Bootstrapping pairs in Distance-Based Regression

La regressió basada en distàncies és un mètode de predicció que consisteix en dos passos: a partir de les distàncies entre observacions obtenim les variables latents, les quals passen a ser els regressors en un model lineal de mínims quadrats ordinaris. Les distàncies les calculem a partir dels predictors originals fent us d'una funció de dissimilaritats adequada. Donat que, en general, els regressors estan relacionats de manera no lineal amb la resposta, la seva selecció amb el test F usual no és possible. En aquest treball proposem una solució a aquest problema de selecció de predictors definint tests estadístics generalitzats i adaptant un mètode de bootstrap no paramètric per a l'estimació dels p-valors. Incluim un exemple numèric amb dades de l'assegurança d'automòbils.

A Mendelian randomization approach to assess the causal relationship of gamma-glutamyl transferase with blood pressure and insulin

Background: Elevated levels of g-glutamyl transferase (GGT) have been associated with subsequent risk of elevated blood pressure (BP), hypertension and diabetes. However, the causality of these relationships has not been addressed. Mendelian randomization refers to the random allocation of alleles at the time of gamete formation. Such allocation is expected to be independent of any behavioural and environmental factors (known or unknown), allowing the analysis of largely unconfounded risk associations that are not due to reverse causation. Methods: We performed a cross-sectional analysis among 4361 participants to the population based CoLaus study. Associations of sex-specific GGT quartiles with systolic BP, diastolic BP and insulin levels were assessed using multivariable linear regression analyses. The rs2017869 GGT1 variant, which explained 1.6% of the variance in GGT levels, was used as an instrument to perform a Mendelian randomization analysis. Results: Median age of the study population was 53 years. After age and sex adjustment, GGT quartiles were strongly associated with systolic and diastolic BP (all p for linear trend <0.0001). After multivariable adjustment, these relationships were significantly attenuated, but remained significant for systolic (b(95%CI)¼1.30 (0.32;2.03), p¼0.007) and diastolic BP (b (95%CI)¼0.57 (0.02;1.13), p¼0.04). Using Mendelian randomization, we observed no positive association of GGT with either systolic BP (b (95%CI)¼-5.68 (-11.51-0.16), p¼0.06) or diastolic BP (b (95%CI)¼ -2.24 (-5.98;1.49) p¼0.24). The association of GGT with insulin was also attenuated after multivariable adjustment. Nevertheless, a strong linear trend persisted in the fully adjusted model (b (95%CI)¼0.07 (0.04;0.09), p<0.0001). Using Mendelian randomization, we observed a similar positive association of GGT with insulin (b (95%CI)¼0.19 (0.01-0.37), p¼0.04). Conclusion: In this study, we found evidence for a direct causal relationship between GGT and insulin, suggesting that oxidative stress may be causally implicated in the pathogenesis of type 2 diabetes mellitus.

Probe-specific mixed-model approach to detect copy number differences using multiplex ligation-dependent probe amplification (MLPA)

Background: MLPA method is a potentially useful semi-quantitative method to detect copy number alterations in targeted regions. In this paper, we propose a method for the normalization procedure based on a non-linear mixed-model, as well as a new approach for determining the statistical significance of altered probes based on linear mixed-model. This method establishes a threshold by using different tolerance intervals that accommodates the specific random error variability observed in each test sample.Results: Through simulation studies we have shown that our proposed method outperforms two existing methods that are based on simple threshold rules or iterative regression. We have illustrated the method using a controlled MLPA assay in which targeted regions are variable in copy number in individuals suffering from different disorders such as Prader-Willi, DiGeorge or Autism showing the best performace.Conclusion: Using the proposed mixed-model, we are able to determine thresholds to decide whether a region is altered. These threholds are specific for each individual, incorporating experimental variability, resulting in improved sensitivity and specificity as the examples with real data have revealed.

Robust accelerated failure time regression

Robust estimators for accelerated failure time models with asymmetric (or symmetric) error distribution and censored observations are proposed. It is assumed that the error model belongs to a log-location-scale family of distributions and that the mean response is the parameter of interest. Since scale is a main component of mean, scale is not treated as a nuisance parameter. A three steps procedure is proposed. In the first step, an initial high breakdown point S estimate is computed. In the second step, observations that are unlikely under the estimated model are rejected or down weighted. Finally, a weighted maximum likelihood estimate is computed. To define the estimates, functions of censored residuals are replaced by their estimated conditional expectation given that the response is larger than the observed censored value. The rejection rule in the second step is based on an adaptive cut-off that, asymptotically, does not reject any observation when the data are generat ed according to the model. Therefore, the final estimate attains full efficiency at the model, with respect to the maximum likelihood estimate, while maintaining the breakdown point of the initial estimator. Asymptotic results are provided. The new procedure is evaluated with the help of Monte Carlo simulations. Two examples with real data are discussed.

Stochastic differential equations with random coefficients

In this paper we establish the existence and uniqueness of a solution for different types of stochastic differential equation with random initial conditions and random coefficients. The stochastic integral is interpreted as a generalized Stratonovich integral, and the techniques used to derive these results are mainly based on the path properties of the Brownian motion, and the definition of the Stratonovich integral.

Blocking hypoxia-induced autophagy in tumors restores cytotoxic T-cell activity and promotes regression.

The relationship between hypoxic stress, autophagy, and specific cell-mediated cytotoxicity remains unknown. This study shows that hypoxia-induced resistance of lung tumor to cytolytic T lymphocyte (CTL)-mediated lysis is associated with autophagy induction in target cells. In turn, this correlates with STAT3 phosphorylation on tyrosine 705 residue (pSTAT3) and HIF-1α accumulation. Inhibition of autophagy by siRNA targeting of either beclin1 or Atg5 resulted in impairment of pSTAT3 and restoration of hypoxic tumor cell susceptibility to CTL-mediated lysis. Furthermore, inhibition of pSTAT3 in hypoxic Atg5 or beclin1-targeted tumor cells was found to be associated with the inhibition Src kinase (pSrc). Autophagy-induced pSTAT3 and pSrc regulation seemed to involve the ubiquitin proteasome system and p62/SQSTM1. In vivo experiments using B16-F10 melanoma tumor cells indicated that depletion of beclin1 resulted in an inhibition of B16-F10 tumor growth and increased tumor apoptosis. Moreover, in vivo inhibition of autophagy by hydroxychloroquine in B16-F10 tumor-bearing mice and mice vaccinated with tyrosinase-related protein-2 peptide dramatically increased tumor growth inhibition. Collectively, this study establishes a novel functional link between hypoxia-induced autophagy and the regulation of antigen-specific T-cell lysis and points to a major role of autophagy in the control of in vivo tumor growth.

Hyponatremia and short-term outcomes in patients with acute pulmonary embolism

BACKGROUND: Hyponatremia, a marker of neurohormonal activation, is associated with poor outcomes in acute cardiorespiratory diseases such as myocardial infarction, right and left ventricular heart failure, and pneumonia. The prognostic value of hyponatremia in patients with acute pulmonary embolism (PE) is unknown. We sought to assess whether hyponatremia at presentation was associated with mortality and hospital readmission in patients hospitalized with PE. METHODS: We studied patient discharges with a primary diagnosis of PE from 185 acute care hospitals in Pennsylvania (1/2000-11/2002). We defined hyponatremia as a serum sodium level ≤135 mmol/l, measured at the time of patient presentation. The study outcomes were 30-day all-cause mortality and hospital readmission. We used random-intercept logistic regression to examine the association between hyponatremia and mortality. We adjusted for baseline patient (race, insurance, severity of illness using the Pulmonary Embolism Severity Index) and hospital characteristics (region, hospital size and teaching status). We used the same approach to examine the association between hyponatremia and readmission among patients who were discharged alive. RESULTS: Among 13,728 patient discharges with PE, 2907 (21.1%) had hyponatremia at the time of presentation. Patients with hyponatremia were older (P<0.001) and more likely to have a history of cancer (P<0.001), heart failure (P<0.001), or chronic lung disease (P=0.002) than patients without hyponatremia. Patients with hyponatremia had a higher unadjusted cumulative 30-day mortality (15.2% vs 8.0%;P<0.001) and readmission rate (15.9% vs 11.8%; P< 0.001) than patients without hyponatremia (Figure). After adjustment for race, insurance, severity of illness, and hospital factors, hyponatremia was associated with a significantly greater odds of death (OR 1.71, 95% CI: 1.50-1.95) and hospital readmission (OR 1.29, 95% CI: 1.14-1.46). CONCLUSIONS: In this large, statewide sample of unselected patients with acute PE, hyponatremia was relatively common and was an independent predictor of short-term mortality and hospital readmission. Given that sodium is a low-cost, easily available laboratory parameter, it may be potentially useful in risk-stratifying patients with PE.

Linear random knots and their scaling behavior

We present here a nonbiased probabilistic method that allows us to consistently analyze knottedness of linear random walks with up to several hundred noncorrelated steps. The method consists of analyzing the spectrum of knots formed by multiple closures of the same open walk through random points on a sphere enclosing the walk. Knottedness of individual "frozen" configurations of linear chains is therefore defined by a characteristic spectrum of realizable knots. We show that in the great majority of cases this method clearly defines the dominant knot type of a walk, i.e., the strongest component of the spectrum. In such cases, direct end-to-end closure creates a knot that usually coincides with the knot type that dominates the random closure spectrum. Interestingly, in a very small proportion of linear random walks, the knot type is not clearly defined. Such walks can be considered as residing in a border zone of the configuration space of two or more knot types. We also characterize the scaling behavior of linear random knots.

Exposure to bioaerosols, respiratory health and lung-specific proteins: a prospective study in garbage and wastewater workers.

Objectives To prospectively assess respiratory health in wastewater workers and garbage collectors over 5 years. Methods Exposure, respiratory symptoms and conditions, spirometry and lung-specific proteins were assessed yearly in a cohort of 304 controls, 247 wastewater workers and 52 garbage collectors. Results were analysed with random coefficient models and linear regression taking into account several potential confounders. Results Symptoms, spirometry and lung-specific proteins were not affected by occupational exposure. Conclusions In this population no effects of occupational exposure to bioaerosols were found, probably because of good working conditions.

Looking for Validity or Testing It? The Perils of Stepwise Regression, Extreme-Scores Analysis, Heteroscedasticity, and Measurement Error

When researchers introduce a new test they have to demonstrate that it is valid, using unbiased designs and suitable statistical procedures. In this article we use Monte Carlo analyses to highlight how incorrect statistical procedures (i.e., stepwise regression, extreme scores analyses) or ignoring regression assumptions (e.g., heteroscedasticity) contribute to wrong validity estimates. Beyond these demonstrations, and as an example, we re-examined the results reported by Warwick, Nettelbeck, and Ward (2010) concerning the validity of the Ability Emotional Intelligence Measure (AEIM). Warwick et al. used the wrong statistical procedures to conclude that the AEIM was incrementally valid beyond intelligence and personality traits in predicting various outcomes. In our re-analysis, we found that the reliability-corrected multiple correlation of their measures with personality and intelligence was up to .69. Using robust statistical procedures and appropriate controls, we also found that the AEIM did not predict incremental variance in GPA, stress, loneliness, or well-being, demonstrating the importance for testing validity instead of looking for it.