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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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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 |
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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 |
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