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 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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xue, Xiaonan [verfasserIn] Agalliu, Ilir Kim, Mimi Y. Wang, Tao Lin, Juan Ghavamian, Reza Strickler, Howard D.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Person-time Loss to follow-up Median survival time Reverse Kaplan-Meier survival curve Competing risk

Anmerkung:	© The Author(s). 2017

Übergeordnetes Werk:	Enthalten in: BMC medical research methodology - London : BioMed Central, 2001, 17(2017), 1 vom: 01. Dez.
Übergeordnetes Werk:	volume:17 ; year:2017 ; number:1 ; day:01 ; month:12

Links:	Volltext

DOI / URN:	10.1186/s12874-017-0436-z

Katalog-ID:	SPR027373754

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520			\|a 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.
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allfields	10.1186/s12874-017-0436-z doi (DE-627)SPR027373754 (SPR)s12874-017-0436-z-e DE-627 ger DE-627 rakwb eng Xue, Xiaonan verfasserin (orcid)0000-0003-2872-8119 aut New methods for estimating follow-up rates in cohort studies 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 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. Person-time (dpeaa)DE-He213 Loss to follow-up (dpeaa)DE-He213 Median survival time (dpeaa)DE-He213 Reverse Kaplan-Meier survival curve (dpeaa)DE-He213 Competing risk (dpeaa)DE-He213 Agalliu, Ilir aut Kim, Mimi Y. aut Wang, Tao aut Lin, Juan aut Ghavamian, Reza aut Strickler, Howard D. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 17(2017), 1 vom: 01. Dez. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:17 year:2017 number:1 day:01 month:12 https://dx.doi.org/10.1186/s12874-017-0436-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 1 01 12
spelling	10.1186/s12874-017-0436-z doi (DE-627)SPR027373754 (SPR)s12874-017-0436-z-e DE-627 ger DE-627 rakwb eng Xue, Xiaonan verfasserin (orcid)0000-0003-2872-8119 aut New methods for estimating follow-up rates in cohort studies 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 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. Person-time (dpeaa)DE-He213 Loss to follow-up (dpeaa)DE-He213 Median survival time (dpeaa)DE-He213 Reverse Kaplan-Meier survival curve (dpeaa)DE-He213 Competing risk (dpeaa)DE-He213 Agalliu, Ilir aut Kim, Mimi Y. aut Wang, Tao aut Lin, Juan aut Ghavamian, Reza aut Strickler, Howard D. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 17(2017), 1 vom: 01. Dez. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:17 year:2017 number:1 day:01 month:12 https://dx.doi.org/10.1186/s12874-017-0436-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 1 01 12
allfields_unstemmed	10.1186/s12874-017-0436-z doi (DE-627)SPR027373754 (SPR)s12874-017-0436-z-e DE-627 ger DE-627 rakwb eng Xue, Xiaonan verfasserin (orcid)0000-0003-2872-8119 aut New methods for estimating follow-up rates in cohort studies 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 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. Person-time (dpeaa)DE-He213 Loss to follow-up (dpeaa)DE-He213 Median survival time (dpeaa)DE-He213 Reverse Kaplan-Meier survival curve (dpeaa)DE-He213 Competing risk (dpeaa)DE-He213 Agalliu, Ilir aut Kim, Mimi Y. aut Wang, Tao aut Lin, Juan aut Ghavamian, Reza aut Strickler, Howard D. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 17(2017), 1 vom: 01. Dez. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:17 year:2017 number:1 day:01 month:12 https://dx.doi.org/10.1186/s12874-017-0436-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 1 01 12
allfieldsGer	10.1186/s12874-017-0436-z doi (DE-627)SPR027373754 (SPR)s12874-017-0436-z-e DE-627 ger DE-627 rakwb eng Xue, Xiaonan verfasserin (orcid)0000-0003-2872-8119 aut New methods for estimating follow-up rates in cohort studies 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 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. Person-time (dpeaa)DE-He213 Loss to follow-up (dpeaa)DE-He213 Median survival time (dpeaa)DE-He213 Reverse Kaplan-Meier survival curve (dpeaa)DE-He213 Competing risk (dpeaa)DE-He213 Agalliu, Ilir aut Kim, Mimi Y. aut Wang, Tao aut Lin, Juan aut Ghavamian, Reza aut Strickler, Howard D. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 17(2017), 1 vom: 01. Dez. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:17 year:2017 number:1 day:01 month:12 https://dx.doi.org/10.1186/s12874-017-0436-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 1 01 12
allfieldsSound	10.1186/s12874-017-0436-z doi (DE-627)SPR027373754 (SPR)s12874-017-0436-z-e DE-627 ger DE-627 rakwb eng Xue, Xiaonan verfasserin (orcid)0000-0003-2872-8119 aut New methods for estimating follow-up rates in cohort studies 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 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. Person-time (dpeaa)DE-He213 Loss to follow-up (dpeaa)DE-He213 Median survival time (dpeaa)DE-He213 Reverse Kaplan-Meier survival curve (dpeaa)DE-He213 Competing risk (dpeaa)DE-He213 Agalliu, Ilir aut Kim, Mimi Y. aut Wang, Tao aut Lin, Juan aut Ghavamian, Reza aut Strickler, Howard D. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 17(2017), 1 vom: 01. Dez. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:17 year:2017 number:1 day:01 month:12 https://dx.doi.org/10.1186/s12874-017-0436-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2017 1 01 12
language	English
source	Enthalten in BMC medical research methodology 17(2017), 1 vom: 01. Dez. volume:17 year:2017 number:1 day:01 month:12
sourceStr	Enthalten in BMC medical research methodology 17(2017), 1 vom: 01. Dez. volume:17 year:2017 number:1 day:01 month:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Person-time Loss to follow-up Median survival time Reverse Kaplan-Meier survival curve Competing risk
isfreeaccess_bool	true
container_title	BMC medical research methodology
authorswithroles_txt_mv	Xue, Xiaonan @@aut@@ Agalliu, Ilir @@aut@@ Kim, Mimi Y. @@aut@@ Wang, Tao @@aut@@ Lin, Juan @@aut@@ Ghavamian, Reza @@aut@@ Strickler, Howard D. @@aut@@
publishDateDaySort_date	2017-12-01T00:00:00Z
hierarchy_top_id	326643818
id	SPR027373754
language_de	englisch
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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. 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author	Xue, Xiaonan
spellingShingle	Xue, Xiaonan misc Person-time misc Loss to follow-up misc Median survival time misc Reverse Kaplan-Meier survival curve misc Competing risk New methods for estimating follow-up rates in cohort studies
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topic_title	New methods for estimating follow-up rates in cohort studies Person-time (dpeaa)DE-He213 Loss to follow-up (dpeaa)DE-He213 Median survival time (dpeaa)DE-He213 Reverse Kaplan-Meier survival curve (dpeaa)DE-He213 Competing risk (dpeaa)DE-He213
topic	misc Person-time misc Loss to follow-up misc Median survival time misc Reverse Kaplan-Meier survival curve misc Competing risk
topic_unstemmed	misc Person-time misc Loss to follow-up misc Median survival time misc Reverse Kaplan-Meier survival curve misc Competing risk
topic_browse	misc Person-time misc Loss to follow-up misc Median survival time misc Reverse Kaplan-Meier survival curve misc Competing risk
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title	New methods for estimating follow-up rates in cohort studies
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title_full	New methods for estimating follow-up rates in cohort studies
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author_browse	Xue, Xiaonan Agalliu, Ilir Kim, Mimi Y. Wang, Tao Lin, Juan Ghavamian, Reza Strickler, Howard D.
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title_sort	new methods for estimating follow-up rates in cohort studies
title_auth	New methods for estimating follow-up rates in cohort studies
abstract	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. © The Author(s). 2017
abstractGer	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. © The Author(s). 2017
abstract_unstemmed	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. © The Author(s). 2017
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title_short	New methods for estimating follow-up rates in cohort studies
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