New results on optimal conditional error functions for adaptive two-stage designs

Unblinded interim analyses in clinical trials with adaptive designs are gaining increasing popularity. Here, the type I error rate is controlled by defining an appropriate conditional error function. Since various approaches to the selection of the conditional error function exist, the question of an optimal choice arises. In this article, we extend existing work on optimal conditional error functions by two results. Firstly, we prove that techniques from variational calculus can be applied to derive existing optimal conditional error functions. Secondly, we answer the question of optimizing the conditional error function of an optimal promising zone design and investigate the efficiency gain.</p

Blinded sample size recalculation in clinical trials with binary composite endpoints

<p>We consider clinical trials with a binary composite endpoint where the trial is successful when a significant result is achieved for the composite or one prespecified main component. Appropriate sample size planning is challenging in this situation, as in addition to the Type I error rate, power, and target difference the overall event rates and the correlation between the test statistics have to be defined. Reliable estimates of these quantities, however, are usually hard to obtain and therefore there is a high risk to not achieve the intended power in a fixed sample size design. In this article, we propose an internal pilot study design where the nuisance parameters are estimated in a blinded way at an interim stage and where the sample size is then revised accordingly. We investigate the characteristics of the proposed design with respect to the actual Type I error rate, power, and sample size. The application of this design is illustrated by a clinical trial example.</p

Adaptive Designs for Two Candidate Primary Time-to-Event Endpoints

<p>In clinical trials, the choice of an adequate primary endpoint is often difficult. Besides its clinical relevance, the endpoint must be measurable within reasonable time and must allow differentiating between the treatments. Often, the most relevant endpoint is ‘’time-to-death,” but if the overall survival prognosis is good, only a few deaths are observed during the study duration. A possible solution is to use surrogate endpoints instead. However, various examples from the literature demonstrate that surrogates do not always perform as intended. Sometimes, the surrogate effect is smaller than for the original endpoint, or the latter shows a higher effect than anticipated so using the surrogate is not reasonable. In this work, different adaptive design strategies for two candidate endpoints are proposed to solve these problems. The idea is to base the efficacy proof on the significance of at least one endpoint. At an interim analysis, both candidates are evaluated. If it is not possible to stop the study early, the sample size is recalculated based on the more promising endpoint. The new methods are illustrated by a clinical study example and compared in terms of power and sample size using Monte Carlo simulations. The software code is provided as supplementary material.</p

Additional file 1 of Extension of a conditional performance score for sample size recalculation rules to the setting of binary endpoints

Additional file 1. Appendices

Estimation of treatment effects in early-phase randomized clinical trials involving external control data

There are good reasons to perform a randomized controlled trial (RCT) even in early phases of clinical development. However, the low sample sizes in those settings lead to high variability of the treatment effect estimate. The variability could be reduced by adding external control data if available. For the common setting of suitable subject-level control group data only available from one external (clinical trial or real-world) data source, we evaluate different analysis options for estimating the treatment effect via hazard ratios. The impact of the external control data is usually guided by the level of similarity with the current RCT data. Such level of similarity can be determined via outcome and/or baseline covariate data comparisons. We provide an overview over existing methods, propose a novel option for a combined assessment of outcome and baseline data, and compare a selected set of approaches in a simulation study under varying assumptions regarding observable and unobservable confounder distributions using a time-to-event model. Our various simulation scenarios also reflect the differences between external clinical trial and real-world data. Data combinations via simple outcome-based borrowing or simple propensity score weighting with baseline covariate data are not recommended. Analysis options which conflate outcome and baseline covariate data perform best in our simulation study.</p

Prognostic relevance of elevated pulmonary arterial pressure assessed non-invasively: Analysis in a large patient cohort with invasive measurements in near temporal proximity - Fig 4

<p><b>Outcome differentiated by PAP for subgroup of patients:</b> left ventricular cardiomyopathy (CMP):A,B; valvular heart disease: C,D; ischemic heart disease (IHD): E,F and rare cardiac diseases: G;H. Invasive measurements by RHC (A,C,E,G) are compared to non-invasively assessment by DE (B,D,F,G). <b>Abbreviations:</b> m/sPAP mean/systolic pulmonary arterial pressure, HR hazard ratio, 95%CI 95% confidence interval, ns not significant.</p

Incremental predictive information for survival of multimodal clinical settings based on complete-case data (n = 395).

<p><b>Abbreviations:</b> Clin: clinical assessment (age, sex, NYHA functional class), Clin+Echo: clinical assessment and transthoracic (Doppler) echocardiography (LV-EF, RV dysfunction, sPAP, RAP), Clin+Sero: clinical assessment and cardiac serological parameters (NT-proBNP, cTnT); Clin+Sero+Echo: clinical assessment, cardiac serological parameters and transthoracic echocardiography combined; Clin+Sero+Echo+RHC: Non-invasive diagnostics and RHC (CI, mPAP, RAP) combined; Clin+RHC: clinical assessment and RHC (CI, mPAP, RAP) combined. <b>**</b>p<0.001. NYHA New York Heart Association, LV-EF left ventricular ejection fraction, sPAP systolic pulmonary arterial pressure, RAP right atrial pressure, NT-proBNP N-terminal pro brain natriuretic peptide, cTnT cardiac troponin T, CI cardiac index, mPAP mean pulmonary arterial pressure, RHC right heart catheterization.</p

Multivariate analysis for independent predictive parameters.

<p>Multivariate analysis for independent predictive parameters.</p

Hemodynamic characteristics of the study population.

<p>Hemodynamic characteristics of the study population.</p

Study protocol.

<p>Flow chart with inclusion criteria from catheter and echocardiography databases, identification of individual patients, and exclusion due to loss of clinical follow-up. <b>Abbreviation:</b> RHC right heart catheterization.</p