Estimating duration in partnership studies: issues, methods and examples. Issue 2 (23rd March 2010)

• Record Type: Journal Article
• Authors: Burington, Bart; Hughes, James P; Whittington, William L H; Stoner, Brad; Garnett, Geoff; Aral, Sevgi O; Holmes, King K
• Is Part Of: Sexually transmitted infections. Volume 86:Issue 2(2010)
• Page Start: 84
• Page End: 89
• Publication Date: 2010-03-23
• Subjects: Biostatistics -- mathematical model -- sexual networks; Sexually transmitted diseases -- Periodicals; HIV infections -- Periodicals; 616.951005
• DOI: 10.1136/sti.2009.037960
• Languages: English
• ISSNs: 1368-4973

Abstract

Abstract : Background and objectives: Understanding the time course of sexual partnerships is important for understanding sexual behaviour, transmission risks for sexually transmitted infections (STI) and development of mathematical models of disease transmission. Study design: The authors describe issues and biases relating to censoring, truncation and sampling that arise when estimating partnership duration. Recommendations for study design and analysis methods are presented and illustrated using data from a sexual-behaviour survey that enrolled individuals from an adolescent-health clinic and two STD clinics. Survey participants were queried, for each of (up to) four partnerships in the last 3 months, about the month and year of first sex, the number of days since last sex and whether partnerships were limited to single encounters. Participants were followed every 4 months for up to 1 year. Results: After adjustment for censoring and truncation, the estimated median duration of sexual partnerships declined from 9 months (unadjusted) to 1.6 months (adjusted). Similarly, adjustment for censoring and truncation reduced the bias in relative risks for the effect of age in a Cox model. Other approaches, such as weighted estimation, also reduced bias in the estimated duration distribution. Conclusion: Methods are available for estimating partnership duration from censored and truncated samples. Ignoring censoring, truncation and other sampling issues results in biased estimates.