Deconstructing the Kaplan-Meier curve : Quantification of treatment effect using the treatment effect process
Copyright © 2022 Elsevier Inc. All rights reserved..
In studies of survival and its association with treatment and other prognostic variables, elapsed time alone will often show itself to be among the strongest, if not the strongest, of the predictor variables. Kaplan-Meier curves will show the overall survival of each group and the general differences between groups due to treatment. However, the time-dependent nature of treatment effects is not always immediately transparent from these curves. More sophisticated tools are needed to spotlight the treatment effects. An important tool in this context is the treatment effect process. This tool can be potent in revealing the complex myriad of ways in which treatment can affect survival time. We look at a recently published study in which the outcome was relapse-free survival, and we illustrate how the use of the treatment effect process can provide a much deeper understanding of the relationship between time and treatment in this trial.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:125 |
|---|---|
| Enthalten in: |
Contemporary clinical trials - 125(2023) vom: 06. Feb., Seite 107043 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Devlin, Sean M [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Biostatistics |
|---|
| Anmerkungen: |
Date Completed 14.02.2023 Date Revised 02.02.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.cct.2022.107043 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM349861102 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM349861102 | ||
| 003 | DE-627 | ||
| 005 | 20240202231910.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.cct.2022.107043 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1278.xml |
| 035 | |a (DE-627)NLM349861102 | ||
| 035 | |a (NLM)36473681 | ||
| 035 | |a (PII)S1551-7144(22)00369-X | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Devlin, Sean M |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Deconstructing the Kaplan-Meier curve |b Quantification of treatment effect using the treatment effect process |
| 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 14.02.2023 | ||
| 500 | |a Date Revised 02.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2022 Elsevier Inc. All rights reserved. | ||
| 520 | |a In studies of survival and its association with treatment and other prognostic variables, elapsed time alone will often show itself to be among the strongest, if not the strongest, of the predictor variables. Kaplan-Meier curves will show the overall survival of each group and the general differences between groups due to treatment. However, the time-dependent nature of treatment effects is not always immediately transparent from these curves. More sophisticated tools are needed to spotlight the treatment effects. An important tool in this context is the treatment effect process. This tool can be potent in revealing the complex myriad of ways in which treatment can affect survival time. We look at a recently published study in which the outcome was relapse-free survival, and we illustrate how the use of the treatment effect process can provide a much deeper understanding of the relationship between time and treatment in this trial | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Biostatistics | |
| 650 | 4 | |a Clinical trials | |
| 650 | 4 | |a Kaplan-Meier survival curves | |
| 700 | 1 | |a O'Quigley, John |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Contemporary clinical trials |d 2005 |g 125(2023) vom: 06. Feb., Seite 107043 |w (DE-627)NLM154869031 |x 1559-2030 |7 nnns |
| 773 | 1 | 8 | |g volume:125 |g year:2023 |g day:06 |g month:02 |g pages:107043 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.cct.2022.107043 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 125 |j 2023 |b 06 |c 02 |h 107043 | ||