Search

AimAttenuated psychosis syndrome (APS), a condition for further study in the Diagnostic and Statistical Manual of Mental Disorders-5, comprises psychotic symptoms that are qualitatively similar to those observed in schizophrenia but are less severe. Patients with APS are at high risk of converting to first-episode psychosis (FEP). As evidence for effective pharmacological interventions in APS is limited, novel treatments may provide symptomatic relief and delay/prevent psychotic conversion. This trial aims to investigate the efficacy, safety, and tolerability of BI 409306, a potent and selective phosphodiesterase-9 inhibitor, versus placebo in APS. Novel biomarkers of psychosis are being investigated.MethodsIn this Phase II, multinational, double-blind, parallel-group trial, randomized (1:1) patients will receive BI 409306 50 mg or placebo twice daily for 52 weeks. Patients (n = 300) will be enrolled to determine time to remission of APS, time to FEP, change in everyday functional capacity (Schizophrenia Cognition Rating Scale), and change from baseline in Brief Assessment of Cognition composite score and Positive and Negative Syndrome Scale scores. Potential biomarkers of psychosis under investigation include functional measures of brain activity and automated speech analyses. Safety is being assessed throughout.ConclusionsThis trial will determine whether BI 409306 is superior to placebo in achieving sustainable remission of APS and improvements in cognition and functional capacity. These advances may provide evidence-based treatment options for symptomatic relief in APS. Furthermore, the study will assess the effect of BI 409306 on psychotic conversion and explore the identification of patients at risk for conversion using novel biomarkers.

Incorporation of external information is becoming increasingly common when designing clinical trials. Availability of multiple sources of information has inspired the development of methodologies that account for potential heterogeneity not only between the prospective trial and the pooled external data sources but also between the different external data sources themselves. Our approach proposes an intuitive way of handling such a scenario for the continuous outcomes setting by using propensity score-based stratification and then utilizing robust meta-analytic predictive priors for each stratum to incorporate the prior data to distinguish among different external data sources in each stratum. Through extensive simulations, our approach proves to be more efficient and less biased than the currently available methods. A real case study using clinical trials that study schizophrenia from multiple different sources is also included. [ABSTRACT FROM AUTHOR]

Purpose: The toddler years are a critical period for language development and growth. We investigated how event-related potentials (ERPs) to repeated and novel nonwords are associated with clinical assessments of language in young children. In addition, nonword repetition (NWR) was used to measure phonological working memory to determine the unique and collective contribution of ERP measures of phonemic discrimination and NWR as predictors of language ability. Method: Forty children between the ages of 24-48 months participated in an ERP experiment to determine phonemic discrimination to repeated and novel nonwords in an old/new design. Participants also completed a NWR task to explore the contribution of phonological working memory in predicting language. Results: ERP analyses revealed that faster responses to novel stimuli correlated with higher language performance on clinical assessments of language. Regression analyses revealed that an earlier component was associated with lower level phonemic sensitivity, and a later component was indexing phonological working memory skills similar to NWR. Conclusion: Our findings suggest that passive ERP responses indexing phonological discrimination and phonological working memory are strongly related to behavioral measures of language.

Incorporating interim analysis into a trial design is gaining popularity in the field of confirmatory clinical trials, where two studies may be conducted in parallel (ie, twin studies) in order to provide substantial evidence per the requirement of FDA guidance. Interim futility analysis provides a chance to check for the "disaster" scenario when the treatment has a high probability to be not more efficacious than the control. Therefore, it is an efficient tool to mitigate risk of running a complete and expansive trial under such scenario. There is no agreement among trial designers that interim analysis should be based on individual study data or pooled data under the twin study scenario. In fact, it is a dilemma for most scientists when specifying the interim analysis strategy at the design stage as the true treatment effects of the twin studies are unknown no matter how similar they are intended to be. To address the issue, we developed a Bayesian hierarchical modeling method to allow dynamic data borrowing between twin studies and demonstrated a favorable characteristic of the new method over the separate and pooled analyses. We evaluated a wide spectrum of the heterogeneity hyperparameters and visualized its critical impact on the Bayesian model's characteristic. Based on the evaluation, we made a suggestion on the heterogeneity hyperparameter selection independent of any a priori knowledge. We also applied our method to a case study where predictive powers of different methods are compared. [ABSTRACT FROM AUTHOR]

Traditionally, statistical methods for futility analysis are developed based on a single study. To establish a drug's effectiveness, usually at least two adequate and well-controlled studies need to demonstrate convincing evidence on its own. Therefore, in a standard clinical development program in chronic diseases, two independent studies are generally conducted for drug registration. This paper proposes a statistical method to combine interim data from two independent and similar studies for interim futility analysis and shows that the conditional power approach based on combined interim data has better operating characteristics compared to the approach based on single-trial interim data, even with small to moderate heterogeneity on the treatment effects between the two studies. [ABSTRACT FROM AUTHOR]

Summary: Multiple comparison procedures combined with modeling techniques (MCP‐Mod) (Bretz et al., 2005) is an efficient and robust statistical methodology for the model‐based design and analysis of dose‐finding studies with an unknown dose–response model. With this approach, multiple comparison methods are used to identify statistically significant contrasts corresponding to a set of candidate dose–response models, and the best model is then used to estimate the target dose. Power and sample size calculations for this methodology require knowledge of the covariance matrix for the estimators of the (placebo‐adjusted) mean responses among the dose groups. In this article, we consider survival endpoints and derive an analytic form of the covariance matrix for the estimators of the log hazard ratios as a function of the total number of events in the study. We then use this closed‐form expression of the covariance matrix to derive the power and sample size formulas. We discuss practical considerations in the application of these formulas. In addition, we provide an illustration with a motivating example on chronic obstructive pulmonary disease. Finally, we demonstrate through simulation studies that the proposed formulas are accurate enough for practical use. [ABSTRACT FROM AUTHOR]

Research focused on assessing weight stigmatization has typically been conducted using cross-sectional, retrospective designs. Such designs may impair the scientific understanding of this stigma by limiting participants’ recall of frequencies and/or details about stigmatizing events. To address this, 50 overweight/obese women were recruited from public weight forums to complete week-long daily diaries. A total of 1077 weight-stigmatizing events were reported on the Stigmatizing Situations Inventory. Hierarchical linear modeling was used to investigate potential relationships between participant-level factors and reported stigmatization. Results indicate that body mass index, education, age, daily activities, and interpersonal interactions all may impact individuals’ levels of stigmatization. [ABSTRACT FROM AUTHOR]