Library Header Image
LSE Research Online LSE Library Services

A Bayesian approach to estimate changes in condom use from limited human immunodeficiency virus prevalence data

Dureau, J., Kalogeropoulos, K., Vickerman, P., Pickles, M. and Boily, M. C. (2016) A Bayesian approach to estimate changes in condom use from limited human immunodeficiency virus prevalence data. Journal of the Royal Statistical Society. Series C: Applied Statistics, 65 (2). pp. 237-257. ISSN 0035-9254

PDF - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview
Identification Number: 10.1111/rssc.12116


Evaluation of HIV large scale interventions programme is becoming increasingly important, but impact estimates frequently hinge on knowledge of changes in behaviour such as the frequency of condom use (CU) over time, or other self-reported behaviour changes, for which we generally have limited or potentially biased data. We employ a Bayesian inference methodology that incorporates a dynamic HIV transmission dynamics model to estimate CU time trends from HIV prevalence data. Estimation is implemented via particle Markov Chain Monte Carlo methods, applied for the first time in this context. The preliminary choice of the formulation for the time varying parameter reflecting the proportion of CU is critical in the context studied, due to the very limited amount of CU and HIV data available We consider various novel formulations to explore the trajectory of CU in time, based on diffusion-driven trajectories and smooth sigmoid curves. Extensive series of numerical simulations indicate that informative results can be obtained regarding the amplitude of the increase in CU during an intervention, with good levels of sensitivity and specificity performance in effectively detecting changes. The application of this method to a real life problem illustrates how it can help evaluate HIV intervention from few observational studies and suggests that these methods can potentially be applied in many different contexts.

Item Type: Article
Official URL:
Additional Information: © 2015 The Authors Journal of the Royal Statistical Society: Series C Applied Statistics published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society. © CC BY 4.0
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Sets: Departments > Statistics
Date Deposited: 05 Dec 2012 10:07
Last Modified: 20 Mar 2019 02:49

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics