM 2009 ANNUAL MEETING OPENING PLENARY SESSION (TOP-RANKED) ABSTRACTS