Two-stage randomized clinical trials with a right-censored endpoint: Comparison of frequentist and Bayesian adaptive designs.

Adaptive randomized clinical trials are of major interest when dealing with a time-to-event outcome in a prolonged observation window. No consensus exists either to define stopping boundaries or to combine p $$ p $$ values or test statistics in the terminal analysis in the case of a frequentist design and sample size adaptation. In a one-sided setting, we compared three frequentist approaches using stopping boundaries relying on α $$ \alpha $$ -spending functions and a Bayesian monitoring setting with boundaries based on the posterior distribution of the log-hazard ratio. All designs comprised a single interim analysis with an efficacy stopping rule and the possibility of sample size adaptation at this interim step. Three frequentist approaches were defined based on the terminal analysis: combination of stagewise statistics (Wassmer) or of p $$ p $$ values (Desseaux), or on patientwise splitting (Jörgens), and we compared the results with those of the Bayesian monitoring approach (Freedman). These different approaches were evaluated in a simulation study and then illustrated on a real dataset from a randomized clinical trial conducted in elderly patients with chronic lymphocytic leukemia. All approaches controlled for the type I error rate, except for the Bayesian monitoring approach, and yielded satisfactory power. It appears that the frequentist approaches are the best in underpowered trials. The power of all the approaches was affected by the violation of the proportional hazards (PH) assumption. For adaptive designs with a survival endpoint and a one-sided alternative hypothesis, the Wassmer and Jörgens approaches after sample size adaptation should be preferred, unless violation of PH is suspected.

Drug combinations screening using a Bayesian ranking approach based on dose-response models.

Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloid leukemia.

Occurrence and severity of cocaine-induced hallucinations: Two distinct phenotypes with shared clinical factors but specific genetic risk factors.

Cocaine-induced transient hallucinations (CIH) are a frequent complication following cocaine intake that is associated with addiction severity. METHODS: Two hundred and forty-two non-psychotic and Caucasian lifetime cocaine users were included in a French multicentric study. Clinical variables and dopamine pathway genotype data were extracted and tested with CIH scores using a zero-inflated binomial model, which allows for the exploration of factors associated with occurrence and severity separately. RESULTS: Cocaine dependence (poccurrence= 6.18 × 10-5, pseverity= 9.25 × 10-8), number of cocaine dependence DSM IV-Tr criteria (poccurrence= 1.22 × 10-7, pseverity= 5.09 × 10-6), and frequency of intake during the worst period of misuse (poccurrence= 8.51 × 10-04, pseverity= 0.04) were associated with greater occurrence and higher severity of CIH. The genetic associations did not yield significant results after correction for multiple tests. However, some nominal associations of SNPs mapped to the VMAT2, DBH, DRD1, and DRD2 genes were significant. In the multivariate model, the significant variables were the number of cocaine dependence criteria, lifetime alcohol dependence, and the nominally associated SNPs. CONCLUSION: Our study shows that CIH occurrence and severity are two distinct phenotypes, with shared clinical risk factors; however, they likely do not share the same genetic background.