Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models.

BACKGROUND: We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. METHODS: Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i) linear regression; (ii) logistic classification; (iii) regression trees; (iv) classification trees (iii and iv are collectively known as "CART"). Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. RESULTS: Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60-80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. CONCLUSIONS: There were no striking differences between either the algebraic (i, ii) vs. non-algebraic (iii, iv), or the regression (i, iii) vs. classification (ii, iv) modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables.

Subjective health assessments and active labor market participation of older men: evidence from a semiparametric binary choice model with nonadditive correlated individual-specific effects

We use panel data from the U. S. Health and Retirement Study, 1992-2002, to estimate the effect of self-assessed health limitations on the active labor market participation of older men. Self-assessments of health are likely to be endogenous to labor supply due to justification bias and individual-specific heterogeneity in subjective evaluations. We address both concerns. We propose a semiparametric binary choice procedure that incorporates nonadditive correlated individual-specific effects. Our estimation strategy identifies and estimates the average partial effects of health and functioning on labor market participation. The results indicate that poor health plays a major role in labor market exit decisions.

A mixed approach for proving non-inferiority in clinical trials with binary endpoints

When a new treatment is compared to an established one in a randomized clinical trial, it is standard practice to statistically test for non-inferiority rather than for superiority. When the endpoint is binary, one usually compares two treatments using either an odds-ratio or a difference of proportions. In this paper, we propose a mixed approach which uses both concepts. One first defines the non-inferiority margin using an odds-ratio and one ultimately proves non-inferiority statistically using a difference of proportions. The mixed approach is shown to be more powerful than the conventional odds-ratio approach when the efficacy of the established treatment is known (with good precision) and high (e.g. with more than 56% of success). The gain of power achieved may lead in turn to a substantial reduction in the sample size needed to prove non-inferiority. The mixed approach can be generalized to ordinal endpoints.

Persuasion, binary choice, and the costs of dishonesty

We study the strategic interaction between a decision maker who needs to take a binary decision but is uncertain about relevant facts and an informed expert who can send a message to the decision maker but has a preference over the decision.We show that the probability that the expert can persuade the decision maker to take the expert's preferred decision is a hump-shaped function of his costs of sending dishonest messages.

A comparison of three methods of Mendelian randomization when the genetic instrument, the risk factor and the outcome are all binary.

The method of instrumental variable (referred to as Mendelian randomization when the instrument is a genetic variant) has been initially developed to infer on a causal effect of a risk factor on some outcome of interest in a linear model. Adapting this method to nonlinear models, however, is known to be problematic. In this paper, we consider the simple case when the genetic instrument, the risk factor, and the outcome are all binary. We compare via simulations the usual two-stages estimate of a causal odds-ratio and its adjusted version with a recently proposed estimate in the context of a clinical trial with noncompliance. In contrast to the former two, we confirm that the latter is (under some conditions) a valid estimate of a causal odds-ratio defined in the subpopulation of compliers, and we propose its use in the context of Mendelian randomization. By analogy with a clinical trial with noncompliance, compliers are those individuals for whom the presence/absence of the risk factor X is determined by the presence/absence of the genetic variant Z (i.e., for whom we would observe X = Z whatever the alleles randomly received at conception). We also recall and illustrate the huge variability of instrumental variable estimates when the instrument is weak (i.e., with a low percentage of compliers, as is typically the case with genetic instruments for which this proportion is frequently smaller than 10%) where the inter-quartile range of our simulated estimates was up to 18 times higher compared to a conventional (e.g., intention-to-treat) approach. We thus conclude that the need to find stronger instruments is probably as important as the need to develop a methodology allowing to consistently estimate a causal odds-ratio.

Disentangling the effects of key innovations on the diversification of Bromelioideae (bromeliaceae).

The evolution of key innovations, novel traits that promote diversification, is often seen as major driver for the unequal distribution of species richness within the tree of life. In this study, we aim to determine the factors underlying the extraordinary radiation of the subfamily Bromelioideae, one of the most diverse clades among the neotropical plant family Bromeliaceae. Based on an extended molecular phylogenetic data set, we examine the effect of two putative key innovations, that is, the Crassulacean acid metabolism (CAM) and the water-impounding tank, on speciation and extinction rates. To this aim, we develop a novel Bayesian implementation of the phylogenetic comparative method, binary state speciation and extinction, which enables hypotheses testing by Bayes factors and accommodates the uncertainty on model selection by Bayesian model averaging. Both CAM and tank habit were found to correlate with increased net diversification, thus fulfilling the criteria for key innovations. Our analyses further revealed that CAM photosynthesis is correlated with a twofold increase in speciation rate, whereas the evolution of the tank had primarily an effect on extinction rates that were found five times lower in tank-forming lineages compared to tank-less clades. These differences are discussed in the light of biogeography, ecology, and past climate change.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Quality of life: a potentially useful measure to indicate subclinical flares in Crohn disease.

Background:  While quality of life (QoL) is a well-recognised outcome measure of Crohn disease (CD) activity, its influence on other outcome measures, including exacerbation of CD is poorly understood. If QoL measures were to be associated with intestinal inflammatory activity, they might be useful for early detection of subclinical flares. Aims:  We hypothesised that low QoL might be associated with subsequent CD flares. Methods:  A cohort of 318 adult CD patients was observed for 1 year after assessment of baseline characteristics. Data were collected in Swiss university hospitals, regional hospitals and private practices. At inclusion, patients completed the Inflammatory Bowel Disease QoL Questionnaire (gastrointestinal QoL; range: 32 to 224 points) and the Short Form-36 Health Survey (general QoL; range: 35 to 145 points). During follow up, flares were recorded. Binary logistic regression was performed to estimate the relation between QoL and the odds of subsequent flares. Results:  A twofold decrease in the odds of flares (99% CI: 1.1; 4.0) per standard deviation of gastrointestinal QoL and a threefold decrease (99% CI: 1.5; 6.2) per standard deviation of general QoL were observed. Conclusions:  The close association between QoL and subsequent flares suggests that QoL measures might be useful in detecting upcoming flares before they become clinically apparent.

Notch1 functions as a tumor suppressor in mouse skin.

Notch proteins are important in binary cell-fate decisions and inhibiting differentiation in many developmental systems, and aberrant Notch signaling is associated with tumorigenesis. The role of Notch signaling in mammalian skin is less well characterized and is mainly based on in vitro studies, which suggest that Notch signaling induces differentiation in mammalian skin. Conventional gene targeting is not applicable to establishing the role of Notch receptors or ligands in the skin because Notch1-/- embryos die during gestation. Therefore, we used a tissue-specific inducible gene-targeting approach to study the physiological role of the Notch1 receptor in the mouse epidermis and the corneal epithelium of adult mice. Unexpectedly, ablation of Notch1 results in epidermal and corneal hyperplasia followed by the development of skin tumors and facilitated chemical-induced skin carcinogenesis. Notch1 deficiency in skin and in primary keratinocytes results in increased and sustained expression of Gli2, causing the development of basal-cell carcinoma-like tumors. Furthermore, Notch1 inactivation in the epidermis results in derepressed beta-catenin signaling in cells that should normally undergo differentiation. Enhanced beta-catenin signaling can be reversed by re-introduction of a dominant active form of the Notch1 receptor. This leads to a reduction in the signaling-competent pool of beta-catenin, indicating that Notch1 can inhibit beta-catenin-mediated signaling. Our results indicate that Notch1 functions as a tumor-suppressor gene in mammalian skin.

Predictors of nonresponse in a questionnaire-based outcome study of vocational rehabilitation patients.

OBJECTIVE: To identify predictors of nonresponse to a self-report study of patients with orthopedic trauma hospitalized for vocational rehabilitation between November 15, 2003, and December 31, 2005. The role of biopsychosocial complexity, assessed using the INTERMED, was of particular interest. DESIGN: Cohort study. Questionnaires with quality of life, sociodemographic, and job-related questions were given to patients at hospitalization and 1 year after discharge. Sociodemographic data, biopsychosocial complexity, and presence of comorbidity were available at hospitalization (baseline) for all eligible patients. Logistic regression models were used to test a number of baseline variables as potential predictors of nonresponse to the questionnaires at each of the 2 time points. SETTING: Rehabilitation clinic. PARTICIPANTS: Patients (N=990) hospitalized for vocational rehabilitation over a period of 2 years. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: Nonresponse to the questionnaires was the binary dependent variable. RESULTS: Patients with high biopsychosocial complexity, foreign native language, or low educational level were less likely to respond at both time points. Younger patients were less likely to respond at 1 year. Those living in a stable partnership were less likely than singles to respond at hospitalization. Sex, psychiatric, and somatic comorbidity and alcoholism were never associated with nonresponse. CONCLUSIONS: We stress the importance of assessing biopsychosocial complexity to predict nonresponse. Furthermore, the factors we found to be predictive of nonresponse are also known to influence treatment outcome and vocational rehabilitation. Therefore, it is important to increase the response rate of the groups of concern in order to reduce selection bias in epidemiologic investigations.

Synchronous versus asynchronous modeling of gene regulatory networks.

MOTIVATION: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. RESULTS: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. AVAILABILITY: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Dynamic simulation of regulatory networks using SQUAD.

BACKGROUND: The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. RESULTS: We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. CONCLUSION: The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.

Measuring an effect size from dichotomized data : contrasted results whether using a correlation or an odds-ratio

It is well known that dichotomizing continuous data has the effect to decrease statistical power when the goal is to test for a statistical association between two variables. Modern researchers however are focusing not only on statistical significance but also on an estimation of the "effect size" (i.e., the strength of association between the variables) to judge whether a significant association is also clinically relevant. In this article, we are interested in the consequences of dichotomizing continuous data on the value of an effect size in some classical settings. It turns out that the conclusions will not be the same whether using a correlation or an odds ratio to summarize the strength of association between the variables: Whereas the value of a correlation is typically decreased by a factor pi/2 after each dichotomization, the value of an odds ratio is at the same time raised to the power 2. From a descriptive statistical point of view, it is thus not clear whether dichotomizing continuous data leads to a decrease or to an increase in the effect size, as illustrated using a data set to investigate the relationship between motor and intellectual functions in children and adolescents

Validation of Logistic Regression Models for Landslide Susceptibility Maps

A wide range of numerical models and tools have been developed over the last decades to support the decision making process in environmental applications, ranging from physical models to a variety of statistically-based methods. In this study, a landslide susceptibility map of a part of Three Gorges Reservoir region of China was produced, employing binary logistic regression analyses. The available information includes the digital elevation model of the region, geological map and different GIS layers including land cover data obtained from satellite imagery. The landslides were observed and documented during the field studies. The validation analysis is exploited to investigate the quality of mapping.