Sensitivity Analysis

Sensitivity analysis provides insight into the stability and reliability of model predictions by investigating how model outcomes change with input parameter variations. In this chapter, we will explore the concept of sensitivity analysis, its significance in health economics, and the different methods employed to conduct it.

The Importance of Sensitivity Analysis in Health Economics

Like any other field, health economics relies on models to make predictions and inform decision-making processes. However, models are inherently simplifications of reality and are built on assumptions and data with varying degrees of certainty. Sensitivity analysis is crucial in health economics for several reasons:

• Uncertainty Management: It enables researchers to identify and manage uncertainties in data or assumptions, leading to more accurate and robust decision-making.
• Model Validation: Sensitivity analysis helps validate models by comparing their output against known or expected results.
• Prioritization: By identifying key parameters that significantly impact the model's outcome, sensitivity analysis allows researchers to prioritize areas where data collection and research efforts should be focused.

Types of Sensitivity Analysis

There are various methods for conducting sensitivity analysis in health economics. The most commonly used approaches include:

• One-Way Sensitivity Analysis: This method involves changing one input parameter at a time while holding all others constant. By analyzing the effect of these changes on the model's outcome, researchers can identify the most influential parameters.
• Multi-Way Sensitivity Analysis: Also known as scenario analysis, this approach involves changing multiple input parameters simultaneously. This is particularly useful for exploring the combined effect of various uncertainties on the model's outcome.
• Probabilistic Sensitivity Analysis: This method incorporates probability distributions for uncertain input parameters, generating a range of possible outcomes. The most common technique in probabilistic sensitivity analysis is Monte Carlo simulation, which involves running the model multiple times with random inputs drawn from the specified probability distributions.

Interpreting and Communicating Sensitivity Analysis Results

The results of sensitivity analysis should be presented clearly and transparently, with a focus on the implications for decision-making. Key steps in this process include:

• Identifying Influential Parameters: Highlight the input parameters that have the most significant impact on the model's outcome. This can help prioritize research efforts and resource allocation.
• Assessing Model Robustness: Determine if the model's conclusions are consistent across a wide range of parameter values. If the results are highly sensitive to changes in specific inputs, it may indicate a need for more accurate data or a reconsideration of the underlying assumptions.
• Communicating Uncertainty: When presenting sensitivity analysis results, it is essential to communicate the degree of uncertainty associated with the model's predictions. This helps decision-makers understand the limitations of the model and make more informed choices.

Conclusion

Sensitivity analysis is a critical component of health economics, enabling researchers to explore the impact of uncertainties in model inputs and assumptions on outcomes. By employing various methods such as one-way, multi-way, and probabilistic sensitivity analysis, health economists can gain valuable insights into model stability, validate their models, and prioritize research efforts. By effectively interpreting and communicating sensitivity analysis results, decision-makers can make better-informed choices in the complex and ever-evolving field of health economics.

References

• This text was originally written by ChatGPT4.0 on 2 April 2023 and edited by Arnold Bosman
• Briggs, A., Claxton, K., & Sculpher, M. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press.