Deterministic sensitivity analysis (DSA) is a method that can be used to investigate the sensitivity of the results from a model-based analysis to variations in a specific input parameter or set of parameters. One or more parameters are manually changed (usually across a pre-specified range) and the results are analysed to determine to what extent the change has an impact on the output values. The range of variation of each parameter is usually pre-specified, and where appropriate it corresponds to the uncertainty in that parameter reported in source studies (for example 95% confidence interval for efficacy from a source trial or meta-analysis). In univariate sensitivity analysis one parameter is varied at a time, whilst in multivariate sensitivity analysis more than one parameter is varied simultaneously. The results of deterministic sensitivity analysis are usually expressed as line graphs or bar charts. A ‘tornado chart’ refers to summary (stack) of bar graphs representing univariate sensitivity analyses for a wide range of input values, ordered according to the extent (spread) of variation of the resulting model output value (with the widest variation on top). It is usually not possible to vary more than 4 to 5 parameters at the same time in this form of analysis: probabilistic sensitivity analysis is required to assess the impact of simultaneous variation of many input parameters. Univariate sensitivity analyses should be viewed with caution where input parameters are highly correlated (i.e. where parameters correlated to the parameter of interest are not varied together with the latter), such as sensitivity and specificity of diagnostic tests or utility of pre- and post-progression health states.


How to cite: Deterministic Sensitivity Analysis [online]. (2016). York; York Health Economics Consortium; 2016.


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