Doubly Penalized Buckley–James Method for Survival Data with High-Dimensional Covariates

Summary .   Recent interest in cancer research focuses on predicting patients' survival by investigating gene expression profiles based on microarray analysis. We propose a doubly penalized Buckley–James method for the semiparametric accelerated failure time model to relate high-dimensional genomic data to censored survival outcomes, which uses the elastic-net penalty that is a mixture of L ₁- and L ₂-norm penalties. Similar to the elastic-net method for a linear regression model with uncensored data, the proposed method performs automatic gene selection and parameter estimation, where highly correlated genes are able to be selected (or removed) together. The two-dimensional tuning parameter is determined by generalized crossvalidation. The proposed method is evaluated by simulations and applied to the Michigan squamous cell lung carcinoma study.     

Bias and Sensitivity Analysis When Estimating Treatment Effects from the Cox Model with Omitted Covariates

Summary

Omission of relevant covariates can lead to bias when estimating treatment or exposure effects from survival data in both randomized controlled trials and observational studies. This paper presents a general approach to assessing bias when covariates are omitted from the Cox model. The proposed method is applicable to both randomized and non‐randomized studies. We distinguish between the effects of three possible sources of bias: omission of a balanced covariate, data censoring and unmeasured confounding. Asymptotic formulae for determining the bias are derived from the large sample properties of the maximum likelihood estimator. A simulation study is used to demonstrate the validity of the bias formulae and to characterize the influence of the different sources of bias. It is shown that the bias converges to fixed limits as the effect of the omitted covariate increases, irrespective of the degree of confounding. The bias formulae are used as the basis for developing a new method of sensitivity analysis to assess the impact of omitted covariates on estimates of treatment or exposure effects. In simulation studies, the proposed method gave unbiased treatment estimates and confidence intervals with good coverage when the true sensitivity parameters were known. We describe application of the method to a randomized controlled trial and a non‐randomized study.     

Estimated Pseudopartial‐Likelihood Method for Correlated Failure Time Data with Auxiliary Covariates

Summary As biological studies become more expensive to conduct, statistical methods that take advantage of existing auxiliary information about an expensive exposure variable are desirable in practice. Such methods should improve the study efficiency and increase the statistical power for a given number of assays. In this article, we consider an inference procedure for multivariate failure time with auxiliary covariate information. We propose an estimated pseudopartial likelihood estimator under the marginal hazard model framework and develop the asymptotic properties for the proposed estimator. We conduct simulation studies to evaluate the performance of the proposed method in practical situations and demonstrate the proposed method with a data set from the studies of left ventricular dysfunction ( SOLVD Investigators, 1991 , New England Journal of Medicine 325 , 293–302).     

Modeling Age and Nest‐Specific Survival Using a Hierarchical Bayesian Approach

Summary : Recent studies have shown that grassland birds are declining more rapidly than any other group of terrestrial birds. Current methods of estimating avian age‐specific nest survival rates require knowing the ages of nests, assuming homogeneous nests in terms of nest survival rates, or treating the hazard function as a piecewise step function. In this article, we propose a Bayesian hierarchical model with nest‐specific covariates to estimate age‐specific daily survival probabilities without the above requirements. The model provides a smooth estimate of the nest survival curve and identifies the factors that are related to the nest survival. The model can handle irregular visiting schedules and it has the least restrictive assumptions compared to existing methods. Without assuming proportional hazards, we use a multinomial semiparametric logit model to specify a direct relation between age‐specific nest failure probability and nest‐specific covariates. An intrinsic autoregressive prior is employed for the nest age effect. This nonparametric prior provides a more flexible alternative to the parametric assumptions. The Bayesian computation is efficient because the full conditional posterior distributions either have closed forms or are log concave. We use the method to analyze a Missouri dickcissel dataset and find that (1) nest survival is not homogeneous during the nesting period, and it reaches its lowest at the transition from incubation to nestling; and (2) nest survival is related to grass cover and vegetation height in the study area.     

Resistance to extinction and behavioral momentum

In the metaphor of behavioral momentum, reinforcement is assumed to strengthen discriminated operant behavior in the sense of increasing its resistance to disruption, and extinction is viewed as disruption by contingency termination and reinforcer omission. In multiple schedules of intermittent reinforcement, resistance to extinction is an increasing function of reinforcer rate, consistent with a model based on the momentum metaphor. The partial-reinforcement extinction effect, which opposes the effects of reinforcer rate, can be explained by the large disruptive effect of terminating continuous reinforcement despite its strengthening effect during training. Inclusion of a term for the context of reinforcement during training allows the model to account for a wide range of multiple-schedule extinction data and makes contact with other formulations. The relation between resistance to extinction and reinforcer rate on single schedules of intermittent reinforcement is exactly opposite to that for multiple schedules over the same range of reinforcer rates; however, the momentum model can give an account of resistance to extinction in single as well as multiple schedules. An alternative analysis based on the number of reinforcers omitted to an extinction criterion supports the conclusion that response strength is an increasing function of reinforcer rate during training.     

Factors confounding cytosolic calcium measurements in Jurkat E6.1 cells during exposure to ELF magnetic fields

Reported changes in the cytosolic calcium concentration ([Ca2+](c)) as a result of exposure to extremely low frequency (ELF) magnetic fields (MF) have been equivocal. In this study, we examine the possibility that some of these differences are attributable to variability associated with the cell cycle, pH of the suspension medium, and response to a calcium agonist. We used a custom designed spectrofluorimeter to measure [Ca2+](c) in Indo 1-AM loaded Jurkat E6.1 cells suspended in conditioned RPMI 1640 medium containing 10% fetal bovine serum. Four exposures were examined: zero static MF (Null), 60 Hz 100 microT(peak) sinusoidal MF (AC), 78 microT static MF (DC), and the combination of the 60 Hz and the 78 microT static MF (AD + DC). A significant decrease in normalized [Ca2+](c) values between 375-495 s for the DC and AC + DC groups was found in comparison to the Null group. However, statistical analysis indicated that cell cycle and quality of the alpha-CD3 monoclonal antibody response were significant covariates, while pH was not a significant covariate. When the effect of these covariates was taken into account, all exposure groups were significantly different from the control. Our results suggest that ELF MF effects may not be seen unless correction is made for biological variability of each cell preparation with respect to cell cycle and [Ca2+](c) response to antigen stimulation.     

Bayesian estimators for conditional hazard functions

This article introduces a new Bayesian approach to the analysis of right-censored survival data. The hazard rate of interest is modeled as a product of conditionally independent stochastic processes corresponding to (1) a baseline hazard function and (2) a regression function representing the temporal influence of the covariates. These processes jump at times that form a time-homogeneous Poisson process and have a pairwise dependency structure for adjacent values. The two processes are assumed to be conditionally independent given their jump times. Features of the posterior distribution, such as the mean covariate effects and survival probabilities (conditional on the covariate), are evaluated using the Metropolis-Hastings-Green algorithm. We illustrate our methodology by an application to nasopharynx cancer survival data.     

Food consumption, utilization and detoxification enzyme activity of larvae of three polyphagous noctuid moth species when fed the botanical insecticide rotenone

We assessed the effects of the isoflavonoid rotenone, an insecticidal allelochemical occurring in various legumes, on larval performance of three polyphagous noctuid species: the corn earworm (CEW), the fall armyworm (FAW) and the southern armyworm (SAW). As rotenone concentration was increased up to 1% fresh mass in an artificial diet, neither mortality (for all three species) nor food consumption (for SAW and FAW) was significantly affected, but developmental time of the latter two species was prolonged. In contrast, for CEW developmental time was shortened and food consumption declined, especially at the two highest rotenone concentrations. Final biomass of all three species declined as dietary rotenone increased.Analysis of covariance (ANCOVA) was used to evaluate larval food utilization analogous to, but without the potential statistical problems of, the traditional ratio-based indices approximate digestibility (AD) and efficiency of conversion of digested food (ECD). Frass output, statistically adjusted to account for differences in consumption, increased with rotenone concentration for all three species, suggesting that, when ingested, this allelochemical interfered with the digestion and/or absorption of ingested food, especially at the higher concentrations tested.Significant statistical interactions in the ANCOVA necessitated the use of utilization plots to examine the linear regressions between biomass gain and absorption at each rotenone concentration. At the two highest concentrations, rotenone tended to reduce the amount of food absorbed by the larvae, as well as their ability to convert the absorbed food to biomass.When piperonyl butoxide, an inhibitor of the polysubstrate monooxygenase (PSMO) system known to metabolize rotenone, was added to the diet along with 0.5 or 1% rotenone, mortality increased significantly only for FAW, approaching or exceeding 50%. The only apparent effects of dietary rotenone on PSMO (aldrin epoxidase) activity (adjusted by ANCOVA for the covariate, crude homogenate protein) were for CEW, where activity decreased in all rotenone treatments compared to the control, and for SAW, where activity at the 3 lowest rotenone concentrations (0.001–0.1% fm) declined about 50% below that of larvae on the control diet, whereas at 0.5 and 1% rotenone, it increased approximately 2-fold over that on the control diet.