USE OF EVIDENCE IN DECISION MODELS: An appraisal of health technology assessments in the UK Nicola Cooper Centre for Biostatistics & Genetic Epidemiology, Department of Health Sciences, University of Leicester, U.K. Acknowledgements to: Doug Coyle, Keith Abrams, Miranda Mugford & Alex Sutton

Increasingly decision models developed to inform complex clinical/economic decisions (e.g. NICE technology appraisals). Technique commonly used for: i) Extrapolation of primary data beyond endpoint of a trial, ii) Indirect comparisons when no head-to-head trials iii) Investigation of how cost-effectiveness of clinical strategies/interventions changes with values of key parameters iv) Linking intermediate endpoints to ultimate measures of health gain (e.g. QALYs) v) Incorporation of country specific data relating to disease history and management. BACKGROUND

USE OF EVIDENCE IN HTA DECISION MODELS (Cooper et al, In press) OBJECTIVE: Review sources & quality of evidence used in the development of economic decision models in health technology assessments in the UK METHODOLOGY: Review included all economic decision models developed as part of the NHS Research & Development Health Technology Assessment (HTA) Programme between 1997 and 2003 inclusively. Quality of evidence was assessed using a hierarchy of data sources developed for economic analyses (Coyle & Lee 2002) & good practice guidelines (Philips et al 2004).

GOOD PRACTICE CRITERIA FOR DECISION MODELS (Philips et al 2004) Statement of perspective Description of strategies/comparators Diagram of model/disease pathways Development of model structure and assumptions discussed Table of model input parameters presented Source of parameters clearly stated Model parameters expressed as distributions Discussion of model assumptions Sensitivity analysis performed Key drivers/influential parameters identified Evaluation of internal consistency undertaken

HIERARCHY OF DATA SOURCES Hierarchy of evidence - a list of potential sources of evidence for each data component of interest: Main clinical effectiveness Baseline clinical data Adverse events and complications Resource use Costs Utilities Sources ranked on increasing scale from 1 to 6, most appropriate (best quality) assigned a rank of 1

HIERARCHY OF DATA SOURCES # Surrogate outcomes = an endpoint measured in lieu of some other so-called true endpoint (including survival at end of clinical trial as predictor of lifetime survival)

FLOW DIAGRAM 22 (out of 42) NICE Appraisals 180 HTA published out of 180 (73%) considered Health Economics 5 out of 42 (12%) Individual Sampling # # One HTA reported both decision & Markov models, one reported both Markov & Individual Patient models, and one model type was unclear. 26 out of 42 (62%) Decision Trees # 12 out of 42 (29%) Markov Models # 48 out of 147 (33%) Developed Decision Models 42 out of 48 (88%) Economic Evaluation Models 6 out of 48 (15%) Cost Analyses Models

GOOD PRACTICE CRITERIA FOR DECISION MODELS (n=42)

RESULTS FROM APPLYING HIERARCHIES OF EVIDENCE (n=42 decision models)

Rank 1 Rank 2 High Rank 3 Rank 4 Medium Rank 5 Rank 6 low Unclear N/A

CONCLUSIONS Evidence on main clinical effect mostly: identified & quality assessed (76%) as part of companion systematic review for HTA reported in a fairly transparent & reproducible way. For all other model inputs (i.e. adverse events, baseline clinical data, resource use, and utilities) search strategies for identifying relevant evidence rarely made explicit sources of specific evidence not always reported

Concerns about decision models confirmed by this study: (1) Use of data from diverse sources (e.g. RCTs, observational studies, expert opinion) - may be subject to varying degrees of bias due to confounding variables, patient selection, or methods of analysis (2) Lack of transparency regarding identification of model input data & key assumptions underlying model structure and evaluation (3) Bias introduced by the researcher with regards to choice of model structure & selection of parameter values to input into the model. CONCLUSIONS

Hierarchies of evidence for different data components provide useful tool for assessing i) quality of evidence, ii) promoting transparency, & iii) informing weakest aspects of model for future work. Acknowledged, highly ranked evidence for certain model parameters may not always be available. Value of evidence input into decision models, regardless of position in hierarchy, depends on its quality & relevance to question of interest. QUANTITY vs. QUALITY ( PRECISION vs. BIAS ) CONCLUSIONS

UNANSWERED QUESTIONS How best to identify the relevant evidence? How much evidence is sufficient and when would there be benefit from identifying additional/supplementary evidence? How to appropriately assess, and where possible adjust for, quality of different types of evidence? - Instruments for assessing quality within study designs but across different study designs non-trivial (Downs & Black 1998) How to appropriately combine/synthesis evidence from different study types? For example, - meta-analyse all data assuming equal weight, - observational data as prior for RCT data, or - hierarchical synthesis model

REFERENCES Cooper NJ, Coyle D, Abrams KR, Mugford M, Sutton AJ. Use of evidence in decision models: An appraisal of health technology assessments in the UK to date. Journal of Health Services Research and Policy (In press 2005). Coyle D, Lee KM. Evidence-based economic evaluation: how the use of different data sources can impact results. Donaldson C, Mugford M, Vale L. Evidence-based health economics: From effectiveness to efficiency in systematic review. London: BMJ Publishing Group, 2002: Philips Z, Ginnelly L, Sculpher M et al. Review of guidelines for good practice in decision-analytic modelling in health technology assessment. Health Technology Assessment. 2004; 8(36). Copy of slides available at: