Proportional hazards models with continuous marks

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 et al. Biometrics 34 (1978) 541--554]. This article studies an extension of this approach to allow a continuum of competing risks, in which the cause of failure is replaced by a continuous mark only observed at the failure time. We develop inference for the proportional hazards model in which the regression parameters depend nonparametrically on the mark and the baseline hazard depends nonparametrically on both time and mark. This work is motivated by the need to assess HIV vaccine efficacy, while taking into account the genetic divergence of infecting HIV viruses in trial participants from the HIV strain that is contained in the vaccine, and adjusting for covariate effects. Mark-specific vaccine efficacy is expressed in terms of one of the regression functions in the mark-specific proportional hazards model. The new approach is evaluated in simulations and applied to the first HIV vaccine efficacy trial.Comment: Published in at http://dx.doi.org/10.1214/07-AOS554 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Evaluating Causal Effect Predictiveness of Candidate Surrogate Endpoints

Most methods for evaluating surrogate endpoints measure validity in terms of net effects (i.e., treatment effects adjusted for the biomarker measured after randomization). Frangakis and Rubin (2002, Biometrics) criticized these approaches because net effects may reflect selection bias, and suggested an alternative definition of a surrogate endpoint (a principal surrogate) based on causal effects. For evaluating principal surrogates we introduce a causal effect predictiveness (CEP) surface, which quantifies how well causal treatment effects on the biomarker predict causal treatment effects on the clinical endpoint. The CEP surface is not identifiable in general due to missing potential outcomes. However, by incorporating baseline covariates that predict the biomarker, the CEP surface is identified under relatively weak assumptions in the important special case that the biomarker has no variability in one treatment arm. For this setting we develop an estimated likelihood method for estimating the CEP surface. The approach is illustrated by the problem of assessing an immune response to a vaccine as a surrogate endpoint for infection

Nonparametric Bounds and Sensitivity Analysis of Treatment Effects

This paper considers conducting inference about the effect of a treatment (or exposure) on an outcome of interest. In the ideal setting where treatment is assigned randomly, under certain assumptions the treatment effect is identifiable from the observable data and inference is straightforward. However, in other settings such as observational studies or randomized trials with noncompliance, the treatment effect is no longer identifiable without relying on untestable assumptions. Nonetheless, the observable data often do provide some information about the effect of treatment, that is, the parameter of interest is partially identifiable. Two approaches are often employed in this setting: (i) bounds are derived for the treatment effect under minimal assumptions, or (ii) additional untestable assumptions are invoked that render the treatment effect identifiable and then sensitivity analysis is conducted to assess how inference about the treatment effect changes as the untestable assumptions are varied. Approaches (i) and (ii) are considered in various settings, including assessing principal strata effects, direct and indirect effects and effects of time-varying exposures. Methods for drawing formal inference about partially identified parameters are also discussed.Comment: Published in at http://dx.doi.org/10.1214/14-STS499 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Data fusion using weakly aligned sources

We introduce a new data fusion method that utilizes multiple data sources to estimate a smooth, finite-dimensional parameter. Most existing methods only make use of fully aligned data sources that share common conditional distributions of one or more variables of interest. However, in many settings, the scarcity of fully aligned sources can make existing methods require unduly large sample sizes to be useful. Our approach enables the incorporation of weakly aligned data sources that are not perfectly aligned, provided their degree of misalignment can be characterized by a prespecified density ratio model. We describe gains in efficiency and provide a general means to construct estimators achieving these gains. We illustrate our results by fusing data from two harmonized HIV monoclonal antibody prevention efficacy trials to study how a neutralizing antibody biomarker associates with HIV genotype.Comment: 33 pages including appendices, 3 figure

Tests for Comparing Mark-Specific Hazards and Cumulative Incidence Functions

It is of interest in some applications to determine whether there is a relationship between a hazard rate function (or a cumulative incidence function) and a mark variable which is only observed at uncensored failure times. We develop nonparametric tests for this problem when the mark variable is continuous. Tests are developed for the null hypothesis that the mark-specific hazard rate is independent of the mark versus ordered and two-sided alternatives expressed in terms of mark-specific hazard functions and mark-specific cumulative incidence functions. The test statistics are based on functionals of a bivariate test process equal to a weighted average of differences between a Nelson--Aalen-type estimator of the mark-specific cumulative hazard function and a nonparametric estimator of this function under the null hypothesis. The weight function in the test process can be chosen so that the test statistics are asymptotically distribution-free.Asymptotically correct critical values are obtained through a simple simulation procedure. The testing procedures are shown to perform well in numerical studies, and are illustrated with an AIDS clinical trial example. Specifically, the tests are used to assess if the instantaneous or absolute risk of treatment failure depends on the amount of accumulation of drug resistance mutations in a subject\u27s HIV virus. This assessment helps guide development of anti-HIV therapies that surmount the problem of drug resistance

Centrally Acting Perindopril Attenuates the Exercise Induced Increase in Muscle Sympathetic Nerve Activity during Heavy Dynamic Exercise

Central angiotensin II (Ang II) linked free radical (FR) production scavenges nitric oxide (NO) enabling an increased central sympathetic neural outflow (SNA). The pathophysiological increase in Ang II linked FR production is recognized as a major mechanism involved in neurogenic hypertension. During exercise, there is a physiological increase in Ang II and muscle sympathetic nerve activity (MSNA) in direct relation to increasing exercise intensity. We tested the hypothesis that the exercise induced increase in Ang II linked FR production and MSNA activity during exercise is located within the brain. Six healthy subjects performed three randomly ordered trials of 70° upright back-supported dynamic leg cycling after ingestion of two different lipid soluble Angiotensin converting enzyme inhibitors ((ACEi) Perindopril (PER) - highly lipid soluble; Captopril (CAP) non-lipid soluble)) and/or placebo (PL). Repeated measurements of whole venous blood, MSNA, and mean arterial pressures (MAP) were obtained at rest and during steady-state heavy intensity exercise at heart rates (HR) of 120 bpm (e120). Peripheral venous superoxide concentrations as measured by electron paramagnetic resonance (EPR) were not significantly altered at rest (P≥0.4) and during E120 by the ACE inhibitors (P≥0.07). Likewise, baseline MSNA (PL, 25 ± 1.5 bust/min; CAP, 21 ± 0.7 bust/min; PER, 25 ± 0.7 bust/min) and MAP (PL, 86 ± 2.8 mmHg vs. CAP, 84 ± 2.6 mmHg; PER, 84 ± 0.7 mmHg) were unchanged at rest (P≥0.1; P≥0.8 respectively). However, during E120 central acting PER attenuated the increases in MSNA and MAP, increasing only 15±6% for MAP and 24±8% for MSNA when compared to PL (26 ± 6% MAP; 57±16% MSNA; P\u3c0.05) and CAP (26±4%MAP; 69±13%MSNA P\u3c0.05). From these data we conclude that centrally acting PER attenuated the central increase in the exercise induced Ang II linked free radical production resulting in an increased central NO activity induced reduction in MSNA during heavy intensity dynamic exercise