Semiparametric Multivariate Accelerated Failure Time Model with Generalized Estimating Equations

The semiparametric accelerated failure time model is not as widely used as the Cox relative risk model mainly due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations provide promising tools to make the accelerate failure time models more attractive in practice. For semiparametric multivariate accelerated failure time models, we propose a generalized estimating equation approach to account for the multivariate dependence through working correlation structures. The marginal error distributions can be either identical as in sequential event settings or different as in parallel event settings. Some regression coefficients can be shared across margins as needed. The initial estimator is a rank-based estimator with Gehan's weight, but obtained from an induced smoothing approach with computation ease. The resulting estimator is consistent and asymptotically normal, with a variance estimated through a multiplier resampling method. In a simulation study, our estimator was up to three times as efficient as the initial estimator, especially with stronger multivariate dependence and heavier censoring percentage. Two real examples demonstrate the utility of the proposed method

Event-Related Potential Correlates of Performance-Monitoring in a Lateralized Time-Estimation Task

Performance-monitoring as a key function of cognitive control covers a wide range of diverse processes to enable goal directed behavior and to avoid maladjustments. Several event-related brain potentials (ERP) are associated with performance-monitoring, but their conceptual background differs. For example, the feedback-related negativity (FRN) is associated with unexpected performance feedback and might serve as a teaching signal for adaptational processes, whereas the error-related negativity (ERN) is associated with error commission and subsequent behavioral adaptation. The N2 is visible in the EEG when the participant successfully inhibits a response following a cue and thereby adapts to a given stop-signal. Here, we present an innovative paradigm to concurrently study these different performance-monitoring-related ERPs. In 24 participants a tactile time-estimation task interspersed with infrequent stop-signal trials reliably elicited all three ERPs. Sensory input and motor output were completely lateralized, in order to estimate any hemispheric processing preferences for the different aspects of performance monitoring associated with these ERPs. In accordance with the literature our data suggest augmented inhibitory capabilities in the right hemisphere given that stop-trial performance was significantly better with left- as compared to right-hand stop-signals. In line with this, the N2 scalp distribution was generally shifted to the right in addition to an ipsilateral shift in relation to the response hand. Other than that, task lateralization affected neither behavior related to error and feedback processing nor ERN or FRN. Comparing the ERP topographies using the Global Map Dissimilarity index, a large topographic overlap was found between all considered components.With an evenly distributed set of trials and a split-half reliability for all ERP components ≥.85 the task is well suited to efficiently study N2, ERN, and FRN concurrently which might prove useful for group comparisons, especially in clinical populations