Impact of random and systematic recall errors and selection bias in case--control studies on mobile phone use and brain tumors in adolescents (CEFALO study)

Whether the use of mobile phones is a risk factor for brain tumors in adolescents is currently being studied. Case--control studies investigating this possible relationship are prone to recall error and selection bias. We assessed the potential impact of random and systematic recall error and selection bias on odds ratios (ORs) by performing simulations based on real data from an ongoing case--control study of mobile phones and brain tumor risk in children and adolescents (CEFALO study). Simulations were conducted for two mobile phone exposure categories: regular and heavy use. Our choice of levels of recall error was guided by a validation study that compared objective network operator data with the self-reported amount of mobile phone use in CEFALO. In our validation study, cases overestimated their number of calls by 9% on average and controls by 34%. Cases also overestimated their duration of calls by 52% on average and controls by 163%. The participation rates in CEFALO were 83% for cases and 71% for controls. In a variety of scenarios, the combined impact of recall error and selection bias on the estimated ORs was complex. These simulations are useful for the interpretation of previous case-control studies on brain tumor and mobile phone use in adults as well as for the interpretation of future studies on adolescents.

Preschool children's eye-movements during pictorial recall

We investigated eye-movements during preschool children’s pictorial recall of seen objects. Thirteen 3- to 4-year-old children completed a perceptual encoding and a pictorial recall task. First, they were exposed to 16 pictorial objects, which were positioned in one of four distinct areas on the computer screen. Subsequently, they had to recall these pictorial objects from memory in order to respond to specific questions about visual details. We found that children spent more time fixating the areas in which the pictorial objects were previously displayed.We conclude that as early as age 3–4 years old, children show specific eye-movements when they recall pictorial contents of previously seen objects.

QTc interval and resting heart rate as long-term predictors of mortality in type 1 and type 2 diabetes mellitus: a 23-year follow-up

We evaluated the association of QT interval corrected for heart rate (QT(c)) and resting heart rate (rHR) with mortality (all-causes, cardiovascular, cardiac, and ischaemic heart disease) in subjects with type 1 and type 2 diabetes.

Comparisons of bacterial patterns present at implant and tooth sites in subjects on supportive periodontal therapy. I. Impact of clinical variables, gender and smoking

OBJECTIVE: (I) To compare the oral microflora at implant and tooth sites in subjects participating in a periodontal recall program, (II) to test whether the microflora at implant and tooth sites differ as an effect of gingival bleeding (bleeding on probing (BOP)), or pocket probing depth (PPD), and (III) to test whether smoking and gender had an impact on the microflora. MATERIAL AND METHODS: Data were collected from 127 implants and all teeth in 56 subjects. Microbiological data were identified by the DNA-DNA checkerboard hybridization. RESULTS: PPD> or =4 mm were found in 16.9% of tooth, and at 26.6% of implant sites (P<0.01). Tooth sites with PPD> or =4 mm had a 3.1-fold higher bacterial load than implant sites (mean difference: 66%, 95% confidence interval (CI): 40.7-91.3, P<0.001). No differences were found for the red, orange, green, and yellow complexes. A higher total bacterial load was found at implant sites with PPD> or =4 mm (mean difference 35.7 x 10(5), 95% CI: 5.2 (10(5)) to 66.1 (10(5)), P<0.02 with equal variance not assumed). At implant sites, BOP had no impact on bacterial load but influenced the load at tooth sites (P<0.01). CONCLUSION: BOP, and smoking had no impact on bacteria at implant sites but influenced the bacterial load at tooth sites. Tooth sites harbored more bacteria than implant sites with comparable PPD. The 4 mm PPD cutoff level influenced the distribution and amounts of bacterial loads. The subject factor is explanatory to bacterial load at both tooth and implant sites.

Estimation with Interval Censored Data in Longitudinal Studies

In biostatistical applications interest often focuses on the estimation of the distribution of a time-until-event variable T. If one observes whether or not T exceeds an observed monitoring time at a random number of monitoring times, then the data structure is called interval censored data. We extend this data structure by allowing the presence of a possibly time-dependent covariate process that is observed until end of follow up. If one only assumes that the censoring mechanism satisfies coarsening at random, then, by the curve of dimensionality, typically no regular estimators will exist. To fight the curse of dimensionality we follow the approach of Robins and Rotnitzky (1992) by modeling parameters of the censoring mechanism. We model the right-censoring mechanism by modeling the hazard of the follow up time, conditional on T and the covariate process. For the monitoring mechanism we avoid modeling the joint distribution of the monitoring times by only modeling a univariate hazard of the pooled monitoring times, conditional on the follow up time, T, and the covariates process, which can be estimated by treating the pooled sample of monitoring times as i.i.d. In particular, it is assumed that the monitoring times and the right-censoring times only depend on T through the observed covariate process. We introduce inverse probability of censoring weighted (IPCW) estimator of the distribution of T and of smooth functionals thereof which are guaranteed to be consistent and asymptotically normal if we have available correctly specified semiparametric models for the two hazards of the censoring process. Furthermore, given such correctly specified models for these hazards of the censoring process, we propose a one-step estimator which will improve on the IPCW estimator if we correctly specify a lower-dimensional working model for the conditional distribution of T, given the covariate process, that remains consistent and asymptotically normal if this latter working model is misspecified. It is shown that the one-step estimator is efficient if each subject is at most monitored once and the working model contains the truth. In general, it is shown that the one-step estimator optimally uses the surrogate information if the working model contains the truth. It is not optimal in using the interval information provided by the current status indicators at the monitoring times, but simulations in Peterson, van der Laan (1997) show that the efficiency loss is small.

Smooth Hazard Function Estimation for Interval Censored Data with Time Varying Covariates

In this paper we propose methods for smooth hazard estimation of a time variable where that variable is interval censored. These methods allow one to model the transformed hazard in terms of either smooth (smoothing splines) or linear functions of time and other relevant time varying predictor variables. We illustrate the use of this method on a dataset of hemophiliacs where the outcome, time to seroconversion for HIV, is interval censored and left-truncated.

The Two-Interval Line-Segment Problem

In this paper, the NPMLE in the one-dimensional line segment problem is defined and studied, where line segments on the real line through two non-overlapping intervals are observed. The self-consistency equations for the NPMLE are defined and a quick algorithm for solving them is provided. Supnorm weak convergence to a Gaussian process and efficiency of the NPMLE is proved. The problem has a strong geological application in the study of the lifespan of species.

On the Accelerated Failure Time Model for Current Status and Interval Censored Data

This paper introduces a novel approach to making inference about the regression parameters in the accelerated failure time (AFT) model for current status and interval censored data. The estimator is constructed by inverting a Wald type test for testing a null proportional hazards model. A numerically efficient Markov chain Monte Carlo (MCMC) based resampling method is proposed to simultaneously obtain the point estimator and a consistent estimator of its variance-covariance matrix. We illustrate our approach with interval censored data sets from two clinical studies. Extensive numerical studies are conducted to evaluate the finite sample performance of the new estimators.