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Open Access Publications from the University of California

Scholarly Works (123 results)

  • Article
  • Peer Reviewed
UC San Francisco Previously Published Works (2020)

Purpose of review

This review considers statistical issues in the design and analysis of the studies used to develop long-acting formulations of antiretrovirals for pre-exposure prophylaxis (PrEP).

Recent finding

An abundant pipeline of products is maturing. Accelerating their evaluation as clinical products requires abandonment of noninferiority standards. Randomized trials should be based on the comparison of principled but innovative estimates of background HIV risk and enrich enrollment for those who do not desire current PrEP products. At every stage of testing, innovative analyses can be applied to help inform and accelerate later studies.

Summary

The development of new long-acting PrEP regimens can be accelerated by innovations in design, ingenuity in synthesizing data sources, and application of causal inference methods.
    Cover page: Statistical approaches to accelerate the development of long-acting antiretrovirals for HIV pre-exposure prophylaxis.
    • Article
    • Peer Reviewed
    UC San Francisco Previously Published Works (2019)
    With the scale-up of HIV pre-exposure prophylaxis (PrEP) with tenofovir (TDF) with or without emtricitabine (FTC), we have entered an era of highly effective HIV prevention with a growing pipeline of potential products to be studied. These studies are likely to be randomized trials with an oral TDF/FTC control arm. These studies require comparison of incident infections and can be time and resource intensive. Conventional approaches for design and analysis active controlled trial can lead to very large sample sizes. We demonstrate the important of assumptions about background infections for interpreting trial results and suggest alternative criteria for demonstrating the efficacy and effectiveness of potential PrEP agents.
      Cover page: Advancing Novel PrEP Products – Alternatives to Non-Inferiority
      • Thesis
      • Peer Reviewed
      UC San Francisco Electronic Theses and Dissertations (2019)

      With the support of several successful randomized placebo-controlled trials, the FDA approved Truvada for daily oral pre-exposure prophylaxis of HIV (PrEP) in the United States in 2012, and shortly thereafter the CDC and WHO released guidelines for widespread PrEP use by all at-risk individuals around the world. However, PrEP rollout is still is infancy, and there are several important questions regarding PrEP implementation that cannot be addressed by randomized trials. The causal transportability theory developed by Pearl and Bareinboim is a mathematically-grounded framework used to consider how effects observed in one setting might be applied to another. This dissertation proposes novel ways transportability can be applied to improve how trial results are used to inform implementation of PrEP. applies transportability to address some of these lingering questions about PrEP implementation.

      The first chapter uses transportability to better understand why randomization to PrEP was effective in preventing HIV among cisgender men but not effective among transgender women in the iPrEx study. We find that after transporting the results of the trial from cisgender men to transgender women, differences in measured baseline characteristics between the populations were sufficient to explain the observed effect heterogeneity in the trial. The second chapter demonstrates how transportability can be applied to subgroup analyses of randomized controlled trials to produce target-specific guidance for how to most efficiently implement new interventions. To illustrate this approach, we transport subgroup analyses of the iPrEx trial to two hypothetical target populations and show that the subgroups with the lowest number needed to treat differs depending on the composition of the target population. The third and final chapter addresses a common practical challenge faced in applying transportability theory to real-world data: how to decide which variables to include in a transport estimator. In this chapter, we discuss the various types of unnecessary variables that may inadvertently be included in transport estimators. We use a Monte Carlo simulation study to identify what types of variables should be included to maximize the performance (with respect to mean-squared error) of the parametric g-computation transport estimator.

      Together these projects highlight how transportability theory can be applied to improve translation of study results to real-world populations.

        Cover page: From Trials to Public Health Impact: Transportability of Causal Effects to Inform Implementation of HIV Pre-exposure Prophylaxis
        • Article
        • Peer Reviewed
        UC San Francisco Previously Published Works (2016)

        Purpose of review

        We discuss selected statistical issues in the design and analysis of preexposure prophylaxis (PrEP) trials. The general principles may inform thinking for other interventions in HIV prevention.

        Recent findings

        To date, four different designs have been used to determine the effectiveness of PrEP: randomized, double-blind, placebo-controlled; randomized, open-label, immediate or delayed access; nonrandomized comparison of HIV incidence according to the level of drug detected; comparison of the observed HIV incidence to the expected rate using historical control data. Open-label trials of PrEP, which assess public health effectiveness, complement the placebo-controlled trials which established the biological efficacy of TDF/FTC. Future trials of PrEP will be highly challenging to design since a no PrEP group is difficult to justify and the natural control regimen, TDF/FTC, is highly efficacious.

        Summary

        Standard statistical paradigms for noninferiority trials should be reconsidered for evaluating alternative PrEP regimens.
          Cover page: Statistical issues in trials of preexposure prophylaxis
          • Article
          • Peer Reviewed
          UC San Francisco Previously Published Works (2019)
          The design and analysis of active-control trials to evaluate experimental HIV pre-exposure prophylaxis (PrEP) agents pose serious statistical challenges. We recently proposed a new outcome measure, the averted infections ratio (AIR) - the proportion of infections that would be averted by using the experimental agent rather than the control agent (compared to no intervention). The main aim of the current paper is to examine the mathematical connection between AIR and the HIV incidence rate ratio, the standard outcome measure. We also consider the sample size implications of the choice of primary outcome measure and explore the connection between effectiveness and efficacy under a simplified model of adherence.
            Cover page: The Connection between the Averted Infections Ratio and the Rate Ratio in Active-control Trials of Pre-exposure Prophylaxis Agents
            • Article
            • Peer Reviewed
            UC San Francisco Previously Published Works (2020)
            Trials of candidate agents for HIV pre-exposure prophylaxis (PrEP) might randomly assign participants to be given a new PrEP agent or oral coformulated tenofovir disoproxil fumarate plus emtricitabine. This design presents unique challenges in interpretation. First, with two active arms, HIV incidence might be low. Second, the effectiveness of tenofovir disoproxil fumarate plus emtricitabine varies across populations; thus, similar HIV incidence between groups could be consistent with a wide range of effectiveness for the new PrEP. We propose a two-part approach to trial results. First, we use Bayesian methods to incorporate assumptions about the background incidence of HIV in the trial in the absence of PrEP, possibly augmented by external data. On the basis of the estimated background incidence, we estimate and compare the number of averted (or prevented) HIV infections in each of the two trial groups, calculating the averted infections ratio. We apply these methods to a completed trial of tenofovir alafenamide plus emtricitabine for PrEP. Our framework shows that leveraging external information to estimate averted infections and the averted infections ratio enhances the efficiency and interpretation of active-controlled PrEP trials.
              Cover page: A Bayesian averted infection framework for PrEP trials with low numbers of HIV infections: application to the results of the DISCOVER trial
              • Article
              • Peer Reviewed
              UC San Francisco Previously Published Works (2021)

              Objectives

              The averted infections ratio (AIR) is a novel measure for quantifying the preservation-of-effect in active-control non-inferiority clinical trials with a time-to-event outcome. In the main formulation, the AIR requires an estimate of the counterfactual placebo incidence rate. We describe two approaches for calculating confidence limits for the AIR given a point estimate of this parameter, a closed-form solution based on a Taylor series expansion (delta method) and an iterative method based on the profile-likelihood.

              Methods

              For each approach, exact coverage probabilities for the lower and upper confidence limits were computed over a grid of values of (1) the true value of the AIR (2) the expected number of counterfactual events (3) the effectiveness of the active-control treatment.

              Results

              Focussing on the lower confidence limit, which determines whether non-inferiority can be declared, the coverage achieved by the delta method is either less than or greater than the nominal coverage, depending on the true value of the AIR. In contrast, the coverage achieved by the profile-likelihood method is consistently accurate.

              Conclusions

              The profile-likelihood method is preferred because of better coverage properties, but the simpler delta method is valid when the experimental treatment is no less effective than the control treatment. A complementary Bayesian approach, which can be applied when the counterfactual incidence rate can be represented as a prior distribution, is also outlined.
                Cover page: Confidence limits for the averted infections ratio estimated via the counterfactual placebo incidence rate
                • Article
                • Peer Reviewed
                UC San Francisco Previously Published Works (2016)

                Background

                In many studies, some information might not be available for the whole cohort, some covariates, or even the outcome, might be ascertained in selected subsamples. These studies are part of a broad category termed two-phase studies. Common examples include the nested case-control and the case-cohort designs. For two-phase studies, appropriate weighted survival estimates have been derived; however, no estimator of cumulative incidence accounting for competing events has been proposed. This is relevant in the presence of multiple types of events, where estimation of event type specific quantities are needed for evaluating outcome.

                Methods

                We develop a non parametric estimator of the cumulative incidence function of events accounting for possible competing events. It handles a general sampling design by weights derived from the sampling probabilities. The variance is derived from the influence function of the subdistribution hazard.

                Results

                The proposed method shows good performance in simulations. It is applied to estimate the crude incidence of relapse in childhood acute lymphoblastic leukemia in groups defined by a genotype not available for everyone in a cohort of nearly 2000 patients, where death due to toxicity acted as a competing event. In a second example the aim was to estimate engagement in care of a cohort of HIV patients in resource limited setting, where for some patients the outcome itself was missing due to lost to follow-up. A sampling based approach was used to identify outcome in a subsample of lost patients and to obtain a valid estimate of connection to care.

                Conclusions

                A valid estimator for cumulative incidence of events accounting for competing risks under a general sampling design from an infinite target population is derived.
                  Cover page: Crude incidence in two-phase designs in the presence of competing risks
                  • Article
                  • Peer Reviewed
                  UC San Francisco Previously Published Works (2016)
                    Cover page: PrEP adherence among trans women in Brazil-access needed for this key population - Authors' reply.
                    • Article
                    • Peer Reviewed
                    UC San Francisco Previously Published Works (2017)
                    Existing HIV care cascade metrics fail to capture whether viremia is equally distributed in a population or concentrated within groups. We applied the Lorenz curve, which has been used to describe disparities in the distribution of income and other resources, to the distribution of viremia in a safety-net HIV clinic in 2012. Among 1855 established clinic patients, 1% of the population held 50% of the virus and 10% of the population held 94% of virus.
                      Cover page: The Lorenz curve
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