Weighted Estimation of the Accelerated Failure Time Model in the Presence of Dependent Censoring

Independent censoring is a crucial assumption in survival analysis. However, this is impractical in many medical studies, where the presence of dependent censoring leads to difficulty in analyzing covariate effects on disease outcomes. The semicompeting risks framework offers one approach to handling dependent censoring. There are two representative estimators based on an artificial censoring technique in this data structure. However, neither of these estimators is better than another with respect to efficiency (standard error). In this paper, we propose a new weighted estimator for the accelerated failure time (AFT) model under dependent censoring. One of the advantages in our approach is that these weights are optimal among all the linear combinations of the previously mentioned two estimators. To calculate these weights, a novel resampling-based scheme is employed. Attendant asymptotic statistical results for the estimator are established. In addition, simulation studies, as well as an application to real data, show the gains in efficiency for our estimator.     

Double Inverse‐Weighted Estimation of Cumulative Treatment Effects Under Nonproportional Hazards and Dependent Censoring

Summary In medical studies of time‐to‐event data, nonproportional hazards and dependent censoring are very common issues when estimating the treatment effect. A traditional method for dealing with time‐dependent treatment effects is to model the time‐dependence parametrically. Limitations of this approach include the difficulty to verify the correctness of the specified functional form and the fact that, in the presence of a treatment effect that varies over time, investigators are usually interested in the cumulative as opposed to instantaneous treatment effect. In many applications, censoring time is not independent of event time. Therefore, we propose methods for estimating the cumulative treatment effect in the presence of nonproportional hazards and dependent censoring. Three measures are proposed, including the ratio of cumulative hazards, relative risk, and difference in restricted mean lifetime. For each measure, we propose a double inverse‐weighted estimator, constructed by first using inverse probability of treatment weighting (IPTW) to balance the treatment‐specific covariate distributions, then using inverse probability of censoring weighting (IPCW) to overcome the dependent censoring. The proposed estimators are shown to be consistent and asymptotically normal. We study their finite‐sample properties through simulation. The proposed methods are used to compare kidney wait‐list mortality by race.     

Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error

Summary : We propose a semiparametric Bayesian method for handling measurement error in nutritional epidemiological data. Our goal is to estimate nonparametrically the form of association between a disease and exposure variable while the true values of the exposure are never observed. Motivated by nutritional epidemiological data, we consider the setting where a surrogate covariate is recorded in the primary data, and a calibration data set contains information on the surrogate variable and repeated measurements of an unbiased instrumental variable of the true exposure. We develop a flexible Bayesian method where not only is the relationship between the disease and exposure variable treated semiparametrically, but also the relationship between the surrogate and the true exposure is modeled semiparametrically. The two nonparametric functions are modeled simultaneously via B‐splines. In addition, we model the distribution of the exposure variable as a Dirichlet process mixture of normal distributions, thus making its modeling essentially nonparametric and placing this work into the context of functional measurement error modeling. We apply our method to the NIH‐AARP Diet and Health Study and examine its performance in a simulation study.     

Time‐Dependent Predictive Accuracy in the Presence of Competing Risks

Summary Competing risks arise naturally in time‐to‐event studies. In this article, we propose time‐dependent accuracy measures for a marker when we have censored survival times and competing risks. Time‐dependent versions of sensitivity or true positive (TP) fraction naturally correspond to consideration of either cumulative (or prevalent) cases that accrue over a fixed time period, or alternatively to incident cases that are observed among event‐free subjects at any select time. Time‐dependent (dynamic) specificity (1–false positive (FP)) can be based on the marker distribution among event‐free subjects. We extend these definitions to incorporate cause of failure for competing risks outcomes. The proposed estimation for cause‐specific cumulative TP/dynamic FP is based on the nearest neighbor estimation of bivariate distribution function of the marker and the event time. On the other hand, incident TP/dynamic FP can be estimated using a possibly nonproportional hazards Cox model for the cause‐specific hazards and riskset reweighting of the marker distribution. The proposed methods extend the time‐dependent predictive accuracy measures of Heagerty, Lumley, and Pepe (2000, Biometrics 56, 337–344) and Heagerty and Zheng (2005, Biometrics 61, 92–105).     

Addressing an Idiosyncrasy in Estimating Survival Curves Using Double Sampling in the Presence of Self-Selected Right Censoring

We investigate the use of follow-up samples of individuals to estimate survival curves from studies that are subject to right censoring from two sources: (i) early termination of the study, namely, administrative censoring, or (ii) censoring due to lost data prior to administrative censoring, so-called dropout. We assume that, for the full cohort of individuals, administrative censoring times are independent of the subjects' inherent characteristics, including survival time. To address the loss to censoring due to dropout, which we allow to be possibly selective, we consider an intensive second phase of the study where a representative sample of the originally lost subjects is subsequently followed and their data recorded. As with double-sampling designs in survey methodology, the objective is to provide data on a representative subset of the dropouts. Despite assumed full response from the follow-up sample, we show that, in general in our setting, administrative censoring times are not independent of survival times within the two subgroups, nondropouts and sampled dropouts. As a result, the stratified Kaplan-Meier estimator is not appropriate for the cohort survival curve. Moreover, using the concept of potential outcomes, as opposed to observed outcomes, and thereby explicitly formulating the problem as a missing data problem, reveals and addresses these complications. We present an estimation method based on the likelihood of an easily observed subset of the data and study its properties analytically for large samples. We evaluate our method in a realistic situation by simulating data that match published margins on survival and dropout from an actual hip-replacement study. Limitations and extensions of our design and analytic method are discussed.     

Cause‐Specific Cumulative Incidence Estimation and the Fine and Gray Model Under Both Left Truncation and Right Censoring

Summary The standard estimator for the cause‐specific cumulative incidence function in a competing risks setting with left truncated and/or right censored data can be written in two alternative forms. One is a weighted empirical cumulative distribution function and the other a product‐limit estimator. This equivalence suggests an alternative view of the analysis of time‐to‐event data with left truncation and right censoring: individuals who are still at risk or experienced an earlier competing event receive weights from the censoring and truncation mechanisms. As a consequence, inference on the cumulative scale can be performed using weighted versions of standard procedures. This holds for estimation of the cause‐specific cumulative incidence function as well as for estimation of the regression parameters in the Fine and Gray proportional subdistribution hazards model. We show that, with the appropriate filtration, a martingale property holds that allows deriving asymptotic results for the proportional subdistribution hazards model in the same way as for the standard Cox proportional hazards model. Estimation of the cause‐specific cumulative incidence function and regression on the subdistribution hazard can be performed using standard software for survival analysis if the software allows for inclusion of time‐dependent weights. We show the implementation in the R statistical package. The proportional subdistribution hazards model is used to investigate the effect of calendar period as a deterministic external time varying covariate, which can be seen as a special case of left truncation, on AIDS related and non‐AIDS related cumulative mortality.     

Censoring the editor in transient forebrain ischemia

A molecular explanation for why some neurons are more vulnerable than others to ischemic injury has long remained elusive. In this issue of Neuron, Peng et al. propose that CREB-dependent downregulation of the RNA editing enzyme ADAR2, resulting in defective Q/R editing of AMPA receptor GluR2 subunits and increased availability of calcium and zinc-permeable death-promoting AMPA receptors, underlies the vulnerability of some neuronal populations to ischemia.     

Cytotoxic and Genotoxic Consequences of Heat Stress Are Dependent on the Presence of Oxygen in Saccharomyces cerevisiae

Lethal heat stress generates oxidative stress in Saccharomyces cerevisiae, and anaerobic cells are several orders of magnitude more resistant than aerobic cells to a 50 degrees C heat shock. Here we characterize the oxidative effects of this heat stress. The thermoprotective effect in anaerobic cells was not due to expression of HSP104 or any other heat shock gene, raising the possibility that the toxicity of lethal heat shock is due mainly to oxidative stress. Aerobic but not anaerobic heat stress caused elevated frequencies of forward mutations and interchromosomal DNA recombination. Oxidative DNA repair glycosylase-deficient strains under aerobic conditions showed a powerful induction of forward mutation frequencies compared to wild-type cells, which was completely abolished under anaerobiosis. We also investigated potential causes for this oxygen-dependent heat shock-induced genetic instability. Levels of sulfhydryl groups, dominated mainly by the high levels of the antioxidant glutathione (reduced form) and levels of vitamin E, decreased after aerobic heat stress but not after anaerobic heat stress. Aerobic heat stress also led to an increase in mitochondrial membrane disruption of several hundredfold, which was 100-fold reduced under anaerobic conditions.     

The Importance of Age Dependent Mortality and the Extrinsic Incubation Period in Models of Mosquito-Borne Disease Transmission and Control

Nearly all mathematical models of vector-borne diseases have assumed that vectors die at constant rates. However, recent empirical research suggests that mosquito mortality rates are frequently age dependent. This work develops a simple mathematical model to assess how relaxing the classical assumption of constant mortality affects the predicted effectiveness of anti-vectorial interventions. The effectiveness of mosquito control when mosquitoes die at age dependent rates was also compared across different extrinsic incubation periods. Compared to a more realistic age dependent model, constant mortality models overestimated the sensitivity of disease transmission to interventions that reduce mosquito survival. Interventions that reduce mosquito survival were also found to be slightly less effective when implemented in systems with shorter EIPs. Future transmission models that examine anti-vectorial interventions should incorporate realistic age dependent mortality rates.     

Quantifying Cancer Absolute Risk and Cancer Mortality in the Presence of Competing Events after a Myotonic Dystrophy Diagnosis

Recent studies show that patients with myotonic dystrophy (DM) have an increased risk of specific malignancies, but estimates of absolute cancer risk accounting for competing events are lacking. Using the Swedish Patient Registry, we identified 1,081 patients with an inpatient and/or outpatient diagnosis of DM between 1987 and 2007. Date and cause of death and date of cancer diagnosis were extracted from the Swedish Cause of Death and Cancer Registries. We calculated non-parametric estimates of absolute cancer risk and cancer mortality accounting for the high non-cancer competing mortality associated with DM. Absolute cancer risk after DM diagnosis was 1.6% (95% CI=0.4-4%), 5% (95% CI=3-9%) and 9% (95% CI=6-13%) at ages 40, 50 and 60 years, respectively. Females had a higher absolute risk of all cancers combined than males: 9% (95% CI=4-14), and 13% (95% CI=9-20) vs. 2% (95%CI= 0.7-6) and 4% (95%CI=2-8) by ages 50 and 60 years, respectively) and developed cancer at younger ages (median age =51 years, range=22-74 vs. 57, range=43-84, respectively, p=0.02). Cancer deaths accounted for 10% of all deaths, with an absolute cancer mortality risk of 2% (95%CI=1-4.5%), 4% (95%CI=2-6%), and 6% (95%CI=4-9%) by ages 50, 60, and 70 years, respectively. No gender difference in cancer-specific mortality was observed (p=0.6). In conclusion, cancer significantly contributes to morbidity and mortality in DM patients, even after accounting for high competing DM mortality from non-neoplastic causes. It is important to apply population-appropriate, validated cancer screening strategies in DM patients.     

IGFBP-3 Can Either Inhibit or Enhance EGF-mediated Growth of Breast Epithelial Cells Dependent upon the Presence of Fibronectin

Progression of breast cancer is associated with remodeling of the extracellular matrix, often involving a switch from estrogen dependence to a dependence on EGF receptor (EGFR)/HER-2 and is accompanied by increased expression of the main binding protein for insulin-like growth factors (IGFBP-3). We have examined the effects of IGFBP-3 on EGF responses of breast epithelial cells in the context of changes in the extracellular matrix. On plastic and laminin with MCF-10A normal breast epithelial cells, EGF and IGFBP-3 each increased cell growth and together produced a synergistic response, whereas with T47D breast cancer cells IGFBP-3 alone had no effect, but the ability of EGF to increase cell proliferation was markedly inhibited in the presence of IGFBP-3. In contrast on fibronectin with MCF-10A cells, IGFBP-3 alone inhibited cell growth and blocked EGF-induced proliferation. With the cancer cells, IGFBP-3 alone had no effect but enhanced the EGF-induced increase in cell growth. The insulin-like growth factor-independent effects of IGFBP-3 alone on cell proliferation were completely abrogated in the presence of an EGFR, tyrosine kinase inhibitor, Iressa. Although IGFBP-3 did not affect EGFR phosphorylation [Tyr¹⁰⁶⁸], it was found to modulate receptor internalization and was associated with activation of Rho and subsequent changes in MAPK phosphorylation. The levels of fibronectin and IGFBP-3 within breast tumors may determine their dependence on EGFR and their response to therapies targeting this receptor.     

Semiparametric regression estimation in the presence of dependent censoring

We propose a semiparametric estimation procedure for estimatingthe regression of an outcome Y, measured at the end of a fixedfollow-up period, on baseline explanatory variables X, measuredprior to start of follow-up, in the presence of dependent censoringgiven X. The proposed estimators are consistent when the dataare ‘missing at random’ but not ‘missing completelyat random’ (Rubin, 1976), and do not require full specificationof the complete data likelihood. Specifically, we assume thatthe probability of censoring at time t is independent of theoutcome Y conditional on the recorded history up to t of a vectorof time-dependent covariates that are correlated with Y. Ourestimators can be used to adjust for dependent censoring andnonrandom noncompliance in randomised trials studying the effectof a treatment on the mean of a response variable of interest.Even with independent censoring, our methods allow the investigatorto increase efficiency by exploiting the correlation of theoutcome with a vector of time-dependent covariates.     

Evidence for the Presence of Glucosensor Mechanisms Not Dependent on Glucokinase in Hypothalamus and Hindbrain of Rainbow Trout (Oncorhynchus mykiss)

We hypothesize that glucosensor mechanisms other than that mediated by glucokinase (GK) operate in hypothalamus and hindbrain of the carnivorous fish species rainbow trout and stress affected them. Therefore, we evaluated in these areas changes in parameters which could be related to putative glucosensor mechanisms based on liver X receptor (LXR), mitochondrial activity, sweet taste receptor, and sodium/glucose co-transporter 1 (SGLT-1) 6h after intraperitoneal injection of 5 mL.Kg^-1 of saline solution alone (normoglycaemic treatment) or containing insulin (hypoglycaemic treatment, 4 mg bovine insulin.Kg^-1 body mass), or D-glucose (hyperglycaemic treatment, 500 mg.Kg^-1 body mass). Half of tanks were kept at a 10 Kg fish mass.m^-3 and denoted as fish under normal stocking density (NSD) whereas the remaining tanks were kept at a stressful high stocking density (70 kg fish mass.m^-3) denoted as HSD. The results obtained in non-stressed rainbow trout provide evidence, for the first time in fish, that manipulation of glucose levels induce changes in parameters which could be related to putative glucosensor systems based on LXR, mitochondrial activity and sweet taste receptor in hypothalamus, and a system based on SGLT-1 in hindbrain. Stress altered the response of parameters related to these systems to changes in glycaemia.     

Semiparametric model-based inference in the presence of missing responses

We consider a semiparametric model that parameterizes the conditionaldensity of the response, given covariates, but allows the marginaldistribution of the covariates to be completely arbitrary. Responsesmay be missing. A likelihood-based imputation estimator anda semi-empirical-likelihood-based estimator for the parametervector describing the conditional density are defined and provedto be asymptotically normal. Semi-empirical loglikelihood functionsfor the parameter vector and the response mean are derived.It is shown that the two semi-empirical loglikelihood functionsare distributed asymptotically as weighted ² and scaled ², respectively.