ROC-guided survival trees and ensembles

Tree-based methods are popular nonparametric tools in studying time-to-event outcomes. In this article, we introduce a novel framework for survival trees and ensembles, where the trees partition the dynamic survivor population and can handle time-dependent covariates. Using the idea of randomized tests, we develop generalized time-dependent receiver operating characteristic (ROC) curves for evaluating the performance of survival trees. The tree-building algorithm is guided by decision-theoretic criteria based on ROC, targeting specifically for prediction accuracy. To address the instability issue of a single tree, we propose a novel ensemble procedure based on averaging martingale estimating equations, which is different from existing methods that average the predicted survival or cumulative hazard functions from individual trees. Extensive simulation studies are conducted to examine the performance of the proposed methods. We apply the methods to a study on AIDS for illustration.     

Low-dose radiation enhances therapeutic HPV DNA vaccination in tumor-bearing hosts

Current therapeutic approaches to treatment of patients with bulky cervical cancer are based on conventional in situ ablative modalities including cisplatin-based chemotherapy and radiation therapy. The 5-year survival of patients with nonresectable disease is dismal. Because over 99% of squamous cervical cancer is caused by persistent infection with an oncogenic strain of human papillomavirus (HPV), particularly type 16 and viral oncoproteins E6 and E7 are functionally required for disease initiation and persistence, HPV-targeted immune strategies present a compelling opportunity in which to demonstrate proof of principle. Sublethal doses of radiation and chemotherapeutic agents have been shown to have synergistic effect in combination with either vaccination against cancer-specific antigens, or with passive transfer of tumor-specific cytotoxic T lymphocytes (CTLs). Here, we explored the combination of low-dose radiation therapy with DNA vaccination with calreticulin (CRT) linked to the mutated form of HPV-16 E7 antigen (E7(detox)), CRT/E7(detox) in the treatment of E7-expressing TC-1 tumors. We observed that TC-1 tumor-bearing mice treated with radiotherapy combined with CRT/E7(detox) DNA vaccination generated significant therapeutic antitumor effects and the highest frequency of E7-specific CD8⁺ T cells in the tumors and spleens of treated mice. Furthermore, treatment with radiotherapy was shown to render the TC-1 tumor cells more susceptible to lysis by E7-specific CTLs. In addition, we observed that treatment with radiotherapy during the second DNA vaccination generated the highest frequency of E7-specific CD8⁺ T cells in the tumors and spleens of TC-1 tumor-bearing mice. Finally, TC-1 tumor-bearing mice treated with the chemotherapy in combination with radiation and CRT/E7(detox) DNA vaccination generate significantly enhanced therapeutic antitumor effects. The clinical implications of the study are discussed. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Generation and characterization of DNA vaccines targeting the nucleocapsid protein of severe acute respiratory syndrome coronavirus

Severe acute respiratory syndrome (SARS) is a serious threat to public health and the economy on a global scale. The SARS coronavirus (SARS-CoV) has been identified as the etiological agent for SARS. Thus, vaccination against SARS-CoV may represent an effective approach to controlling SARS. DNA vaccines are an attractive approach for SARS vaccine development, as they offer many advantages over conventional vaccines, including stability, simplicity, and safety. Our investigators have previously shown that DNA vaccination with antigen linked to calreticulin (CRT) dramatically enhances major histocompatibility complex class I presentation of linked antigen to CD8(+) T cells. In this study, we have employed this CRT-based enhancement strategy to create effective DNA vaccines using SARS-CoV nucleocapsid (N) protein as a target antigen. Vaccination with naked CRT/N DNA generated the most potent N-specific humoral and T-cell-mediated immune responses in vaccinated C57BL/6 mice among all of the DNA constructs tested. Furthermore, mice vaccinated with CRT/N DNA were capable of significantly reducing the titer of challenging vaccinia virus expressing the N protein of the SARS virus. These results show that a DNA vaccine encoding CRT linked to a SARS-CoV antigen is capable of generating strong N-specific humoral and cellular immunity and may potentially be useful for control of infection with SARS-CoV.     

Nonparametric estimation of the bivariate recurrence time distribution

This article considers statistical models in which two different types of events, such as the diagnosis of a disease and the remission of the disease, occur alternately over time and are observed subject to right censoring. We propose nonparametric estimators for the joint distribution of bivariate recurrence times and the marginal distribution of the first recurrence time. In general, the marginal distribution of the second recurrence time cannot be estimated due to an identifiability problem, but a conditional distribution of the second recurrence time can be estimated non-parametrically. In the literature, statistical methods have been developed to estimate the joint distribution of bivariate recurrence times based on data on the first pair of censored bivariate recurrence times. These methods are inefficient in the model considered here because recurrence times of higher orders are not used. Asymptotic properties of the proposed estimators are established. Numerical studies demonstrate the estimators perform well with practical sample sizes. We apply the proposed method to the South Verona, Italy, psychiatric case register (PCR) data set for illustration of the methods and theory.     

Semiparametric regression analysis on longitudinal pattern of recurrent gap times

In longitudinal studies, individual subject may experience recurrent events of the same type over a relatively long period of time. The longitudinal pattern of gaps between successive recurrent events is often of great research interest. In this article, the probability structure of the recurrent gap times is first explored in the presence of censoring. According to the discovered structure, we introduce the stratified proportional reverse-time hazards models with unspecified baseline functions to accommodate individual heterogeneity, when the longitudinal pattern parameter is of main interest. Inference procedures are proposed and studied by way of proper riskset construction. The proposed methodology is demonstrated by the Monte Carlo simulations and an application to a well-known Denmark schizophrenia cohort study data set.     

Estimating incident population distribution from prevalent data

A prevalent sample consists of individuals who have experienced disease incidence but not failure event at the sampling time. We discuss methods for estimating the distribution function of a random vector defined at baseline for an incident disease population when data are collected by prevalent sampling. Prevalent sampling design is often more focused and economical than incident study design for studying the survival distribution of a diseased population, but prevalent samples are biased by design. Subjects with longer survival time are more likely to be included in a prevalent cohort, and other baseline variables of interests that are correlated with survival time are also subject to sampling bias induced by the prevalent sampling scheme. Without recognition of the bias, applying empirical distribution function to estimate the population distribution of baseline variables can lead to serious bias. In this article, nonparametric and semiparametric methods are developed for distribution estimation of baseline variables using prevalent data.     

Joint inference for competing risks data using multiple endpoints

Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations and/or are of simultaneous interest. In clinical trials, often more than one competing event has meaningful clinical interpretations even though the trial effects of different events could be different or even opposite to each other. In this paper, we develop estimation procedures and inferential properties for the joint use of multiple cumulative incidence functions (CIFs). Additionally, by incorporating longitudinal marker information, we develop estimation and inference procedures for weighted CIFs and related metrics. The proposed methods are applied to a COVID-19 in-patient treatment clinical trial, where the outcomes of COVID-19 hospitalization are either death or discharge from the hospital, two competing events with completely different clinical implications.     

Cohort case-control design and analysis for clustered failure-time data

Cohort case-control design is an efficient and economical design to study risk factors for disease incidence or mortality in a large cohort. In the last few decades, a variety of cohort case-control designs have been developed and theoretically justified. These designs have been exclusively applied to the analysis of univariate failure-time data. In this work, a cohort case-control design adapted to multivariate failure-time data is developed. A risk set sampling method is proposed to sample controls from nonfailures in a large cohort for each case matched by failure time. This method leads to a pseudolikelihood approach for the estimation of regression parameters in the marginal proportional hazards model (Cox, 1972, Journal of the Royal Statistical Society, Series B 34, 187-220), where the correlation structure between individuals within a cluster is left unspecified. The performance of the proposed estimator is demonstrated by simulation studies. A bootstrap method is proposed for inferential purposes. This methodology is illustrated by a data example from a child vitamin A supplementation trial in Nepal (Nepal Nutrition Intervention Project-Sarlahi, or NNIPS).     

Semiparametric modelling and estimation of covariate-adjusted dependence between bivariate recurrent events

A time-dependent measure, termed the rate ratio, was proposed to assess the local dependence between two types of recurrent event processes in one-sample settings. However, the one-sample work does not consider modeling the dependence by covariates such as subject characteristics and treatments received. The focus of this paper is to understand how and in what magnitude the covariates influence the dependence strength for bivariate recurrent events. We propose the covariate-adjusted rate ratio, a measure of covariate-adjusted dependence. We propose a semiparametric regression model for jointly modeling the frequency and dependence of bivariate recurrent events: the first level is a proportional rates model for the marginal rates and the second level is a proportional rate ratio model for the dependence structure. We develop a pseudo-partial likelihood to estimate the parameters in the proportional rate ratio model. We establish the asymptotic properties of the estimators and evaluate the finite sample performance via simulation studies. We illustrate the proposed models and methods using a soft tissue sarcoma study that examines the effects of initial treatments on the marginal frequencies of local/distant sarcoma recurrence and the dependence structure between the two types of cancer recurrence.