1 / 27

Economic evaluation of health programmes

Economic evaluation of health programmes. Department of Epidemiology, Biostatistics and Occupational Health Class no. 22: Applying the net benefit framework, assessing the value of information, reporting economic evaluations Nov 17, 2008. Plan of class. Net benefit framework

wynona
Download Presentation

Economic evaluation of health programmes

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Economic evaluation of health programmes Department of Epidemiology, Biostatistics and Occupational Health Class no. 22: Applying the net benefit framework, assessing the value of information, reporting economic evaluations Nov 17, 2008

  2. Plan of class • Net benefit framework • Cost-effectiveness acceptability curves • ‘Marrying econometrics and cost-effectiveness analysis’

  3. Why are p-values for the NMB regression coefficient estimates equal to those obtained with effect as the dependent variable? When λ goes to infinity in the variation in Ci becomes less and less important compared to that in Ei, so that the regression: is mostly explaining variation in Ei: the Ci part represents a smaller and smaller share of the variation in NMBi. Hence, while the coefficients are different (NMBi is becoming larger and larger), the p-values are the same.

  4. Additional points concerning Hoch et al. 06

  5. Nature of outcome (1) • If a fainting episode (syncope) occurs, and the device that measures heart rhythm is activated on time, it is possible to see whether the syncope is or is not associated with heart arrhythmia. • Success occurs if : • a) a syncope occurs • b) the device is activated.

  6. Nature of outcome (2) • Loop recorder: 63.27% of patients have symptom reccurrence and successful activation, vs. 23.53% of Holter group • The loop recorder is more expensive but has a higher chance of detecting whether arrhythmia is the cause of syncope

  7. How the dependent variable (net benefit) is constructed

  8. Review: why the p-value on the intervention dummy from the net benefit regression can be used to construct the CEAC (1) • The CEAC shows, for a given value of λ, the probability that the intervention is cost-effective. • In the regression: statistical software computes a t-statistic for the estimate of δ to test the null hypothesis that δ=0; if the p-value is small enough (usually less than 0.05), we reject the null.

  9. (Under what condition does this become the probability density function of a t-distribution?)

  10. Review: why the p-value on the intervention dummy from the net benefit regresion can be used to construct the CEAC (2) • But we want to test the null hypothesis that δ <= (negative or equal to) 0. • If the estimate of δ is positive, then the associated t-statistic is also positive – so we locate it on the right-hand side of the t-distribution. Half of the corresponding p-value gives us the area to the right of that value. This is the significance level at which we can reject the null that δ <= (negative or equal to) 0. In other words, the probability that δ>0 is 1- half of the p-value.

  11. Review: why the p-value on the intervention dummy from the net benefit regresion can be used to construct the CEAC (3) • If the estimate of δ is negative, then the associated t-statistic is also negative – so we locate it on the left-hand side of the distribution. Half of the corresponding p-value gives us the area to the left of that value. This is the significance level at which we can reject the null that δ  >= (positive or equal to) 0. In other words, the probability that δ>0 is half of the p-value.

  12. Regression estimates for different values of λ

  13. Calculating probabilities of cost-effectiveness Note how similar probabilities of cost-effectiveness derived from regression and from bootstrapping are

  14. CEAC from probabilities of cost-effectiveness derived from one-sided p-values in previous table

  15. Comments • Here, no variation in cost across individuals with same Tx • When there is variation in cost, skewness or heteroskedasticity may make p-values less valid – then use bootstrapping • NBRF enables estimates of mean net benefit of : • Usual care (β0) • New treatment (β0+ β1) as well as incremental net benefit (β1)

  16. Using models to assess value of additional research

  17. Concept of decision uncertainty • Methodological uncertainty • Sampling variation/ parameter uncertainty • Modelling uncertainty • Generalizability Decision uncertainty Is additional research necessary?

  18. Expected value of perfect information (EVPI) • Probabilistic sensitivity analysis (PSA) to yield expected costs and effects of alternative options: identify preferred option • Determine probability of making wrong decision = 1 - probability that this is indeed best option (use CEAC) • Use PSA to determine cost of making wrong decision: • Foregone health • Wasted resources • Calculate expected cost of uncertainty by multiplication (in terms of health and dollars) • Multiply by number of patients to get population EVPI

  19. Example of EVPI analysis

  20. Implications of EVPI analysis • Additional research must cost less than EVPI • Can also use EVPI analysis to assess value of information to be yielded by alternative research designs • Frontier area of investigation • EVPI absolutely not taken into account by CIHR

  21. Presentation and use of economic evaluation results

  22. Economic evaluation is widely used… • Oregon Medicaid experiment • Combined with public deliberative process • Requirement for formal economic evaluation for drugs to be approved for reimbursement • Australia • Several Canadian provinces • U.K. • Wider use in England (NICE)

  23. …but has many limitations • Validity can be hard to assess • => Standardize reporting • Generalizability may be issue • Very method of funding interventions meeting $/QALY threshold has been criticized

  24. Common reporting format • Alleged benefits: • Transparency • Comparability across studies • Limits of league tables • Improve quality of evaluations? • Stifle innovation? • US Public Health Services Panel on Cost-effectiveness in Health and Medicine • Include reference case in report • British Medical Journal Working Party on Economic Evaluation (1996) • Too many methodological controversies, so just focus on common reporting standards

  25. Some common recommendations

  26. Concept of league table • Rank interventions in order of $/QALY • Why? • Place findings in broader context • Inform decisions about which interventions to fund • Use of league tables for this purpose has been criticized

  27. Examplesfrompublishedstudies (1998 US$) Source: http://www.hsph.harvard.edu/cearegistry/comprehensive-revised.pdf

More Related