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TAKING PATIENT HETEROGENEITY AND PREFERENCES INTO ACCOUNT IN HEALTH TECHNOLOGY ASSESSMENTS

Published online by Cambridge University Press:  25 October 2017

Wietske Kievit ,
Marcia Tummers ,
Ralph van Hoorn ,
Andrew Booth ,
Kati Mozygemba ,
Pietro Refolo ,
Dario Sacchini ,
Lisa Pfadenhauer ,
Ansgar Gerhardus  and
Gert Jan Van der Wilt
Wietske Kievit
Affiliation:
Radboud Institute for Health [email protected]
Marcia Tummers
Affiliation:
Radboud Institute for Health Sciences
Ralph van Hoorn
Affiliation:
Radboud Institute for Health Sciences
Andrew Booth
Affiliation:
Health Economics and Decision Science (HEDS)
Kati Mozygemba
Affiliation:
Institute for Public Health and Nursing Research (IPP)
Pietro Refolo
Affiliation:
Institute of Bioethics and Medical Humanities
Dario Sacchini
Affiliation:
Institute of Bioethics and Medical Humanities
Lisa Pfadenhauer
Affiliation:
Institute for Biometrics, Epidemiology and Medical Informatics
Ansgar Gerhardus
Affiliation:
Institute for Public Health and Nursing Research (IPP)
Gert Jan Van der Wilt
Affiliation:
Donders Institute for Brain, Cognition and Behaviour
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Abstract

Objectives: The INTEGRATE-HTA project provided methodology to evaluate complex technologies. This study provides guidance on how to retrieve and critically appraise available evidence on moderators and predictors of treatment effects and on patient preferences for treatment outcomes as a source of complexity.

Methods: Search filters for PubMed were developed by hand-searching a large volume of articles reporting on relevant aspects. Search terms were retrieved from selected papers and algorithmically combined to find the optimal combination of search terms. For the development of the appraisal checklists literature was searched in PubMed and Google Scholar together with citation chasing. For the CHecklist for the Appraisal of Moderators and Predictors (CHAMP) a Delphi procedure was used to value a set of eligible appraisal criteria retrieved from the literature.

Results: Search filters were developed optimized for different accuracy measures. The final version of CHAMP consists of a seventeen questions covering the design, analysis, results and transferability of results of moderator and predictor analysis. The final checklist for appraisal of literature on patient preferences for treatment outcomes consist of six questions meant to help the user to identify relevant quality issues together with a guidance toward existing tools concerning the appraisal of specific preference elicitation methods.

Conclusions: Incorporating knowledge on subgroups for whom a specific treatment will produce more benefit holds the promise of better targeting and, ultimately, enhancing overall effectiveness and efficiency of healthcare technology. Finally, incorporating information on preferences for treatment outcomes will foster health technology assessment that addresses outcomes that are important to patients.

Keywords

INTEGRATE-HTAModeratorsPredictorsPatient preferencesComplex interventionsHealth technology assessment
Type
Theme Submissions
Information
International Journal of Technology Assessment in Health Care , Volume 33 , Issue 5: Assessing Health Technologies in an Integrated Way-Results of the INTEGRATE-HTA Project , 2017 , pp. 562 - 569
Copyright
Copyright © Cambridge University Press 2017 

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References

REFERENCES

1. Gerhardus, A. Integrated health technology assessment for evaluating complex technologies (INTEGRATE-HTA): An introduction to the guidances. 2016. http://www.integrate-hta.eu/downloads/ (accessed July 20, 2017).Google Scholar
2. Van Hoorn, R, Tummers, M, Kievit, W, Van der Wilt, GJ. Guidance for the assessment of treatment moderation and patients’ preferences. 2016. http://www.integrate-hta.eu/downloads/ (accessed July 20, 2017).Google Scholar
3. Kraemer, HC, Wilson, GT, Fairburn, CG, Agras, WS. Mediators and moderators of treatment effects in randomized clinical trials. Arch Gen Psychiatry. 2002;59:877–883.CrossRefGoogle ScholarPubMed
4. Nicholson, RA, Hursey, KG, Nash, JM. Moderators and mediators of behavioral treatment for headache. Headache. 2005;45:513–519.CrossRefGoogle ScholarPubMed
5. Baron, RM, Kenny, DA. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51:1173–82.CrossRefGoogle ScholarPubMed
6. MacCorquodale, K, Meehl, PE. On a distinction between hypothetical constructs and intervening variables. Psychol Rev. 1948;55:95–107.CrossRefGoogle ScholarPubMed
7. March, JS, Curry, JF. Predicting the outcome of treatment. J Abnorm Child Psychol. 1998;26:39–51.CrossRefGoogle ScholarPubMed
8. Kraemer, HC, Kiernan, M, Essex, M, Kupfer, DJ. How and why criteria defining moderators and mediators differ between the Baron & Kenny and MacArthur approaches. Health Psychol. 2008;27:S101–S108.CrossRefGoogle ScholarPubMed
9. Viechtbauer, W. 31 Analysis of moderator effects in metaanalysis. Best practices in quantitative methods. Thousand Oaks CA: SAGE Publications, Inc; 2008:471–487.Google Scholar
10. Jenkins, M. Evaluation of methodological search filters–a review. Health Info Libr J. 2004;21:148–163.CrossRefGoogle ScholarPubMed
11. White, VJ, Glanville, JM, Lefebvre, C, Sheldon, TA. A statistical approach to designing search filters to find systematic reviews: Objectivity enhances accuracy. J Info Sci. 2001;27:357–370.CrossRefGoogle Scholar
12. Bachmann, LM, Coray, R, Estermann, P, Ter Riet, G. Identifying diagnostic studies in MEDLINE: Reducing the number needed to read. J Am Med Inform Assoc. 2002;9:653–658.CrossRefGoogle Scholar
13. Sun, X, Briel, M, Busse, JW, et al. Credibility of claims of subgroup effects in randomised controlled trials: Systematic review. BMJ. 2012;344:e1553.CrossRefGoogle ScholarPubMed
14. Pincus, T, Miles, C, Froud, R, et al. Methodological criteria for the assessment of moderators in systematic reviews of randomised controlled trials: A consensus study. BMC Med Res Methodol. 2011;11:14.CrossRefGoogle ScholarPubMed
15. Fitch, K, Bernstein, SJ, Aguilar, MD, Burnand, B, LaCalle, JR. The RAND/UCLA appropriateness method user's manual. DTIC Document; 2001.Google Scholar
16. Higgins, JPT, Altman, DG, Gøtzsche, PC, et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ. 2011;343.CrossRefGoogle ScholarPubMed
17. Reuzel, RP, van der Wilt, GJ, ten Have, HA, de Vries Robbe, PF. Interactive technology assessment and wide reflective equilibrium. J Med Philos. 2001;26:245–261.CrossRefGoogle ScholarPubMed
18. Janssen, IM, Scheibler, F, Gerhardus, A. Importance of hemodialysis-related outcomes: Comparison of ratings by a self-help group, clinicians, and health technology assessment authors with those by a large reference group of patients. Patient Prefer Adherence. 2016;10:2491–500.CrossRefGoogle ScholarPubMed
19. Jansen, SJ, Kievit, J, Nooij, MA, Stiggelbout, AM. Stability of patients’ preferences for chemotherapy: The impact of experience. Med Decis Making. 2001;21:295–306.CrossRefGoogle ScholarPubMed
20. Ely, JW, Osheroff, JA, Chambliss, ML, Ebell, MH, Rosenbaum, ME. Answering physicians’ clinical questions: Obstacles and potential solutions. J Am Med Inform Assoc. 2005;12:217–224.CrossRefGoogle ScholarPubMed
21. Eiring, O, Landmark, BF, Aas, E, et al. What matters to patients? A systematic review of preferences for medication-associated outcomes in mental disorders. BMJ Open. 2015;5:e007848.CrossRefGoogle ScholarPubMed
22. Opmeer, BC, de Borgie, CA, Mol, BW, Bossuyt, PM. Assessing preferences regarding healthcare interventions that involve non-health outcomes: An Overview of clinical studies. Patient. 2010;3:1–10.CrossRefGoogle ScholarPubMed
23. Janssen, IM, Gerhardus, A, Schroer-Gunther, MA, Scheibler, F. A descriptive review on methods to prioritize outcomes in a health care context. Health Expect. 2015;18:1873–1893.CrossRefGoogle Scholar
24. van Hoorn R, Kievit W, Booth, A, et al. The development of PubMed search strategies for patient preferences for treatment outcomes. BMC Med Res Methodol. 2016;16:88.CrossRefGoogle ScholarPubMed
25. Wahlster, P, Brereton, L, Burns, J, et al. Guidance on the integrated assessment of complex health technologies - The INTEGRATE-HTA Model. http://www.integrate-hta.eu/downloads/ (accessed July 20, 2017).Google Scholar
26. Rohwer, AB, Booth, A, Pfadenhauer, L, et al. Guidance on the use of logic models in health technology assessments of complex interventions. http://www.integrate-hta.eu/downloads/ (accessed July 20, 2017).Google Scholar
27. Brereton, L, Wahlster, P, Lysdahl, KB, et al. Integrated assessment of home based palliative care with and without reinforced caregiver support: ‘A Demonstration of INTEGRATE-HTA methodological guidances’. 2016. http://www.integrate-hta.eu/downloads/ (accessed July 20, 2017).Google Scholar
28. Barnato, AE, Anthony, DL, Skinner, J, Gallagher, PM, Fisher, ES. Racial and ethnic differences in preferences for end-of-life treatment. J Gen Intern Med. 2009;24:695–701.CrossRefGoogle ScholarPubMed
29. Isaacs, JD, Ferraccioli, G. The need for personalised medicine for rheumatoid arthritis. Ann Rheum Dis. 2011;70:4–7.CrossRefGoogle ScholarPubMed
30. Jakka, S, Rossbach, M. An economic perspective on personalized medicine. HUGO J. 2013;7:1–6.CrossRefGoogle Scholar
31. Bridges, JF, Jones, C. Patient-based health technology assessment: A vision of the future. Int J Technol Assess Health Care. 2007;23: 30–35.CrossRefGoogle ScholarPubMed
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