Using models to predict the future: what to do when the data run out?

Although clinical trials often provide “best evidence” comparing the effectiveness of alternative management strategies, such evidence can be limited in duration or the results reported, causing clinicians and policy analysts to wonder “What if?”. Models of the clinical prognosis, often over patients’ lifetimes (the long haul), are perhaps weaker evidence but can help answer questions about the management of individual patients and can place that best evidence into the context of clinical reality. Clinicians treating patients after percutaneous coronary intervention face complex choices among antithrombotic agents. Although thienopyridines plus aspirin have emerged as the standard of care, uncertainty exists when balancing risks with benefits of newer agents such as prasugrel. Certain variants of the pro-drug activating enzyme CYP2C19 produce less active drug and have decreased efficacy in preventing ischemic events (1). Guzauskas presents deterministic and probabilistic models (2) comparing the outcomes of the 15-month TRITON trial (3) of two thienopyridines and a 2C19 genotype-guided strategy. This careful analysis provides important insights but leaves unanswered questions. Investigators, clinicians and policy makers often muse on how to translate a trial of limited duration into clinical policy and action. Clearly, the post-trial time horizon can have important impacts on the benefits and risks (and indeed the costs) of a therapy. Longer windows (e.g., waiting for half the patients to die to observe median survival or for all the trial patients to die to observe average survival or life expectancy) may be impractical and expensive. We often think of different horizons when considering a trial. First, we have the trial or data collection horizon, sometimes referred to as the hard data or evidence period. Some policy analysts only consider this hard evidence horizon. Second, we have the modeling or analytic horizon, which often extends beyond the trial horizon and may often be based on observational or registry data. Finally, we have the lifetime horizon that can extend even beyond the observational data and reflect assumptions about the future. We typically have less confidence about the longer horizons. With an analysis of the 1995 GUSTO trial of thrombolytic therapy in patients with acute myocardial infarction, Mark (4) modeled with all three horizons---the trial, observational data from the Duke Cardiovascular Database and population-wide vital statistics. The concept of multiple modeling horizons with differing data sources can be seen in Figure 1, which illustrates hypothetical survival from three data sources: TRITON, Nottingham, and population wide vital statistics. Beyond using registry data, the emergence of electronic medical records and claims databases makes it possible to examine long-term outcome data, although selection biases and the lack of formal criteria for certain events remains problematic. Open in a separate window Figure 1 Hypothetical Survival Curves in Three Horizons The solid line represents the data from the TRITON trial. The dotted lower curve represents older data from the Nottingham Heart Attack Register (NHAR) with assumed constant mortality rates, Guzauskas’s assumption. The dashed line represents an alternative assumption based vital statistics data from life tables and if true would suggest even better survival with antithrombotic treatment. The sharp inflection at 15 months reflects Grazaukas’s arbitrary assumption that thienopyridine’s therapeutic efficacy disappears suddenly after the TRITON trial horizon.