Evaluating Response to Tuberculosis Therapy: A Generalized Estimating Equation Model Open Access

Kyle, Ryan Patrick (2012)

Permanent URL: https://etd.library.emory.edu/concern/etds/wm117p83g?locale=en
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Abstract

A standard course of therapy for pulmonary tuberculosis (TB) requires six to nine
months of medication. Shortening the time required for stable cure is a priority in current
TB research. Current Phase 2 trials are large and expensive, given a shortage of surrogate
markers for therapy response and small differences in treatment effect between current
candidate drugs. Improved identification of those less responsive to therapy may reduce the
time and cost needed to identify more effective treatments. The author examined data for
531 participants from TB Trials Consortium Study 29, a randomized, open-label, multicenter
clinical trial comparing rifapentine versus rifampin administered 5 days per week during the
first 8 weeks of combination therapy for pulmonary TB. Individuals without liquid culture
results at baseline or at the end of intensive phase therapy were not included in the analysis;
the final sample included 375 participants. The probability of negative culture in liquid
media by week of therapy and treatment arm was obtained using a generalized estimating
equation model that adjusted for geographic region, presence of productive cough at baseline
or by week of therapy, cavitation at baseline, presence of diabetes, HIV status, cigarette use
history, bilateral involvement at baseline, days to detection on MGIT 960 at baseline, sex and
baseline smear grade. Presence of productive cough by week (adjusted odds ratio = 0.58; 95%
confidence interval: 0.42, 0.81) and decreased days to detection at baseline (aOR = 1.09; 95%
CI: 1.02, 1.16) were significantly associated with delayed culture conversion. Interactions
between weeks on therapy and diabetes or history of cigarette use were identified. These
findings may suggest that further clinical examination of the relationship between TB,
diabetes, and cigarette use is needed.

Table of Contents

Chapter 1 - Background

1.1 Epidemiology of tuberculosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Challenges in the treatment of pulmonary tuberculosis: the problem of per-
sistence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 The pursuit of superior sterilizing activity . . . . . . . . . . . . . . . . . . . . 3

1.4 Improving methods for the evaluation of new regimens: surrogate markers of
sterilizing activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4.1 Two-month culture conversion . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4.2 Sputum smear . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4.3 Serial sputum colony counting on solid media . . . . . . . . . . . . . . . . 7
1.4.4 Automated liquid culture systems . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4.5 Nucleic acid amplification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4.6 Biomarkers yet to be used in clinical trials . . . . . . . . . . . . . . . . . . 10
1.5 Factors affecting response to therapy . . . . . . . . . . . . . . . . . . . . . . 11
1.5.1 Cavitary tuberculosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.5.2 Diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.5.3 Smoking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.5.4 Geographic region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Chapter 2 - Manuscript

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 Study population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.2 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.1 Model selection, and assessment of interaction and confounding . . . 20
2.3.2 Selected GEE model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Appendix A: Notes on GEE correlation structure selection . . . . . . . . . . . . 26
Appendix B: Code used to fit final GEE model . . . . . . . . . . . . . . . . . . . . 30

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32


Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

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