Project description:In population-based health research, the so-called population attributable fraction is an important quantity that calculates the percentage of excess risk of morbidity and mortality associated with modifiable risk factors for a given population. While the concept of "risk" is usually measured by event probabilities, in practice it may be of a more direct interest to know the excess life expectancy associated with the modifiable risk factors instead, particularly when mortality is of the ultimate concern. In this paper, we thus propose to study a novel quantity, termed "attributable life expectancy," to measure the population attributable fraction of life expectancy. We further develop a model-based approach for the attributable life expectancy under the Oakes-Dasu proportional mean residual life model, and establish its asymptotic properties for inferences. Numerical studies that includes Monte-Carlo simulations and an actual analysis of the mortality associated with smoking cessation in an Asia Cohort Consortium, are conducted to evaluate the performance of our proposed method.
Project description:An objective of preventive HIV vaccine efficacy trials is to understand how vaccine-induced immune responses to specific protein sequences of HIV-1 associate with subsequent infection with specific sequences of HIV, where the immune response biomarkers are measured in vaccine recipients via a two-phase sampling design. Motivated by this objective, we investigate the stratified mark-specific proportional hazards model under two-phase biomarker sampling, where the mark is the genetic distance of an infecting HIV-1 sequence to an HIV-1 sequence represented inside the vaccine. Estimation and inference procedures based on inverse probability weighting of complete-cases and on augmented inverse probability weighting of complete-cases are developed. Asymptotic properties of the estimators are derived and their finite-sample performances are examined in simulation studies. The methods are shown to have satisfactory performance, and are applied to the RV144 vaccine trial to assess whether immune response correlates of HIV-1 infection are stronger for HIV-1 infecting sequences similar to the vaccine than for sequences distant from the vaccine. Augmented inverse probability weighting; auxiliary variables; case-cohort design; censored failure time; competing risks; HIV vaccine efficacy trial.
Project description:PurposeSeoul and Busan are the two largest cities in Korea. However, life expectancy (LE) in Busan is shorter than in Seoul and among the total Korean population. This study was conducted to decompose age- and cause-specific contributions to the LE difference between Seoul and Busan.Materials and methodsWe obtained population and mortality data for Seoul and Busan between 2015 and 2017 from Statistics Korea. We applied Arriaga's decomposition method to life table data to estimate age- and cause-specific contributions to the LE difference between Seoul and Busan.ResultsDuring 2015-2017, LE in Busan was shorter than in Seoul by 2.22 years. Roughly two-thirds of the LE gap between Seoul and Busan was due to excess mortality among elderly people in Busan. The ≥85 age group alone contributed to approximately 20% of the LE gap, while no meaningful contribution was made by the 1-24 age groups. Cardiovascular disease accounted for over 40% of the total LE gap between Seoul and Busan, and this factor was more prominent in women. The top 15 leading specific causes of deaths explained nearly the entire LE difference between Seoul and Busan.ConclusionThe difference in LE between Seoul and Busan was due to higher mortality rate in Busan than in Seoul, especially in the elderly population and from cardiovascular diseases. Information on age- and cause-specific contributions to the LE difference between Seoul and Busan may guide health policy-makers to plan strategies for reducing the gap in LE.
Project description:BackgroundStandard futility analyses designed for a proportional hazards setting may have serious drawbacks when non-proportional hazards are present. One important type of non-proportional hazards occurs when the treatment effect is delayed. That is, there is little or no early treatment effect but a substantial later effect.MethodsWe define optimality criteria for futility analyses in this setting and propose simple search procedures for deriving such rules in practice.ResultsWe demonstrate the advantages of the optimal rules over commonly used rules in reducing the average number of events, the average sample size, or the average study duration under the null hypothesis with minimal power loss under the alternative hypothesis.ConclusionOptimal futility rules can be derived for a non-proportional hazards setting that control the loss of power under the alternative hypothesis while maximizing the gain in early stopping under the null hypothesis.
Project description:For either the equivalence trial or the non-inferiority trial with survivor outcomes from two treatment groups, the most popular testing procedure is the extension (e.g., Wellek, A log-rank test for equivalence of two survivor functions, Biometrics, 1993; 49: 877-881) of log-rank based test under proportional hazards model. We show that the actual type I error rate for the popular procedure of Wellek is higher than the intended nominal rate when survival responses from two treatment arms satisfy the proportional odds survival model. When the true model is proportional odds survival model, we show that the hypothesis of equivalence of two survival functions can be formulated as a statistical hypothesis involving only the survival odds ratio parameter. We further show that our new equivalence test, formulation, and related procedures are applicable even in the presence of additional covariates beyond treatment arms, and the associated equivalence test procedures have correct type I error rates under the proportional hazards model as well as the proportional odds survival model. These results show that use of our test will be a safer statistical practice for equivalence trials of survival responses than the commonly used log-rank based tests.
Project description:For time-to-event data with finitely many competing risks, the proportional hazards model has been a popular tool for relating the cause-specific outcomes to covariates (Prentice and others, 1978. The analysis of failure time in the presence of competing risks. Biometrics 34, 541-554). Inspired by previous research in HIV vaccine efficacy trials, the cause of failure is replaced by a continuous mark observed only in subjects who fail. This article studies an extension of this approach to allow a multivariate continuum of competing risks, to better account for the fact that the candidate HIV vaccines tested in efficacy trials have contained multiple HIV sequences, with a purpose to elicit multiple types of immune response that recognize and block different types of HIV viruses. We develop inference for the proportional hazards model in which the regression parameters depend parametrically on the marks, to avoid the curse of dimensionality, and the baseline hazard depends nonparametrically on both time and marks. Goodness-of-fit tests are constructed based on generalized weighted martingale residuals. The finite-sample performance of the proposed methods is examined through extensive simulations. The methods are applied to a vaccine efficacy trial to examine whether and how certain antigens represented inside the vaccine are relevant for protection or anti-protection against the exposing HIVs.
Project description:We consider methods for estimating the treatment effect and/or the covariate by treatment interaction effect in a randomized clinical trial under noncompliance with time-to-event outcome. As in Cuzick et al. (2007), assuming that the patient population consists of three (possibly latent) subgroups based on treatment preference: the ambivalent group, the insisters, and the refusers, we estimate the effects among the ambivalent group. The parameters have causal interpretations under standard assumptions. The article contains two main contributions. First, we propose a weighted per-protocol (Wtd PP) estimator through incorporating time-varying weights in a proportional hazards model. In the second part of the article, under the model considered in Cuzick et al. (2007), we propose an EM algorithm to maximize a full likelihood (FL) as well as the pseudo likelihood (PL) considered in Cuzick et al. (2007). The E step of the algorithm involves computing the conditional expectation of a linear function of the latent membership, and the main advantage of the EM algorithm is that the risk parameters can be updated by fitting a weighted Cox model using standard software and the baseline hazard can be updated using closed-form solutions. Simulations show that the EM algorithm is computationally much more efficient than directly maximizing the observed likelihood. The main advantage of the Wtd PP approach is that it is more robust to model misspecifications among the insisters and refusers since the outcome model does not impose distributional assumptions among these two groups.
Project description:As an alternative to the local partial likelihood method of Tibshirani and Hastie and Fan, Gijbels, and King, a global partial likelihood method is proposed to estimate the covariate effect in a nonparametric proportional hazards model, λ(t|x) = exp{ψ(x)}λ(0)(t). The estimator, ψ̂(x), reduces to the Cox partial likelihood estimator if the covariate is discrete. The estimator is shown to be consistent and semiparametrically efficient for linear functionals of ψ(x). Moreover, Breslow-type estimation of the cumulative baseline hazard function, using the proposed estimator ψ̂(x), is proved to be efficient. The asymptotic bias and variance are derived under regularity conditions. Computation of the estimator involves an iterative but simple algorithm. Extensive simulation studies provide evidence supporting the theory. The method is illustrated with the Stanford heart transplant data set. The proposed global approach is also extended to a partially linear proportional hazards model and found to provide efficient estimation of the slope parameter. This article has the supplementary materials online.