SMIM : A unified framework of survival sensitivity analysis using multiple imputation and martingale
© 2021 The International Biometric Society..
Censored survival data are common in clinical trial studies. We propose a unified framework for sensitivity analysis to censoring at random in survival data using multiple imputation and martingale, called SMIM. The proposed framework adopts the δ-adjusted and control-based models, indexed by the sensitivity parameter, entailing censoring at random and a wide collection of censoring not at random assumptions. Also, it targets a broad class of treatment effect estimands defined as functionals of treatment-specific survival functions, taking into account missing data due to censoring. Multiple imputation facilitates the use of simple full-sample estimation; however, the standard Rubin's combining rule may overestimate the variance for inference in the sensitivity analysis framework. We decompose the multiple imputation estimator into a martingale series based on the sequential construction of the estimator and propose the wild bootstrap inference by resampling the martingale series. The new bootstrap inference has a theoretical guarantee for consistency and is computationally efficient compared to the nonparametric bootstrap counterpart. We evaluate the finite-sample performance of the proposed SMIM through simulation and an application on an HIV clinical trial.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:79 |
|---|---|
| Enthalten in: |
Biometrics - 79(2023), 1 vom: 27. März, Seite 230-240 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Yang, Shu [VerfasserIn] |
|---|
| Links: |
|---|
| Anmerkungen: |
Date Completed 24.03.2023 Date Revised 28.03.2023 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1111/biom.13555 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM329961357 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM329961357 | ||
| 003 | DE-627 | ||
| 005 | 20231225210247.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1111/biom.13555 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1099.xml |
| 035 | |a (DE-627)NLM329961357 | ||
| 035 | |a (NLM)34453313 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Yang, Shu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a SMIM |b A unified framework of survival sensitivity analysis using multiple imputation and martingale |
| 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 24.03.2023 | ||
| 500 | |a Date Revised 28.03.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2021 The International Biometric Society. | ||
| 520 | |a Censored survival data are common in clinical trial studies. We propose a unified framework for sensitivity analysis to censoring at random in survival data using multiple imputation and martingale, called SMIM. The proposed framework adopts the δ-adjusted and control-based models, indexed by the sensitivity parameter, entailing censoring at random and a wide collection of censoring not at random assumptions. Also, it targets a broad class of treatment effect estimands defined as functionals of treatment-specific survival functions, taking into account missing data due to censoring. Multiple imputation facilitates the use of simple full-sample estimation; however, the standard Rubin's combining rule may overestimate the variance for inference in the sensitivity analysis framework. We decompose the multiple imputation estimator into a martingale series based on the sequential construction of the estimator and propose the wild bootstrap inference by resampling the martingale series. The new bootstrap inference has a theoretical guarantee for consistency and is computationally efficient compared to the nonparametric bootstrap counterpart. We evaluate the finite-sample performance of the proposed SMIM through simulation and an application on an HIV clinical trial | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 4 | |a delta adjustment | |
| 650 | 4 | |a jump-to-reference | |
| 650 | 4 | |a restrictive mean survival time | |
| 650 | 4 | |a restrictive mean time loss | |
| 650 | 4 | |a wild-bootstrap | |
| 700 | 1 | |a Zhang, Yilong |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Guanghan Frank |e verfasserin |4 aut | |
| 700 | 1 | |a Guan, Qian |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Biometrics |d 1946 |g 79(2023), 1 vom: 27. März, Seite 230-240 |w (DE-627)NLM00179423X |x 1541-0420 |7 nnns |
| 773 | 1 | 8 | |g volume:79 |g year:2023 |g number:1 |g day:27 |g month:03 |g pages:230-240 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1111/biom.13555 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 79 |j 2023 |e 1 |b 27 |c 03 |h 230-240 | ||