Cargando…

New methods for estimating follow-up rates in cohort studies

BACKGROUND: The follow-up rate, a standard index of the completeness of follow-up, is important for assessing the validity of a cohort study. A common method for estimating the follow-up rate, the “Percentage Method”, defined as the fraction of all enrollees who developed the event of interest or ha...

Descripción completa

Detalles Bibliográficos
Autores principales: Xue, Xiaonan, Agalliu, Ilir, Kim, Mimi Y., Wang, Tao, Lin, Juan, Ghavamian, Reza, Strickler, Howard D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709923/
https://www.ncbi.nlm.nih.gov/pubmed/29191174
http://dx.doi.org/10.1186/s12874-017-0436-z
_version_ 1783282874131152896
author Xue, Xiaonan
Agalliu, Ilir
Kim, Mimi Y.
Wang, Tao
Lin, Juan
Ghavamian, Reza
Strickler, Howard D.
author_facet Xue, Xiaonan
Agalliu, Ilir
Kim, Mimi Y.
Wang, Tao
Lin, Juan
Ghavamian, Reza
Strickler, Howard D.
author_sort Xue, Xiaonan
collection PubMed
description BACKGROUND: The follow-up rate, a standard index of the completeness of follow-up, is important for assessing the validity of a cohort study. A common method for estimating the follow-up rate, the “Percentage Method”, defined as the fraction of all enrollees who developed the event of interest or had complete follow-up, can severely underestimate the degree of follow-up. Alternatively, the median follow-up time does not indicate the completeness of follow-up, and the reverse Kaplan-Meier based method and Clark’s Completeness Index (CCI) also have limitations. METHODS: We propose a new definition for the follow-up rate, the Person-Time Follow-up Rate (PTFR), which is the observed person-time divided by total person-time assuming no dropouts. The PTFR cannot be calculated directly since the event times for dropouts are not observed. Therefore, two estimation methods are proposed: a formal person-time method (FPT) in which the expected total follow-up time is calculated using the event rate estimated from the observed data, and a simplified person-time method (SPT) that avoids estimation of the event rate by assigning full follow-up time to all events. Simulations were conducted to measure the accuracy of each method, and each method was applied to a prostate cancer recurrence study dataset. RESULTS: Simulation results showed that the FPT has the highest accuracy overall. In most situations, the computationally simpler SPT and CCI methods are only slightly biased. When applied to a retrospective cohort study of cancer recurrence, the FPT, CCI and SPT showed substantially greater 5-year follow-up than the Percentage Method (92%, 92% and 93% vs 68%). CONCLUSIONS: The Person-time methods correct a systematic error in the standard Percentage Method for calculating follow-up rates. The easy to use SPT and CCI methods can be used in tandem to obtain an accurate and tight interval for PTFR. However, the FPT is recommended when event rates and dropout rates are high. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-017-0436-z) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-5709923
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-57099232017-12-06 New methods for estimating follow-up rates in cohort studies Xue, Xiaonan Agalliu, Ilir Kim, Mimi Y. Wang, Tao Lin, Juan Ghavamian, Reza Strickler, Howard D. BMC Med Res Methodol Research Article BACKGROUND: The follow-up rate, a standard index of the completeness of follow-up, is important for assessing the validity of a cohort study. A common method for estimating the follow-up rate, the “Percentage Method”, defined as the fraction of all enrollees who developed the event of interest or had complete follow-up, can severely underestimate the degree of follow-up. Alternatively, the median follow-up time does not indicate the completeness of follow-up, and the reverse Kaplan-Meier based method and Clark’s Completeness Index (CCI) also have limitations. METHODS: We propose a new definition for the follow-up rate, the Person-Time Follow-up Rate (PTFR), which is the observed person-time divided by total person-time assuming no dropouts. The PTFR cannot be calculated directly since the event times for dropouts are not observed. Therefore, two estimation methods are proposed: a formal person-time method (FPT) in which the expected total follow-up time is calculated using the event rate estimated from the observed data, and a simplified person-time method (SPT) that avoids estimation of the event rate by assigning full follow-up time to all events. Simulations were conducted to measure the accuracy of each method, and each method was applied to a prostate cancer recurrence study dataset. RESULTS: Simulation results showed that the FPT has the highest accuracy overall. In most situations, the computationally simpler SPT and CCI methods are only slightly biased. When applied to a retrospective cohort study of cancer recurrence, the FPT, CCI and SPT showed substantially greater 5-year follow-up than the Percentage Method (92%, 92% and 93% vs 68%). CONCLUSIONS: The Person-time methods correct a systematic error in the standard Percentage Method for calculating follow-up rates. The easy to use SPT and CCI methods can be used in tandem to obtain an accurate and tight interval for PTFR. However, the FPT is recommended when event rates and dropout rates are high. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-017-0436-z) contains supplementary material, which is available to authorized users. BioMed Central 2017-12-01 /pmc/articles/PMC5709923/ /pubmed/29191174 http://dx.doi.org/10.1186/s12874-017-0436-z Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Xue, Xiaonan
Agalliu, Ilir
Kim, Mimi Y.
Wang, Tao
Lin, Juan
Ghavamian, Reza
Strickler, Howard D.
New methods for estimating follow-up rates in cohort studies
title New methods for estimating follow-up rates in cohort studies
title_full New methods for estimating follow-up rates in cohort studies
title_fullStr New methods for estimating follow-up rates in cohort studies
title_full_unstemmed New methods for estimating follow-up rates in cohort studies
title_short New methods for estimating follow-up rates in cohort studies
title_sort new methods for estimating follow-up rates in cohort studies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709923/
https://www.ncbi.nlm.nih.gov/pubmed/29191174
http://dx.doi.org/10.1186/s12874-017-0436-z
work_keys_str_mv AT xuexiaonan newmethodsforestimatingfollowupratesincohortstudies
AT agalliuilir newmethodsforestimatingfollowupratesincohortstudies
AT kimmimiy newmethodsforestimatingfollowupratesincohortstudies
AT wangtao newmethodsforestimatingfollowupratesincohortstudies
AT linjuan newmethodsforestimatingfollowupratesincohortstudies
AT ghavamianreza newmethodsforestimatingfollowupratesincohortstudies
AT stricklerhowardd newmethodsforestimatingfollowupratesincohortstudies