Pairwise comparison models with non-binary outcomes
Pairwise Comparison Estimation of Censored Transformation Models
... which we can rearrange to express as: E[(I[y 1i ≥ y 0j ] − I[y 1j ≥ y 0i ])I[x 0 i β 0 ≥ x 0 j β 0 , x 0 i b < x 0 j b]] (A.19) By the previous lemma, the above expectation is non-negative, and only equal to 0 ...
37
Semiparametric Selection Models with Binary Outcomes
... the non-threshold- crossing designs, we report the median and median absolute deviation (MAD) for the bi- variate probit estimators because there were a number of replications where bivariate probit performed ...
45
Non-linear mixed models in the analysis of mediated longitudinal data with binary outcomes
... link models in ...longitudinal binary data, scaling will also be ...linear models for continuous outcome data, the product of coefficients method is not equivalent to the “ difference of coeffi- ...
10
Logistic random effects regression models: a comparison of statistical packages for binary and ordinal outcomes
... The frequentist approaches differ mainly in the way the integrated likelihood is computed in order to obtain the parameter estimates called maximum likelihood esti- mate (MLE) or restricted maximum likelihood estimate ...
11
Copula regression spline models for binary outcomes
... functions obtained using the other copula models (not reported here but available upon request) were similar. The effects of bmi, income, age and education in the treatment and outcome equations show different ...
41
Copula regression spline models for binary outcomes
... functions obtained using the other copula models (not reported here but available upon request) were similar. The effects of bmi, income, age and education in the treatment and outcome equations show different ...
41
Use of ordinal outcomes in vascular prevention trials: comparison with binary outcomes in published trials
... The conventional approach to analyzing vascular preven- tion trials is to perform time to event analyses, as visualized using Kaplan–Meier curves and analyzed with Cox regres- sion. When the frequency of events is high, ...
28
Comparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcomes: a simulation study
... Missing data may be a serious problem in some CRTs due to the lack of direct contact with individual subjects and lengthy follow-up [2]. The impact of missing data on estimation of the treatment effect and its confidence ...
16
Learning Latent Variable Models by Pairwise Cluster Comparison: Part I - Theory and Overview
... resulting models might not have any correspondence to real causal mechanisms (Silva et ...of binary and Gaussian variables has been sug- gested (Pearl, ...(HLC) models, which are rooted trees where ...
52
Learning Latent Variable Models by Pairwise Cluster Comparison: Part II - Algorithm and Evaluation
... of pairwise cluster comparison (PCC) to identify causal relation- ships from clusters of data points and an overview of a two-stage algorithm for learning PCC ...using pairwise comparisons between ...
45
Identification of Causal Effects on Binary Outcomes Using Structural Mean Models
... on binary outcomes with non- compliance using structural mean models, additional, non-standard assumptions need to be ...for binary outcomes, meaning that treatment e¤ects ...
25
Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors
... For binary outcomes, sample selection methods rely on a different ...of binary outcomes, the use of a bivariate probit model and a one-step maximum likelihood estimator is mandatory ...
13
Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study
... approach models the prob- ability of having the outcome ...to non-MLE ...the non-convergence issue, a SAS macro called “COPY” was developed [16] and later en- hanced [17] to increase the chance of ...
8
A Comparison of Three Probabilistic Models of Binary Discrete Choice Under Risk
... die from a box of six-sided dice (rolling them until satisfied if they wished), and their selected die was then rolled by the attendant to determine the payment. Here is the reasoning behind the protocol’s features. I ...
40
Mixed Binary-Continuous Copula Regression Models with Application to Adverse Birth Outcomes
... (including non-linearity in continuous predictors and spatial dependence), and other outcomes of interest, one of which is binary and one of which is continuous, which are expected to be ...
27
Longitudinal Joint Modelling of Binary and Continuous Outcomes: A Comparison of Bridge and Normal Distributions
... logistic models are estimated almost the same for different distributional assumptions for the random effect [24, ...the models and the accuracy of ...the models, we assumed that the random intercept ...
11
A comparison of analytic approaches for individual patient data meta-analyses with binary outcomes
... one-stage models when study sizes are ...it models the exact binomial distribution of the data and offers more flexibility in model specification over the two-stage approach ...
12
Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods
... the non-zero edges in a Markov graph, as N → ∞ even for increasing number of parameters p or neighborhood sizes of the graph d, as long as N grows more quickly than d 3 log p (see Wainwright et ...the ...
24
Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study
... mixed models; LOR: Log-odds ratio; MCMC: Markov-chain Monte Carlo; NCHGN: Non-central-hypergeometric-normal model; OR: Odds ratio; PQL: Penalized quasi likelihood; REM: Random effects model; RIM: Random ...
18
Submodularization for Binary Pairwise Energies
... as binary deconvolution, segmentation with repulsion, cur- vature regularization and ...of non-linear submodular ap- proximations over linear approximations, we also compare to a version of LSA-TR where ...
8