ICEBERG study: an indirect adjusted comparison estimating the long-term benefit of esketamine nasal spray when compared with routine treatment of treatment resistant depression in general psychiatry.

Background: Treatment resistant depression (TRD) affects 10-30% of patients with major depressive disorder. In 4-week trials, esketamine nasal spray (NS) was efficacious vs. placebo when both were initiated in addition to a new selective serotonin or serotonin norepinephrine reuptake inhibitor.

Indirect adjusted comparison of 6-month clinical outcomes between esketamine nasal spray and other real-world polypharmacy treatment strategies for treatment resistant depression: results from the ICEBERG study.

Background: The efficacy of esketamine nasal spray (NS) as a rapid-acting agent for treatment resistant depression (TRD) was demonstrated in comparisons with placebo, when both were given in addition to a newly initiated selective serotonin reuptake inhibitor (SSRI)/serotonin norepinephrine reuptake inhibitor (SNRI). How esketamine NS compares with commonly used real-world (RW) polypharmacy treatment strategies is not known.

Method: ICEBERG was an adjusted indirect treatment comparison that analysed data from SUSTAIN-2 (NCT02497287; clinicaltrials.

An Adjusted Treatment Comparison Comparing Amivantamab Versus Real-World Clinical Practice in Europe and the United States for Patients with Advanced Non-Small Cell Lung Cancer with Activating Epidermal Growth Factor Receptor Exon 20 Insertion Mutations.

Introduction: Patients with advanced, epidermal growth factor receptor (EGFR)-mutated, non-small cell lung cancer (NSCLC) with Exon 20 insertion mutations (Exon20ins) have poor prognoses, exacerbated by a previous lack of specific treatment guidelines and unmet need for targeted therapies. Amivantamab, an EGFR and MET bispecific antibody, demonstrated efficacy and tolerability in patients with advanced EGFR-mutated NSCLC with Exon20ins following platinum-based therapy in CHRYSALIS (NCT02609776; Cohort D+). Since CHRYSALIS was single-arm, individual patient data (IPD)-based adjusted analyses versus similar patients in real-world clinical practice (RWCP) were conducted to generate comparative evidence.

Adjusted comparison of outcomes between patients from CARTITUDE-1 multiple myeloma patients with prior exposure to proteasome inhibitors, immunomodulatory drugs and anti-CD38 antibody from the prospective, multinational LocoMMotion study of real-world clinical practice.

Ciltacabtagene autoleucel (cilta-cel) is a chimeric antigen receptor T-cell therapy studied in patients with multiple myeloma exposed to three classes of treatment in the single-arm CARTITUDE-1 study. To assess the effectiveness of cilta-cel compared to real-world clinical practice (RWCP), we performed adjusted comparisons using individual patients' data from CARTITUDE-1 and LocoMMotion, a prospective, multinational study of patients with multiple myeloma triple-class exposed of treatment. Comparisons were performed using inverse probability weighting.

Adjusted Comparison of Outcomes between Patients from CARTITUDE-1 versus Multiple Myeloma Patients with Prior Exposure to PI, Imid and Anti-CD-38 from a German Registry.

Ciltacabtagene autoleucel (cilta-cel) is a Chimeric antigen receptor T-cell therapy with the potential for long-term disease control in heavily pre-treated patients with relapsed/refractory multiple myeloma (RRMM). As cilta-cel was assessed in the single-arm CARTITUDE-1 clinical trial, we used an external cohort of patients from the Therapie Monitor registry fulfilling the CARTITUDE-1 inclusion criteria to evaluate the effectiveness of cilta-cel for overall survival (OS) and time to next treatment (TTNT) vs. real-world clinical practice.

: An R Package for performing kernel change point detection on the running statistics of multivariate time series.

In many scientific disciplines, researchers are interested in discovering when complex systems such as stock markets, the weather or the human body display abrupt changes. Essentially, this often comes down to detecting whether a multivariate time series contains abrupt changes in one or more statistics, such as means, variances or pairwise correlations. To assist researchers in this endeavor, this paper presents the package for performing kernel change point (KCP) detection on user-selected running statistics of multivariate time series.

Detecting long-lived autodependency changes in a multivariate system via change point detection and regime switching models.

Long-lived simultaneous changes in the autodependency of dynamic system variables characterize crucial events as epileptic seizures and volcanic eruptions and are expected to precede psychiatric conditions. To understand and predict such phenomena, methods are needed that detect such changes in multivariate time series. We put forward two methods: First, we propose KCP-AR, a novel adaptation of the general-purpose KCP (Kernel Change Point) method.

Testing for the Presence of Correlation Changes in a Multivariate Time Series: A Permutation Based Approach.

Detecting abrupt correlation changes in multivariate time series is crucial in many application fields such as signal processing, functional neuroimaging, climate studies, and financial analysis. To detect such changes, several promising correlation change tests exist, but they may suffer from severe loss of power when there is actually more than one change point underlying the data. To deal with this drawback, we propose a permutation based significance test for Kernel Change Point (KCP) detection on the running correlations.

Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods.

Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP.