Covariates used in the analysis
Power Analysis of Longitudinal Data with Time-Dependent Covariates Using Generalized Method of Moments
... data analysis; hence, the methodology could be adopted by researchers in different fields when using real data with unexpected behavior over ...the covariates used in the simulation study made it ...
167
Propensity score analysis with partially observed covariates: How should multiple imputation be used?
... some covariates are still unbalanced even after ...binary covariates, large standardized differences can occur even for slight imbalance when the prevalence is ...observed covariates were not strong ...
43
Propensity score analysis with partially observed covariates: How should multiple imputation be used?
... on covariates: covariates are imputed and a PS analysis is performed in each imputed dataset to estimate the treatment ...PS analysis: (i) should we apply Rubin’s rules to the IPTW treatment ...
43
Accommodating Covariates in ROC Analysis
... The final step is to plot and compare the ROC curves for the linear predictions from the two models. This can be accomplished using the programs roccurve and comproc. This procedure is simplistic in at least two ...
25
Choosing covariates in the analysis of cluster randomised trials.
... In any simulation it is important to choose appropriate parameter values and variable distributions, except where trial data is sampled. Multiple values of a parameter are typically investigated in any one simulation ...
304
Multiclass analysis and prediction with network structured covariates
... discriminant analysis (LDA), K-nearest neighbor (KNN), and extreme gradient boosting ...commonly used, and then we briefly review the four classification ...
25
Analysis of kiwifruit orchard financial performance, including covariates
... the analysis has been ...the analysis were the contribution of the management systems to the financial performance of orchards, and the contribution of other factors as ...is used to determine the ...
26
A semiparametric inference to regression analysis with missing covariates in survey data
... Shu Yang and Jae Kwang Kim North Carolina State University and Iowa State University Abstract: Parameter estimation in parametric regression models with missing co- variates is considered under a survey sampling setup. ...
27
A comparison of methods to adjust for continuous covariates in the analysis of randomised trials
... the covariates be pre-specified [33, ...general analysis approach ...which covariates should be kept in the final model, and which covariates should be discarded (usu- ally based on a ...
10
Analysis of Mixed Outcomes: Misclassified Binary Responses and Measurement Error in Covariates
... Indian Institute of Management, Ahmedabad Abstract The focus of this paper is on regression models for mixed binary and continuous outcomes, when the true predictor is measured with error and the binary responses are ...
25
Bayesian Survival Analysis without Covariates Using Optimization and Simulation Tools
... The analysis of survival data is a major focus of the statistics ...the analysis of survival data using two parametric models, namely Weibull and ...are used quite effectively to analyze skewed data ...
10
Survival Analysis with High-Dimensional c\Covariates, with Applications to Cancer Genomics
... we used the methods above we would still need to choose a false positive rate or false discovery rate, but so far it is not clear what choices would give optimal ...of covariates, fit the regularized ...
112
Improving upon the efficiency of complete case analysis when covariates are MNAR.
... observed covariates and ...be used since this model only involves fully observed ...given covariates, CCA and our proposed augmented CCA estimator are both consistent, provided the missingness model ...
13
Recurrent Events Analysis in the Presence of Time Dependent Covariates and Dependent Censoring
... 2 Methods In this section, we will address the estimation of the regression parameters in the full data model (4) based on the observed data. We will firstly review the estimation problem based on the full data. ...
26
Dealing with partially observed covariates in propensity score analysis of observational data
... Primary analysis: exposure defined as time exposed to a single class of antihypertensive Our primary analysis will compare the incidence of AKI in the five exposure ...be used to account for ...
239
Bayesian Imputation of Missing Covariates
... commonly used (base) R functions. By modelling the analysis model of interest jointly with the incomplete covariates, analysis and imputation can be performed simultaneously while assuring ...
265
Sensitivity models for missing covariates in the analysis of survival data from multiple surveys
... case analysis; a full likelihood model in which the covariates are assumed to be missing at random and in which each of the binary predictor variables are mod- elled as independent Bernoulli random ...
27
Empirical Analysis of Multiple Infrastructural Covariates: An Application of Gravity Model on Asian Economies
... We used the econometric techniques’ two-stage sample selection model to correct several issues such as zero trade and heterogeneity, the two-stage model to focus on sample standard selection bias which is ...
19
Multiple imputation for IPD meta-analysis: allowing for heterogeneity and studies with missing covariates.
... model used. Using the correctly specified meta-analysis model, that is, fixed effect IVW for scenario 3 and random effects IVW for scenario (10), we present the results of the estimation of 𝛽 2 with ...
18
Bayesian analysis of a time series of counts with covariates: an application to the control of an infectious disease
... In this section we examine a data set that was obtained from Morton et al. (1999). The Klebsiella data set consists of the monthly number of cases of ESBL-producing K.pneumoniae and the number of grams of ...
12