Estimating Human Immunodeficiency Virus (HIV) Prevention Effects in Low-incidence Settings

Abstract

Background: Randomized controlled trials (RCTs) for determining efficacy of preexposure prophylaxis (PrEP) in preventing human immunodeficiency virus (HIV) infection have not been conducted among US women because their lower HIV incidence requires impractically large studies. Results from higher-incidence settings, like Sub-Saharan Africa, may not apply to US women owing to differences in age, sexual behavior, coinfections, and adherence.

Methods: We propose a novel strategy for evaluating PrEP efficacy in the United States using data from both settings to obtain four parameters: (1) intention-to-treat (ITT) and (2) per-protocol effects in the higher-incidence setting, (3) per-protocol effect generalized to the lower-incidence setting, and (4) back-calculated ITT effect using adherence data from the lower-incidence setting. To illustrate, we simulated two RCTs comparing PrEP against placebo: one in 4000 African women and another in 500 US women. We estimated all parameters using g-computation and report risk ratios averaged over 2000 simulations, alongside the 2.5th and 97.5th percentiles of the simulation results.

Results: Twelve months after randomization, the African ITT and per-protocol risk ratios were 0.65 (0.47, 0.88) and 0.20 (0.08, 0.34), respectively. The US ITT and per-protocol risk ratios were 0.42 (0.20, 0.62) and 0.17 (0.03, 0.38), respectively. These results matched well the simulated true effects.

Conclusions: Our simple demonstration informs the design of future studies seeking to estimate the effectiveness of a treatment (like PrEP) in lower-incidence settings where a traditional RCT would not be feasible. See video abstract at, http://links.lww.com/EDE/B506.

Figure 1.

Causal diagram used in simulations, showing relations between randomization to pre-exposure prophylaxis, adherence to study protocol, lack of sexually transmitted infection (STI) at baseline (an example effect measure modifier), age >21 years (an example confounder), and HIV seroconversion. In our simulation, older age was positively associated with adherence to protocol; older age, adherence, and the interaction between lack of an STI and adherence were negatively associated with HIV seroconversion.

Figure 2.

Boxplots of the simulation risk ratios with individual simulation estimates for all 4 parameters estimated using g-computation. The filled diamonds are the true risk ratios, and the unfilled diamonds are the estimated risk ratios. The boxes represent the interquartile range, with the median risk ratio being the line within the box. Each of the light gray circles represents the result from a single simulation. The breadth of the whiskers is 1.5 times the interquartile range. Extreme outliers were removed from the picture (for the US ITT, 15 risk ratios <1×10⁻⁴ and, for the US per-protocol, 15 risk ratios <1×10⁻¹⁰).