Decision models are usually populated 1 parameter at a time, with 1 item of information
informing each parameter. Often, however, data may not be available on the parameters …

Prediction models used in support of clinical and health policy decision making often need
to consider the course of a disease over an extended period of time, and draw evidence …

There has been an increasing interest in using expected value of information (EVI) theory in
medical decision making, to identify the need for further research to reduce uncertainty in …

Clinical decisions often have long-term implications. Analysts encounter difficulties when
employing conventional decision-analytic methods to model these scenarios. This occurs …

Alongside the development of meta-analysis as a tool for summarizing research literature,
there is renewed interest in broader forms of quantitative synthesis that are aimed at …

Markov transition models are frequently used to model disease progression. The authors
show how the solution to Kolmogorov's forward equations can be exploited to map between …

Markov models are useful when a decision problem involves risk that is continuous over
time, when the timing of events is important, and when important events may happen more …

Increasingly complex models are being used to evaluate the cost‐effectiveness of medical
interventions. We describe the multiple sources of uncertainty that are relevant to such …

Decision-analytic models must often be informed using data that are only indirectly related to
the main model parameters. The authors outline how to implement a Bayesian synthesis of …

Meta-analysis has been well-established for many years, but has been largely confined to
pooling evidence on pair-wise contrasts. Broader forms of synthesis have also been …