... the analysesof randomised clinical trials. With this study, we developed suggestions on howto test and validate underlying assumptions behind logistic regression, linearregression, and Cox regression when ...
This paper focuses on the problem of testing the null hypothesis that the variance functions of two populations are equal versus one-sided alternatives under a general nonparametric heteroscedastic regression ...
... discriminant analysis, logistic regression, support vector machines, and random forest) in two different scenarios, urban and forest, formed by artificial and natural objects, respectively. The results ...
... EBUS-TBNA. Methylation of p16/INK4a, MGMT, SHOX2, E-cadherin, DLEC1, and RASSF1A was quantified by nested methylation-specific qPCR in 218 EBUS-TBNA lymph node samples. Cross-validated linear regression ...
... unaffected female relatives were genotyped. The 161-SNP PRS was calculated and standardised to 327 population controls (sPRS). Association analysis was performed using a Cox-type random effect regression ...
... on kinship coefficients as analytical weights in a proportional hazard regression on the cause-specific hazards. Confidence intervals (CIs) were obtained by bootstrapping at the family level. Results: ...
... An important application field of SOP is penalised regression estimation of models where multiple quadratic penalties act on the same regression coefficients. We discuss in detail two of those models: ...
... different methods proposed in the literature when correcting for the optimism of the estimated area under the receiver operating characteristic curve in logistic regression models. A simulation study (where ...
... relationships between GWG and offspring bone measures (bone mineral content [BMC], bone areal density [aBMD], size‐corrected BMC [scBMC], and height) at 7 years were fitted through local polynomial regression ...
... regression model was used to identify time associated with Center for Disease Control and Prevention (CDC) categories change. Results: A high proportion of HIV-infected patients (66.7%) had initiated ART ...
... regression analysis carried out at the Cambre Health Center, A Coruña. A sample of 707 patients was included. Anthropometric variables, comorbidity and clinical examination of the knees were assessed. ...
... the results in 327 participants from the same subcohorts. The association of MAT2β-AAb levels with KOA incidence was assessed by combining several robust biostatistics analysis (logistic regression, Receiver ...
In this paper, we consider a general regression model where missing data occur in the response and in the covariates. Our aim is to estimate the marginal distribution function and a marginal functional, ...
Goodness–of–fit tests for quantile regression models, in the presence of missing observations in the response variable, are introduced and analyzed in this paper. The different proposals are based on the ...
... smoothers to estimate a semiparametric additive quantile regression model. We also propose a statistical hypothesis test to determine critical values, i.e., the values of the concentration that are significant ...
... the management of this species. In particular, not enough is known about its growth pattern. Thus, we model the length–weight relationship of this resource for each harvest site. We use regression models ...
... Kaplan-Meier estimation. We assessed the crude, adjusted, and propensity-matched (total matched pairs, n = 4261) hazard ratio (HR) of mortality for CEA vs CAS using Cox regression.
Results
The unadjusted ...
... from the index hospitalization. We used classification and regression tree modeling to inform a multivariable Cox-regression model predicting reintervention after EVR.
Results:
We studied 12,911 patients ...
Two‐stage instrumental variable methods are commonly used for estimating average causal effects in the presence of an unmeasured confounder. In the context of the proportional hazard Cox regression models, ...
... Then, we use the result of the previous step as the input of a support vector regression model (SVM), where RUL is the response variable. The validity of the method was checked on an extensive public database, ...
... a new regression model is introduced by considering the proposed
distribution, which is adequate for situations where the response variable is restricted
to a bounded interval, as an alternative to the ...
The problem of testing for the parametric form of the conditional variance is considered
in a fully nonparametric regression model. A test statistic based on a weighted
L2-distance between the empirical ...
A test for the equality of error distributions in two nonparametric regression models is proposed. The test statistic is based on comparing the empirical characteristic functions of the residuals calculated ...
... of life after the surgical intervention was added to the HRQoL questionnaire. Risk factors related to impaired quality of life were identified using univariate comparisons and multivariate regression techniques.
RESULTS: ...
Objective
Use logistic regression to determine a mathematical model which is able to predict injuries in aerobic gymnastics (AG) athletes, according to certain anthropometric characteristics.
Subjects ...
... design was used (n=101; =0.05; precision=10%), studying anthropometric, sociocultural data, Fagerstrom test and the BREAST-Q (c) questionnaire. A logistic regression analysis was performed to identify ...
... regression models. We propose several performance indices to summarize the two-stage prediction procedure and a permutation test to formally assess the added predictive value of a second omic set of predictors ...
... An important application field of SOP is penalised regression estimation of models where multiple quadratic penalties act on the same regression coefficients. We discuss in detail two of those models: ...
... were also associated with the presence of pathogenic mutations in regression and ROC analyses. This signature also discriminates between LMNA (WT) healthy subjects and LMNA (MUT) carriers who are phenotypically ...
In this paper, we study the linear quantile regression model when response data are missing at random. Based on the inverse probability weight method, we establish an estimation equation on quantile regression ...
Based on the inverse probability weight method, we, in this article, construct the empirical likelihood (EL) and penalized empirical likelihood (PEL) ratios of the parameter in the linear quantile regression ...
This article proposes a new general methodology for constructing nonparametric and semiparametric Asymptotically Distribution-Free (ADF) tests for semiparametric hypotheses in regression models for possibly ...
Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are ...
... we focus on an estimator for the covariate-specific receiver-operating characteristic curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class ...
Several procedures have been proposed for testing goodness-of-fit to the error distribution in nonparametric regression models. The null distribution of the associated test statistics is usually approximated ...
... truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to ...
In this paper, we focus on the partially linear varying-coefficient quantile regression model when the data are right censored and the censoring indicator is missing at random. Based on the calibration ...
... different functional regression models to the data and compares the models to test the viability of estimating water content from reflectance. It then applies our methodology to select a small number of ...
... of health traits. We review several approaches for the combination of omic markers in the context of binary outcome prediction, all based on double cross-validation and regularized regression models. We ...
Several procedures have been proposed for testing the equality of error distributions in two or more nonparametric regression models. Here we deal with methods based on comparing estimators of the cumulative ...
... by clinical researchers. This paper focuses on categorising a continuous predictor within a logistic regression model, in such a way that the best discriminative ability is obtained in terms of the highest ...
... and regression trees (CARTs) were used to develop an algorithm for allocating patients to these subgroups. This algorithm was tested in 3651 patients from the COPD Cohorts Collaborative International Assessment ...
... Matrix Metalloproteinases −1, −7, −9, CEA and CYFRA 21.1 were determined in 140 patients with respiratory symptoms (lung cancer and controls with/without benign pathology). Logistic Lasso regression was ...
... hazards regression model. In particular, we compared the c-index and the concordance probability estimator. We evaluated the empirical performance of both estimators through simulations. To categorise ...
We present the R npregfast package via some applications involved with the study of living organisms. The package implements nonparametric estimation procedures in regression models with or without factor-by-curve ...
... as resistance indicators. Univariate linear regression analyses indicated that quantitation of cisplatin-induced G–G intra-strand adducts, measured 1 h after treatment, was the best predictor for viability ...
... with cSCC. Cumulative/dynamic receiver operating characteristic curve was used to determine the optimal PD-L1 threshold. Kaplan-Meier estimators and Cox proportional hazards regression models were also ...