ArticleStatistical communications in infectious diseases2020
Accounting for Informative Sampling in Estimation of Associations between Sexually Transmitted Infections and Hormonal Contraceptive Methods.
Article in Statistical communications in infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 1 citations in OpenAlex.
- Contraceptive effects on the cervicovaginal microbiome: Recent evidence including randomized trials.American journal of reproductive immunology (New York, N.Y. : 1989) · 2023Review
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The relationship between hormonal contraceptive method use and sexually transmitted infections (STIs) 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 STI 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 DMPA users have a significantly decreased hazard of
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Registered trials
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