When epidemiological databases inform injury mechanisms: biomechanical analysis of injury associations.

BACKGROUND: Vehicle accidents are still a heavy social burden despite improvements due the latest technologies and policies. To pursue the trend of decrease, having a more detailed view and understanding of the injury patterns would contribute to inform both the rescue team to optimize victim's management and policymakers in order for them to tackle at best this issue. METHODS: Two complementary analyses of injury associations were performed, one using a biomechanical classification and the other an anatomic one, computed on data stratified by car accident type (lateral or frontal). Our objective is to understand whether these two categories of crash lead to similar or heterogeneous injury association patterns, and analyze these findings from an impact mechanics point of view. Indeed, having an improved understanding of the injury mechanisms would facilitate their diagnosis and prevention. RESULTS: While each type of accident possesses its own injury profile, most injury associations are found for both types. Injuries such as clavicle and rib fractures were identified as involved in a high number of associations. Several associations between fractures and blood vessel injuries were found. CONCLUSIONS: The results suggests three main conclusions: (i) Injury associations are rather independent from crash characteristics, (ii) Clavicle and rib fractures are typical of poly-traumatized victims, (iii) Certain fractures can be used to early detect victims at higher risk of hemorrhage. Overall, this study provide paramedics and doctors with data to orientate them toward a faster and more appropriate decision. Moreover, this exploratory work revealed the potential that injury association analyses have to inform policy-making and issue recommendations to decrease road accident mortality and morbidity.

On the use of cross-validation for the calibration of the adaptive lasso.

Cross-validation is the standard method for hyperparameter tuning, or calibration, of machine learning algorithms. The adaptive lasso is a popular class of penalized approaches based on weighted L1 -norm penalties, with weights derived from an initial estimate of the model parameter. Although it violates the paramount principle of cross-validation, according to which no information from the hold-out test set should be used when constructing the model on the training set, a "naive" cross-validation scheme is often implemented for the calibration of the adaptive lasso. The unsuitability of this naive cross-validation scheme in this context has not been well documented in the literature. In this work, we recall why the naive scheme is theoretically unsuitable and how proper cross-validation should be implemented in this particular context. Using both synthetic and real-world examples and considering several versions of the adaptive lasso, we illustrate the flaws of the naive scheme in practice. In particular, we show that it can lead to the selection of adaptive lasso estimates that perform substantially worse than those selected via a proper scheme in terms of both support recovery and prediction error. In other words, our results show that the theoretical unsuitability of the naive scheme translates into suboptimality in practice, and call for abandoning it.

Sparse estimation for case-control studies with multiple disease subtypes.

The analysis of case-control studies with several disease subtypes is increasingly common, e.g. in cancer epidemiology. For matched designs, a natural strategy is based on a stratified conditional logistic regression model. Then, to account for the potential homogeneity among disease subtypes, we adapt the ideas of data shared lasso, which has been recently proposed for the estimation of stratified regression models. For unmatched designs, we compare two standard methods based on $L_1$-norm penalized multinomial logistic regression. We describe formal connections between these two approaches, from which practical guidance can be derived. We show that one of these approaches, which is based on a symmetric formulation of the multinomial logistic regression model, actually reduces to a data shared lasso version of the other. Consequently, the relative performance of the two approaches critically depends on the level of homogeneity that exists among disease subtypes: more precisely, when homogeneity is moderate to high, the non-symmetric formulation with controls as the reference is not recommended. Empirical results obtained from synthetic data are presented, which confirm the benefit of properly accounting for potential homogeneity under both matched and unmatched designs, in terms of estimation and prediction accuracy, variable selection and identification of heterogeneities. We also present preliminary results from the analysis of a case-control study nested within the EPIC (European Prospective Investigation into Cancer and nutrition) cohort, where the objective is to identify metabolites associated with the occurrence of subtypes of breast cancer.

Structure estimation of binary graphical models on stratified data: Application to the description of injury tables for victims of road accidents.

Graphical models are used in many applications such as medical diagnostics and computer security. Increasingly often, the estimation of such models has to be performed on several predefined strata of the whole population. For instance, in epidemiology and clinical research, strata are often defined according to age, gender, treatment, or disease type. In this article, we propose new approaches dedicated to the estimation of binary graphical models on such strata. These approaches are implemented by combining well-known methods that have been developed in the context of a single binary graphical model, with penalties encouraging structured sparsity, which have recently been shown to be appropriate when dealing with stratified data. Empirical comparisons on synthetic data highlight that our approaches generally outperform its competitors. We present an application of the approach to study associations among the injuries suffered by victims of road accidents according to road user type.