A Role for Decision Analysis in PHIAC? Mark Sculpher Centre for Health Economics University of York

Rationale for decision analysis Contrasting paradigms Measurement Testing hypotheses about individual endpoints (parameters) Relatively few parameters of interest Primary role for trials and other primary data collection Focus on parameter uncertainty Decision making What do we do now based on all sources of current knowledge? Decision making involves synthesis Decisions cannot be avoided A decision is always taken under conditions of uncertainty Can be based on implicit or explicit analysis 

Key elements of decision analysis Option A Option B Option C Population Outcome 1 Outcome 2 Outcome 1 Outcome 2 Outcome 1 Outcome 2 p1 p2 p3 p4 p5 p6 Value A1 Value A2 Value B1 Value B2 Value C1 Value C2 (p1 x Value A1 ) + (p2 x Value A2 ) Expected value (p3 x Value B1 ) + (p4 x Value B2 ) (p5 x Value C1 ) + (p6 x Value C2 )

Key features of decision analysis Quantitative Explicit Accepts role of judgements/assumptions Helps to understand implications of uncertainty

Example – lead poisoning in children Brown MJ. Medical Decision Making 2002; 22: 482.

Key role for sensitivity analysis Brown MJ. Medical Decision Making 2002; 22: 482.

What would decision analysis require of PHIAC? Absolute clarity about the appropriate population and relevant options (i.e. the ‘decision question’) Clear view about objectives –Health gain –Issue of equity Appropriateness of expected value maximisation Appropriate synthesis for all parameters –Dealing with heterogeneity Random effects Increase options Sub-groups –Not just trial evidence –Dealing with ‘no evidence’ Role for explicit opinion and judgement