Statistical analyses
Cancer
ADVERSE EVENT, PAD, REGRESSION, EVENTS, CANCER
The primary analysis population was a modified intention-to-treat (mITT) population, which was defined as all randomized patients who were eligible and received at least one dose of treatment. These patients were considered evaluable for efficacy analysis. Patients were analyzed in the arm they were allocated. A patient was considered to be eligible if they did not have any major deviations from the patient entry criteria. The assessment of the presence of major deviations was assessed centrally by the principal investigator and her medical team.CBR, ORR, DOR, PFS, and OS were estimated in the mITT population.The trial was powered to detect an increase in CBR from 40% to 60% at week 24. Therefore, a sample size of 136 (68 per arm) evaluable patients was required (calculated using EAST, with one-sided alpha = 0.1, power of 80%, and Casagrande–Pike–Smith correction).An efficacy interim analysis for futility was performed after the enrolment of 68 evaluable patients (34 per treatment arm). The interim analysis was carried out when CBR assessment was possible for all patients, i.e., maximum 24 weeks for the last of the 68 evaluable patients. The primary purpose of efficacy interim analysis was to check the likelihood of observing the expected treatment effect. For planning the interim analysis, a Lan-Demets approach was used (O’Brien and Fleming spending functions for β error). On the Comparisons of binary endpoints (Clinical Benefit, Objective Response) by arms or other categorical variables were performed using Fisher’s exact tests. Non-parametric Wilcoxon rank-sum tests were used to compare continuous variables. Possible associations between two continuous variables were assessed using Spearman’s correlation. Logistic regression models were used to assess the probability of the occurrence of binary events explained by some variables of interest and their interactions, represented by the odds ratios. Time-to-event outcomes, such as OS, PFS curves, the medians or survival at any time-point were estimated using Kaplan–Meier analysis. Log-rank test was used to compare the distributions of the survival curves. Cox regression analysis was used to estimate the hazard ratio between two survival curves and to assess the association between the time-to-event outcomes and potential prognostic factors. Univariate and multivariate models (logistic for binary and Cox for time-to-event outcomes) have been used to explore the associations between outcomes and these factors. Statistical tests were using one-sided for the primary efficacy analyses specified in the protocol and two-sided for exploratory subgroup analyses. If not stated in the text, the test is based on a two-sided test.The safety population was defined as patients who received at least one dose of the assigned study treatment. AEs were graded by National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0, and the safety data were descriptively compared between the arms.Statistical analyses were done in SAS Enterprise Guide version 8.3. Graph Pad Prism version 9.5.1 was used for graphical representation.