Exposicions CEIB (Biometria)
Conferenciants: Klaus, Nuria Pérez - Dijous 6 d'octubre 2011
Dijous 6 d’octubre
De 11:00 a 14:00h
* 11:00 - 12:00: Exposició klaus
Títol: Linear regression models with an interval–censored covariate. A comparison of different residuals.
Resum:
We consider the analysis of a linear regression model with an interval–censored covariate. For this model, we propose a new definition of residuals and compare their behavior with other proposed residuals for this setting.
1.Introduction: Interval–censored survival data refer to a situation where the event of interest is not exactly observed and the time T to this event is only known to lie between two time points, L and R, that define a censoring interval for T. This kind of censoring is quite usual in longitudinal studies where subjects are followed over time and the event of interest is observed within consecutive visits. An extensive literature for interval–censored data exists, see for instance the review by Gómez et al. (2009). The vast majority of methods for interval censoring consider the interval–censored variable as the response variable. In this work, we consider the situation where the interval–censored observation is a covariate in a linear regression model. In this setting, Gómez et al. (2003) developed a likelihood approach to jointly estimate the regression coefficients of the model as well as the marginal distribution of the covariate
2. Residuals for a linear regression model with an interval–censored covariate: Checking the underlying model assumptions is essential to assure the validity of the inferences and conclusions of a linear regression analysis. These assumptions include a correctly specified regression function as well as independent and identically distributed model errors. Gómez et al. (2003) and Topp et al. (2004) proposed three different kinds of residuals for linear models that incorporate an interval–censored covariate. In this work, we propose new residuals for this setting. The existence of an interval–censored covariate generates interval–censored residuals. The new residuals are defined as their expected value conditional on the observed censoring interval. We compare through simulation the behavior of the new proposed residuals with that of the residuals considered in Topp et al. (2004).
* 12:10 – 12:45. Exposició Nuria Pérez
Títol: Study of the factors related to interleukin-6 production. Modelling an outcome with under detection limit.
Resum:
A detection limit on the technique that measures an outcome variable is a drawback that should be taken into account in the modelling. In this case, two approaches were applied and compared: linear regression after a single imputation and regression analyses for left-censored data using maximum likelihood estimation.
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