Effective HIV prevention requires knowledge of the structure and dynamics of the social networks across which infections are transmitted. These networks are most commonly comprised of chains of sexual relationships. Whereas network data have long been collected during survey interviews, new data sources have become increasingly common in recent years. In this article, we review current and emerging methods for collecting HIV-related network data, as well as modelling frameworks commonly used to infer network parameters and map potential HIV transmission pathways within the network.
Common indicators such as the number of new sexual partners in a given year and the lifetime number of sexual partners are used in several analyses to predict the risk of contracting HIV. However, are these indicators consistent?
In the coming months two short courses will be organised under the auspices of SACEMA: Bayesian Biostatistics from 4-8 April 2016 (registration deadline: 17 March 2016) and Using quantitative bias analysis with epidemiologic data from 18-20 May 2016 (early bird registration deadline: 1 April 2016).
Prof. Emmanuel Lesaffre of the Leuven Biostatistics and Statistical Bioinformatics Centre, Catholic University of Leuven, Belgium, will be presenting an intensive course on Bayesian analysis of longitudinal studies on 26 and 27 November 2015 at Stellenbosch University. The course will be oriented towards an applied audience with a good knowledge of various regression models.
Agent-based modelling, also called microsimulation, is a way of modelling epidemics that is growing in popularity. Instead of the traditional way of modelling using differential equations, an agent-based model consists of, perhaps, thousands of agents, each representing a person, and each behaving according to a simple set of rules. Instead of outputs such as infection and mortality rates being derived from equations, they are derived from the interactions of the agents over many iterations. These models are providing rich insights into the HIV epidemic.
While traditional regression methods have been proven as a powerful research tool, no technique is suitable for all circumstances. Two common situations in which regression techniques alone are likely to produce biased results is when the outcome is rare and the number of measured confounders is large; and/or when important confounders are neglected.
Analysing briefly an example in which both circumstances are present simultaneously, this article shows how propensity score matching associated with Monte Carlo sensitivity analysis can be considered an interesting complement to traditional multivariate modelling.
Those who study sexual behaviour often rely on self-reported information from surveys. However, results from surveys may be inaccurate due to social desirability bias (SDB). One way to combat SDB is to change the mode of inquiry. Typically surveys are conducted using face-to-face-interviewing. The use of audio computer-assisted self-interviewing (ACASI) has been proposed as a better alternative. There is evidence from Africa, that use of ACASI may elicit more adequate reporting of sensitive sexual behaviours. Here we describe a sexual behaviour survey we conducted in three disadvantaged communities of Cape Town using ACASI methods.
Brown International Advanced Research Institutes (BIARI) represents a unique professional development initiative to provide a platform for outstanding young faculty and practitioners from the global south and emerging economies to engage in a sustained, high-level intellectual and policy dialogue with leading scholars in their fields. The institutes will be held June 8-22, 2013. To learn Read More
In 1994 Douglas Altman wrote a highly influential paper in the British Medical Journal entitled ‘the scandal of poor medical research’. It focused on the prevalence of poor design and analysis in medical research, due to a general failure to appreciate the principles underlying scientific research. Addressing these shortcomings would entail better science education, not only of researchers, but also of the public and judiciary. But there is another factor at work; ‘confirmation bias’. This is probably the most widespread and insidious form of bias, where scientists search much harder for evidence to support their pet idea than for evidence to refute it – and weight that evidence accordingly. To show that such matters are not merely of academic interest, two recent examples are considered.