An Introduction to Factored Regression Models with Blimp

In this paper, we provide an introduction to the factored regression framework. This modeling framework applies the rules of probability to break up or “factor” a complex joint distribution into a product of conditional regression models. Using this framework, we can easily specify the complex multivariate models that missing data modeling requires. The article provides a brief conceptual overview of factored regression and describes the functional notation used to conceptualize the models. Furthermore, we present a conceptual overview of how the models are estimated and imputations are obtained. Finally, we discuss how users can use the free software package, Blimp, to estimate the models in the context of a mediation example.

Download Full-text

Power and sample size for random coefficient regression models in randomized experiments with monotone missing data

Biometrical Journal ◽

10.1002/bimj.202000184 ◽

2021 ◽

Vol 63 (4) ◽

pp. 806-824

Author(s):

Nan Hu ◽

Howard Mackey ◽

Ronald Thomas

Keyword(s):

Missing Data ◽

Sample Size ◽

Regression Models ◽

Random Coefficient ◽

Randomized Experiments ◽

Random Coefficient Regression

Download Full-text

Identification of the most specific markers to differentiate primary pulmonary carcinoma from metastatic gastrointestinal carcinoma to the lung

Diagnostic Pathology ◽

10.1186/s13000-021-01184-2 ◽

2022 ◽

Vol 17 (1) ◽

Author(s):

Bachar Alabdullah ◽

Amir Hadji-Ashrafy

Keyword(s):

Gastrointestinal Tract ◽

Predictive Value ◽

Regression Models ◽

Area Under The Curve ◽

Roc Curves ◽

Multivariate Models ◽

Gastrointestinal Carcinoma ◽

Lung Tumours ◽

Napsin A ◽

Sensitivity Specificity

Abstract Background A number of biomarkers have the potential of differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract, however, a standardised panel for that purpose does not exist yet. We aimed to identify the smallest panel that is most sensitive and specific at differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract. Methods A total of 170 samples were collected, including 140 primary and 30 non-primary lung tumours and staining for CK7, Napsin-A, TTF1, CK20, CDX2, and SATB2 was performed via tissue microarray. The data was then analysed using univariate regression models and a combination of multivariate regression models and Receiver Operating Characteristic (ROC) curves. Results Univariate regression models confirmed the 6 biomarkers’ ability to independently predict the primary outcome (p < 0.001). Multivariate models of 2-biomarker combinations identified 11 combinations with statistically significant odds ratios (ORs) (p < 0.05), of which TTF1/CDX2 had the highest area under the curve (AUC) (0.983, 0.960–1.000 95% CI). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 75.7, 100, 100, and 37.5% respectively. Multivariate models of 3-biomarker combinations identified 4 combinations with statistically significant ORs (p < 0.05), of which CK7/CK20/SATB2 had the highest AUC (0.965, 0.930–1.000 95% CI). The sensitivity, specificity, PPV, and NPV were 85.1, 100, 100, and 41.7% respectively. Multivariate models of 4-biomarker combinations did not identify any combinations with statistically significant ORs (p < 0.05). Conclusions The analysis identified the combination of CK7/CK20/SATB2 to be the smallest panel with the highest sensitivity (85.1%) and specificity (100%) for predicting tumour origin with an ROC AUC of 0.965 (p < 0.001; SE: 0.018, 0.930–1.000 95% CI).

Download Full-text

Modelling semi-continuous data using mixture regression models with an application to cattle production yields

The Journal of Agricultural Science ◽

10.1017/s0021859611000608 ◽

2011 ◽

Vol 150 (1) ◽

pp. 109-121 ◽

Cited By ~ 2

Author(s):

E. J. BELASCO ◽

S. K. GHOSH

Keyword(s):

Regression Models ◽

Continuous Variable ◽

Continuous Variables ◽

Multivariate Models ◽

Mixture Regression ◽

Potential Applications ◽

Mixture Regression Models ◽

Mixture Regression Model ◽

The Relationship ◽

Positive Half

SUMMARYThe present paper develops a mixture regression model that allows for distributional flexibility in modelling the likelihood of a semi-continuous outcome that takes on zero value with positive probability while continuous on the positive half of the real line. A multivariate extension is also developed that builds on past multivariate models by systematically capturing the relationship between continuous and semi-continuous variables, while allowing for the semi-continuous variable to be characterized by a mixture model. The flexibility associated with this model provides potential applications in many production system studies. The empirical model is shown to provide a more accurate measure of mortality rates in cattle feedlots, both independently and within a system including other performance and health factors.

Download Full-text

Acute Medical Ward for better care coordination of patients admitted with infection – evidence from a tertiary hospital in Singapore

Acute Medicine Journal ◽

10.52964/amja.0679 ◽

2017 ◽

Vol 16 (4) ◽

pp. 170-176

Author(s):

Tharmmambal Balakrishnan ◽

◽

Pek Siang Edmund Teo ◽

Wan Tin Lim ◽

Xiao Hui Xin ◽

...

Keyword(s):

Regression Models ◽

Linear Models ◽

Tertiary Hospital ◽

General Ward ◽

Medical Ward ◽

South East Asia ◽

Multivariate Models ◽

Readmission Rates ◽

Logistic Regression Models ◽

Acute Admissions

Coordination and consolidation of care provided in acute care hospitals need reconfiguration and reorganization to meet the demand of large number of acute admissions. We report on the effectiveness of an Acute Medical Ward AMW (AMW) receiving cases that were suspected to have infection related diagnosis on admission by Emergency Department (ED), addressing this in a large tertiary hospital in South East Asia. Mean Length of Stay (LOS) was compared using Gamma Generalized Linear Models with Log-link while odds of readmissions and mortality were compared using logistic regression models. The LOS (mean: 5.8 days, SD: 9.1 days) of all patients admitted to AMW was similar to discharge diagnosis-matched general ward (GW) patients admitted before AMW implementation, readmission rates were lower (15-day: 5.3%, 30-day: 8.1%). Bivariate and multivariate models revealed that mean LOS after AMW implementation was not significantly different from before AMW implementation (Ratio: 0.99, p=0.473). Our AMW had reduced readmission rates for patients with infection but has not made an overall impact on the LOS and readmission rates for the epartment as a whole.

Download Full-text

Technical approach for the design of a high-resolution spectral model on a sphere: Application to decaying turbulence

Nonlinear Processes in Geophysics ◽

10.5194/npg-7-105-2000 ◽

2000 ◽

Vol 7 (1/2) ◽

pp. 105-110 ◽

Cited By ~ 6

Author(s):

K. Ishioka ◽

M. Yamada ◽

Y.-Y. Hayashi ◽

S. Yoden

Keyword(s):

High Resolution ◽

Software Package ◽

Free Software ◽

Spectral Model ◽

Technical Approach ◽

Barotropic Model ◽

Test Experiment ◽

Decaying Turbulence

Abstract. Several technical suggestions to construct a high-resolution spectral model on a sphere (the T682 barotropic model) are presented and their implementation of FORTRAN77 libraries is provided as a free software package ISPACK (http://www.gfd-dennou.org/arch/ispack/). A test experiment on decaying turbulence is conducted to demonstrate the ability of the model.

Download Full-text

Nonstandard conditionally specified models for nonignorable missing data

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1815563117 ◽

2020 ◽

Vol 117 (32) ◽

pp. 19045-19053

Author(s):

Alexander M. Franks ◽

Edoardo M. Airoldi ◽

Donald B. Rubin

Keyword(s):

Systems Biology ◽

Missing Data ◽

Joint Distribution ◽

Exponential Family ◽

Nonignorable Missing Data ◽

Data Analyses ◽

Missingness Mechanism ◽

Conditionally Specified Models ◽

Nonignorable Missing ◽

Family Models

Data analyses typically rely upon assumptions about the missingness mechanisms that lead to observed versus missing data, assumptions that are typically unassessable. We explore an approach where the joint distribution of observed data and missing data are specified in a nonstandard way. In this formulation, which traces back to a representation of the joint distribution of the data and missingness mechanism, apparently first proposed by J. W. Tukey, the modeling assumptions about the distributions are either assessable or are designed to allow relatively easy incorporation of substantive knowledge about the problem at hand, thereby offering a possibly realistic portrayal of the data, both observed and missing. We develop Tukey’s representation for exponential-family models, propose a computationally tractable approach to inference in this class of models, and offer some general theoretical comments. We then illustrate the utility of this approach with an example in systems biology.

Download Full-text

A joint regression modeling framework for analyzing bivariate binary data in R

Dependence Modeling ◽

10.1515/demo-2017-0016 ◽

2017 ◽

Vol 5 (1) ◽

pp. 268-294 ◽

Cited By ~ 7

Author(s):

Giampiero Marra ◽

Rosalba Radice

Keyword(s):

Binary Data ◽

Regression Models ◽

Joint Modeling ◽

Data Sets ◽

Modeling Framework ◽

Flexible Joint ◽

Sampling Schemes ◽

Multivariate Response ◽

Interpretable Models ◽

Non Gaussian

Abstract We discuss some of the features of the R add-on package GJRM which implements a flexible joint modeling framework for fitting a number of multivariate response regression models under various sampling schemes. In particular,we focus on the case inwhich the user wishes to fit bivariate binary regression models in the presence of several forms of selection bias. The framework allows for Gaussian and non-Gaussian dependencies through the use of copulae, and for the association and mean parameters to depend on flexible functions of covariates. We describe some of the methodological details underpinning the bivariate binary models implemented in the package and illustrate them by fitting interpretable models of different complexity on three data-sets.

Download Full-text