Effectiveness of potent antiretroviral therapy on progression of human immunodeficiency virus: Bayesian modelling and model checking via counterfactual replicates

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

Volumn 53, Issue 4, 2004, Pages 633-650

  Berzuini, Carlo ^a,b,c   Allemani, Claudia ^b  

^a MEDICAL RESEARCH COUNCIL   (United Kingdom)

^b UNIVERSITY OF PAVIA   (Italy)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Acquired immune deficiency syndrome; Bayesian modelling; CD4+ T cells; Failure time data; Graphical model; Highly active antiretroviral therapy; Human immunodeficiency virus; Longitudinal modelling; Markov chain Monte Carlo methods; Model criticism

Indexed keywords

EID: 4644261143     PISSN: 00359254     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1467-9876.2004.04985.x     Document Type: Review

References (33)

1. Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B, 57, 289-300.
2. Berzuini, C. and Larizza, C. (1996) A unified approach for modeling longitudinal and failure time data, with application in medical monitoring. IEEE Trans. Pattn Anal. Mach. Intell., 18, 109-123.
3. Binquet, C., Chene, G., Jacqmin-Gadda, H., Journot, V., Saves, M., Lacoste, D., Dabis, F. and the Groupe d'Epidemiologie Clinique du SIDA en Aquitaine (2001) Modeling changes in CD4-positive T-lymphocyte counts after the start of highly active antiretroviral therapy and the relation with risk of opportunistic infections: the Aquitaine Cohort, 1996-1997. Am. J. Epidem., 153, 386-393.
4. + cell count and the risk of opportunistic infections or death after highly active antiretroviral treatment. AIDS, 12, 2313-2320.
5. Cox, D. R. (1999) Some remarks on failure-times, surrogate markers, degradation, wear, and the quality of life. Lifetime Data Anal., 5, 307-314.
6. Detels, R., Munoz, A., McFarlane, G., Kingsley, L. A., Margolick, J. B., Giorgi, J., Schrager, L. K., Phair, J. P. and the Multicenter AIDS Cohort Study Investigators (1998) Effectiveness of potent antiretroviral therapy on time to AIDS and death in men with known HIV infection duration. J. Am. Med. Ass., 280, 1497-1503.
7. Detels, R., Tarwater, P. M., Phair, J. P., Margolick, J., Riddler, S. A., Munoz, A. and the Multicenter AIDS Cohort Study Investigators (2001) Effectiveness of potent antiretroviral therapies on the incidence of opportunistic infections before and after AIDS diagnosis. AIDS, 15, 347-355.
8. Dorrucci, M., Balducci, M., Pezzotti, P., Sinicco, A., Alberici, F., Rezza, G. and the Italian HIV Seroconversion Study Group (1999) Temporal changes in the rate of progression to death among Italians with known date of HIV seroconversion: estimates of the population effect of treatment. J. Acq. Immune Defic. Syndr., 22, 65-70.
9. Faucett, C. L. and Thomas, D. C. (1996) Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach. Statist. Med., 15, 1663-1685.
10. Gelfand, A. E., Dey, D. K. and Chang, H. (1992) Model determination using predictive distributions, with implementation via sampling-based methods (with discussion). In Bayesian Statistics 4 (eds J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith), pp. 147-167. Oxford: Oxford University Press.
11. Gelman, A., Meng, X. L. and Stern, H. S. (1996) Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statist. Sin., 6, 733-807.
12. Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (eds) (1996) Markov Chain Monte Carlo in Practice. London: Chapman and Hall.
13. Guihenneuc-Jouyaux, C., Richardson, S. and Longini, Jr, I. M. (2000) Modeling markers of disease progression by a hidden Markov process: application to characterizing CD4 cell decline. Biometrics, 56, 733-741.
14. Hastings, W. K. (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109.
15. Henderson, R., Diggle, P. and Dobson, A. (2000) Joint modelling of longitudinal measurements and event time data. Biostatistics, 4, 465-480.
16. Italian Seroconversion Study Team (1992) Disease progression and early predictors of AIDS in HIV-seroconverted injecting drug users. AIDS, 6, 421-426.
17. Jewell, N. P. and Nielsen, J. P. (1993) A framework for consistent prediction rules based on markers. Biometrika, 80, 153-164.
18. Lauritzen, S. L. (1996) Graphical Models. Oxford: Clarendon.
19. Lauritzen, S. L., Dawid, A. P., Larsen, B. N. and Leimer, H. G. (1990) Independence properties of directed Markov fields. Networks, 20, 491-505.
20. Meng, X.-L. (1994) Posterior predictive p-values. Ann. Statist., 22, 1142-1160.
21. Miller, V., Staszewski, S., Nisius, G., Lepri, A. C., Sabin, C. and Phillips, A. N. (1999) Risk of new AIDS diseases in people on triple therapy. Lancet, 353, 463.
22. Palella, Jr, F. J., Delaney, K. M., Moorman, A. C., Loveless, M. O., Fuhrer, J., Satten, G. A., Aschman, D. J. and Holmberg, S. D. (1998) Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection: HIV Outpatient Study Investigators. New Engl. J. Med., 338, 853-860.
23. Pezzotti, P., Pappagallo, M., Phillips, A. N., Boros, S., Valdarchi, C., Sinicco, A., Zaccarelli, M. and Rezza, G. (2001) Response to highly active antiretroviral therapy according to duration of HIV infection. J. Acq. Immune Defic. Syndr., 26, 473-479.
24. Phillips, A. N., Grabar, S., Tassie, J. M., Costagliola, D., Lundgren, J. D. and Egger, M. (1999) Use of observational databases to evaluate the effectiveness of antiretroviral therapy for HIV infection: comparison of cohort studies with randomized trials. AIDS, 13, 2075-2082.
25. Rezza, G., Lazzarin, A., Angarano, G., Sinicco, A., Pristera, R., Ortona, L., Barbanera, M., Gafa, S., Tirelli, U. and Salassa, B. (1989) The natural history of HIV infection in intravenous drug users: risk of disease progression in a cohort of seroconverters. AIDS, 3, 87-90.
26. Satten, G. A. and Longini, Jr, I. M. (1996) Markov chains with measurement errors: estimating the 'true' course of a marker of the progression of human immunodeficiency virus disease (with discussion). Appl. Statist., 45, 275-309.
27. Singpurwalla, N. D. (1995) Survival in dynamic environments. Statist. Sci., 10, 86-103.
28. Spiegelhalter, D. J., Thomas, A., Best, N. G. and Gilks, W. R. (1995) BUGS: Bayesian Inference using Gibbs Sampling, Version 0.50. Cambridge: Medical Research Council Biostatistics Unit.
29. Tarwater, P. M., Margolik, J. B., Jin, J., Phair, J. P., Detels, R., Rinaldo, C., Giorgi, J. and Munoz, A. (2001) Increase and plateau of CD4 T-cell counts in the 3 1/2 years after initiation of potent antiretroviral therapy. J. Acq. Immune Defic. Syndr., 27, 168-175.
30. Taylor, J. M. G., Cumberland, W. G. and Sy, J. P. (1994) A stochastic model for analysis of longitudinal AIDS data. J. Am. Statist. Ass., 89, 727-737.
31. Taylor, J. M. G., Wang, Y., Ahdieh, L., Chmiel, J. S., Detels, R., Giorgi, J. V., Kaslow, R., Kingsley, L. and Margolok, J. (2000) Causal pathways for CCR5 genotype and HIV progression. J. AIDS, 23, 160-171.
32. Tierney, L. (1994) Markov chains for exploring posterior distributions. Ann. Statist., 22, 1701-1728.
33. Whittaker, J. (1990) Graphical Models in Applied Multivariate Analysis. Chichester: Wiley.