Deep historical borrowing framework to prospectively and simultaneously synthesize control information in confirmatory clinical trials with multiple endpoints / Tianyu Zhan, Yiwang Zhou, Ziqian Geng, Yihua Gu, Jian Kang, Li Wang, Xiaohong Huang, Elizabeth H. Slate
ABSTRACT In current clinical trial development, historical information is receiving more attention as it provides utility beyond sample size calculation. Meta-analytic-predictive (MAP) priors and robust MAP priors have been proposed for prospectively borrowing historical data on a single endpoint. To simultaneously synthesize control information from multiple endpoints in confirmatory clinical trials, we propose to approximate posterior probabilities from a Bayesian hierarchical model and estimate critical values by deep learning to construct pre-specified strategies for hypothesis testing. This feature is important to ensure study integrity by establishing prospective decision functions before the trial conduct. Simulations are performed to show that our method properly controls family-wise error rate and preserves power as compared with a typical practice of choosing constant critical values given a subset of null space. Satisfactory performance under prior-data conflict is also demonstrated. We further illustrate our method using a case study in Immunology.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:32 |
|---|---|
| Enthalten in: |
Journal of biopharmaceutical statistics - 32(2022), 1, Seite 90-106 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Zhan, Tianyu [VerfasserIn] |
|---|
| Links: |
FID Access [lizenzpflichtig] |
|---|
| Themen: |
Bayesian hierarchical model |
|---|
| Umfang: |
1 Online-Ressource (17 p) |
|---|
| doi: |
10.1080/10543406.2021.1975128 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
KFL011136278 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | KFL011136278 | ||
| 003 | DE-627 | ||
| 005 | 20230713210907.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230609s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1080/10543406.2021.1975128 |2 doi | |
| 035 | |a (DE-627)KFL011136278 | ||
| 035 | |a (KFL)prod_LgpH_10.1080/10543406.2021.1975128 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 084 | |a PHARM |q DE-84 |2 fid | ||
| 100 | 1 | |a Zhan, Tianyu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Deep historical borrowing framework to prospectively and simultaneously synthesize control information in confirmatory clinical trials with multiple endpoints |c Tianyu Zhan, Yiwang Zhou, Ziqian Geng, Yihua Gu, Jian Kang, Li Wang, Xiaohong Huang, Elizabeth H. Slate |
| 264 | 1 | |c 2022 | |
| 300 | |a 1 Online-Ressource (17 p) | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a ABSTRACT In current clinical trial development, historical information is receiving more attention as it provides utility beyond sample size calculation. Meta-analytic-predictive (MAP) priors and robust MAP priors have been proposed for prospectively borrowing historical data on a single endpoint. To simultaneously synthesize control information from multiple endpoints in confirmatory clinical trials, we propose to approximate posterior probabilities from a Bayesian hierarchical model and estimate critical values by deep learning to construct pre-specified strategies for hypothesis testing. This feature is important to ensure study integrity by establishing prospective decision functions before the trial conduct. Simulations are performed to show that our method properly controls family-wise error rate and preserves power as compared with a typical practice of choosing constant critical values given a subset of null space. Satisfactory performance under prior-data conflict is also demonstrated. We further illustrate our method using a case study in Immunology | ||
| 653 | |a Bayesian hierarchical model | ||
| 653 | |a deep learning | ||
| 653 | |a family-wise error rate control | ||
| 653 | |a power preservation | ||
| 653 | |a prospective algorithm | ||
| 700 | 1 | |a Zhou, Yiwang |e verfasserin |4 aut | |
| 700 | 1 | |a Geng, Ziqian |e verfasserin |4 aut | |
| 700 | 1 | |a Gu, Yihua |e verfasserin |4 aut | |
| 700 | 1 | |a Kang, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Xiaohong |e verfasserin |4 aut | |
| 700 | 1 | |a Slate, Elizabeth H. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of biopharmaceutical statistics |d Philadelphia, PA : Taylor & Francis, 1991 |g 32(2022), 1, Seite 90-106 |h Online-Ressource |w (DE-627)KFL000088641 |w (DE-600)2008925-9 |w (DE-576)27387828X |x 1520-5711 |7 nnns |
| 773 | 1 | 8 | |g volume:32 |g year:2022 |g number:1 |g pages:90-106 |
| 856 | 4 | 0 | |u http://pharmazie.proxy.fid-lizenzen.de/fid/tandf-ejournals-pharmazie/doi.org/10.1080/10543406.2021.1975128 |m X:KFL |x Resolving-System |y FID Access |z lizenzpflichtig |
| 912 | |a ZDB-1-TFE | ||
| 912 | |a GBV_KFL | ||
| 912 | |a FID-PHARM | ||
| 935 | |i IMPORT_0628_prod_LgpH_05 | ||
| 951 | |a AR | ||
| 952 | |d 32 |j 2022 |e 1 |h 90-106 | ||