Swipe om te navigeren naar een ander artikel
Missing health-related quality of life (HRQOL) data in clinical trials can impact conclusions but the effect has not been thoroughly studied in HIV clinical trials. Despite repeated recommendations to avoid complete case (CC) analysis and last observation carried forward (LOCF), these approaches are commonly used to handle missing data. The goal of this investigation is to describe the use of different analytic methods under assumptions of missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) using HIV as an empirical example.
Medical Outcomes Study HIV (MOS-HIV) Health Survey data were combined from two large open-label multinational HIV clinical trials comparing treatments A and B over 48 weeks. Inclusion in the HRQOL analysis required completion of the MOS-HIV at baseline and at least one follow-up visit (weeks 8, 16, 24, 40, 48). Primary outcomes for the analysis were change from week 0 to 48 in mental health summary (MHS), physical health summary (PHS), pain and health distress scores analyzed using CC, LOCF, generalized estimating equations (GEE), direct likelihood and sensitivity analyses using joint mixed-effects model, and Markov chain Monte Carlo (MCMC) multiple imputation. Time and treatment were included in all models. Baseline and longitudinal variables (adverse event and reason for discontinuation) were only used in the imputation model.
A total of 511 patients randomized to treatment A and 473 to treatment B completed the MOS-HIV at baseline and at least one follow-up visit. At week 48, 71% of patients on treatment A and 31% on treatment B completed the MOS-HIV survey. Examining changes within each treatment group, CC and MCMC generally produced the largest or most positive changes. The joint model was most conservative; direct likelihood and GEE produced intermediate results; LOCF showed no consistent trend. There was greater spread for within-group changes than between-group differences (within MHS scores for treatment A: −0.1 to 1.6, treatment B: 0.4 to 2.0; between groups: −0.7 to 0.4; within PHS scores for treatment A: −1.5 to 0.4, treatment B: −1.7 to −0.2; between groups: 0.1 to 1.1). The size of within-group changes and between-group differences was of similar magnitude for the pain and health distress scores. In all cases, the range of estimates was small <0.2 SD (less than 2 points for the summary scores and 5 points for the subscale scores).
Use of the recommended likelihood-based models that do not require assumptions of MCAR was very feasible. Sensitivity analyses using auxiliary information can help to investigate the potential effect that missing data have on results but require planning to ensure that relevant data are prospectively collected.
Log in om toegang te krijgen
Met onderstaand(e) abonnement(en) heeft u direct toegang:
Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: John Wiley & Sons.
Donaldson, G. W., & Moinpour, C. M. (2005). Learning to live with missing quality-of-life data in advanced-stage disease trials. Journal of Clinical Oncology, 22(1), 1–5.
Fairclough, D. L. (2002). Design and analysis of quality of life studies in clinical trials. Boca Raton, Florida: Chapman & Hall/CRC.
FDA. (2006). Draft guidance for industry on patient-reported outcome measures: use in medicinal product development to support labeling claims. Federal Register, 71, 5862–5863.
Mallinckrodt, C. H., Christopher, J. Kaiser, C. J., Watkin, J. G., Detke, M. J., Molenberghs, G., & Carroll, R. J. (2004). Type I error rates from likelihood-based repeated measures analyses of incomplete longitudinal data. Pharmaceutical Statistics, 3, 171–186.
Revicki, D. A., Swartz, C., Wu, A. W., Haubrich, R., & Collier, A. C. (1999). Quality of life outcomes of saquinavir, zalcitabine and combination saquinavir plus zalcitabine therapy for adults with advanced HIV infection with CD4 counts between 50 and 300 cells/mm 3. Antiviral Therapy, 4(1), 35–44. PubMed
van Leth, F., Conway, B., Laplume, H., Martin, D., Fisher, M., Jelaska, A., Wit, F. W., Lange, J. M. & 2NN study group. (2004). Quality of life in patients treated with first-line antiretroviral therapy containing nevirapine and/or efavirenz. Antiviral Therapy, 9(5), 721–728.
Casado, A., Badia, X., Consiglio, E., Ferrer, E., Gonzalez, A., Pedrol, E., Gatell, J. M., Azuaje, C., Llibre, J. M., Aranda, M., Barrufet, P., Martinez-Lacasa, J., Podzamczer, D., & COMBINE Study Team. (2004). Health-related quality of life in HIV-infected naive patients treated with nelfinavir or nevirapine associated with ZDV/3TC (the COMBINE-QoL substudy). HIV Clinical Trials, 5(3), 132–139.
Badia, X., Podzamczer, D., Moral, I., Roset, M., Arnaiz, J. A., Lonca, M., Casiro, A., Roson, B., Gatell, J. M., & BestQol Study Group. (2004). Health-related quality of life in HIV patients switching to twice-daily indinavir/ritonavir regimen or continuing with three-times-daily indinavir-based therapy. Antiviral Therapy, 9(6), 979–985.
Hicks, C. B., Cahn, P., Cooper, D. A. et al. (2006). Durable efficacy of tipranavir-ritonavir in combination with an optimised background regimen of antiretroviral drugs for treatment-experienced HIV-1-infected patients at 48 weeks in the randomized evaluation of strategic intervention in multi-drug reSistant patients with tipranavir (RESIST) studies: An analysis of combined data from two randomised open-label trials. Lancet, 36, 466–475. CrossRef
Revicki, D. A., Sorensen, S., & Wu, A. W. (1998). Reliability and validity of physical and mental health summary scores from the Medical Outcomes Study HIV Health Survey. Medical Care, 38, 126–137. CrossRef
DeGruttola, V., & Tu, X. M. (1994). Modeling progression of CD4-lymphocyte count and its relationship to survival time. Biometrics, 50(4), 1003–1014. CrossRef
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581–592. CrossRef
Rubin, D. B., & Schenker, N. (1986). Multiple imputation for interval estimation from simple random samples with ignorable nonresponse. Journal of the American Statistical Association, 81, 366–374. CrossRef
Schafer, J. L. (1997). Analysis of incomplete multivariate data. Chapman and Hall, London.
Tanner, M. A., & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation (with discussion). Journal of the American Statistical Association, 82, 528–550. CrossRef
Dempster, A. P., & Rubin, D. B. (1983). Overview. In W. G. Madow, I. Olkin, & D. B. Rubin (Eds.), Incomplete data in sample surveys (Vol. II). Theory and annotated bibliography. New York: Academic Press (pp. 3–10).
Guo, X., Carlin, B. P. (2004). Separate and joint modeling of longitudinal and event time data using standard computer packages. The American Statistician, 58, 16–24. CrossRef
- Handling missing quality of life data in HIV clinical trials: what is practical?
Diane L. Fairclough
Henrik W. Finnern
Albert W. Wu
- Springer Netherlands