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Decision and cost-effectiveness analysis: Understanding sensitivity analysis

Decision and cost-effectiveness analysis: Understanding sensitivity analysis. Advanced Training in Clinical Research Lecture 5 UCSF Department of Epidemiology and Biostatistics February 17, 2011. Objectives. To understand the purpose of sensitivity analysis.

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Decision and cost-effectiveness analysis: Understanding sensitivity analysis

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  1. Decision and cost-effectiveness analysis: Understanding sensitivity analysis Advanced Training in Clinical Research Lecture 5 UCSF Department of Epidemiology and Biostatistics February 17, 2011

  2. Objectives • To understand the purpose of sensitivity analysis. • To understand techniques used for sensitivity analysis. Health Strategies International, UCSF

  3. Why do Sensitivity Analyses? • All CEAs have substantial uncertainty. • Sensitivity analyses deal with uncertainty systematically. • Convince audience that results are robust.

  4. Four Topics • Types of uncertainty. • Deterministic sensitivity analyses. • One-way, multi-way, scenario. • Probabilistic sensitivity analyses. • Monte Carlo. • Uses of sensitivity analyses. Health Strategies International, Super Models for Global Health

  5. Types of Uncertainty • Truth uncertainty: • What are the correct input values? • Trait uncertainty: • What if population characteristics or other circumstances change? • Methodological uncertainty: • What if the analysis were done differently? Health Strategies International, Super Models for Global Health

  6. Deterministic Sensitivity Analyses • One-way/univariate: • Vary one input at a time. • Multi-way/multivariate: • Vary 2+ inputs at a time. Health Strategies International, Super Models for Global Health

  7. Deterministic Sensitivity Analyses • Scenario (variant of multi-way): • Tests set of relevant conditions. • Threshold analysis (one-way or multi-way): • Input values beyond which cost-effectiveness is achieved (or lost). Health Strategies International, Super Models for Global Health

  8. One-way Sensitivity Analysis Base case est. of annual rupture risk = 0.0005

  9. Univariate Sensitivity Analyses: Base case and range of outcomes for 1,000 FC users

  10. Automating one-way SAs: • Male circumcision for HIV prevention in South Africa

  11. Two-way Sensitivity AnalysisKahn, JAIDS, 2001

  12. Three-way Sensitivity Analysis Adult male circumcision (Kahn at al, PlosMedicine 2006) Health Strategies International, Super Models for Global Health

  13. Threshold Analysis: NVP for Prevention of Vertical Transmission of HIV in Sub-Saharan Africa Input values needed for $50/DALLYMarseille at a,l Lancet, 1999

  14. Using scenario analysis to quantify effect of unknown parameterMarseille, BMGF White Paper, 2009,. Health Strategies International, Super Models for Global Health

  15. Probabilistic Sensitivity Analysis What is it? What is it good for?

  16. Probabilistic Sensitivity Analysis • Operational definition: • Outputs are calculated based on random assignment of values to inputs drawn from user-selected probability distribution. • Examples: • Monte Carlo, Latin Hypercube Software: @Risk®; Crystal Ball® TreeAge ® Health Strategies International, Super Models for Global Health

  17. The Problem with Deterministic SAs No estimate of the probability of achieving a particular outcome. Probabilistic SAs are the remedy.

  18. Probabilistic Sensitivity Analyses • Value: • Return the likelihood of attaining a particular outcome or outcome range. • Everything known about each input is expressed at once. • Particularly valuable when many inputs are important. Health Strategies International, Super Models for Global Health

  19. Probabilistic Sensitivity Analyses • Drawbacks: • Need to be able to make decent estimates of the underlying probability distribution. • “Black box” Health Strategies International, Super Models for Global Health

  20. Other Uses of SA:(The Inner Teachings) • Planning the analysis. • Debugging the model. • Documenting relationships between inputs and outputs. • Identifying thresholds. • Influencing policy. Health Strategies International, Super Models for Global Health

  21. Planning the Analysis • Program software to permit SAs on likely SA variables. • SA curves provide a check on the integrity of the model. • Identify candidates for more data collection early. Health Strategies International, Super Models for Global Health

  22. Debugging the ModelTricks of the Trade • One-ways are best: simple and intuitive. • Plug in extreme values. • Separate diagnosis of numerator from denominator. • Break outputs down further if necessary • (intervention versus control arms).

  23. Documenting Relationships Between Inputs and Outputs • Distinguish between ‘bugs’ and insights. • Examples of insights: • Slowing disease progression can increase costs. • Higher disease prevalence can mean lower benefits. • Benefits decrease with age.

  24. Unexpected Dynamic Uncovered by SA

  25. Identify Thresholds – Influence Policy Preventing HIV vertical transmission in sub-Saharan Africa • Cost of ARVs to prevent vertical transmission. • Universal versus targeted provision of NVP. • Hard-to quantify potential benefits of FC

  26. Cost per DALY of HIVNET 012 NVP regimen as function of HIV seroprevalence and type of counseling/testing regimen

  27. Summary • SA is a set of techniques for the explicit management of uncertainty. • Essential part of establishing key findings. • Indispensable for convincing an audience that results are technically sound and policy-relevant.

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