Estimation of the Overall Treatment Effect in the Presence of Interference in Cluster-Randomized Trials of Infectious Disease Prevention

Epidemiologic Methods

Volumn 5, Issue 1, 2016, Pages 57-68

  Carnegie, Nicole Bohme ^a   Wang, Rui ^b,c   De Gruttola, Victor ^c  

^a UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

additive hazard; causal inference; community randomized trials; group randomized trials; spillover effects

Indexed keywords

ARTICLE; BOTSWANA; CLINICAL OUTCOME; CLUSTER ANALYSIS; CONCEPTUAL FRAMEWORK; DISEASE TRANSMISSION; EPIDEMIC; EPIDEMIOLOGICAL DATA; EXPOSURE; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; INCIDENCE; INFECTION PREVENTION; POPULATION MODEL; POPULATION SIZE; PRIORITY JOURNAL; RANDOMIZED CONTROLLED TRIAL (TOPIC); SIMULATION; THERAPY EFFECT;

EID: 85012069379     PISSN: 21949263     EISSN: 2161962X     Source Type: Journal    
DOI: 10.1515/em-2015-0016     Document Type: Article

References (35)

1. Aalen, O. O. (1989). A linear regression model for the analysis of life times. Statistics in Medicine, 8:907-925.
2. Aalen, O. O. (1993). Further results on the non-parametric linear regression model in survival analysis. Statistics in Medicine, 12:1569-1588.
3. Anderson, R. M., and May, R. M. (1991). Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press.
4. Attia, S., Egger, M., Möller, M., Zwahlen, M., and Low, N. (2009). Sexual transmission of HIV According To viral load and antiretroviral therapy: Systematic review and meta-Analysis. AIDS, 23:1397-1404.
5. Choi, D. (2015). Estimation of monotone treatment effects in network experiments. arXiv preprint, arXiv, 1408.4102v3[stat.ME].
6. ClinicalTrials.gov (2016). Botswana Combination Prevention Project https://clinicaltrials.gov/show/NCT01965470, Accessed: 10 Feb 2016
7. Eckles, D., Karrer, B., and Ugander, J. (2014). Design and analysis of experiments in networks: reducing bias from interference. arXiv preprint, arXiv, 1404.7530v2[stat.ME].
8. Goodreau, S. M. (2011). A decade of modelling research yields considerable evidence for the importance of concurrency: A response to sawers and stillwaggon. Journal of the International AIDS Society, 14:12.
9. Halloran, M. E., and Struchiner, C. J. (1991). Study designs for dependent happenings. Epidemiology, 2:331-338.
10. Hong, G., and Raudenbush, S. W. (2006). Evaluating kindergarten retention policy: A case study of causal inference for multilevel observational data. Journal of the American Statistical Association, 101:901-910.
11. Hudgens, M. G., and Halloran, M. E. (2008). Toward causal inference with interference. Journal of the American Statistical Association, 103:832-842.
12. Jewkes, R., Nduna, M., Levin, J., Jama, N., Dunkle, K., Puran, A., and Duwury, N. (2008). Impact of stepping stones on incidence of HIV and HSV-2 and sexual behaviour in rural south africa: Cluster randomised controlled trial. BMJ, 337:a606.
13. Kamali, A., Quigley, M., Nakiyingi, J., Kinsman, J., Kengeya-Kayondo, J., Gopal, R., Ojwiya, A., Hughes, P., Carpenter, L. M., and Whitworth, J. (2003). Syndromic management of sexually-Transmitted infections and behaviour change interventions on transmission of HIV-1 in rural Uganda: A community randomised trial. Lancet, 361:645-652.
14. Keeling, M. J., and Rohani, P. (2008). Modeling Infectious Diseases in Humans and Animals. Princeton, NJ: Princeton University Press.
15. Kulich, M., and Lin, D. (2000). Additive hazards regression with covariate measurement error. Journal of the American Statistical Association, 95:238-248.
16. Lange, T., and Hansen, J. V. (2011). Direct and indirect effects in a survival context. Epidemiology, 22:575-581.
17. Lin, D., and Ying, Z. (1994). Semiparametric analysis of the additive risk model. Biometrika, 81:61-71.
18. Lingappa, J. R., Hughes, J. P., Wang, R. S., Baeten, J. M., Celum, C., Gray, G. E., Stevens, W. S., Donnell, D., Campbell, M. S., Farquhar, C. et al (2010). Estimating the impact of plasma HIV-1 RNA reductions on heterosexual HIV-1 transmission risk. PloS ONE, 5:e12598.
19. Martinussen, T., and Vansteelandt, S. (2013). On collapsibility and confounding bias in Cox and Aalen regression models. Lifetime Data Analysis, 19:279-296.
20. Neyman, J. (1923). On the application of probability theory to agricultural experiments. Essay on principles (with discussion). Section 9 (translated). Statistical Science, 5:465-480.
21. Novitsky, V., and Essex, M. (2012). Using HIV viral load to guide treatment-for-prevention interventions. Current Opinion in HIV and AIDS, 7:117-124.
22. Pan, D., Liu, Y. Y., and Wu, Y. S. (2015). Additive hazards regression with random effects for clustered failure times. Acta Mathematica Sinica, 31:511-525.
23. Perez-Heydrich, C., Hudgens, M. G., Halloran, M. E., Clemens, J. D., Ali, M., and Emch, M. E. (2014). Assessing effects of cholera vaccination in the presence of interference. Biometrics, 70:731-741.
24. Quinn, T. C., Wawer, M. J., Sewankambo, N., Serwadda, D., Li, C., Wabwire-Mangen, F., Meehan, M. O., Lutalo, T., and Gray, R. H. (2000). Viral load and heterosexual transmission of human immunodeficiency virus type 1. New England Journal of Medicine, 342:921-929.
25. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66:688-701.
26. Scheike, T. H., and Zhang, M.-J. (2011). Analyzing competing risk data using the R timereg package. Journal of Statistical Software, 38:1-15, http://www.jstatsoft.org/v38/i02/.
27. Small, D. S., Ten Have, T. R., and Rosenbaum, P. R. (2008). Randomization inference in a group-randomized trial of treatments for depression: Covariate adjustment, noncompliance, and quantile effects. Journal of the American Statistical Association, 103:271-279.
28. Sobel, M. E. (2006). What do randomized studies of housing mobility demonstrate?: Causal inference in the face of interference. Journal of the American Statistical Association, 101:1398-1407.
29. Tchetgen Tchetgen, E. J., and Vander Weele, T. J. (2012). On causal inference in the presence of interference. Statistical Methods in Medical Research, 21:55-75.
30. Therneau, T. M. (2015). A package for survival analysis in S. http://CRAN.R-project.org/package = survival, version 2.38.
31. Thomas, D., Stram, D., and Dwyer, J. (1993). Exposure measurement error: Influence on exposure-disease relationships and methods of correction. Annual Review of Public Health, 14:69-93.
32. Tiono, A. B., Ouédraogo, A., Ogutu, B., Diarra, A., Coulibaly, S., Gansané, A., Sirima, S. B., ONeil, G., Mukhopadhyay, A., and Hamed, K. (2013). A controlled, parallel, cluster-randomized trial of community-wide screening and treatment of asymptomatic carriers of plasmodium falciparum in burkina faso. Malaria Journal, 12:79.
33. Verbitsky-Savitz, N., and Raudenbush, S. W. (2012). Causal inference under interference in spatial settings: A case study evaluating community policing program in Chicago. Epidemiologic Methods, 1. Article 6, 1:107-130.
34. Wang, R., Goyal, R., Lei, Q., Essex, M., and De Gruttola, V. (2014). Sample size considerations in the design of cluster randomized trials of combination HIV prevention. Clinical Trials, 11:309-318.
35. Watson, R. K. (1972). On an epidemic in a stratified population. Journal of Applied Probability, 9:659-666.