Accounting for informative sampling in estimation of associations between sexually transmitted infections and hormonal contraceptive methods

Anu Mishrahttp://orcid.org/https://orcid.org/0000-0002-4662-0374 4 , Petra Bůžková 1 , Jennifer E. Balkus 2 , 3 , and Elizabeth R. Brown 2 , 3
  • 1 University of Washington, Department of Biostatistics, Seattle, USA
  • 2 Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, USA
  • 3 University of Washington, Department of Epidemiology, Seattle, USA
  • 4 University of Washington, Biostatistics, Box 357232, Seattle
Anu MishraORCID iD: https://orcid.org/0000-0002-4662-0374, Petra Bůžková, Jennifer E. Balkus
  • Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, WA, USA
  • University of Washington, Department of Epidemiology, Seattle, WA, USA
  • Search for other articles:
  • degruyter.comGoogle Scholar
and Elizabeth R. Brown
  • Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, WA, USA
  • University of Washington, Department of Epidemiology, Seattle, WA, USA
  • Search for other articles:
  • degruyter.comGoogle Scholar

Abstract

The relationship between hormonal contraceptive method use and sexually transmitted infections is not well understood. Studies that implement routine screening for STIs among different contraceptive users, such as the ASPIRE HIV-1 prevention trial, can be useful for identifying potential risk factors of STIs. However, the complex nature of non-random data can lead to challenges in estimation of associations for potential risk factors. In particular, if screening for the disease is not random (i. e. it is driven by symptoms or other clinical indicators), estimates of association can suffer from bias, often referred to as informative sampling bias. Time-varying predictors and potential stratification variables can further contribute to difficulty in obtaining unbiased estimates. In this paper, we estimate the association between time- varying contraceptive use and Sexually transmitted infections acquisition, in the presence of informative sampling, by extending the work Buzkova (2010). We use a two-step procedure to jointly model the non-random screening process and sexually transmitted infection risk. In the first step, inverse intensity rate ratios (IIRR) weights are estimated. In the second step, a weighted proportional rate model is fit to estimate the IIRR weighted hazard ratio. We apply the method to evaluate the relationship between hormonal contraception and risk of sexually transmitted infections among women participating in a biomedical HIV-1 prevention trial. We compare our results using the proposed weighted method to those generated using conventional approaches that do not account for potential informative sampling bias or do not use the full potential of the data. Using the IIRR weighted approach we found depot medroxyprogesterone acetate users have a significantly decreased hazard of Trichomonas vaginalis acquisition compared to IUD users (hazard ratio: 0.44, 95% CI: (0.25, 0.83)), which is consistent with the literature. We did not find significant increased or decreased hazard of other STIs for hormonal contraceptive users compared to non-hormonal IUD users.

  • Alkema, L., V. Kantorova, C. Menozzi, and A. Biddlecom. 2013. “National, regional, and global rates and trends in contraceptive prevalence and unmet need for family planning between 1990 and 2015: a systematic and comprehensive analysis.” The Lancet 381: 1642–1652. https://doi.org/10.1016/s0140-6736(12)62204-1.

  • Andersen, P. K. and R. D. Gill. 1982. “Cox’s regression model for counting processes: a large sample study.” The annals of statistics: 1100–1120. https://doi.org/10.1214/aos/1176345976.

  • Baeten, J. M., P. M. Nyange, B. A. Richardson, L. Lavreys, B. Chohan, H. L. Martin, K. Mandaliya, J. O. Ndinya-Achola, J. J. Bwayo, and J. K. Kreiss. 2001. “Hormonal contraception and risk of sexually transmitted disease acquisition: results from a prospective study.” American Journal of Obstetrics & Gynecology 185: 380–385. https://doi.org/10.1067/mob.2001.115862.

  • Baeten, J. M., T. Palanee-Phillips, E. R. Brown, K. Schwartz, L. E. Soto-Torres, V. Govender, N. M. Mgodi, F. Matovu Kiweewa, G. Nair, F. Mhlanga, et al.. 2016. “Use of a vaginal ring containing dapivirine for HIV-1 prevention in women.” New England Journal of Medicine 375: 2121–2132. https://doi.org/10.3410/f.726166218.793526926.

  • Borgdorff, H., M. C. Verwijs, F. W. Wit, E. Tsivtsivadze, G. F. Ndayisaba, R. Verhelst, F. H. Schuren, and J. H. van de Wijgert. 2015. “The impact of hormonal contraception and pregnancy on sexually transmitted infections and on cervicovaginal microbiota in african sex workers.” Sexually transmitted diseases 42: 143–152. https://doi.org/10.1097/olq.0000000000000245.

  • Buzkova, P. 2010 “Panel count data regression with informative observation times.” The International Journal of Biostatistics 6. https://doi.org/10.2202/1557-4679.1239.

  • Buzkova, P. and T. Lumley. 2007. “Longitudinal data analysis for generalized linear models with follow-up dependent on outcome-related variables.” Canadian Journal of Statistics 35: 485–500. https://doi.org/10.1002/cjs.5550350402.

  • Gursahaney, P. R., L. A. Meyn, S. L. Hillier, R. L. Sweet, and H. C. Wiesenfeld. 2010. “Combined hormonal contraception may be protective against neisseria gonorrhoeae infection.” Sexually Transmitted Diseases 37: 356–360. https://doi.org/10.1097/olq.0b013e3181d40ff1.

  • Huang, C.-Y., M.-C. Wang, and Y. Zhang. 2006. “Analysing panel count data with informative observation times.” Biometrika 93: 763–775. https://doi.org/10.1093/biomet/93.4.763.

  • Liang, Y., W. Lu, and Z. Ying. 2009. “Joint modeling and analysis of longitudinal data with informative observation times.” Biometrics 65: 377–384. https://doi.org/10.1111/j.1541-0420.2008.01104.x.

  • Lin, D. and Z. Ying. 2001. “Semiparametric and nonparametric regression analysis of longitudinal data.” Journal of the American Statistical Association 96: 103–126. https://doi.org/10.1198/016214501750333018.

  • Matovu, F., E. Brown, A. Mishra, G. Nair, T. Palanee-Phillips, N. Mgodi, C. Nakabiito, N. Chakhtoura, S. Hillier, and J. Baeten. 2017. “Acquisition of sexually transmitted infections among women using a variety of contraceptive options: a prospective study among high-risk African women.” Journal of the International AIDS Society 23: 94–95. https://doi.org/10.1002/jia2.25257.

  • Mohllajee, A. P., K. M. Curtis, S. L. Martins, and H. B. Peterson. 2006. “Hormonal contraceptive use and risk of sexually transmitted infections: a systematic review.” Contraception 73: 154–165. https://doi.org/10.1016/j.contraception.2005.08.012.

  • Morrison, C. S., A. N. Turner, and L. B. Jones. 2009. “Highly effective contraception and acquisition of HIV and other sexually transmitted infections.” Best practice & research Clinical obstetrics & gynaecology 23: 263–284. https://doi.org/10.1016/j.bpobgyn.2008.11.004.

  • Newman, L., J. Rowley, S. Vander Hoorn, N. S. Wijesooriya, M. Unemo, N. Low, G. Stevens, S. Gottlieb, J. Kiarie, and M. Temmerman. 2015. “Global estimates of the prevalence and incidence of four curable sexually transmitted infections in 2012 based on systematic review and global reporting.” PloS one 10: e0143304. https://doi.org/10.1371/journal.pone.0143304.

  • Pettifor, A., S. Delany, I. Kleinschmidt, W. C. Miller, J. Atashili, and H. Rees. 2009. “Use of injectable progestin contraception and risk of STI among South African women,” Contraception 80: 555–560. https://doi.org/10.1016/j.contraception.2009.06.007.

  • Song, X., X. Mu, and L. Sun. 2012. “Regression analysis of longitudinal data with time-dependent covariates and informative observation times.” Scandinavian Journal of Statistics 39: 248–258. https://doi.org/10.1111/j.1467-9469.2011.00776.x.

  • Tan, K. S., B. French, and A. B. Troxel. 2014. “Regression modeling of longitudinal data with outcome-dependent observation times: extensions and comparative evaluation.” Statistics in medicine 33: 4770–4789. https://doi.org/10.1002/sim.6262.

Purchase article
Get instant unlimited access to the article.
$42.00
Log in
Already have access? Please log in.


or
Log in with your institution

Journal + Issues

Search