Estimating the Burden of Recurrent Events in the Presence of Competing Risks: The Method of Mean Cumulative Count
Cumulative incidence has been widely used to estimate the cumulative probability of developing an event of interest by a given time, in the presence of competing risks. When it is of interest to measure the total burden of recurrent events in a population, however, the cumulative incidence method is...
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| Published in | American journal of epidemiology Vol. 181; no. 7; pp. 532 - 540 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Oxford Publishing Limited (England)
01.04.2015
Oxford University Press |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0002-9262 1476-6256 1476-6256 |
| DOI | 10.1093/aje/kwu289 |
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| Abstract | Cumulative incidence has been widely used to estimate the cumulative probability of developing an event of interest by a given time, in the presence of competing risks. When it is of interest to measure the total burden of recurrent events in a population, however, the cumulative incidence method is not appropriate because it considers only the first occurrence of the event of interest for each individual in the analysis: Subsequent occurrences are not included. Here, we discuss a straightforward and intuitive method termed "mean cumulative count," which reflects a summarization of all events that occur in the population by a given time, not just the first event for each subject. We explore the mathematical relationship between mean cumulative count and cumulative incidence. Detailed calculation of mean cumulative count is described by using a simple hypothetical example, and the computation code with an illustrative example is provided. Using follow-up data from January 1975 to August 2009 collected in the Childhood Cancer Survivor Study, we show applications of mean cumulative count and cumulative incidence for the outcome of subsequent neoplasms to demonstrate different but complementary information obtained from the 2 approaches and the specific utility of the former. |
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| AbstractList | Cumulative incidence has been widely used to estimate the cumulative probability of developing an event of interest by a given time, in the presence of competing risks. When it is of interest to measure the total burden of recurrent events in a population, however, the cumulative incidence method is not appropriate because it considers only the first occurrence of the event of interest for each individual in the analysis: Subsequent occurrences are not included. Here, we discuss a straightforward and intuitive method termed "mean cumulative count," which reflects a summarization of all events that occur in the population by a given time, not just the first event for each subject. We explore the mathematical relationship between mean cumulative count and cumulative incidence. Detailed calculation of mean cumulative count is described by using a simple hypothetical example, and the computation code with an illustrative example is provided. Using follow-up data from January 1975 to August 2009 collected in the Childhood Cancer Survivor Study, we show applications of mean cumulative count and cumulative incidence for the outcome of subsequent neoplasms to demonstrate different but complementary information obtained from the 2 approaches and the specific utility of the former. Cumulative incidence has been widely used to estimate the cumulative probability of developing an event of interest by a given time, in the presence of competing risks. When it is of interest to measure the total burden of recurrent events in a population, however, the cumulative incidence method is not appropriate because it considers only the first occurrence of the event of interest for each individual in the analysis: Subsequent occurrences are not included. Here, we discuss a straightforward and intuitive method termed "mean cumulative count," which reflects a summarization of all events that occur in the population by a given time, not just the first event for each subject. We explore the mathematical relationship between mean cumulative count and cumulative incidence. Detailed calculation of mean cumulative count is described by using a simple hypothetical example, and the computation code with an illustrative example is provided. Using follow-up data from January 1975 to August 2009 collected in the Childhood Cancer Survivor Study, we show applications of mean cumulative count and cumulative incidence for the outcome of subsequent neoplasms to demonstrate different but complementary information obtained from the 2 approaches and the specific utility of the former.Cumulative incidence has been widely used to estimate the cumulative probability of developing an event of interest by a given time, in the presence of competing risks. When it is of interest to measure the total burden of recurrent events in a population, however, the cumulative incidence method is not appropriate because it considers only the first occurrence of the event of interest for each individual in the analysis: Subsequent occurrences are not included. Here, we discuss a straightforward and intuitive method termed "mean cumulative count," which reflects a summarization of all events that occur in the population by a given time, not just the first event for each subject. We explore the mathematical relationship between mean cumulative count and cumulative incidence. Detailed calculation of mean cumulative count is described by using a simple hypothetical example, and the computation code with an illustrative example is provided. Using follow-up data from January 1975 to August 2009 collected in the Childhood Cancer Survivor Study, we show applications of mean cumulative count and cumulative incidence for the outcome of subsequent neoplasms to demonstrate different but complementary information obtained from the 2 approaches and the specific utility of the former. |
| Author | Martin, L. J. Robison, L. L. Yasui, Y. Dong, H. Leisenring, W. M. Armstrong, G. T. |
| Author_xml | – sequence: 1 givenname: H. surname: Dong fullname: Dong, H. – sequence: 2 givenname: L. L. surname: Robison fullname: Robison, L. L. – sequence: 3 givenname: W. M. surname: Leisenring fullname: Leisenring, W. M. – sequence: 4 givenname: L. J. surname: Martin fullname: Martin, L. J. – sequence: 5 givenname: G. T. surname: Armstrong fullname: Armstrong, G. T. – sequence: 6 givenname: Y. surname: Yasui fullname: Yasui, Y. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25693770$$D View this record in MEDLINE/PubMed |
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| Copyright | The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. Copyright Oxford Publishing Limited(England) Apr 1, 2015 The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. 2015 |
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| Keywords | disease burden recurrent events mean cumulative count cumulative incidence |
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| Notes | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 Abbreviations: CumI, cumulative incidence; KM, Kaplan-Meier; MCC, mean cumulative count; RT, radiation therapy. |
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| SubjectTerms | Adolescent Cancer Child Correlation analysis Cost of Illness Data Interpretation, Statistical Epidemiologic Research Design Humans Incidence Neoplasms - economics Neoplasms - mortality Neoplasms - therapy Practice of Epidemiology Probability Radiotherapy - adverse effects Radiotherapy - mortality Recurrence Risk Assessment Survival Analysis Tumors Young Adult |
| Title | Estimating the Burden of Recurrent Events in the Presence of Competing Risks: The Method of Mean Cumulative Count |
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