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In clinical settings, the diagnosis of medical conditions is often aided by measurement of various serum biomarkers through the use of laboratory tests. These biomarkers provide information about different aspects of a patient's health and overall function of multiple organ systems. We have developed a statistical procedure that condenses the information from a variety of health biomarkers into a composite index, which could be used as a risk score for predicting all-cause mortality. It could also be viewed as a holistic measure of overall physiological health status. This health status metric is computed as a function of standardized values of each biomarker measurement, weighted according to their empirically determined relative strength of association with mortality. The underlying risk model was developed using the biomonitoring and mortality data of a large sample of US residents obtained from the National Health and Nutrition Examination Survey (NHANES) and the National Death Index (NDI). Biomarker concentration levels were standardized using spline-based Cox regression models, and optimization algorithms were used to estimate the weights. The predictive accuracy of the tool was optimized by bootstrap aggregation. We also demonstrate how stacked generalization, a machine learning technique, can be used for further enhancement of the prediction power. The index was shown to be highly predictive of all-cause mortality and long-term outcomes for specific health conditions. It also exhibited a robust association with concurrent chronic conditions, recent hospital utilization, and current health status as assessed by self-rated health.

Background Many clinical trials focus on the comparison of the treatment effect between two or more groups concerning a rarely occurring event. In this situation, showing a relevant effect with an acceptable power requires the observation of a large number of patients over a long period of time. For feasibility issues, it is therefore often considered to include several event types of interest, non-fatal or fatal, and to combine them within a composite endpoint. Commonly, a composite endpoint is analyzed with standard survival analysis techniques by assessing the time to the first occurring event. This approach neglects that an individual may experience more than one event which leads to a loss of information. As an alternative, composite endpoints could be analyzed by models for recurrent events. There exists a number of such models, e.g. regression models based on count data or Cox-based models such as the approaches of Andersen and Gill, Prentice, Williams and Peterson or, Wei, Lin and Weissfeld. Although some of the methods were already compared within the literature there exists no systematic investigation for the special requirements regarding composite endpoints. Methods Within this work a simulation-based comparison of recurrent event models applied to composite endpoints is provided for different realistic clinical trial scenarios. Results We demonstrate that the Andersen-Gill model and the Prentice- Williams-Petersen models show similar results under various data scenarios whereas the Wei-Lin-Weissfeld model delivers effect estimators which can considerably deviate under commonly met data scenarios. Conclusion Based on the conducted simulation study, this paper helps to understand the pros and cons of the investigated methods in the context of composite endpoints and provides therefore recommendations for an adequate statistical analysis strategy and a meaningful interpretation of results.

The evaluation of health benefits and harms of an intervention with GRADE Evidence to Decision (EtD) frameworks includes judgments if the effects are “trivial,” “small,” “moderate,” or “large.” Such judgments ideally require the a priori establishment of decision thresholds (DTs), whose empirical derivation for single outcomes has been previously described. In this article, we provide a methodological approach to estimate DTs for composite endpoints based on disutilities. We generated an approach that involves the computation of pooled disutilities, in which the disutility of each outcome comprised in the composite endpoint is weighted by the respective relative frequency. We also applied a modeling approach based on probability distributions to account for uncertainty in the estimates. We present a practical example of the determination of DTs associated with the development of at least one adverse event following treatment with intranasal medications for rhinitis that we used in the Allergic Rhinitis and its Impact on Asthma guidelines. We provide the methodological steps of a modeling-based approach to compute pooled disutilities and, as a result, DTs for composite endpoints. We have developed a webtool (https://compositedt.med.up.pt/) that allows for a simple implementation of the proposed approach. Applying our approach to a practical example, we concluded that rhinitis nasal medications, compared to placebo, were associated with a trivial harm from adverse events. We propose an approach for estimating DTs for composite endpoints, which may be particularly valuable whenever composite endpoints are used for clinical research, clinical practice, and decision-making. •An approach has been developed to compute DTs for composite endpoints.•The approach is based on weighted disutilities and allows to consider uncertainty.•We provide detailed stepwise guidance and a webtool to implement this approach.

Studies often combine several events, for example, death or myocardial infarction or stroke, into a single study outcome. This is called a composite endpoint. Composite endpoints make doing trials easier by reducing the sample size or follow-up period required to demonstrate the effectiveness of an intervention. However, interpreting the results of composite endpoints can be confusing. To avoid misleading conclusions about the effectiveness of an intervention, it is important for readers of studies reporting a composite endpoint to ascertain that the clinical importance, the frequency of events, and the effect of the intervention on each component of the composite endpoint are similar. •Studies often combine several events into a single study outcome, the composite endpoint.•Using composite endpoints reduces the sample size or follow-up period required to demonstrate the effectiveness of an intervention.•To avoid misleading conclusions about the effectiveness of an intervention, it is important to ascertain that the clinical importance, the frequency of events, and the effect of the intervention on each component of the composite endpoint are similar.

Abstract Background Randomized clinical trials (RCTs) in hospital-acquired and ventilator-associated bacterial pneumonia (HABP and VABP, respectively) are important for the evaluation of new antimicrobials. However, the heterogeneity in endpoints used in RCTs evaluating treatment of HABP/VABP may puzzle clinicians. The aim of this work was to reach a consensus on clinical endpoints to consider in future clinical trials evaluating antimicrobial treatment efficacy for HABP/VABP. Methods Twenty-six international experts from intensive care, infectious diseases, and the pharmaceutical industry were polled using the Delphi method. Results The panel recommended a hierarchical composite endpoint including, by priority order, (1) survival at day 28, (2) mechanical ventilation–free days through day 28, and (3) clinical cure between study days 7 and 10 for VABP; and (1) survival (day 28) and (2) clinical cure (days 7–10) for HABP. Clinical cure was defined as the combination of resolution of signs and symptoms present at enrollment and improvement or lack of progression of radiological signs. More than 70% of the experts agreed to assess survival and mechanical ventilation–free days though day 28, and clinical cure between day 7 and day 10 after treatment initiation. Finally, the hierarchical order of endpoint components was reached after 3 Delphi rounds (72% agreement). Conclusions We provide a multinational expert consensus on separate hierarchical composite endpoints for VABP and HABP, and on a definition of clinical cure that could be considered for use in future HABP/VABP clinical trials. Using the Delphi method, this study provides a multinational expert consensus on separate hierarchical composite endpoints for hospital-acquired and ventilator-associated bacterial pneumonia (HABP/VABP), and on a definition of clinical cure that could be considered for use in future HABP/VABP clinical trials.

INTRODUCTION We evaluated the statistical power for a theoretical randomized trial of anti‐amyloid treatment in preclinical Alzheimer's Disease across five cognitive composites in preclinical Alzheimer's Disease across five cognitive composites: Alzheimer's Prevention Initiative Preclinical Composite Cognitive Test (APCC); Preclinical Alzheimer's Composite with Semantic Processing (PACC5); Preclinical Alzheimer's Cognitive Composite (PACC); and global and episodic memory composites. METHODS We utilized annual cognitive assessments from 517 decedents (78.2 ± 4.7years; 72% female) with post mortem pathologic Alzheimer's disease (AD) to represent amyloid positivity. We calculated sample sizes to detect 30% reduction in 5‐year slopes of cognitive decline for equal size treatment versus placebo groups across composites. RESULTS Estimated sample sizes for APCC (n = 1633, 95% confidence interval [CI] 1400–1823), PACC (n = 1822, 95% CI 1612–2122), and episodic memory (n = 3141 95%CI 2563–3732) were larger than for PACC5 (n = 1424, 95% CI 1249–1575). Sample size estimates were similar between PACC5 and the global composite (n = 1267, 95%CI 1336–1407). DISCUSSION Small changes in composites, such as addition of semantic fluency in PACC5, could be considered as part of approaches to improve statistical power. HIGHLIGHTS We evaluated statistical power of a theoretical 5‐year randomized trial testing anti‐amyloid treatments in early Alzheimer's across five cognitive composite endpoints. We leveraged annual cognitive assessment in Rush Alzheimer's Disease Center cohorts and used post mortem pathologic AD to represent amyloid positivity. Preclinical Alzheimer's Composite with Semantic Processing (PACC5) required significantly lower sample size to achieve power for a 30% reduction in cognitive slope than Alzheimer's Disease Cooperative Study‐Preclinical Alzheimer's Cognitive Composite (PACC). PACC5 had better statistical power than Alzheimer's Prevention Initiative Preclinical Composite Cognitive Test (APCC) and an episodic memory composite. Small changes in cognitive composites can improve detection of cognitive decline.

Pocock et al. (2012), following Finkelstein & Schoenfeld (1999), has popularized the win ratio for analysis of controlled clinical trials with multiple types of outcome event. The approach uses pairwise comparisons between patients in the treatment and control groups using a primary outcome, say the time to death, with ties broken using a secondary outcome, say the time to hospitalization. In general the observed pairwise preferences and the weight they attach to the component rankings will depend on the distribution of potential follow-up time. We present expressions for the win and loss probabilities for general bivariate survival models when follow-up of all patients is limited to a specified time horizon. In the special case of a bivariate Lehmann model we show that the win ratio does not depend on this horizon. We show how the win ratio may be estimated nonparametrically or from a parametric model. Extensions to events of three or more types are described. Application of the method of marginal estimation due to Wei et al. (1989) to this problem is described.

Background The win ratio has been widely used in the analysis of hierarchical composite endpoints, which prioritize critical outcomes such as mortality over nonfatal, secondary events. Although a regression framework exists to incorporate covariates, it is limited to low-dimensional datasets and may struggle with numerous predictors. This gap necessitates a robust variable selection method tailored to the win ratio framework. Methods We propose an elastic net-type regularization approach for win ratio regression, extending the proportional win-fractions (PW) model in low-dimensional settings. The method addresses key challenges, including adapting pairwise comparisons to penalized regression, optimizing model selection through subject-level cross-validation, and defining performance metrics via a generalized concordance index. The procedures are implemented in the wrnet R-package, publicly available at https://lmaowisc.github.io/wrnet/ . Results Simulation studies demonstrate that wrnet outperforms traditional (regularized) Cox regression for time-to-first-event analysis, particularly in scenarios with differing covariate effects on mortality and nonfatal events. When applied to data from the HF-ACTION trial, the method identified prognostic variables and achieved superior predictive accuracy compared to regularized Cox models, as measured by overall and component-specific concordance indices. Conclusion The wrnet approach combines the interpretability and clinical relevance of the win ratio with the scalability and robustness of elastic net regularization. The accompanying R-package provides a user-friendly interface for routine application of the procedures, whenever appropriate. Future research could explore additional applications or refine the methodology to address non-proportionalities in win-loss risks and nonlinearities in covariate effects.

BACKGROUND: Measures of therapeutic efficacy in pain studies have historically focused on pain scores, such as the Visual Analog Scale (VAS) or the Numeric Rating Scale. However, pain scores capture a univariate measure of a multivariate condition present in patients with chronic pain, where the pain condition can affect activities of daily living, sleep, quality of life, and mood. Hence, examining composite endpoints, which incorporate outcomes from multiple facets of pain, may allow investigators to better assess improvements in chronic pain patients with various new treatments. OBJECTIVES: This trial was designed to evaluate the performance of the Nalu™ Neurostimulation System (Nalu Medical, Inc.), a miniature implanted pulse generator (micro-IPG), in the treatment of low-back pain and leg pain with spinal cord stimulation therapy. STUDY DESIGN: This was a prospective, single arm, multicenter, open-label, postmarket study that followed patients for 90 days postimplantation of the Nalu Neurostimulation System. SETTING: Patients were recruited from, and treated at, 15 US-based comprehensive pain centers. METHODS: Patients with chronic, intractable, neuropathic pain of the back and/or leg(s), with a VAS pain score of at least 6 at the time of screening, were included. The micro-IPG was implanted per standard clinical practice. Patient-reported outcomes (PROs), including VAS pain scores, Oswestry Disability Index (ODI), Beck Depression Inventory, quality-of-life metric (EQ-5D-5L), Patient Global Impression of Change (PGIC), and sleep disturbance Patient-reported Outcomes Measurement Information System (PROMIS) were recorded. Literature-based minimal clinically important differences (MCIDs) were used to define the MCID responder rates as well as a composite endpoint analysis. RESULTS: Ninety-four percent (94%) of the study patients reached the MCID in at least 2 of the PROs. Five out of 6 PROs demonstrated a responder rate of > 75%. Forty-nine percent (49%) of the patients were holistic responders, meaning they responded in each of the 6 outcome measures under consideration. Overall VAS pain scores reached the MCID in 86% of the patients. PGIC demonstrated the largest MCID responder rate: 100%. The ODI score reached the MCID in 94% of the patients; the BDI score reached the MCID rate in 84% of the patients; the EQ-5D-5L score reached the MCID in 77% of the patients; and the PROMIS score reached the MCID in 67% of the patients. LIMITATIONS: While this was a multicenter, prospective study, it was also a single arm, nonrandomized trial. The 35 study patients were only followed for 90 days post micro-IPG implant. CONCLUSION: In the face of improving spinal cord stimulation pain outcomes, composite PROs are likely to become more common in evaluating therapeutic response. Responder rates, defined by the MCID, may help to establish composite endpoints. Since MCID was achieved across a variety of endpoints indicates that treatment with the Nalu Neurostimulation System provided a robust treatment response. KEY WORDS: Spinal cord stimulation (SCS), chronic pain, radiculopathy, micro-IPG, battery-free, persistent spinal pain syndrome (PSPS), Failed Back Surgery Syndrome (FBSS), composite endpoints, holistic responders