Evaluation of Indian Prediction Models for Lung Function Parameters: A Statistical Approach

Background: Interpretation of lung function test parameters is usually based on comparisons of data with reference (predicted) values based on healthy subjects. Predicted values are obtained from studies of “normal” or “healthy” subjects with similar anthropometric and ethnic characteristics. Regres...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Ritul Kamal, Sheela Misra
Formato: article
Lenguaje:EN
Publicado: Ubiquity Press 2019
Materias:
Acceso en línea:https://doaj.org/article/7f7ef8bf154f40b4982a0290736c668b
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:7f7ef8bf154f40b4982a0290736c668b
record_format dspace
spelling oai:doaj.org-article:7f7ef8bf154f40b4982a0290736c668b2021-12-02T00:17:13ZEvaluation of Indian Prediction Models for Lung Function Parameters: A Statistical Approach2214-999610.5334/aogh.2397https://doaj.org/article/7f7ef8bf154f40b4982a0290736c668b2019-03-01T00:00:00Zhttps://annalsofglobalhealth.org/articles/2397https://doaj.org/toc/2214-9996Background: Interpretation of lung function test parameters is usually based on comparisons of data with reference (predicted) values based on healthy subjects. Predicted values are obtained from studies of “normal” or “healthy” subjects with similar anthropometric and ethnic characteristics. Regression models are generally used to obtain the reference values from measurements observed in a representative sample of healthy subjects. Objectives: The study aims to carry out a statistical evaluation of the Indian prediction models of lung function parameters and critically evaluate the reference values for the same in an Indian context. Methods: The screening and inclusion of the articles for the study was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Evaluation of the prediction models has been done with respect to modeling approach, regression diagnostics and methodology protocol. The suitability of the models has also been evaluated using a checklist comprising of 8 criteria developed using the American Thoracic Society (ATS) guidelines. Results: Using the PRISMA guidelines 32 articles with a total sample size of 25,289 subjects were included in the final synthesis. Multiple linear regression models were used in 27 articles, with one additionally using weighted least squares technique and 4 using step-wise regression method. Regression diagnostics as per the ATS guidelines were performed and reported by 22 articles. The prediction models were traditionally developed using ordinary least squares method (OLS) without examining the homoskedasticity of residuals. The quality assessment using the checklist developed revealed that only 5 articles satisfied more than 7 out of 8 criteria, and a further 8 articles satisfied less than 3 criteria of suitability of prediction models. Conclusions: Indian prediction models for lung function models are traditionally based on linear regression models, however with more advancement in computational power for sophisticated statistical techniques, more robust prediction models are required in the Indian context.Ritul KamalSheela MisraUbiquity Pressarticleprediction models, lung function, regression diagnostics, modelingInfectious and parasitic diseasesRC109-216Public aspects of medicineRA1-1270ENAnnals of Global Health, Vol 85, Iss 1 (2019)
institution DOAJ
collection DOAJ
language EN
topic prediction models, lung function, regression diagnostics, modeling
Infectious and parasitic diseases
RC109-216
Public aspects of medicine
RA1-1270
spellingShingle prediction models, lung function, regression diagnostics, modeling
Infectious and parasitic diseases
RC109-216
Public aspects of medicine
RA1-1270
Ritul Kamal
Sheela Misra
Evaluation of Indian Prediction Models for Lung Function Parameters: A Statistical Approach
description Background: Interpretation of lung function test parameters is usually based on comparisons of data with reference (predicted) values based on healthy subjects. Predicted values are obtained from studies of “normal” or “healthy” subjects with similar anthropometric and ethnic characteristics. Regression models are generally used to obtain the reference values from measurements observed in a representative sample of healthy subjects. Objectives: The study aims to carry out a statistical evaluation of the Indian prediction models of lung function parameters and critically evaluate the reference values for the same in an Indian context. Methods: The screening and inclusion of the articles for the study was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Evaluation of the prediction models has been done with respect to modeling approach, regression diagnostics and methodology protocol. The suitability of the models has also been evaluated using a checklist comprising of 8 criteria developed using the American Thoracic Society (ATS) guidelines. Results: Using the PRISMA guidelines 32 articles with a total sample size of 25,289 subjects were included in the final synthesis. Multiple linear regression models were used in 27 articles, with one additionally using weighted least squares technique and 4 using step-wise regression method. Regression diagnostics as per the ATS guidelines were performed and reported by 22 articles. The prediction models were traditionally developed using ordinary least squares method (OLS) without examining the homoskedasticity of residuals. The quality assessment using the checklist developed revealed that only 5 articles satisfied more than 7 out of 8 criteria, and a further 8 articles satisfied less than 3 criteria of suitability of prediction models. Conclusions: Indian prediction models for lung function models are traditionally based on linear regression models, however with more advancement in computational power for sophisticated statistical techniques, more robust prediction models are required in the Indian context.
format article
author Ritul Kamal
Sheela Misra
author_facet Ritul Kamal
Sheela Misra
author_sort Ritul Kamal
title Evaluation of Indian Prediction Models for Lung Function Parameters: A Statistical Approach
title_short Evaluation of Indian Prediction Models for Lung Function Parameters: A Statistical Approach
title_full Evaluation of Indian Prediction Models for Lung Function Parameters: A Statistical Approach
title_fullStr Evaluation of Indian Prediction Models for Lung Function Parameters: A Statistical Approach
title_full_unstemmed Evaluation of Indian Prediction Models for Lung Function Parameters: A Statistical Approach
title_sort evaluation of indian prediction models for lung function parameters: a statistical approach
publisher Ubiquity Press
publishDate 2019
url https://doaj.org/article/7f7ef8bf154f40b4982a0290736c668b
work_keys_str_mv AT ritulkamal evaluationofindianpredictionmodelsforlungfunctionparametersastatisticalapproach
AT sheelamisra evaluationofindianpredictionmodelsforlungfunctionparametersastatisticalapproach
_version_ 1718403810808823808