Time-to-event analysis
Survival analysis (or time-to-event analysis) deals with data where the outcome of interest is the length of time until the occurrence of an event. This type of analysis is unique because the event may not occur in all participants (known as censoring). A previous article in this journal covered the...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2025-04-01
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| Series: | Perspectives in Clinical Research |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.4103/picr.picr_52_25 |
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| _version_ | 1850033893160255488 |
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| author | Priya Ranganathan Vishal Deo C. S. Pramesh |
| author_facet | Priya Ranganathan Vishal Deo C. S. Pramesh |
| author_sort | Priya Ranganathan |
| collection | DOAJ |
| description | Survival analysis (or time-to-event analysis) deals with data where the outcome of interest is the length of time until the occurrence of an event. This type of analysis is unique because the event may not occur in all participants (known as censoring). A previous article in this journal covered the basic aspects of conventional survival analysis. In this article, we discuss two unique features – nonproportional hazards (PH) and competing risks. |
| format | Article |
| id | doaj-art-9788e7bfbdc64490b148e48bf85963de |
| institution | DOAJ |
| issn | 2229-3485 2229-5488 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Perspectives in Clinical Research |
| spelling | doaj-art-9788e7bfbdc64490b148e48bf85963de2025-08-20T02:58:00ZengWolters Kluwer Medknow PublicationsPerspectives in Clinical Research2229-34852229-54882025-04-0116210210510.4103/picr.picr_52_25Time-to-event analysisPriya RanganathanVishal DeoC. S. PrameshSurvival analysis (or time-to-event analysis) deals with data where the outcome of interest is the length of time until the occurrence of an event. This type of analysis is unique because the event may not occur in all participants (known as censoring). A previous article in this journal covered the basic aspects of conventional survival analysis. In this article, we discuss two unique features – nonproportional hazards (PH) and competing risks.https://journals.lww.com/10.4103/picr.picr_52_25kaplan–meier estimateproportional hazards modelsurvival analysis |
| spellingShingle | Priya Ranganathan Vishal Deo C. S. Pramesh Time-to-event analysis Perspectives in Clinical Research kaplan–meier estimate proportional hazards model survival analysis |
| title | Time-to-event analysis |
| title_full | Time-to-event analysis |
| title_fullStr | Time-to-event analysis |
| title_full_unstemmed | Time-to-event analysis |
| title_short | Time-to-event analysis |
| title_sort | time to event analysis |
| topic | kaplan–meier estimate proportional hazards model survival analysis |
| url | https://journals.lww.com/10.4103/picr.picr_52_25 |
| work_keys_str_mv | AT priyaranganathan timetoeventanalysis AT vishaldeo timetoeventanalysis AT cspramesh timetoeventanalysis |