Future considerations for clinical trials of combination antifungal therapy
| 1. Usefulness of antifungal combination therapy in biomarker-driven clinical trials |
| 2. Usefulness of antifungal combination therapy in theragnostic (e.g. immuno-PET) clinical trials |
| 3. To work out optimal drug dosing/scheduling of drugs in combinations |
| 4. Bayesian adaptive design models incorporating data analysis from multiple doses and schedules |
| 5. To identify subgroups of patients who would benefit most from an intensive antifungal approach |
| 6. To assess which combinations work best against different degrees of azole resistance |
| 1. Usefulness of antifungal combination therapy in biomarker-driven clinical trials |
| 2. Usefulness of antifungal combination therapy in theragnostic (e.g. immuno-PET) clinical trials |
| 3. To work out optimal drug dosing/scheduling of drugs in combinations |
| 4. Bayesian adaptive design models incorporating data analysis from multiple doses and schedules |
| 5. To identify subgroups of patients who would benefit most from an intensive antifungal approach |
| 6. To assess which combinations work best against different degrees of azole resistance |
Future considerations for clinical trials of combination antifungal therapy
| 1. Usefulness of antifungal combination therapy in biomarker-driven clinical trials |
| 2. Usefulness of antifungal combination therapy in theragnostic (e.g. immuno-PET) clinical trials |
| 3. To work out optimal drug dosing/scheduling of drugs in combinations |
| 4. Bayesian adaptive design models incorporating data analysis from multiple doses and schedules |
| 5. To identify subgroups of patients who would benefit most from an intensive antifungal approach |
| 6. To assess which combinations work best against different degrees of azole resistance |
| 1. Usefulness of antifungal combination therapy in biomarker-driven clinical trials |
| 2. Usefulness of antifungal combination therapy in theragnostic (e.g. immuno-PET) clinical trials |
| 3. To work out optimal drug dosing/scheduling of drugs in combinations |
| 4. Bayesian adaptive design models incorporating data analysis from multiple doses and schedules |
| 5. To identify subgroups of patients who would benefit most from an intensive antifungal approach |
| 6. To assess which combinations work best against different degrees of azole resistance |
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