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Statistical design and analysis plan for an impact evaluation of an HIV treatment and prevention intervention for female sex workers in Zimbabwe: a study protocol for a cluster randomised controlled trial

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      Pragmatic cluster-randomised trials should seek to make unbiased estimates of effect and be reported according to CONSORT principles, and the study population should be representative of the target population. This is challenging when conducting trials amongst ‘hidden’ populations without a sample frame. We describe a pair-matched cluster-randomised trial of a combination HIV-prevention intervention to reduce the proportion of female sex workers (FSW) with a detectable HIV viral load in Zimbabwe, recruiting via respondent driven sampling (RDS).


      We will cross-sectionally survey approximately 200 FSW at baseline and at endline to characterise each of 14 sites. RDS is a variant of chain referral sampling and has been adapted to approximate random sampling. Primary analysis will use the ‘RDS-2’ method to estimate cluster summaries and will adapt Hayes and Moulton’s ‘2-step’ method to adjust effect estimates for individual-level confounders and further adjust for cluster baseline prevalence. We will adapt CONSORT to accommodate RDS. In the absence of observable refusal rates, we will compare the recruitment process between matched pairs. We will need to investigate whether cluster-specific recruitment or the intervention itself affects the accuracy of the RDS estimation process, potentially causing differential biases. To do this, we will calculate RDS-diagnostic statistics for each cluster at each time point and compare these statistics within matched pairs and time points. Sensitivity analyses will assess the impact of potential biases arising from assumptions made by the RDS-2 estimation.


      We are not aware of any other completed pragmatic cluster RCTs that are recruiting participants using RDS. Our statistical design and analysis approach seeks to transparently document participant recruitment and allow an assessment of the representativeness of the study to the target population, a key aspect of pragmatic trials. The challenges we have faced in the design of this trial are likely to be shared in other contexts aiming to serve the needs of legally and/or socially marginalised populations for which no sampling frame exists and especially when the social networks of participants are both the target of intervention and the means of recruitment.

      The trial was registered at Pan African Clinical Trials Registry (PACTR201312000722390) on 9 December 2013.

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      Most cited references 36

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      Respondent-Driven Sampling: A New Approach to the Study of Hidden Populations

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        Burden of HIV among female sex workers in low-income and middle-income countries: a systematic review and meta-analysis.

        Female sex workers are a population who are at heightened risk of HIV infection secondary to biological, behavioural, and structural risk factors. However, three decades into the HIV pandemic, understanding of the burden of HIV among these women remains limited. We aimed to assess the burden of HIV in this population compared with that of other women of reproductive age. We searched PubMed, Embase, Global Health, SCOPUS, PsycINFO, Sociological Abstracts, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and POPLine for studies of female sex workers in low-income and middle-income countries published between Jan 1, 2007, and June 25, 2011. Studies of any design that measured the prevalence or incidence of HIV among female sex workers, even if sex workers were not the main focus of the study, were included. Meta-analyses were done with the Mantel-Haenszel method with a random-effects model characterising an odds ratio for the prevalence of HIV among female sex workers compared with that for all women of reproductive age. Of 434 selected articles and surveillance reports, 102 were included in the analyses, representing 99,878 female sex workers in 50 countries. The overall HIV prevalence was 11·8% (95% CI 11·6-12·0) with a pooled odds ratio for HIV infection of 13·5 (95% CI 10·0-18·1) with wide intraregional ranges in the pooled HIV prevalence and odds ratios for HIV infection. In 26 countries with medium and high background HIV prevalence, 30·7% (95% CI 30·2-31·3; 8627 of 28,075) of sex workers were HIV-positive and the odds ratio for infection was 11·6 (95% CI 9·1-14·8). Although data characterising HIV risk among female sex workers is scarce, the burden of disease is disproportionately high. These data suggest an urgent need to scale up access to quality HIV prevention programmes. Considerations of the legal and policy environments in which sex workers operate and actions to address the important role of stigma, discrimination, and violence targeting female sex workers is needed. The World Bank, UN Population Fund. Copyright © 2012 Elsevier Ltd. All rights reserved.
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          Simple sample size calculation for cluster-randomized trials.

           R Hayes,  S. Bennett (1999)
          Cluster-randomized trials, in which health interventions are allocated randomly to intact clusters or communities rather than to individual subjects, are increasingly being used to evaluate disease control strategies both in industrialized and in developing countries. Sample size computations for such trials need to take into account between-cluster variation, but field epidemiologists find it difficult to obtain simple guidance on such procedures. In this paper, we provide simple formulae for sample size determination for both unmatched and pair-matched trials. Outcomes considered include rates per person-year, proportions and means. For simplicity, formulae are expressed in terms of the coefficient of variation (SD/mean) of cluster rates, proportions or means. Guidance is also given on the estimation of this value, with or without the use of prior data on between-cluster variation. The methods are illustrated using two case studies: an unmatched trial of the impact of impregnated bednets on child mortality in Kenya, and a pair-matched trial of improved sexually-transmitted disease (STD) treatment services for HIV prevention in Tanzania.

            Author and article information

            [ ]Centre for Evaluation Department for Social and Environmental Health Research, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH UK
            [ ]Research Department of Infection and Population Health, Institute of Epidemiology and Health Care, Faculty of Population Health Sciences, University College London, Gower Street, London, WC1E 6BT UK
            [ ]Centre for Sexual Health & HIV/AIDS Research (CeSHHAR) Zimbabwe, 9 Monmouth Road Avondale West, Harare, Zimbabwe
            BioMed Central (London )
            5 January 2016
            5 January 2016
            : 17
            4700631 1095 10.1186/s13063-015-1095-1
            © Hargreaves et al. 2015

            Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

            Funded by: FundRef, United Nations Fund for Population Activities (US);
            Award ID: Zimbabwe's Integrated Support Fund
            Award Recipient :
            Study Protocol
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            © The Author(s) 2016


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