Random regression models for human immunodeficiency virus ribonucleic acid data subject to left censoring and informative drop-outs

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volumn 49, Issue 4, 2000, Pages 485-497

  Lyles, Robert H ^a,d   Lyles, Cynthia M ^b   Taylor, Douglas J ^c  

^a EMORY UNIVERSITY   (United States)

^b JOHNS HOPKINS UNIVERSITY   (United States)

^c Family Health International   (United States)

^d EMORY UNIVERSITY   (United States)

Author keywords

Bias; Detection limit; Maximum likelihood; Missing data; Random effects

Indexed keywords

EID: 0034354618     PISSN: 00359254     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-9876.00207     Document Type: Article

References (29)

1. Allison, P. D. (1995) Survival Analysis using the SAS System: a Practical Guide. Gary: SAS Institute.
2. Beckman, R. J., Nachtsheim, C. J. and Cook, R. D. (1987) Diagnostics for mixed-model analysis of variance. Technometrics, 29, 413-426.
3. Carriquiry, A. L., Gianola, D. and Fernando, R. L. (1987) Mixed-model analysis of a censored normal distribution with reference to animal breeding. Biometrics, 43, 929-939.
4. Christensen, R., Pearson, L. M. and Johnson, W. (1992) Case-deletion diagnostics for mixed models. Technometrics, 34, 38-45.
5. De Gruttola, V. and Tu, X. M. (1994) Modelling progression of CD4-lymphocyte count and its relationship to survival time. Biometrics, 50, 1003-1014.
6. Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. R. Statist. Soc. B, 39, 1-38.
7. Diggle, P. and Kenward, M. G. (1994) Informative drop-outs in longitudinal data analysis (with discussion). Appl. Statist., 43, 49-93.
8. Evans, M. and Swartz, T. (1995) Methods for approximating integrals in statistics with special emphasis on Bayesian integration problems. Statist. Sci., 10, 254-272.
9. Fairclough, D. L. (1998) Multiple imputation for non-random missing data in longitudinal studies of health-related quality of life. Joint Statist. Meet. Am. Statist. Ass. Biometrie Soc., Dallas, abstract 157.
10. Hornung, R. W. and Reed, L. D. (1990) Estimation of average concentration in the presence of nondetectable values. App. Occup. Environ. Hyg., 5, 46-51.
11. Hughes, J. P. (1999) Mixed effects models with censored data with application to HIV RNA levels. Biometrics, 55, 625-629.
12. Laird, N. M. and Ware, J. H. (1982) Random-effects models for longitudinal data. Biometrics, 38, 963-974.
13. Lange, N. and Ryan, L. (1989) Assessing normality in random effects models. Ann. Statist., 17, 624-642.
14. Liang, K.-Y. andZeger, S. L. (1986) Longitudinal data analysis using generalized linear models. Biometrika,73, 13-22.
15. Little, R. J. A. and Rubin, D. B. (1987) Statistical Analysis with Missing Data. New York: Wiley.
16. Louis, T. A. (1982) Finding the observed information matrix when using the EM algorithm. J. R. Statist. Soc. B, 44, 226-233.
17. Lyles, C. M., Dorrucci, M., Vlahov, D., Pezzotti, P., Angarano, G., Sinicco, A., Alberici, F., Alcorn, T. M., Vella, S. and Rezza, G. (1999) Longitudinal human immunodeficiency virus type 1 load in the Italian seroconversion study: correlates and temporal trends of virus load. J. Infect. Dis., 180, 1018-1024.
18. + lymphocytes as prognostic markers of HIV-1 infection. Ann. Intern. Med., 126, 946-954.
19. O'Brien, T. R., Rosenberg, P. S., Yellin, F. and Goedert, J. J. (1998) Longitudinal HIV-I RNA levels in a cohort of homosexual men. J. AIDS, 18, 155-161.
20. Paxton, W. B., Coombs, R. W., McElrath, M. J., Reefer, M. C., Hughes, J., Sinangil, F., Chernoff. D., Demeter, L., Williams, B. and Corey, L. (1997) Longitudinal analysis of quantitative virologie measures in human immunodeficiency virus-infected subjects with >= 400 CD4 lymphocytes: implications for applying measurements to individual patients. J. Infect. Dis., 175, 247-254.
21. Pettitt, A. N. (1986) Censored observations, repeated measures and mixed effects models: an approach using the EM algorithm and normal errors. Biometrika, 73, 635-643.
22. SAS Institute (1995) SAS/IML Software: Changes and Enhancements through Release 6.11. Cary: SAS Institute.
23. Schluchter, M. D. (1992) Methods for the analysis of informatively censored longitudinal data. Statist. Med., 11, 1861-1870.
24. Smith, D. K., Warren, D. L., Vlahov, D., Schuman, P., Stein, M. D., Greenberg, B. L. and Holmberg, S. D. (1997) Design and baseline participant characteristics of the Human Immunodeficiency Virus Epidemiology Research (HER) study: a prospective cohort study of human immunodeficiency virus infection in US women. Am. J. Epidem., 146, 459-469.
25. Todd, J., Pachl, C., White, R., Yeghiazarian, T., Johnson, P., Taylor, B., Holodniy, M., Kern, D., Hamren, S., Chernoff, D. and Urdea, M. (1995) Performance characteristics for the quantitation of plasma HIV-1 RNA using branched DNA signal amplification technology. J. AIDS, 10, suppl. 2, S35-S44.
26. Venables, W. N. and Ripley, B. D. (1994) Modern Applied Statistics with S-Plus. New York: Springer.
27. Vlahov, D., Graham, N., Hoover, D., Flynn, C., Bartlett, J. G., Margolick, J. B., Lyles, C. M., Nelson, K. E., Smith, D., Holmberg, S. and Farzadegan, H. (1998) Prognostic indicators for AIDS and infectious disease death in HIV-infected injection drug users: plasma viral load and CD4+ cell count. J. Am. Med. Ass., 279, 35-40.
28. Wu, M. C. and Bailey, K. R. (1990) Estimaticn and comparison of changes in the presence of informative right censoring: conditional linear model. Biometrics, 45, 939-955.
29. Wu, M. C. and Carroll, R. J. (1988) Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics, 44, 175-188.