scholarly journals Accounting for Misclassified Outcomes in Binary Regression Models Using Multiple Imputation With Internal Validation Data

2013 ◽  
Vol 177 (9) ◽  
pp. 904-912 ◽  
Author(s):  
Jessie K. Edwards ◽  
Stephen R. Cole ◽  
Melissa A. Troester ◽  
David B. Richardson
Keyword(s):  
Multiple Imputation ◽  
Regression Models ◽  
Binary Regression ◽  
Validation Data ◽  
Internal Validation

Design effects for binary regression models fitted to dependent data

1993 ◽  
Vol 12 (13) ◽  
pp. 1259-1268 ◽  
Author(s):  
John M. Neuhaus ◽  
Mark R. Segal
Keyword(s):  
Regression Models ◽  
Dependent Data ◽  
Binary Regression ◽  
Design Effects

scholarly journals A combined strategy of feature selection and machine learning to identify predictors of prediabetes

2019 ◽  
Vol 27 (3) ◽  
pp. 396-406 ◽  
Author(s):  
Kushan De Silva ◽  
Daniel Jönsson ◽  
Ryan T Demmer
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
National Health ◽  
Screening Tool ◽  
Model Performance ◽  
Nutrition Examination Survey ◽  
Validation Data ◽  
Internal Validation ◽  
Health And Nutrition ◽  
Wide Range

Abstract Objective To identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population. Materials and Methods We analyzed n = 6346 men and women enrolled in the National Health and Nutrition Examination Survey 2013–2014. Prediabetes was defined using American Diabetes Association guidelines. The sample was randomly partitioned to training (n = 3174) and internal validation (n = 3172) sets. Feature selection algorithms were run on training data containing 156 preselected exposure variables. Four machine learning algorithms were applied on 46 exposure variables in original and resampled training datasets built using 4 resampling methods. Predictive models were tested on internal validation data (n = 3172) and external validation data (n = 3000) prepared from National Health and Nutrition Examination Survey 2011–2012. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Predictors were assessed by odds ratios in logistic models and variable importance in others. The Centers for Disease Control (CDC) prediabetes screening tool was the benchmark to compare model performance. Results Prediabetes prevalence was 23.43%. The CDC prediabetes screening tool produced 64.40% AUROC. Seven optimal (≥ 70% AUROC) models identified 25 predictors including 4 potentially novel associations; 20 by both logistic and other nonlinear/ensemble models and 5 solely by the latter. All optimal models outperformed the CDC prediabetes screening tool (P < 0.05). Discussion Combined use of feature selection and machine learning increased predictive performance outperforming the recommended screening tool. A range of predictors of prediabetes was identified. Conclusion This work demonstrated the value of combining feature selection with machine learning to identify a wide range of predictors that could enhance prediabetes prediction and clinical decision-making.

scholarly journals Semiparametric binary regression models under shape constraints with an application to Indian schooling data

2009 ◽  
Vol 149 (2) ◽  
pp. 101-117 ◽  
Author(s):  
Moulinath Banerjee ◽  
Debasri Mukherjee ◽  
Santosh Mishra
Keyword(s):  
Regression Models ◽  
Binary Regression ◽  
Shape Constraints

Validating vascular access data in the Swedish Renal Registry SRR

2020 ◽  
pp. 112972982095473
Author(s):  
Gunilla Welander ◽  
Birgitta Sigvant
Keyword(s):  
Vascular Access ◽  
Medical Records ◽  
External Validation ◽  
Internal Validity ◽  
Validation Data ◽  
Internal Validation ◽  
Clinical Utilization ◽  
Surgical Units ◽  
National Patient ◽  
Access Data

Background: All Swedish dialysis units register data on vascular access in the Swedish Renal Registry (SRR). This study assessed external and internal validity of vascular access data in the SRR and its use as a tool in clinical practice. Methods: For external validation, all procedures for placed fistulas, open and endovascular reinterventions registered in the SRR in 2011 to 2017 were cross-matched with data from the Swedish National Patient Registry. A two-stage sampling selected 12/60 dialysis units for internal validation. Data on current vascular access for 10 randomly selected patients at each unit were compared with medical record data. SRR data on placed fistulas from 2017 were cross-checked with data from local surgical units. Registrations of central venous catheters (CVCs) as temporary or permanent were used as a proxy for clinical utilization of the registry and analyzed separately. Results: External validity increased from 74% to 83% during the observation period. In all, 1037 datapoints were used in internal validation, with a 95% match between SRR registrations and medical records. Registrations of CVCs, fistulas, and interventions were reliable, with few missing data or mismatches. Vascular access type initiating hemodialysis was missing or incorrect in either the SRR or medical records for 14/120 patients. Registrations of placed fistulas in 2017 matched in all but four (pre-dialysis stage) of 135 cases. Some 35% of the CVCs validated ( n = 49) at 7/12 units were not categorized as temporary or permanent. Conclusion: The SRR provides a reliable resource on current vascular access care.

Fitting binary regression models with case-augmented samples

Biometrika ◽  
2006 ◽  
Vol 93 (2) ◽  
pp. 385-397 ◽  
Author(s):  
A. J. Lee ◽  
A. J. Scott ◽  
C. J. Wild
Keyword(s):  
Regression Models ◽  
Binary Regression
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