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METHODOLOGY FOR META-ANALYSIS OF TIME TO EVENT TYPE OUTCOMES TO INFORM ECONOMIC EVALUATIONS

METHODOLOGY FOR META-ANALYSIS OF TIME TO EVENT TYPE OUTCOMES TO INFORM ECONOMIC EVALUATIONS. Nicola Cooper, Alex Sutton, Keith Abrams Department of Health Sciences, University of Leicester, UK. XI Cochrane Colloquium Barcelona, 26-31 st October 2003. BACKGROUND.

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METHODOLOGY FOR META-ANALYSIS OF TIME TO EVENT TYPE OUTCOMES TO INFORM ECONOMIC EVALUATIONS

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  1. METHODOLOGY FOR META-ANALYSIS OF TIME TO EVENT TYPE OUTCOMES TO INFORM ECONOMIC EVALUATIONS Nicola Cooper, Alex Sutton, Keith Abrams Department of Health Sciences, University of Leicester, UK XI Cochrane Colloquium Barcelona, 26-31st October 2003

  2. BACKGROUND • In clinical studies with time to event data as the principal outcome, mediantime to event usually reported. • However, for economic evaluations the statistic of interest is the mean => Area under survival curve • (i.e. provides best estimate of expected time to an event) • Often mean time to an event can NOT be determined from observed data alone due to right-censoring • (i.e. actual time to an event for some individuals unknown either due to loss of follow-up or event not incurred by end of study)

  3. PROBLEM: Mean undefined Last observation censored => mean undefined Trt 1 Trt 2

  4. OBJECTIVE • Determine how best to use time to event data for the purpose of economic evaluation. • i.e. how to estimate mean time to an event (& associated uncertainty) in the presence of right-censoring: • 1) using published summary statistics • 2) using individual patient data

  5. ESTIMATING THE MEAN FROM SUMMARY STATISTICS • Often only median time to an event reported. BUT for economic evaluation need mean time • From median only – a exponential distribution can be assumed to estimate the mean • From survival curve may be possible to derive individual patient data (IPD) • Mean = 1/Var = 1/2

  6. ESTIMATING MEAN FROM IPD 1 .8 .6 Survival probability .4 .2 0 0 250 500 750 1000 1250 1500 1750 2000 Time to event • Restricted mean: If longest time censored use: • Censored time as event (biased underestimate) • Maximum feasible time as event (biased overestimate) Maximum feasible time Censored time

  7. ESTIMATING MEAN FROM IPD Exponentially extending the survival curve to zero • Extrapolation outside of the observation period by fitting parametric survival distributions (e.g. Weibull, exponential).

  8. EXAMPLE • Use ofNeuraminidase Inhibitors(NIs)to treat influenza in otherwise healthy adults • 3 published trials comparing NIs to standard care • Main outcome: Time to symptoms alleviated • Meta-analysis to obtain a pooled estimate of the absolute mean difference in time to symptoms alleviated between NIs and standard care to inform an economic evaluation

  9. EXAMPLE Same as above Same as above • Turner D, Wailoo A, Nicholson K, Cooper N, Sutton A and Abrams K. Systematic review and economic decision modelling for the prevention and treatment of influenza A and B.Health Technology Assessment Report. 2003.

  10. COMPARISON OF M-As USING ALTERNATIVE APPROACHES TO ESTIMATE MEAN • Restricted mean – Assume event occurs at time of censoring (IPD) • Restricted mean – Assume event occurs at maximum feasible value (IPD) • Extrapolation beyond data applying an exponential distribution (IPD) • Exponential distribution assumption (summary)

  11. ALTERNATIVE META-ANALYSES

  12. RESULTS: Cost-Effectiveness Plane

  13. RESULTS: CE Acceptability Curve

  14. CONCLUSIONS • Inferences don’t change in this example but estimates of the mean (and associated uncertainty) do – therefore this may be a critical issue for other applications. • Problematic even if IPD available – i.e. still do not know the “correct” answer if the last value is right-censored • Sensitivity analysis would seem the best way to proceed

  15. FURTHER ISSUES • Combining the data from different trials • – Could use different distributional assumptions to estimate the mean for different trials and different arms of the same trial? • Model averaging?

  16. REFERENCES • Neymark N, Adriaenssen I., Gorlia T, Caleo S and Bolla M. Estimating survival gain for economic evaluations with survival time as principal endpointA cost-effectiveness analysis of adding early hormonal therapy to radiotherapy in patients with locally advanced prostate cancer.Health Economics. 2002; 11(3)233-248. • Turner D, Wailoo A, Nicholson K, Cooper N, Sutton A and Abrams K. Systematic review and economic decision modelling for the prevention and treatment of influenza A and B.Health Technology Assessment Report. 2003.

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