Uncertainty & Value of Information

Monte Carlo simulation uses repeated random sampling to show how uncertainty in model inputs produces uncertainty in model outputs.

An analysis systematically varying two or more input parameters together across a defined range of combinations, examining their combined effect on results.

A normal distribution is a continuous, symmetric probability distribution characterised by a mean that sets its centre and a positive standard deviation that sets its spread.

One-way sensitivity analysis is a deterministic method that changes one model input at a time across a specified range while holding all other inputs at their base-case values.

A study sample size selected to meet a stated design objective, such as adequate power or precision, or to maximise the expected value of information after accounting for research costs and decision delays.

A parameter distribution is a probability model for the plausible values of an uncertain model input, specified from evidence and assumptions so its uncertainty can be propagated through an analysis.

Parameter uncertainty is uncertainty about the true values of quantities used as model inputs because those values are estimated from incomplete or imperfect evidence.

The expected value of perfect information calculated for an entire affected patient population over time, rather than for a single patient.