Update of the risk assessment of inorganic arsenic in food.

The European Commission asked EFSA to update its 2009 risk assessment on arsenic in food carrying out a hazard assessment of inorganic arsenic (iAs) and using the revised exposure assessment issued by EFSA in 2021. Epidemiological studies show that the chronic intake of iAs via diet and/or drinking water is associated with increased risk of several adverse outcomes including cancers of the skin, bladder and lung. The CONTAM Panel used the benchmark dose lower confidence limit based on a benchmark response (BMR) of 5% (relative increase of the background incidence after adjustment for confounders, BMDL05) of 0.06 µg iAs/kg bw per day obtained from a study on skin cancer as a Reference Point (RP). Inorganic As is a genotoxic carcinogen with additional epigenetic effects and the CONTAM Panel applied a margin of exposure (MOE) approach for the risk characterisation. In adults, the MOEs are low (range between 2 and 0.4 for mean consumers and between 0.9 and 0.2 at the 95th percentile exposure, respectively) and as such raise a health concern despite the uncertainties.

Alternative methods to evaluate trial level surrogacy.

BACKGROUND: The evaluation and validation of surrogate endpoints have been extensively studied in the last decade. Prentice [1] and Freedman, Graubard and Schatzkin [2] laid the foundations for the evaluation of surrogate endpoints in randomized clinical trials. Later, Buyse et al. [5] proposed a meta-analytic methodology, producing different methods for different settings, which was further studied by Alonso and Molenberghs [9], in their unifying approach based on information theory. PURPOSE: In this article, we focus our attention on the trial-level surrogacy and propose alternative procedures to evaluate such surrogacy measure, which do not pre-specify the type of association. A promising correction based on cross-validation is investigated. As well as the construction of confidence intervals for this measure. METHODS: In order to avoid making assumption about the type of relationship between the treatment effects and its distribution, a collection of alternative methods, based on regression trees, bagging, random forests, and support vector machines, combined with bootstrap-based confidence interval and, should one wish, in conjunction with a cross-validation based correction, will be proposed and applied. We apply the various strategies to data from three clinical studies: in opthalmology, in advanced colorectal cancer, and in schizophrenia. RESULTS: The results obtained for the three case studies are compared; they indicate that using random forest or bagging models produces larger estimated values for the surrogacy measure, which are in general stabler and the confidence interval narrower than linear regression and support vector regression. For the advanced colorectal cancer studies, we even found the trial-level surrogacy is considerably different from what has been reported. LIMITATIONS: In general the alternative methods are more computationally demanding, and specially the calculation of the confidence intervals, require more computational time that the delta-method counterpart. CONCLUSIONS: First, more flexible modeling techniques can be used, allowing for other type of association. Second, when no cross-validation-based correction is applied, overly optimistic trial-level surrogacy estimates will be found, thus cross-validation is highly recommendable. Third, the use of the delta method to calculate confidence intervals is not recommendable since it makes assumptions valid only in very large samples. It may also produce range-violating limits. We therefore recommend alternatives: bootstrap methods in general. Also, the information-theoretic approach produces comparable results with the bagging and random forest approaches, when cross-validation correction is applied. It is also important to observe that, even for the case in which the linear model might be a good option too, bagging methods perform well too, and their confidence intervals were more narrow.

Reintroduction of oxaliplatin is associated with improved survival in advanced colorectal cancer.

PURPOSE: In the OPTIMOX1 trial, previously untreated patients with advanced colorectal cancer were randomly assigned to two different schedules of leucovorin, fluorouracil, and oxaliplatin that were administered until progression in the control arm or in a stop-and-go fashion in the experimental arm. The randomly assigned treatment groups did not differ significantly in terms of response rate, progression-free survival, and overall survival (OS). However, the impact of oxaliplatin reintroduction on OS was potentially masked by the fact that a large number of patients did not receive the planned oxaliplatin reintroduction or received oxaliplatin after second-line therapy in both treatment groups. PATIENTS AND METHODS: A Cox model was fitted with all significant baseline factors plus time-dependent variables reflecting tumor progression, reintroduction of oxaliplatin, and use of second-line irinotecan. A shared frailty model was fitted with all significant baseline factors plus the number of lines of chemotherapy received by the patient and the percentage of patients with oxaliplatin reintroduction in the center. An adjusted hazard ratio (HR) was calculated for three reintroduction classes (1% to 20%, 21% to 40%, and > 40%), using centers with no reintroduction (0%) as the reference group. RESULTS: Oxaliplatin reintroduction had an independent and significant impact on OS (HR = 0.56, P = .009). The percentage of patients with oxaliplatin reintroductions also had a significant impact on OS. Centers in which more than 40% of the patients were reintroduced had an adjusted HR for OS of 0.59 compared with centers in which no patient was reintroduced. CONCLUSION: Oxaliplatin reintroduction is associated with improved survival in patients with advanced colorectal cancer.

A version of the EM algorithm for proportional hazard model with random effects.

Proportional hazard models with multivariate random effects (frailties) acting multiplicatively on the baseline hazard have recently become a topic of an intensive research. One of the main practical problems related to the models is the estimation of parameters. To this aim, several approaches based on the EM algorithm have been proposed. The major difference between these approaches is the method of the computation of conditional expectations required at the E-step. In this paper an alternative implementation of the EM algorithm is proposed, in which the expected values are computed with the use of the Laplace approximation. The method is computationally less demanding than the approaches developed previously. Its performance is assessed based on a simulation study and compared to a non-EM based estimation approach proposed by Ripatti and Palmgren (2000).