The estimation and modelling of cause-specific cumulative incidence functions using time-dependent weights
Competing risks occur in survival analysis when an individual is at risk of more than one type of event and the occurrence of one event precludes the occurrence of any other event. A measure of interest with competing risks data is the cause-specific cumulative incidence function (CIF) which gives t...
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| Published in: | The Stata journal Vol. 17; no. 1; p. 181 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
01.03.2017
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| Subjects: | |
| ISSN: | 1536-867X |
| Online Access: | Get more information |
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| Summary: | Competing risks occur in survival analysis when an individual is at risk of more than one type of event and the occurrence of one event precludes the occurrence of any other event. A measure of interest with competing risks data is the cause-specific cumulative incidence function (CIF) which gives the absolute (or crude) risk of having the event by time
, accounting for the fact that it is impossible to have the event if a competing event is experienced first. The user written command, stcompet, calculates non-parametric estimates of the cause-specific CIF and the official Stata command, stcrreg, fits the Fine and Gray model for competing risks data. Geskus (2011) has recently shown that some of the key measures in competing risks can be estimated in standard software by restructuring the data and incorporating weights. This has a number of advantages as any tools developed for standard survival analysis can then be used for the analysis of competing risks data. This paper describes the stcrprep command that restructures the data and calculates the appropriate weights. After using stcrprep a number of standard Stata survival analysis commands can then be used for the analysis of competing risks. For example, sts graph, failure will give a plot of the cause-specific CIF and stcox will fit the Fine and Gray proportional subhazards model. Using stcrprep together with stcox is computationally much more efficient than using stcrreg. In addition, the use of stcrprep opens up new opportunities for competing risk models. This is illustrated by fitting flexible parametric survival models to the expanded data to directly model the cause-specific CIF. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1536-867X |
| DOI: | 10.1177/1536867x1701700110 |