Accounting for Competing Events When Evaluating Long-Term Outcomes in Survivors of Critical Illness
The clinical trajectory of survivors of critical illness after hospital discharge can be complex and highly unpredictable. Assessing long-term outcomes after critical illness can be challenging because of possible competing events, such as all-cause death during follow-up (which precludes the occurrence of an event of particular interest). In this perspective, we explore challenges and methodological implications of competing events during the assessment of long-term outcomes in survivors of critical illness. In the absence of competing events, researchers evaluating long-term outcomes commonly use the Kaplan-Meier method and the Cox proportional hazards model to analyze time-to-event (survival) data. However, traditional analytical and modeling techniques can yield biased estimates in the presence of competing events. We present different estimands of interest and the use of different analytical approaches, including changes to the outcome of interest, Fine and Gray regression models, cause-specific Cox proportional hazards models, and generalized methods (such as inverse probability weighting). Finally, we provide code and a simulated dataset to exemplify the application of the different analytical strategies in addition to overall reporting recommendations.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:208 |
|---|---|
| Enthalten in: |
American journal of respiratory and critical care medicine - 208(2023), 11 vom: 01. Dez., Seite 1158-1165 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Angriman, Federico [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Competing events |
|---|
| Anmerkungen: |
Date Completed 04.12.2023 Date Revised 27.02.2024 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.1164/rccm.202305-0790CP |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM362653682 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM362653682 | ||
| 003 | DE-627 | ||
| 005 | 20240229163633.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1164/rccm.202305-0790CP |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1308.xml |
| 035 | |a (DE-627)NLM362653682 | ||
| 035 | |a (NLM)37769125 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Angriman, Federico |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Accounting for Competing Events When Evaluating Long-Term Outcomes in Survivors of Critical Illness |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 04.12.2023 | ||
| 500 | |a Date Revised 27.02.2024 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a The clinical trajectory of survivors of critical illness after hospital discharge can be complex and highly unpredictable. Assessing long-term outcomes after critical illness can be challenging because of possible competing events, such as all-cause death during follow-up (which precludes the occurrence of an event of particular interest). In this perspective, we explore challenges and methodological implications of competing events during the assessment of long-term outcomes in survivors of critical illness. In the absence of competing events, researchers evaluating long-term outcomes commonly use the Kaplan-Meier method and the Cox proportional hazards model to analyze time-to-event (survival) data. However, traditional analytical and modeling techniques can yield biased estimates in the presence of competing events. We present different estimands of interest and the use of different analytical approaches, including changes to the outcome of interest, Fine and Gray regression models, cause-specific Cox proportional hazards models, and generalized methods (such as inverse probability weighting). Finally, we provide code and a simulated dataset to exemplify the application of the different analytical strategies in addition to overall reporting recommendations | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a competing events | |
| 650 | 4 | |a critical care survivors | |
| 650 | 4 | |a proportional hazards model | |
| 650 | 4 | |a research design | |
| 650 | 4 | |a survival analysis | |
| 700 | 1 | |a Ferreyro, Bruno L |e verfasserin |4 aut | |
| 700 | 1 | |a Harhay, Michael O |e verfasserin |4 aut | |
| 700 | 1 | |a Wunsch, Hannah |e verfasserin |4 aut | |
| 700 | 1 | |a Rosella, Laura C |e verfasserin |4 aut | |
| 700 | 1 | |a Scales, Damon C |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t American journal of respiratory and critical care medicine |d 1994 |g 208(2023), 11 vom: 01. Dez., Seite 1158-1165 |w (DE-627)NLM074657305 |x 1535-4970 |7 nnns |
| 773 | 1 | 8 | |g volume:208 |g year:2023 |g number:11 |g day:01 |g month:12 |g pages:1158-1165 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1164/rccm.202305-0790CP |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 208 |j 2023 |e 11 |b 01 |c 12 |h 1158-1165 | ||