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On the analysis of tuberculosis studies with intermittent missing sputum data. (English) Zbl 1397.62471

Summary: In randomized studies evaluating treatments for tuberculosis (TB), individuals are scheduled to be routinely evaluated for the presence of TB using sputum cultures. One important endpoint in such studies is the time of culture conversion, the first visit at which a patient’s sputum culture is negative and remains negative. This article addresses how to draw inference about treatment effects when sputum cultures are intermittently missing on some patients. We discuss inference under a novel benchmark assumption and under a class of assumptions indexed by a treatment-specific sensitivity parameter that quantify departures from the benchmark assumption. We motivate and illustrate our approach using data from a randomized trial comparing the effectiveness of two treatments for adult TB patients in Brazil.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis

References:

[1] American Thoracic Society (2000). Diagnostic standards and classification of tuberculosis in adults and children. Am. J. Respir. Crit. Care Med. 161 1376-1395.
[2] Barndorff-Nielsen, O. E. and Cox, D. R. (1994). Inference and Asymptotics . Chapman & Hall, London. · Zbl 0826.62004
[3] Conde, M. B., Efron, A., Loredo, C., Souza, G. R. M. D., Graça, N. P., Cezar, M. C., Ram, M., Chaudhary, M. A., Bishai, W. R., Kritski, A. L. and Chaisson, R. E. (2009). Moxifloxacin versus ethambutol in the initial treatment of tuberculosis: A double-blind, randomised, controlled phase II trial. Lancet 373 1183-1189.
[4] Cox, D. R. (1972). Regression models and life-tables. J. Roy. Statist. Soc. Ser. B 34 187-220. · Zbl 0243.62041
[5] European Medicines Agency, Committee for Medicinal Products for Human Use (2010). Addendum to the Note for Guidance on Evaluation of Medicinal Products Indicated for Treatment of Bacterial Infections to Specifically Address the Clinical Development of New Agents to Treat Disease Due to Mycobacterium Tuberculosis . European Medicines Agency, London.
[6] Gill, R. D., Van der Laan, M. J. and Robins, J. M. (1997). Coarsening at random: Characterizations, conjectures and counter-examples. In Proceedings of the First Seattle Symposium in Biostatistics : Survival Analysis (D. Y. Lin and T. R. Fleming, eds.) 255-294. Springer, Berlin. · Zbl 0918.62003
[7] Heitjan, D. F. (1993). Ignorability and coarse data: Some biomedical examples. Biometrics 49 1099-1109. · Zbl 0825.62755 · doi:10.2307/2532251
[8] Heitjan, D. F. (1994). Ignorability in general incomplete-data models. Biometrika 81 701-708. · Zbl 0810.62008 · doi:10.1093/biomet/81.4.701
[9] Heitjan, D. F. and Rubin, D. B. (1991). Ignorability and coarse data. Ann. Statist. 19 2244-2253. · Zbl 0745.62004 · doi:10.1214/aos/1176348396
[10] Nelson, D. R., Zeuzem, S., Andreone, P., Ferenci, P., Herring, R., Jensen, D. M., Marcellin, P., Pockros, P. J., Rodríguez-Torres, M., Rossaro, L. et al. (2012). Balapiravir plus peginterferon alfa-2a (40KD)/ribavirin in a randomized trial of hepatitis C genotype 1 patients. Annals of Hepatology 11 15.
[11] Newey, W. K. and McFadden, D. (1994). Large sample estimation and hypothesis testing. In Handbook of Econometrics , Vol. IV (R. F. Engle and D. L. McFadden, eds.) 2111-2245. North-Holland, Amsterdam. · doi:10.1016/S1573-4412(05)80005-4
[12] Robins, J. M. and Gill, R. D. (1997). Non-response models for the analysis of non-monotone ignorable missing data. Stat. Med. 16 39-56.
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