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Sun Y, Heng F, Lee U, Gilbert PB. Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials. CAN J STAT 2023; 51:235-257. [PMID: 36937899 PMCID: PMC10022693 DOI: 10.1002/cjs.11693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 10/14/2021] [Indexed: 11/07/2022]
Abstract
This article studies generalized semiparametric regression models for conditional cumulative incidence functions with competing risks data when covariates are missing by sampling design or happenstance. A doubly-robust augmented inverse probability weighted complete-case (AIPW) approach to estimation and inference is investigated. This approach modifies IPW complete-case estimating equations by exploiting the key features in the relationship between the missing covariates and the phase-one data to improve efficiency. An iterative numerical procedure is derived to solve the nonlinear estimating equations. The asymptotic properties of the proposed estimators are established. A simulation study examining the finite-sample performances of the proposed estimators shows that the AIPW estimators are more efficient than the IPW estimators. The developed method is applied to the RV144 HIV-1 vaccine efficacy trial to investigate vaccine-induced IgG binding antibodies to HIV-1 as correlates of acquisition of HIV-1 infection while taking account of whether the HIV-1 sequences are near or far from the HIV-1 sequences represented in the vaccine construct.
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Affiliation(s)
- Yanqing Sun
- University of North Carolina at Charlotte, Charlotte, NC 28223, U.S.A
| | - Fei Heng
- University of North Florida, Jacksonville, FL 32224, U.S.A
| | - Unkyung Lee
- CBER, Food and Drug Administration, Silver Spring, MD 20993, U.S.A
| | - Peter B. Gilbert
- University of Washington, Seattle, WA 98195, U.S.A
- Fred Hutchinson Cancer Research Center, Seattle, WA 98109, U.S.A
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Qi L, Sun Y, Juraska M, Moodie Z, Magaret CA, Heng F, Carpp LN, Gilbert PB. Neutralizing antibody correlates of sequence specific dengue disease in a tetravalent dengue vaccine efficacy trial in Asia. Vaccine 2022; 40:5912-5923. [PMID: 36068106 PMCID: PMC9881745 DOI: 10.1016/j.vaccine.2022.08.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 07/13/2022] [Accepted: 08/23/2022] [Indexed: 01/31/2023]
Abstract
In the CYD14 trial of the CYD-TDV dengue vaccine in 2-14 year-olds, neutralizing antibody (nAb) titers to the vaccine-insert dengue strains correlated inversely with symptomatic, virologically-confirmed dengue (VCD). Also, vaccine efficacy against VCD was higher against dengue prM/E amino acid sequences closer to the vaccine inserts. We integrated the nAb and sequence data types by assessing nAb titers as a correlate of sequence-specific VCD separately in the vaccine arm and in the placebo arm. In both vaccine and placebo recipients the correlation of nAb titer with sequence-specific VCD was stronger for dengue nAb contact site sequences closer to the vaccine (p = 0.005 and p = 0.012, respectively). The risk of VCD in vaccine (placebo) recipients was 6.7- (1.80)-fold lower at the 90th vs 10th percentile of nAb for viruses perfectly matched to CYD-TDV, compared to 2.1- (0.78)-fold lower at the 90th vs 10th percentile for viruses with five amino acid mismatches. The evidence for a stronger sequence-distance dependent correlate of risk for the vaccine arm indicates departure from the Prentice criteria for a valid sequence-distance specific surrogate endpoint and suggests that the nAb marker may affect dengue risk differently depending on whether nAbs arise from infection or also by vaccination. However, when restricting to baseline-seropositive 9-14 year-olds, the correlation pattern became more similar between the vaccine and placebo arms, supporting nAb titers as an approximate surrogate endpoint in this population. No sequence-specific nAb titer correlates of VCD were seen in baseline-seronegative participants. Integrated immune response/pathogen sequence data correlates analyses could help increase knowledge of correlates of risk and surrogate endpoints for other vaccines against genetically diverse pathogens. Trial registration: EU Clinical Trials Register 2014-001708-24; registration date 2014-05-26.
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Affiliation(s)
- Li Qi
- Biostatistics and Programming, Sanofi, 55 Corporate Drive, Bridgewater, NJ 08807, United States.
| | - Yanqing Sun
- Department of Mathematics and Statistics, University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC 28223, United States.
| | - Michal Juraska
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, 1100 Fairview Avenue North, Seattle, WA 98109, United States.
| | - Zoe Moodie
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, 1100 Fairview Avenue North, Seattle, WA 98109, United States.
| | - Craig A Magaret
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, 1100 Fairview Avenue North, Seattle, WA 98109, United States.
| | - Fei Heng
- Department of Mathematics and Statistics, University of North Florida, 1 UNF Drive, Jacksonville, FL 32224, United States.
| | - Lindsay N Carpp
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, 1100 Fairview Avenue North, Seattle, WA 98109, United States.
| | - Peter B Gilbert
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, 1100 Fairview Avenue North, Seattle, WA 98109, United States; Department of Biostatistics, University of Washington, 3980 15th Avenue NE, Seattle, WA 98109, United States.
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Sun Y, Qi L, Heng F, Gilbert PB. A Hybrid Approach for the Stratified Mark-Specific Proportional Hazards Model with Missing Covariates and Missing Marks, with Application to Vaccine Efficacy Trials. J R Stat Soc Ser C Appl Stat 2020; 69:791-814. [PMID: 33191955 DOI: 10.1111/rssc.12417] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Deployment of the recently licensed CYD-TDV dengue vaccine requires understanding of how the risk of dengue disease in vaccine recipients depends jointly on a host biomarker measured after vaccination (neutralization titer - NAb) and on a "mark" feature of the dengue disease failure event (the amino acid sequence distance of the dengue virus to the dengue sequence represented in the vaccine). The CYD14 phase 3 trial of CYD-TDV measured NAb via case-cohort sampling and the mark in dengue disease failure events, with about a third missing marks. We addressed the question of interest by developing inferential procedures for the stratified mark-specific proportional hazards model with missing covariates and missing marks. Two hybrid approaches are investigated that leverage both augmented inverse probability weighting and nearest neighborhood hot deck multiple imputation. The two approaches differ in how the imputed marks are pooled in estimation. Our investigation shows that NNHD imputation can lead to biased estimation without properly selected neighborhood. Simulations show that the developed hybrid methods perform well with unbiased NNHD imputations from proper neighborhood selection. The new methods applied to CYD14 show that NAb is strongly inversely associated with risk of dengue disease in vaccine recipients, more strongly against dengue viruses with shorter distances.
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Affiliation(s)
- Yanqing Sun
- University of North Carolina at Charlotte, Charlotte, U.S.A
| | - Li Qi
- Sanofi, Bridgewater, U.S.A
| | - Fei Heng
- University of North Florida, Jacksonville, U.S.A
| | - Peter B Gilbert
- University of Washington and Fred Hutchinson Cancer Research Center, Seattle, U.S.A
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