Bayesian Sequential Design for Identifying and Ranking Effective Patient Subgroups in Precision Medicine in the Case of Counting Outcome Data with Inflated Zeros
Precision medicine is revolutionizing health care, particularly by addressing patient variability due to different biological profiles. As traditional treatments may not always be appropriate for certain patient subsets, the rise of biomarker-stratified clinical trials has driven the need for innova...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Journal of Personalized Medicine |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4426/13/11/1560 |
| _version_ | 1797458741780545536 |
|---|---|
| author | Valentin Vinnat Djillali Annane Sylvie Chevret |
| author_facet | Valentin Vinnat Djillali Annane Sylvie Chevret |
| author_sort | Valentin Vinnat |
| collection | DOAJ |
| description | Precision medicine is revolutionizing health care, particularly by addressing patient variability due to different biological profiles. As traditional treatments may not always be appropriate for certain patient subsets, the rise of biomarker-stratified clinical trials has driven the need for innovative methods. We introduced a Bayesian sequential scheme to evaluate therapeutic interventions in an intensive care unit setting, focusing on complex endpoints characterized by an excess of zeros and right truncation. By using a zero-inflated truncated Poisson model, we efficiently addressed this data complexity. The posterior distribution of rankings and the surface under the cumulative ranking curve (SUCRA) approach provided a comprehensive ranking of the subgroups studied. Different subsets of subgroups were evaluated depending on the availability of biomarker data. Interim analyses, accounting for early stopping for efficacy, were an integral aspect of our design. The simulation study demonstrated a high proportion of correct identification of the subgroup which is the most predictive of the treatment effect, as well as satisfactory false positive and true positive rates. As the role of personalized medicine grows, especially in the intensive care setting, it is critical to have designs that can manage complicated endpoints and that can control for decision error. Our method seems promising in this challenging context. |
| first_indexed | 2024-03-09T16:41:31Z |
| format | Article |
| id | doaj.art-d84493ad620a43c9ba7dd66b7f288c39 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-09T16:41:31Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-d84493ad620a43c9ba7dd66b7f288c392023-11-24T14:51:25ZengMDPI AGJournal of Personalized Medicine2075-44262023-10-011311156010.3390/jpm13111560Bayesian Sequential Design for Identifying and Ranking Effective Patient Subgroups in Precision Medicine in the Case of Counting Outcome Data with Inflated ZerosValentin Vinnat0Djillali Annane1Sylvie Chevret2ECSTRRA Team, INSERM U1153, Université Paris Cité, 75010 Paris, FranceIntensive Care Unit, Raymond Poincaré Hospital, 78266 Garches, FranceECSTRRA Team, INSERM U1153, Université Paris Cité, 75010 Paris, FrancePrecision medicine is revolutionizing health care, particularly by addressing patient variability due to different biological profiles. As traditional treatments may not always be appropriate for certain patient subsets, the rise of biomarker-stratified clinical trials has driven the need for innovative methods. We introduced a Bayesian sequential scheme to evaluate therapeutic interventions in an intensive care unit setting, focusing on complex endpoints characterized by an excess of zeros and right truncation. By using a zero-inflated truncated Poisson model, we efficiently addressed this data complexity. The posterior distribution of rankings and the surface under the cumulative ranking curve (SUCRA) approach provided a comprehensive ranking of the subgroups studied. Different subsets of subgroups were evaluated depending on the availability of biomarker data. Interim analyses, accounting for early stopping for efficacy, were an integral aspect of our design. The simulation study demonstrated a high proportion of correct identification of the subgroup which is the most predictive of the treatment effect, as well as satisfactory false positive and true positive rates. As the role of personalized medicine grows, especially in the intensive care setting, it is critical to have designs that can manage complicated endpoints and that can control for decision error. Our method seems promising in this challenging context.https://www.mdpi.com/2075-4426/13/11/1560personalized medicinebiomarkersBayesian inferenceidentification of subset |
| spellingShingle | Valentin Vinnat Djillali Annane Sylvie Chevret Bayesian Sequential Design for Identifying and Ranking Effective Patient Subgroups in Precision Medicine in the Case of Counting Outcome Data with Inflated Zeros Journal of Personalized Medicine personalized medicine biomarkers Bayesian inference identification of subset |
| title | Bayesian Sequential Design for Identifying and Ranking Effective Patient Subgroups in Precision Medicine in the Case of Counting Outcome Data with Inflated Zeros |
| title_full | Bayesian Sequential Design for Identifying and Ranking Effective Patient Subgroups in Precision Medicine in the Case of Counting Outcome Data with Inflated Zeros |
| title_fullStr | Bayesian Sequential Design for Identifying and Ranking Effective Patient Subgroups in Precision Medicine in the Case of Counting Outcome Data with Inflated Zeros |
| title_full_unstemmed | Bayesian Sequential Design for Identifying and Ranking Effective Patient Subgroups in Precision Medicine in the Case of Counting Outcome Data with Inflated Zeros |
| title_short | Bayesian Sequential Design for Identifying and Ranking Effective Patient Subgroups in Precision Medicine in the Case of Counting Outcome Data with Inflated Zeros |
| title_sort | bayesian sequential design for identifying and ranking effective patient subgroups in precision medicine in the case of counting outcome data with inflated zeros |
| topic | personalized medicine biomarkers Bayesian inference identification of subset |
| url | https://www.mdpi.com/2075-4426/13/11/1560 |
| work_keys_str_mv | AT valentinvinnat bayesiansequentialdesignforidentifyingandrankingeffectivepatientsubgroupsinprecisionmedicineinthecaseofcountingoutcomedatawithinflatedzeros AT djillaliannane bayesiansequentialdesignforidentifyingandrankingeffectivepatientsubgroupsinprecisionmedicineinthecaseofcountingoutcomedatawithinflatedzeros AT sylviechevret bayesiansequentialdesignforidentifyingandrankingeffectivepatientsubgroupsinprecisionmedicineinthecaseofcountingoutcomedatawithinflatedzeros |