Skip to main content

Statistical Considerations in Analyzing Health-Related Quality of Life Data

  • 161 Accesses

Abstract

Health-related quality of life in oncology studies is often measured using patient-reported outcome (PRO) instruments which quantify latent concepts, such as pain and fatigue, experienced by the patient. Certain statistical considerations are helpful when analyzing PRO data for results interpretable to a variety of stakeholders. For longitudinal studies, valid results depend on approaches that accommodate missing PRO data. While comparison of group means is a common way to evaluate interventions, other endpoints reporting proportions of people who have experienced meaningful improvement or the likelihood of improving, for example, are palatable to many end-users. This chapter begins by describing basic properties of PRO instruments. We discuss ways to develop PRO research questions to guide analytics and methods to analyze endpoints while accounting for missing data. We consider ways to analyze multiple PRO endpoints including the use of standardized effect sizes, effective visualization, and accounting for testing of multiple endpoints.

Keywords

  • Health-related quality of life
  • Patient-reported outcomes
  • Oncology
  • Study design
  • Statistical considerations
  • Interpretability

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-84702-9_10
  • Chapter length: 20 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-84702-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Fig. 10.1
Fig. 10.2
Fig. 10.3

References

  1. Altman DG, Royston P. The cost of dichotomising continuous variables. Br Med J. 2006;332:1080. https://doi.org/10.1136/bmj.332.7549.1080.

    CrossRef  Google Scholar 

  2. Basch E, Abernethy AP, Mullins CD, Reeve BB, Lou SM, Coons SJ, Sloan J, Wenzel K, Chauhan C, Eppard W, Frank ES, Lipscomb J, Raymond SA, Spencer M, Tunis S. Recommendations for incorporating patient-reported outcomes into clinical comparative effectiveness research in adult oncology. J Clin Oncol. 2012;30:4249–55.

    CrossRef  Google Scholar 

  3. Bell ML, Fairclough DL. Practical and statistical issues in missing data for longitudinal patient reported outcomes. Stat Methods Med Res. 2013;625:1–20. https://doi.org/10.1177/0962280213476378.

    CrossRef  Google Scholar 

  4. Bell ML, Fairclough DL, Fiero MH, Butow PN. Handling missing items in the Hospital Anxiety and Depression Scale (HADS): a simulation study public health. BMC Res Notes. 2016;9 https://doi.org/10.1186/s13104-016-2284-z.

  5. Bell ML, Fiero M, Horton NJ, Hsu C-H. Handling missing data in RCTs; a review of the top medical journals. BMC Med Res Methodol. 2014;14:1–8. https://doi.org/10.1186/1471-2288-14-118.

    CrossRef  Google Scholar 

  6. Bell ML, Fiero MH, Dhillon HM, Bray VJ, Vardy JL. Statistical controversies in cancer research: using standardized effect size graphs to enhance interpretability of cancer-related clinical trials with patient-reported outcomes. Ann Oncol. 2017;28 https://doi.org/10.1093/annonc/mdx064.

  7. Bell ML, Floden L, Rabe BA, Hudgens S, Dhillon H, Bray V, Hardy J. Analytical approaches and estimands to take account of missing patient-reported data in longitudinal studies. Patient Rep Outcome Meas. 2019;10:129–40.

    CrossRef  Google Scholar 

  8. Bell ML, Horton NJ, Dhillon HM, Bray VJ, Vardy J. Using generalized estimating equations and extensions in randomized trials with missing longitudinal patient reported outcome data. Psychooncology. 2018;27 https://doi.org/10.1002/pon.4777.

  9. Bell ML, Kenward MG, Fairclough DL, Horton NJ. Differential dropout and bias in randomised controlled trials: when it matters and when it may not. BMJ. 2013;346:e8668. https://doi.org/10.1136/bmj.e8668.

    CrossRef  Google Scholar 

  10. Bonnetain F, Dahan L, Maillard E, Ychou M, Mitry E, Hammel P, Legoux J-L, Rougier P, Bedenne L, Seitz J-F. Time until definitive quality of life score deterioration as a means of longitudinal analysis for treatment trials in patients with metastatic pancreatic adenocarcinoma. Eur J Cancer. 2010;46:2753–62. https://doi.org/10.1016/j.ejca.2010.07.023.

    CrossRef  Google Scholar 

  11. Bottomley A, Reijneveld JC, Koller M, Flechtner H, Tomaszewski KA, Greimel E, Ganz PA, Ringash J, O’Connor D, Kluetz PG, Tafuri G, Grønvold M, Snyder C, Gotay C, Fallowfield DL, Apostolidis K, Wilson R, Stephens R, Schünemann H, Calvert M, Holzner B, Musoro JZ, Wheelwright S, Martinelli F, Dueck AC, Pe M, Coens C, Velikova G, Kuliś D, Taphoorn MJB, Darlington AS, Lewis I, van de Poll-Franse L. Current state of quality of life and patient-reported outcomes research. Eur J Cancer. 2019;121 https://doi.org/10.1016/j.ejca.2019.08.016.

  12. Bray VJ, Dhillon HM, Bell ML, Kabourakis M, Fiero MH, Yip D, Boyle F, Price MA, Vardy JL. Evaluation of a web-based cognitive rehabilitation program in cancer survivors reporting cognitive symptoms after chemotherapy. J Clin Oncol. 2017;35 https://doi.org/10.1200/JCO.2016.67.8201.

  13. Cappelleri JC, Zou KH, Bushmakin AG, Alvir JMJ, Alemayehu D, Symonds T. Patient-reported outcomes: measurement, implementation and interpretation. CRC Press; 2013.

    CrossRef  Google Scholar 

  14. Carpenter JR, Roger JH, Kenward MG. Analysis of longitudinal trials with protocol deviation: a framework for relevant, accessible assumptions, and inference via multiple imputation. J Biopharm Stat. 2013;23:1352–71. https://doi.org/10.1080/10543406.2013.834911.

    CrossRef  Google Scholar 

  15. Coens C, Pe M, Dueck AC, Sloan J, Basch E, Calvert M, Campbell A, Cleeland C, Cocks K, Collette L, Devlin N, Dorme L, Flechtner HH, Gotay C, Griebsch I, Groenvold M, King M, Kluetz PG, Koller M, Malone DC, Martinelli F, Mitchell SA, Musoro JZ, O’Connor D, Oliver K, Piault-Louis E, Piccart M, Quinten C, Reijneveld JC, Schürmann C, Smith AW, Soltys KM, Taphoorn MJB, Velikova G, Bottomley A. International standards for the analysis of quality-of-life and patient-reported outcome endpoints in cancer randomised controlled trials: recommendations of the SISAQOL Consortium. Lancet Oncol. 2020;21(2):e83–96.

    CrossRef  Google Scholar 

  16. Cohen J. Statistical power analysis for the behavioural sciences. Hillside: Lawrence Erlbaum Assoc; 1988.

    Google Scholar 

  17. Committee for Medicinal Products for Human Use (CHMP). Guideline on missing data in confirmatory clinical trials. London Eur Med Agency. 2011;44:1–12. https://doi.org/10.2307/2290157.

    CrossRef  Google Scholar 

  18. Dawson J, Doll H, Coffey J, Jenkinson C, on behalf of the Oxford. Responsiveness and minimally important change for the Manchester-Oxford foot questionnaire (MOXFQ) compared with AOFAS and SF-36 assessments following surgery for hallux valgus. Osteoarthr Cartil. 2007;15 https://doi.org/10.1016/j.joca.2007.02.003.

  19. Fairclough D. Design and analysis of quality of life studies in clinical trials. Chapman and Hall/CRC; 2010.

    CrossRef  Google Scholar 

  20. Fayers P, Aaronson N, Bjordal K. EORTC QLQ-C30 scoring manual. Brussels: EORTC; 2001.

    Google Scholar 

  21. Fiero MH, Pe M, Weinstock C, King-Kallimanis BL, Komo S, Klepin HD, Gray SW, Bottomley A, Kluetz PG, Sridhara R. Demystifying the estimand framework: a case study using patient-reported outcomes in oncology. Lancet Oncol. 2020;21:E488–94.

    CrossRef  Google Scholar 

  22. Fitzmaurice G, Laird N, Ware J. Applied longitudinal analysis. 2nd ed. Wiley; 2011.

    CrossRef  Google Scholar 

  23. Floden L, Bell ML. Imputation strategies when a continuous outcome is to be dichotomized for responder analysis: a simulation study. BMC Med Res Methodol. 2019;19 https://doi.org/10.1186/s12874-019-0793-x.

  24. Gottschall AC, West SG, Enders CK. A comparison of item-level and scale-level multiple imputation for questionnaire batteries. Multivariate Behav Res. 2012;47 https://doi.org/10.1080/00273171.2012.640589.

  25. Graham JW. Missing data analysis: making it work in the real world. Annu Rev Psychol. 2009;60:549–76.

    CrossRef  Google Scholar 

  26. Hamasaki T, Bretz F, LaVange LM, Müller P, Pennello G, Pinheiro JC. Editorial: roles of hypothesis testing, p-values and decision making in biopharmaceutical research. Stat Biopharm Res. 2021;13:1–5.

    CrossRef  Google Scholar 

  27. Hamidou Z, Dabakuyo TS, Mercier M, Fraisse J, Causeret S, Tixier H, Padeano M-M, Loustalot C, Cuisenier J, Sauzedde J-M, Smail M, Combier J-P, Chevillote P, Rosburger C, Arveux P, Bonnetain F. Time to deterioration in quality of life score as a modality of longitudinal analysis in patients with breast cancer. Oncologist. 2011;16:1458–68. https://doi.org/10.1634/theoncologist.2011-0085.

    CrossRef  Google Scholar 

  28. Hedges LV. Distribution theory for Glass’s estimator of effect size and related estimators. J Educ Stat. 1981;6 https://doi.org/10.3102/10769986006002107.

  29. Hochberg Y. A sharper bonferroni procedure for multiple tests of significance. Biometrika. 1988;75 https://doi.org/10.1093/biomet/75.4.800.

  30. Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat. 1979;6:65–70.

    Google Scholar 

  31. Horton M, Tennant A. Patient reported outcomes: misinference from ordinal scales? Trials. 2011;12 https://doi.org/10.1186/1745-6215-12-s1-a65.

  32. Hudgens S, Gable J, Kulke MH, Bergsland E, Anthony LB, Caplin ME, Oberg KE, Pavel ME, Banks P, Yang QM, Lapuerta P. Evaluation of meaningful change in bowel move frequency for patients with carcinoid syndrome. J Clin Oncol. 2017;35 https://doi.org/10.1200/jco.2017.35.4_suppl.583.

  33. ICH. Addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles for clinical trials E9(R1). Fed Regist No. FDA-20. 2019.

    Google Scholar 

  34. Jaeschke R, Singer J, Guyatt GH. Measurement of health status. Ascertaining the minimal clinically important difference. Control Clin Trials. 1989;10 https://doi.org/10.1016/0197-2456(89)90005-6.

  35. Lawrance R, Degtyarev E, Griffiths P, Trask P, Lau H, D’Alessio D, Griebsch I, Wallenstein G, Cocks K, Rufibach K. What is an estimand & how does it relate to quantifying the effect of treatment on patient-reported quality of life outcomes in clinical trials? J Patient Rep Outcomes. 2020;4 https://doi.org/10.1186/s41687-020-00218-5.

  36. Leucht S, Davis JM, Engel RR, Kane JM, Wagenpfeil S. Defining ‘response’ in antipsychotic drug trials: recommendations for the use of scale-derived Cutoffs. Neuropsychopharmacology. 2007;32:1903–10. https://doi.org/10.1038/sj.npp.1301325.

    CrossRef  Google Scholar 

  37. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73 https://doi.org/10.1093/biomet/73.1.13.

  38. Little RJ, D’Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, Frangakis C, Hogan JW, Molenberghs G, Murphy SA, Neaton JD, Rotnitzky A, Scharfstein D, Shih WJ, Siegel JP, Stern H. The prevention and treatment of missing data in clinical trials. N Engl J Med. 2012;367:1355–60. https://doi.org/10.1056/NEJMsr1203730.

    CrossRef  Google Scholar 

  39. Little RJA, Rubin DB. Statistical analysis with missing data. Wiley; 2002.

    CrossRef  Google Scholar 

  40. Mallinckrod CH, Lane PW, Schnell D, Peng Y, Mancuso JP. Recommendations for the primary analysis of continuous endpoints in longitudinal clinical trials. Drug Inf J. 2008;42:303–19. https://doi.org/10.1177/009286150804200402.

    CrossRef  Google Scholar 

  41. Mallinckrodt CH, Bell J, Liu G, Ratitch B, O’Kelly M, Lipkovich I, Singh P, Xu L, Molenberghs G. Aligning estimators with estimands in clinical trials: putting the ICH E9(R1) guidelines into practice. Ther Innov Regul Sci. 2019;216847901983697 https://doi.org/10.1177/2168479019836979.

  42. Mazza GL, Enders CK, Ruehlman LS. Addressing item-level missing data: a comparison of proration and full information maximum likelihood estimation. Multivariate Behav Res. 2015;50 https://doi.org/10.1080/00273171.2015.1068157.

  43. McGlothlin AE, Lewis RJ. Minimal clinically important difference: defining what really matters to patients. JAMA. 2014;312:1342–3.

    CrossRef  Google Scholar 

  44. Pe M, Dorme L, Coens C, Basch E, Calvert M, Campbell A, Cleeland C, Cocks K, Collette L, Dirven L, Dueck AC, Devlin N, Flechtner HH, Gotay C, Griebsch I, Groenvold M, King M, Koller M, Malone DC, Martinelli F, Mitchell SA, Musoro JZ, Oliver K, Piault-Louis E, Piccart M, Pimentel FL, Quinten C, Reijneveld JC, Sloan J, Velikova G, Bottomley A. Statistical analysis of patient-reported outcome data in randomised controlled trials of locally advanced and metastatic breast cancer: a systematic review. Lancet Oncol. 2018;19

    Google Scholar 

  45. Permutt T. A taxonomy of estimands for regulatory clinical trials with discontinuations. Stat Med. 2016;35:2865–75. https://doi.org/10.1002/sim.6841.

    CrossRef  Google Scholar 

  46. Piaggio G, Elbourne DR, Pocock SJ, Evans SJW, Altman DG. Reporting of noninferiority and equivalence randomized trials: extension of the CONSORT 2010 statement. JAMA. 2012;308:2594–604.

    CrossRef  Google Scholar 

  47. Revicki D, Hays RD, Cella D, Sloan J. Recommended methods for determining responsiveness and minimally important differences for patient-reported outcomes. J Clin Epidemiol. 2008;61:102–9.

    CrossRef  Google Scholar 

  48. Revicki DA, Erickson PA, Sloan JA, Dueck A, Guess H, Santanello NC. Interpreting and reporting results based on patient-reported outcomes. Value Health. 2007;10:S116–24.

    CrossRef  Google Scholar 

  49. Rubin DB. Multiple imputation for nonresponse in surveys. Wiley-Interscience; 2004.

    Google Scholar 

  50. Sloan JA, Dueck AC, Erickson PA, Guess H, Revicki DA, Santanello NC. Analysis and interpretation of results based on patient-reported outcomes. Value Health. 2007;10:S106–15.

    CrossRef  Google Scholar 

  51. Snapinn SM, Jiang Q. Responder analyses and the assessment of a clinically relevant treatment effect. Trials. 2007;8:31. https://doi.org/10.1186/1745-6215-8-31.

    CrossRef  Google Scholar 

  52. Snyder C, Smith K, Holzner B, Rivera YM, Bantug E, Brundage M, Weber D, Basch E, Aaronson N, Reeve B, Velikova G, Heckert A, Stotsky-Himelfarb E, Chauhan C, Hoffman V, Ganz P, Barbera L, Frank E, Lou SM, Durazo A, Needham J, Nasso SF, Miller R, Smith T, Struth D, Rein A, Dias A, Roberts C, Smider N, Cook G, Bjorner J, Witteman H, Dolan JG, Blazeby J, Golub RM, Laine C, Ramsey S. Making a picture worth a thousand numbers: recommendations for graphically displaying patient-reported outcomes data. Qual Life Res. 2019:28. https://doi.org/10.1007/s11136-018-2020-3.

  53. Stewart AK, Dimopoulos MA, Masszi T, Špička I, Oriol A, Hájek R, Rosiñol L, Siegel DS, Niesvizky R, Jakubowiak AJ, San-Miguel JF, Ludwig H, Buchanan J, Cocks K, Yang X, Xing B, Zojwalla N, Tonda M, Moreau P, Palumbo A. Health-related quality of life results from the open-label, randomized, phase III ASPIRE trial evaluating carfilzomib, lenalidomide, and dexamethasone versus lenalidomide and dexamethasone in patients with relapsed multiple myeloma. J Clin Oncol. 2016;34:3921–30. https://doi.org/10.1200/JCO.2016.66.9648.

    CrossRef  Google Scholar 

  54. Stockler MR, Hilpert F, Friedlander M, King MT, Wenzel L, Lee CK, Joly F, De Gregorio N, Arranz JA, Mirza MR, Sorio R, Freudensprung U, Sneller V, Hales G, Pujade-Lauraine E. Patient-reported outcome results from the open-label phase III AURELIA trial evaluating bevacizumab-containing therapy for platinum-resistant ovarian cancer. J Clin Oncol. 2014;32:1309–16. https://doi.org/10.1200/JCO.2013.51.4240.

    CrossRef  Google Scholar 

  55. US Food and Drug Administration (2019) Incorporating clinical outcome assessments into endpoints for regulatory decision-making.

    Google Scholar 

  56. Verbeke G, Molenberghs G. Linear mixed models for longitudinal data. Springer; 2009.

    Google Scholar 

  57. Wagner LI, Sweet J, Butt Z, Lai J, Cella D. Measuring patient self-reported cognitive function: development of the functional assessment of cancer therapy – cognitive function instrument. J Support Oncol. 2009;7(6):W32–9.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lysbeth Floden .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Floden, L., Bell, M. (2022). Statistical Considerations in Analyzing Health-Related Quality of Life Data. In: Kassianos, A.P. (eds) Handbook of Quality of Life in Cancer. Springer, Cham. https://doi.org/10.1007/978-3-030-84702-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-84702-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84701-2

  • Online ISBN: 978-3-030-84702-9

  • eBook Packages: MedicineMedicine (R0)