Literature DB >> 33647505

Statistical Models in Clinical Studies.

Shigeyuki Matsui1, Jennifer Le-Rademacher2, Sumithra J Mandrekar2.   

Abstract

Although statistical models serve as the foundation of data analysis in clinical studies, their interpretation requires sufficient understanding of the underlying statistical framework. Statistical modeling is inherently a difficult task because of the general lack of information of the nature of observable data. In this article, we aim to provide some guidance when using regression models to aid clinical researchers to better interpret results from their statistical models and to encourage investigators to collaborate with a statistician to ensure that their studies are designed and analyzed appropriately.
Copyright © 2021 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Classification and prediction; Effect Assessment; Multivariable models; Regression models; Statistical models; Univariable models

Mesh:

Year:  2021        PMID: 33647505      PMCID: PMC8085106          DOI: 10.1016/j.jtho.2021.02.021

Source DB:  PubMed          Journal:  J Thorac Oncol        ISSN: 1556-0864            Impact factor:   15.609


  13 in total

1.  What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models.

Authors:  Michael A Babyak
Journal:  Psychosom Med       Date:  2004 May-Jun       Impact factor: 4.312

2.  Causal diagrams for epidemiologic research.

Authors:  S Greenland; J Pearl; J M Robins
Journal:  Epidemiology       Date:  1999-01       Impact factor: 4.822

Review 3.  Five myths about variable selection.

Authors:  Georg Heinze; Daniela Dunkler
Journal:  Transpl Int       Date:  2017-01       Impact factor: 3.782

Review 4.  Introduction to causal diagrams for confounder selection.

Authors:  Elizabeth J Williamson; Zoe Aitken; Jock Lawrie; Shyamali C Dharmage; John A Burgess; Andrew B Forbes
Journal:  Respirology       Date:  2014-01-22       Impact factor: 6.424

5.  Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.

Authors:  Miguel A Hernán; James M Robins
Journal:  Am J Epidemiol       Date:  2016-03-18       Impact factor: 4.897

6.  Calculating the sample size required for developing a clinical prediction model.

Authors:  Richard D Riley; Joie Ensor; Kym I E Snell; Frank E Harrell; Glen P Martin; Johannes B Reitsma; Karel G M Moons; Gary Collins; Maarten van Smeden
Journal:  BMJ       Date:  2020-03-18

7.  Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology.

Authors:  Susan Halabi; Cai Li; Sheng Luo
Journal:  JCO Precis Oncol       Date:  2019-10-24

8.  A prognostic model for advanced stage nonsmall cell lung cancer. Pooled analysis of North Central Cancer Treatment Group trials.

Authors:  Sumithra J Mandrekar; Steven E Schild; Shauna L Hillman; Katie L Allen; Randolph S Marks; James A Mailliard; James E Krook; Andrew W Maksymiuk; Kari Chansky; Karen Kelly; Alex A Adjei; James R Jett
Journal:  Cancer       Date:  2006-08-15       Impact factor: 6.860

9.  Assessing the performance of prediction models: a framework for traditional and novel measures.

Authors:  Ewout W Steyerberg; Andrew J Vickers; Nancy R Cook; Thomas Gerds; Mithat Gonen; Nancy Obuchowski; Michael J Pencina; Michael W Kattan
Journal:  Epidemiology       Date:  2010-01       Impact factor: 4.822

10.  Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration.

Authors:  Karel G M Moons; Douglas G Altman; Johannes B Reitsma; John P A Ioannidis; Petra Macaskill; Ewout W Steyerberg; Andrew J Vickers; David F Ransohoff; Gary S Collins
Journal:  Ann Intern Med       Date:  2015-01-06       Impact factor: 25.391

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