UK National Institute for Health Research.

This paper examines the synthetic control method in contrast to commonly used difference‐in‐differences (DiD) estimation, in the context of a re‐evaluation of a pay‐for‐performance (P4P) initiative, the Advancing Quality scheme. The synthetic control method aims to estimate treatment effects by constructing a weighted combination of control units, which represents what the treated group would have experienced in the absence of receiving the treatment. While DiD estimation assumes that the effects of unobserved confounders are constant over time, the synthetic control method allows for these effects to change over time, by re‐weighting the control group so that it has similar pre‐intervention characteristics to the treated group.We extend the synthetic control approach to a setting of evaluation of a health policy where there are multiple treated units. We re‐analyse a recent study evaluating the effects of a hospital P4P scheme on risk‐adjusted hospital mortality. In contrast to the original DiD analysis, the synthetic control method reports that, for the incentivised conditions, the P4P scheme did not significantly reduce mortality and that there is a statistically significant increase in mortality for non‐incentivised conditions. This result was robust to alternative specifications of the synthetic control method. © 2015 The Authors. Health Economics published by John Wiley & Sons Ltd.

Difference-in-differences (DiD) estimators provide unbiased treatment effect estimates when, in the absence of treatment, the average outcomes for the treated and control groups would have followed parallel trends over time. This assumption is implausible in many settings. An alternative assumption is that the potential outcomes are independent of treatment status, conditional on past outcomes. This paper considers three methods that share this assumption: the synthetic control method, a lagged dependent variable (LDV) regression approach, and matching on past outcomes. Our motivating empirical study is an evaluation of a hospital pay-for-performance scheme in England, the best practice tariffs programme. The conclusions of the original DiD analysis are sensitive to the choice of approach. We conduct a Monte Carlo simulation study that investigates these methods' performance. While DiD produces unbiased estimates when the parallel trends assumption holds, the alternative approaches provide less biased estimates of treatment effects when it is violated. In these cases, the LDV approach produces the most efficient and least biased estimates.

Health economic models rely on data from trials to project the risk of events (e.g., death) over time beyond the span of the available data. Parametric survival analysis methods can be applied to identify an appropriate statistical model for the observed data, which can then be extrapolated to derive a complete time-to-event curve. This paper describes the properties of the most commonly used statistical distributions as a basis for these models and describes an objective process of identifying the most suitable parametric distribution in a given dataset. The approach can be applied with both individual-patient data as well as with survival probabilities derived from published Kaplan-Meier curves. Both are illustrated with analyses of overall survival from the Sorafenib Hepatocellular Carcinoma Assessment Randomised Protocol trial.