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. 2017 Dec 29;15(1):223.
doi: 10.1186/s12916-017-0985-3.

Simulations for designing and interpreting intervention trials in infectious diseases

M Elizabeth Halloran  1   2 , Kari Auranen  3 , Sarah Baird  4 , Nicole E Basta  5 , Steven E Bellan  6 , Ron Brookmeyer  7 , Ben S Cooper  8 , Victor DeGruttola  9 , James P Hughes  10 , Justin Lessler  11 , Eric T Lofgren  12 , Ira M Longini  13 , Jukka-Pekka Onnela  9 , Berk Özler  14 , George R Seage  15 , Thomas A Smith  16   17 , Alessandro Vespignani  18 , Emilia Vynnycky  19   20 , Marc Lipsitch  15
Affiliations

Simulations for designing and interpreting intervention trials in infectious diseases

M Elizabeth Halloran et al. BMC Med. .

Abstract

Background: Interventions in infectious diseases can have both direct effects on individuals who receive the intervention as well as indirect effects in the population. In addition, intervention combinations can have complex interactions at the population level, which are often difficult to adequately assess with standard study designs and analytical methods.

Discussion: Herein, we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly with regard to emerging infectious diseases, one that more accurately reflects the dynamics of the transmission process. In an increasingly complex world, simulations can explicitly represent transmission dynamics, which are critical for proper trial design and interpretation. Certain ethical aspects of a trial can also be quantified using simulations. Further, after a trial has been conducted, simulations can be used to explore the possible explanations for the observed effects.

Conclusion: Much is to be gained through a multidisciplinary approach that builds collaborations among experts in infectious disease dynamics, epidemiology, statistical science, economics, simulation methods, and the conduct of clinical trials.

Keywords: Clinical trial design; Infectious diseases; Mathematical modeling; Simulations; Vaccine.

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Competing interests

The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
Study designs for dependent happenings. Two clusters, or populations, are considered under two different scenarios. In the left-hand side scenario, a certain portion of individuals in the cluster receive vaccination (or other treatment) (Z = 1) and the remaining portion receive the control intervention (Z = 0). In the right-hand side scenario, everyone receives the control intervention. Control is defined as current best practice, placebo, or nothing. The direct, indirect, total, and overall effects of intervention are defined by the indicated contrasts (adapted from Halloran and Struchiner [2, 3]). The effects have recently been given alternative terms in the economics literature [4], where ‘direct effect’ is termed as ‘value of treatment’, ‘indirect effect’ as ‘spillover effect on the non-treated’, ‘overall effect’ as ‘total causal effect’, and ‘total effect’ as ‘intention-to-treat effect’
Fig. 2
Fig. 2
Role of simulations for design and analysis of infectious disease intervention trials. Simulations (red) can provide inputs to the usual process of statistical analysis (purple) by which considerations of trial population, choice of intervention and control intervention, randomization scheme, estimands and estimators, and sample size lead to a choice of design (blue). The simulations take assumptions about the transmission setting of the trial, the individual-level effects of the intervention and the trial itself, and an approach to gathering data from the trial, and create a database of simulated results from many stochastic realizations of the trial. This database contains information on the mean and variability of quantities that would be estimated in the trial under various conditions, which can then inform the design choices
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