Impact of stochastically generated heterogeneity in hazard rates on individually randomized vaccine efficacy trials

Abstract

Background/aims

Network structure and individuals’ level of exposure to a pathogen can impact results from efficacy evaluation studies of interventions against infectious diseases. Heterogeneity in infection risk can cause randomized groups to increasingly differ as a trial progresses and as more high-risk individuals become infected (described in prior work as the “frailty” phenomenon). Here, we show the impact this phenomenon can have on an individually randomized trial of a leaky vaccine in which all participants are exchangeable a priori.

Methods

We model a vaccine trial by generating a network of individuals grouped into communities, which are connected to a larger main population. We then simulate an epidemic, deterministically and with time-varying transmission rates in the main population and stochastically in the communities. The disease natural history follows a susceptible-exposed-infectious-recovered model. Simulation results are used to estimate vaccine efficacy $(\widehat{\mathrm{VE}})$ with a Cox proportional hazards model.

Results

We find downward bias in $\widehat{\mathrm{VE}}$ associated with low connectivity between communities in the study population and high force of infection, even when all participants in the trial are exchangeable at the time of randomization. This phenomenon arises because the stochastic dynamics in such a setting randomly lead to community-level variation in the force of infection. Stratifying a Cox model by community alleviates this bias with no loss of power.

Conclusion

Understanding and accounting for the impact of heterogeneous hazard rates can allow for more accurate estimates of $\widehat{\mathrm{VE}}$ in epidemic settings.