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Statistical projection methods for lung cancer incidence and mortality: a systematic review

OBJECTIVES: To identify and summarise all studies using statistical methods to project lung cancer incidence or mortality rates more than 5 years into the future. STUDY TYPE: Systematic review. METHODS: We performed a systematic literature search in multiple electronic databases to identify studies...

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Autores principales: Yu, Xue Qin, Luo, Qingwei, Hughes, Suzanne, Wade, Stephen, Caruana, Michael, Canfell, Karen, O'Connell, Dianne L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6720154/
https://www.ncbi.nlm.nih.gov/pubmed/31462469
http://dx.doi.org/10.1136/bmjopen-2018-028497
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author Yu, Xue Qin
Luo, Qingwei
Hughes, Suzanne
Wade, Stephen
Caruana, Michael
Canfell, Karen
O'Connell, Dianne L
author_facet Yu, Xue Qin
Luo, Qingwei
Hughes, Suzanne
Wade, Stephen
Caruana, Michael
Canfell, Karen
O'Connell, Dianne L
author_sort Yu, Xue Qin
collection PubMed
description OBJECTIVES: To identify and summarise all studies using statistical methods to project lung cancer incidence or mortality rates more than 5 years into the future. STUDY TYPE: Systematic review. METHODS: We performed a systematic literature search in multiple electronic databases to identify studies published from 1 January 1988 to 14 August 2018, which used statistical methods to project lung cancer incidence and/or mortality rates. Reference lists of relevant articles were checked for additional potentially relevant articles. We developed an organisational framework to classify methods into groups according to the type of data and the statistical models used. Included studies were critically appraised using prespecified criteria. RESULTS: One hundred and one studies met the inclusion criteria; six studies used more than one statistical method. The number of studies reporting statistical projections for lung cancer increased substantially over time. Eighty-eight studies used projection methods, which did not incorporate data on smoking in the population, and 16 studies used a method which did incorporate data on smoking. Age–period–cohort models (44 studies) were the most commonly used methods, followed by other generalised linear models (35 studies). The majority of models were developed using observed rates for more than 10 years and used data that were considered to be good quality. A quarter of studies provided comparisons of fitted and observed rates. While validation by withholding the most recent observed data from the model and then comparing the projected and observed rates for the most recent period provides important information on the model’s performance, only 12 studies reported doing this. CONCLUSION: This systematic review provides an up-to-date summary of the statistical methods used in published lung cancer incidence or mortality projections. The assessment of the strengths of existing methods will help researchers to better apply and develop statistical methods for projecting lung cancer rates. Some of the common methods described in this review can be applied to the projection of rates for other cancer types or other non-infectious diseases.
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spelling pubmed-67201542019-09-17 Statistical projection methods for lung cancer incidence and mortality: a systematic review Yu, Xue Qin Luo, Qingwei Hughes, Suzanne Wade, Stephen Caruana, Michael Canfell, Karen O'Connell, Dianne L BMJ Open Epidemiology OBJECTIVES: To identify and summarise all studies using statistical methods to project lung cancer incidence or mortality rates more than 5 years into the future. STUDY TYPE: Systematic review. METHODS: We performed a systematic literature search in multiple electronic databases to identify studies published from 1 January 1988 to 14 August 2018, which used statistical methods to project lung cancer incidence and/or mortality rates. Reference lists of relevant articles were checked for additional potentially relevant articles. We developed an organisational framework to classify methods into groups according to the type of data and the statistical models used. Included studies were critically appraised using prespecified criteria. RESULTS: One hundred and one studies met the inclusion criteria; six studies used more than one statistical method. The number of studies reporting statistical projections for lung cancer increased substantially over time. Eighty-eight studies used projection methods, which did not incorporate data on smoking in the population, and 16 studies used a method which did incorporate data on smoking. Age–period–cohort models (44 studies) were the most commonly used methods, followed by other generalised linear models (35 studies). The majority of models were developed using observed rates for more than 10 years and used data that were considered to be good quality. A quarter of studies provided comparisons of fitted and observed rates. While validation by withholding the most recent observed data from the model and then comparing the projected and observed rates for the most recent period provides important information on the model’s performance, only 12 studies reported doing this. CONCLUSION: This systematic review provides an up-to-date summary of the statistical methods used in published lung cancer incidence or mortality projections. The assessment of the strengths of existing methods will help researchers to better apply and develop statistical methods for projecting lung cancer rates. Some of the common methods described in this review can be applied to the projection of rates for other cancer types or other non-infectious diseases. BMJ Publishing Group 2019-08-27 /pmc/articles/PMC6720154/ /pubmed/31462469 http://dx.doi.org/10.1136/bmjopen-2018-028497 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Epidemiology
Yu, Xue Qin
Luo, Qingwei
Hughes, Suzanne
Wade, Stephen
Caruana, Michael
Canfell, Karen
O'Connell, Dianne L
Statistical projection methods for lung cancer incidence and mortality: a systematic review
title Statistical projection methods for lung cancer incidence and mortality: a systematic review
title_full Statistical projection methods for lung cancer incidence and mortality: a systematic review
title_fullStr Statistical projection methods for lung cancer incidence and mortality: a systematic review
title_full_unstemmed Statistical projection methods for lung cancer incidence and mortality: a systematic review
title_short Statistical projection methods for lung cancer incidence and mortality: a systematic review
title_sort statistical projection methods for lung cancer incidence and mortality: a systematic review
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6720154/
https://www.ncbi.nlm.nih.gov/pubmed/31462469
http://dx.doi.org/10.1136/bmjopen-2018-028497
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