Safety analysis of new medications in clinical trials: a simulation study to assess the differences between cause-specific and subdistribution frameworks in the presence of competing events.

Safety is an essential part of the evaluation of new medications and competing risks that occur in most clinical trials are a well identified challenge in the analysis of adverse events. Two statistical frameworks exist to consider competing risks: the cause-specific and the subdistribution framework. To date, the application of the cause-specific framework is the standard practice in safety analyses. Here we analyze how the safety analysis results of new medications would be affected if instead of the cause-specific the subdistribution framework was chosen. We conducted a simulation study with 600 participants, equally allocated to verum and control groups and a 30 months follow-up period. Simulated trials were analyzed for safety in a competing risk (death) setting using both the cause-specific and subdistribution frameworks. Results show that comparing safety profiles in a subdistribution setting is always more pessimistic than in a cause-specific setting. For the group with the longest survival and a safety advantage in a cause-specific setting, the advantage either disappeared or a disadvantage was found in the subdistribution analysis setting. These observations are not contradictory but show different perspectives. To evaluate the safety of a new medication over its comparator, one needs to understand the origin of both the risks and the benefits associated with each therapy. These requirements are best met with a cause-specific framework. The subdistribution framework seems better suited for clinical prediction, and therefore more relevant for providers or payers, for example.

Surrogate threshold effect based on a meta-analysis for the predictive value of progression-free survival for overall survival in hormone receptor-positive, HER2-negative metastatic breast cancer.

PURPOSE: Clinical trials investigating therapies for metastatic breast cancer (mBC) generally use progression-free survival (PFS) as primary endpoint, which is not accepted as patient-relevant outcome within the German benefit assessment. Hence a validation of PFS as surrogate endpoint for overall survival (OS) is needed, e.g., in the indication of HR+, HER2-negative mBC. METHODS: A systematic search was conducted. RCT were included if at least one study arm investigated fulvestrant, letrozole, tamoxifen, exemestane, or anastrozole. Additionally, hazard ratios reported for OS/PFS including confidence interval or standard error were mandatory. Pearson correlation coefficient was calculated to estimate the relation of surrogate endpoint PFS and patient-relevant outcome OS as well as the surrogate threshold effect (STE) which is used to determine thresholds for the estimate of the surrogate endpoint. RESULTS: 16 studies with 5324 patients in total were included in the analyses. The correlation between hazard ratios of PFS and OS was statistically significant (r = 0.72, 95% CI 0.35-0.90) representing a positive linear relationship. STE analysis was applied. The meta-regression model revealed a STE for HRPFS of 0.60 and sensitivity analyses underlined robustness of the results. CONCLUSIONS: Based on the derived STE, it is possible to draw conclusions on a significant effect in OS for a hypothetical trial demonstrating an upper confidence limit of HRPFS < 0.60 in PFS. However, only final OS results are able to confirm if a clinical relevant difference in survival time can be achieved.

Burden of hospitalization in acute lymphoblastic leukemia patients treated with Inotuzumab Ozogamicin versus standard chemotherapy treatment.

BACKGROUND: Inotuzumab Ozogamicin (INO), has demonstrated an improvement in overall survival, high rate of complete remission, favorable patient-reported outcomes, and manageable safety profile vs standard of care (SoC; intensive chemotherapy) for relapsed/refractory (R/R) acute lymphoblastic leukemia (ALL) in the phase 3 INO-VATE trial. With a one-hour weekly dosing schedule, INO might be associated with lower healthcare system burden. This study analyses hospitalizations for INO vs SoC. METHODS: All patients receiving study treatment in the INO-VATE trial were included. The days hospitalized during study treatment was calculated. Due to different treatment durations for INO and SoC (median of 3 vs 1 cycles), number of hospital days was mainly reported per observed patient month. Hospital days per patient month were analyzed for different treatment cycles, subgroups, and main reasons for hospitalization. Differences between treatments were analyzed by the incidence rate ratio (IRR). RESULTS: Overall, 82.9% and 94.4% INO and SoC patients experienced at least one hospitalization. The mean hospitalization days per patient month was 7.6 and 18.4 days for INO and SoC (IRR = 0.413, P < .001), which corresponds to patients spending 25.0% and 60.5% of their treatment time in a hospital. Main hospitalization reasons were R/R ALL treatment (5.2 (INO) vs 14.0 (SoC) days, IRR = 0.368, P < .001), treatment toxicities (1.4 vs 2.8 days, IRR = 0.516, P < .001) or other reasons (1.0 vs 1.6 days, IRR 0.629, P < .001). CONCLUSIONS: Inotuzumab Ozogamicin treatment in R/R ALL is associated with a lower hospitalization burden compared with SoC. It is likely this lower burden has a favorable impact on healthcare budgets and cost-effectiveness considerations.