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Simultaneous Inference of Treatment Effect Modification by Intermediate Response Endpoint Principal Strata with Application to Vaccine Trials.

ABSTRACT: In randomized clinical trials, researchers are often interested in identifying an inexpensive intermediate study endpoint (typically a biomarker) that is a strong effect modifier of the treatment effect on a longer-term clinical endpoint of interest. Motivated by randomized placebo-controlled preventive vaccine efficacy trials, within the principal stratification framework a pseudo-score type estimator has been proposed to estimate disease risks conditional on the counter-factual biomarker of interest under each treatment assignment to vaccine or placebo, yielding an estimator of biomarker conditional vaccine efficacy. This method can be used for trial designs that use baseline predictors of the biomarker and/or designs that vaccinate disease-free placebo recipients at the end of the trial. In this article, we utilize the pseudo-score estimator to estimate the biomarker conditional vaccine efficacy adjusting for baseline covariates. We also propose a perturbation resampling method for making simultaneous inference on conditional vaccine efficacy over the values of the biomarker. We illustrate our method with datasets from two phase 3 dengue vaccine efficacy trials.

SUBMITTER: Zhuang Y

PROVIDER: S-EPMC6939148 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Simultaneous Inference of Treatment Effect Modification by Intermediate Response Endpoint Principal Strata with Application to Vaccine Trials.

Zhuang Yingying Y Huang Ying Y Gilbert Peter B PB

The international journal of biostatistics 20190702 1

In randomized clinical trials, researchers are often interested in identifying an inexpensive intermediate study endpoint (typically a biomarker) that is a strong effect modifier of the treatment effect on a longer-term clinical endpoint of interest. Motivated by randomized placebo-controlled preventive vaccine efficacy trials, within the principal stratification framework a pseudo-score type estimator has been proposed to estimate disease risks conditional on the counter-factual biomarker of in ...[more]

PMID: 31265429

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Project description:We develop a transport formula for predicting overall cumulative vaccine efficacy through time t (VE(t)) to prevent clinically significant infection with a genetically diverse pathogen (e.g., HIV infection) in a new setting for which a Phase III preventive vaccine efficacy trial that would directly estimate VE(t) has not yet been conducted. The formula integrates data from (1) a previous Phase III trial, (2) a Phase I/II immune response biomarker endpoint trial in the new setting where a follow-up Phase III trial is planned, (3) epidemiological data on background HIV infection incidence in the new setting; and (4) genomic epidemiological data on HIV sequence distributions in the previous and new settings. For (1), the randomized vaccine versus placebo Phase III trial yields estimates of vaccine efficacy to prevent particular genotypes of HIV in participant subgroups defined by baseline covariates X and immune responses to vaccination S(1) measured at a fixed time point ? (potential outcomes if assigned vaccine); often one or more immune responses to vaccination are available that modify genotype-specific vaccine efficacy. The formula focuses on subgroups defined by X and S(1) and being at-risk for HIV infection at ? under both the vaccine and placebo treatment assignments. For (2), the Phase I/II trial tests the same vaccine in a new setting, or a refined new vaccine in the same or new setting, and measures the same baseline covariates and immune responses as the original Phase III trial. For (3), epidemiological data in the new setting are used to project overall background HIV infection rates in the baseline covariate subgroups in the planned Phase III trial, hence re-calibrating for HIV incidence differences in the two settings; whereas for (4), data bases of HIV sequences measured from HIV infected individuals are used to re-calibrate for differences in the distributions of the circulating HIV genotypes in the two settings. The transport formula incorporates a user-specified bridging assumption function that measures differences in HIV genotype-specific conditional biological-susceptibility vaccine efficacies in the two settings, facilitating a sensitivity analysis. We illustrate the transport formula with application to HIV Vaccine Trials Network (HVTN) research. One application of the transport formula is to use predicted VE(t) as a rational criterion for ranking a set of candidate vaccines being studied in Phase I/II trials for their priority for down-selection into the follow-up Phase III trial.

Dataset Information

Simultaneous Inference of Treatment Effect Modification by Intermediate Response Endpoint Principal Strata with Application to Vaccine Trials.

Publications

Simultaneous Inference of Treatment Effect Modification by Intermediate Response Endpoint Principal Strata with Application to Vaccine Trials.

Similar Datasets

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