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Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy.

ABSTRACT: In randomized placebo-controlled preventive HIV vaccine efficacy trials, an objective is to evaluate the relationship between vaccine efficacy to prevent infection and genetic distances of the exposing HIV strains to the multiple HIV sequences included in the vaccine construct, where the set of genetic distances is considered as the continuous multivariate "mark" observed in infected subjects only. This research develops a multivariate mark-specific hazard ratio model in the competing risks failure time analysis framework for the assessment of mark-specific vaccine efficacy. It allows improved efficiency of estimation by employing the semiparametric method of maximum profile likelihood estimation in the vaccine-to-placebo mark density ratio model. The model also enables the use of a more efficient estimation method for the overall log hazard ratio in the Cox model. In addition, we propose testing procedures to evaluate two relevant hypotheses concerning mark-specific vaccine efficacy. The asymptotic properties and finite-sample performance of the inferential procedures are investigated. Finally, we apply the proposed methods to data collected in the Thai RV144 HIV vaccine efficacy trial.

SUBMITTER: Juraska M

PROVIDER: S-EPMC3940058 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy.

Juraska M M Gilbert P B PB

Biometrics 20130219 2

In randomized placebo-controlled preventive HIV vaccine efficacy trials, an objective is to evaluate the relationship between vaccine efficacy to prevent infection and genetic distances of the exposing HIV strains to the multiple HIV sequences included in the vaccine construct, where the set of genetic distances is considered as the continuous multivariate "mark" observed in infected subjects only. This research develops a multivariate mark-specific hazard ratio model in the competing risks fail ...[more]

PMID: 23421613

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Project description:BackgroundIn clinical and epidemiological researches, continuous predictors are often discretized into categorical variables for classification of patients. When the relationship between a continuous predictor and log relative hazards is U-shaped in survival data, there is a lack of a satisfying solution to find optimal cut-points to discretize the continuous predictor. In this study, we propose a novel approach named optimal equal-HR method to discretize a continuous variable that has a U-shaped relationship with log relative hazards in survival data.MethodsThe main idea of the optimal equal-HR method is to find two optimal cut-points that have equal log relative hazard values and result in Cox models with minimum AIC value. An R package 'CutpointsOEHR' has been developed for easy implementation of the optimal equal-HR method. A Monte Carlo simulation study was carried out to investigate the performance of the optimal equal-HR method. In the simulation process, different censoring proportions, baseline hazard functions and asymmetry levels of U-shaped relationships were chosen. To compare the optimal equal-HR method with other common approaches, the predictive performance of Cox models with variables discretized by different cut-points was assessed.ResultsSimulation results showed that in asymmetric U-shape scenarios the optimal equal-HR method had better performance than the median split method, the upper and lower quantiles method, and the minimum p-value method regarding discrimination ability and overall performance of Cox models. The optimal equal-HR method was applied to a real dataset of small cell lung cancer. The real data example demonstrated that the optimal equal-HR method could provide clinical meaningful cut-points and had good predictive performance in Cox models.ConclusionsIn general, the optimal equal-HR method is recommended to discretize a continuous predictor with right-censored outcomes if the predictor has an asymmetric U-shaped relationship with log relative hazards based on Cox regression models.

Dataset Information

Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy.

Publications

Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy.

Similar Datasets

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