The Predictive Individual Effect for Survival Data.
Bayesian predictive inference
Non-proportional hazards
Patient-centric measure
Rank preservation
Survival gain
Time-to-event endpoint
Journal
Therapeutic innovation & regulatory science
ISSN: 2168-4804
Titre abrégé: Ther Innov Regul Sci
Pays: Switzerland
ID NLM: 101597411
Informations de publication
Date de publication:
05 2022
05 2022
Historique:
received:
18
09
2021
accepted:
18
02
2022
pubmed:
17
3
2022
medline:
5
4
2022
entrez:
16
3
2022
Statut:
ppublish
Résumé
The call for patient-focused drug development is loud and clear, as expressed in the twenty-first Century Cures Act and in recent guidelines and initiatives of regulatory agencies. Among the factors contributing to modernized drug development and improved health-care activities are easily interpretable measures of clinical benefit. In addition, special care is needed for cancer trials with time-to-event endpoints if the treatment effect is not constant over time. To quantify the potential clinical survival benefit for a new patient, would he/she be treated with the test or control treatment. We propose the predictive individual effect which is a patient-centric and tangible measure of clinical benefit under a wide variety of scenarios. It can be obtained by standard predictive calculations under a rank preservation assumption that has been used previously in trials with treatment switching. We discuss four recent Oncology trials that cover situations with proportional as well as non-proportional hazards (delayed treatment effect or crossing of survival curves). It is shown that the predictive individual effect offers valuable insights beyond p-values, estimates of hazard ratios or differences in median survival. Compared to standard statistical measures, the predictive individual effect is a direct, easily interpretable measure of clinical benefit. It facilitates communication among clinicians, patients, and other parties and should therefore be considered in addition to standard statistical results.
Sections du résumé
BACKGROUND
The call for patient-focused drug development is loud and clear, as expressed in the twenty-first Century Cures Act and in recent guidelines and initiatives of regulatory agencies. Among the factors contributing to modernized drug development and improved health-care activities are easily interpretable measures of clinical benefit. In addition, special care is needed for cancer trials with time-to-event endpoints if the treatment effect is not constant over time.
OBJECTIVE
To quantify the potential clinical survival benefit for a new patient, would he/she be treated with the test or control treatment.
METHODS
We propose the predictive individual effect which is a patient-centric and tangible measure of clinical benefit under a wide variety of scenarios. It can be obtained by standard predictive calculations under a rank preservation assumption that has been used previously in trials with treatment switching.
RESULTS
We discuss four recent Oncology trials that cover situations with proportional as well as non-proportional hazards (delayed treatment effect or crossing of survival curves). It is shown that the predictive individual effect offers valuable insights beyond p-values, estimates of hazard ratios or differences in median survival.
CONCLUSION
Compared to standard statistical measures, the predictive individual effect is a direct, easily interpretable measure of clinical benefit. It facilitates communication among clinicians, patients, and other parties and should therefore be considered in addition to standard statistical results.
Identifiants
pubmed: 35294767
doi: 10.1007/s43441-022-00386-0
pii: 10.1007/s43441-022-00386-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
492-500Informations de copyright
© 2022. The Drug Information Association, Inc.
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