Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions
Survival time is the primary endpoint of many randomized controlled trials, and a treatment effect is typically quantified by the hazard ratio under the assumption of proportional hazards. Awareness is increasing that in many settings this assumption is a priori violated, for example, due to delayed onset of drug effect. In these cases, interpretation of the hazard ratio estimate is ambiguous and statistical inference for alternative parameters to quantify a treatment effect is warranted. We consider differences or ratios of milestone survival probabilities or quantiles, differences in restricted mean survival times, and an average hazard ratio to be of interest. Typically, more than one such parameter needs to be reported to assess possible treatment benefits, and in confirmatory trials, the according inferential procedures need to be adjusted for multiplicity. A simple Bonferroni adjustment may be too conservative because the different parameters of interest typically show considerable correlation. Hence simultaneous inference procedures that take into account the correlation are warranted. By using the counting process representation of the mentioned parameters, we show that their estimates are asymptotically multivariate normal and we provide an estimate for their covariance matrix. We propose according to the parametric multiple testing procedures and simultaneous confidence intervals. Also, the logrank test may be included in the framework. Finite sample type I error rate and power are studied by simulation. The methods are illustrated with an example from oncology. A software implementation is provided in the R package nph.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:33 |
|---|---|
| Enthalten in: |
Statistical methods in medical research - 33(2024), 4 vom: 06. Apr., Seite 589-610 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Ristl, Robin [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Journal Article |
|---|
| Anmerkungen: |
Date Completed 18.04.2024 Date Revised 25.04.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1177/09622802241231497 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM369553314 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM369553314 | ||
| 003 | DE-627 | ||
| 005 | 20240425233115.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240311s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1177/09622802241231497 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1386.xml |
| 035 | |a (DE-627)NLM369553314 | ||
| 035 | |a (NLM)38465602 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Ristl, Robin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions |
| 264 | 1 | |c 2024 | |
| 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 18.04.2024 | ||
| 500 | |a Date Revised 25.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Survival time is the primary endpoint of many randomized controlled trials, and a treatment effect is typically quantified by the hazard ratio under the assumption of proportional hazards. Awareness is increasing that in many settings this assumption is a priori violated, for example, due to delayed onset of drug effect. In these cases, interpretation of the hazard ratio estimate is ambiguous and statistical inference for alternative parameters to quantify a treatment effect is warranted. We consider differences or ratios of milestone survival probabilities or quantiles, differences in restricted mean survival times, and an average hazard ratio to be of interest. Typically, more than one such parameter needs to be reported to assess possible treatment benefits, and in confirmatory trials, the according inferential procedures need to be adjusted for multiplicity. A simple Bonferroni adjustment may be too conservative because the different parameters of interest typically show considerable correlation. Hence simultaneous inference procedures that take into account the correlation are warranted. By using the counting process representation of the mentioned parameters, we show that their estimates are asymptotically multivariate normal and we provide an estimate for their covariance matrix. We propose according to the parametric multiple testing procedures and simultaneous confidence intervals. Also, the logrank test may be included in the framework. Finite sample type I error rate and power are studied by simulation. The methods are illustrated with an example from oncology. A software implementation is provided in the R package nph | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Survival analysis | |
| 650 | 4 | |a multiple testing | |
| 650 | 4 | |a non-proportional hazards | |
| 650 | 4 | |a randomized clinical trial | |
| 650 | 4 | |a simultaneous inference | |
| 700 | 1 | |a Götte, Heiko |e verfasserin |4 aut | |
| 700 | 1 | |a Schüler, Armin |e verfasserin |4 aut | |
| 700 | 1 | |a Posch, Martin |e verfasserin |4 aut | |
| 700 | 1 | |a König, Franz |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Statistical methods in medical research |d 1997 |g 33(2024), 4 vom: 06. Apr., Seite 589-610 |w (DE-627)NLM013229338 |x 1477-0334 |7 nnns |
| 773 | 1 | 8 | |g volume:33 |g year:2024 |g number:4 |g day:06 |g month:04 |g pages:589-610 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1177/09622802241231497 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 33 |j 2024 |e 4 |b 06 |c 04 |h 589-610 | ||