Prognostic score for predicting risk of dementia over 10 years while accounting for competing risk of death

Early detection of subjects at high risk of developing dementia is essential. By dealing with censoring and competing risk of death, we developed a score for predicting 10-year dementia risk by combining cognitive tests, and we assessed whether inclusion of cognitive change over the previous year increased its discrimination. Data came from the French prospective cohort study Personnes Agées QUID (PAQUID) and included 3,777 subjects aged 65 years or older (1988-1998). The combined prediction score was estimated by means of an illness-death model handling interval censoring and competing risk of death. Its predictive ability was measured using the receiver operating characteristic (ROC) curve, with 2 different definitions depending on the way subjects who died without a dementia diagnosis were considered. To account for right-censoring and interval censoring, we estimated the ROC curves by means of a weighting approach and a model-based imputation estimator. The combined score exhibited an area under the ROC curve (AUROC) of 0.81 for discriminating future demented subjects from subjects alive and nondemented 10 years later and an AUROC of 0.75 for discriminating future demented subjects from all other subjects (including deceased persons). Adjustment for cognitive change over the previous year did not improve prediction.

Receiver operating characteristic curve estimation for time to event with semicompeting risks and interval censoring

Semicompeting risks and interval censoring are frequent in medical studies, for instance when a disease may be diagnosed only at times of visit and disease onset is in competition with death. To evaluate the ability of markers to predict disease onset in this context, estimators of discrimination measures must account for these two issues. In recent years, methods for estimating the time-dependent receiver operating characteristic curve and the associated area under the ROC curve have been extended to account for right censored data and competing risks. In this paper, we show how an approximation allows to use the inverse probability of censoring weighting estimator for semicompeting events with interval censored data. Then, using an illness-death model, we propose two model-based estimators allowing to rigorously handle these issues. The first estimator is fully model based whereas the second one only uses the model to impute missing observations due to censoring. A simulation study shows that the bias for inverse probability of censoring weighting remains modest and may be less than the one of the two parametric estimators when the model is misspecified. We finally recommend the nonparametric inverse probability of censoring weighting estimator as main analysis and the imputation estimator based on the illness-death model as sensitivity analysis.

Disease-specific adverse events following nonlive vaccines: a paradoxical placebo effect or a nocebo phenomenon?

Vaccines can cause adverse reactions (AR), i.e. adverse events following immunization (AEFIs) due to the vaccine, such as local reactions or fever. In addition, live attenuated vaccines which replicate in vaccinees can cause disease-specific AR, e.g. measles-like rash following measles vaccination. However, nonlive vaccines because they are inactivated and they do not replicate in vaccinees, are not likely to cause disease-specific AR. The aim of the study was to assess whether safety signals could be generated by an undescribed bias in spontaneous reporting of disease-specific AEFIs with nonlive vaccines. All AEFIs of Sanofi Pasteur MSD vaccines spontaneously reported in France from January 2000 to June 2010, coded according to MedDRA terms and collected in the company's pharmacovigilance database were analyzed. Vaccine-event pairs of interest were selected a priori. The disproportionality reporting rate methodology was used, comparing the proportion of a given event reported following a given vaccine to its proportion reported following all other studied vaccines. The Reporting Odds Ratio (ROR) was used for signals detection for each vaccine-event pair selected. A total of 33,275 AEFIs were analyzed. The calculated ROR showed a statistically disproportionate reporting rate and generated false safety signals for almost all the pairs tested. Three nonlive vaccine pairs were striking: gynaecological symptoms and the quadrivalent human papillomavirus (qHPV) vaccine; trismus and tetanus vaccines; hepatobiliary disorders and hepatitis B vaccines. In conclusion we have identified a new vaccine AE spontaneous reporting bias: "disease-specific adverse events following nonlive vaccines", showing that vaccinees and healthcare professionals tend to report preferentially the symptoms of the disease against which the nonlive vaccine was administered. We suggest that bias is subordinate to a paradoxical placebo effect and/or a nocebo phenomenon.