Abstract
BackgroundThe success of an intervention to prevent the complications of an infection is influenced by the natural history of the infection. Assumptions about the temporal relationship between infection and the development of sequelae can affect the predicted effect size of an intervention and the sample size calculation. This study investigates how a mathematical model can be used to inform sample size calculations for a randomised controlled trial (RCT) using the example of Chlamydia trachomatis infection and pelvic inflammatory disease (PID).MethodsWe used a compartmental model to imitate the structure of a published RCT. We considered three different processes for the timing of PID development, in relation to the initial C. trachomatis infection: immediate, constant throughout, or at the end of the infectious period. For each process we assumed that, of all women infected, the same fraction would develop PID in the absence of an intervention. We examined two sets of assumptions used to calculate the sample size in a published RCT that investigated the effect of chlamydia screening on PID incidence. We also investigated the influence of the natural history parameters of chlamydia on the required sample size.ResultsThe assumed event rates and effect sizes used for the sample size calculation implicitly determined the temporal relationship between chlamydia infection and PID in the model. Even small changes in the assumed PID incidence and relative risk (RR) led to considerable differences in the hypothesised mechanism of PID development. The RR and the sample size needed per group also depend on the natural history parameters of chlamydia.ConclusionsMathematical modelling helps to understand the temporal relationship between an infection and its sequelae and can show how uncertainties about natural history parameters affect sample size calculations when planning a RCT.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-015-0953-5) contains supplementary material, which is available to authorized users.
Highlights
The success of an intervention to prevent the complications of an infection is influenced by the natural history of the infection
We used a constant force of infection, assuming that a single screening test in women participating in the randomised controlled trial (RCT) did not change the population prevalence of chlamydia, i.e. susceptible women can become infected at a constant rate λ
Assuming a constant progression rate from chlamydia to pelvic inflammatory disease (PID) results in a relative risk (RR) of 0.49, which is close to the original RR assumption in the Prevention Of Pelvic Infection (POPI) trial (RR = 0.48)
Summary
The success of an intervention to prevent the complications of an infection is influenced by the natural history of the infection. Assumptions about the temporal relationship between infection and the development of sequelae can affect the predicted effect size of an intervention and the sample size calculation. This study investigates how a mathematical model can be used to inform sample size calculations for a randomised controlled trial (RCT) using the example of Chlamydia trachomatis infection and pelvic inflammatory disease (PID). The success of an intervention to prevent the disease complications by reducing exposure to infection is influenced by the natural history of the infection. Researchers have suggested that mathematical models could help to improve the design of randomised controlled trials (RCTs) of Herzog et al BMC Infectious Diseases (2015) 15:233 complex interventions to prevent infectious disease transmission [1,2,3,4]. The results obtained for the effect size can be used to determine the required sample size
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
