Browse
Submit Data
Databases
API
Help

Dataset Information

18 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Employing a latent variable framework to improve efficiency in composite endpoint analysis.

ABSTRACT: Composite endpoints that combine multiple outcomes on different scales are common in clinical trials, particularly in chronic conditions. In many of these cases, patients will have to cross a predefined responder threshold in each of the outcomes to be classed as a responder overall. One instance of this occurs in systemic lupus erythematosus, where the responder endpoint combines two continuous, one ordinal and one binary measure. The overall binary responder endpoint is typically analysed using logistic regression, resulting in a substantial loss of information. We propose a latent variable model for the systemic lupus erythematosus endpoint, which assumes that the discrete outcomes are manifestations of latent continuous measures and can proceed to jointly model the components of the composite. We perform a simulation study and find that the method offers large efficiency gains over the standard analysis, the magnitude of which is highly dependent on the components driving response. Bias is introduced when joint normality assumptions are not satisfied, which we correct for using a bootstrap procedure. The method is applied to the Phase IIb MUSE trial in patients with moderate to severe systemic lupus erythematosus. We show that it estimates the treatment effect 2.5 times more precisely, offering a 60% reduction in required sample size.

SUBMITTER: McMenamin M

PROVIDER: S-EPMC8172253 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework.

Project description:Latent variable models have been playing a central role in psychometrics and related fields. In many modern applications, the inference based on latent variable models involves one or several of the following features: (1) the presence of many latent variables, (2) the observed and latent variables being continuous, discrete, or a combination of both, (3) constraints on parameters, and (4) penalties on parameters to impose model parsimony. The estimation often involves maximizing an objective function based on a marginal likelihood/pseudo-likelihood, possibly with constraints and/or penalties on parameters. Solving this optimization problem is highly non-trivial, due to the complexities brought by the features mentioned above. Although several efficient algorithms have been proposed, there lacks a unified computational framework that takes all these features into account. In this paper, we fill the gap. Specifically, we provide a unified formulation for the optimization problem and then propose a quasi-Newton stochastic proximal algorithm. Theoretical properties of the proposed algorithms are established. The computational efficiency and robustness are shown by simulation studies under various settings for latent variable model estimation.

| S-EPMC9636119 | biostudies-literature

Stringing Bimetallic Metal-Organic Framework-Derived Cobalt Phosphide Composite for High-Efficiency Overall Water Splitting.

Project description:Water electrolysis is an emerging energy conversion technology, which is significant for efficient hydrogen (H2) production. Based on the high-activity transition metal ions and metal alloys of ultrastable bifunctional catalyst, the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) are the key to achieving the energy conversion method by overall water splitting (OWS). This study reports that the Co-based coordination polymer (ZIF-67) anchoring on an indium-organic framework (InOF-1) composite (InOF-1@ZIF-67) is treated followed by carbonization and phosphorization to successfully obtain CoP nanoparticles-embedded carbon nanotubes and nitrogen-doped carbon materials (CoP-InNC@CNT). As HER and OER electrocatalysts, it is demonstrated that CoP-InNC@CNT simultaneously exhibit high HER performance (overpotential of 153 mV in 0.5 m H2SO4 and 159 mV in 1.0 m KOH) and OER performance (overpotential of 270 mV in 1.0 m KOH) activities to reach the current density of 10 mA cm-2. In addition, these CoP-InNC@CNT rods, as a cathode and an anode, can display an excellent OWS performance with ?10 = 1.58 V and better stability, which shows the satisfying electrocatalyst for the OWS compared to control materials. This method ensures the tight and uniform growth of the fast nucleating and stable materials on substrate and can be further applied for practical electrochemical reactions.

| S-EPMC7055562 | biostudies-literature

A Composite Endpoint for Treatment Benefit According to Patient Preference.

Project description:Complex disorders usually affect multiple symptom domains measured by several outcomes. The importance of these outcomes is often different among patients. Current approaches integrate multiple outcomes without considering patient preferences at the individual level. In this paper, we propose a new composite Desirability of Outcome Ranking (DOOR) that integrates individual level ranking of outcome importance and define a winning probability measuring the overall treatment effect. Stratified randomization can be performed based on the participants' baseline outcome rankings. A Wilcoxon-Mann-Whitney U-statistic is used to average the pairwise DOOR between one treated and one control patient, considering the difference in these patients' ranking of outcome importance. We use both theoretical and empirical methods to examine the statistical properties of our method and to compare with conventional approaches. We conclude that the proposed composite DOOR properly reflects patient-level preferences and can be used in pivotal trials or comparative effectiveness trials for a patient-centered evaluation of overall treatment benefits.

| S-EPMC10655937 | biostudies-literature

Home monitoring improves endpoint efficiency in idiopathic pulmonary fibrosis.

Project description:The objective of this study was to investigate the reliability, feasibility and analytical impact of home-based measurement of forced vital capacity (FVC) and dyspnoea as clinical endpoints in idiopathic pulmonary fibrosis (IPF).Patients with IPF performed weekly home-based assessment of FVC and dyspnoea using a mobile hand-held spirometer and self-administered dyspnoea questionnaires. Weekly variability in FVC and dyspnoea was estimated, and sample sizes were simulated for a hypothetical 24-week clinical trial using either traditional office-based interval measurement or mobile weekly assessment.In total, 25 patients were enrolled. Mean adherence to weekly assessments over 24?weeks was greater than 90%. Compared with change assessment using baseline and 24-week measurements only, weekly assessment of FVC resulted in enhanced precision and power. For example, a hypothetical 24-week clinical trial with FVC as the primary endpoint would require 951 patients using weekly home spirometry compared with 3840 patients using office spirometry measures at weeks 1 and 24 only. The ability of repeated measures to reduce clinical trial sample size was influenced by the correlation structure of the data.Home monitoring can improve the precision of endpoint assessments, allowing for greater efficiency in clinical trials of therapeutics for IPF.

| S-EPMC6029860 | biostudies-literature

Development of a genetic framework to improve the efficiency of bioactive delivery from blueberry.

Project description:In the present study, we applied a novel high-throughput in vitro gastrointestinal digestion model to phenotype bioaccessibility of phenolics in a diverse germplasm collection representing cultivated highbush blueberries. Results revealed significant (P < 0.05) differences between accessions, years, and accession by year interaction for relative and absolute bioaccessibility of flavonoids and phenolic acids. Broad sense heritability estimates revealed low to moderate inheritances of relative and absolute bioaccessibility, suggesting that besides environmental variables, genetics factors could control bioaccessibility of phenolics. Acylated anthocyanins had significantly higher relative bioaccessibility than non-acylated anthocyanins. Correlation analysis indicated that relative bioaccessibility did not show significant association with fruit quality or raw concentration of metabolites. The study also identified accessions that have high relative and absolute bioaccessibility values. Overall, combining the bioaccessibility of phenolics with genetic and genomic approaches will enable the identification of genotypes and genetic factors influencing these traits in blueberry.

| S-EPMC7560831 | biostudies-literature

A latent variable model for improving inference in trials assessing the effect of dose on toxicity and composite efficacy endpoints.

Project description:It is often of interest to explore how dose affects the toxicity and efficacy properties of a novel treatment. In oncology, efficacy is often assessed through response, which is defined by a patient having no new tumour lesions and their tumour size shrinking by 30%. Usually response and toxicity are analysed as binary outcomes in early phase trials. Methods have been proposed to improve the efficiency of analysing response by utilising the continuous tumour size information instead of dichotomising it. However, these methods do not allow for toxicity or for different doses. Motivated by a phase II trial testing multiple doses of a treatment against placebo, we propose a latent variable model that can estimate the probability of response and no toxicity (or other related outcomes) for different doses. We assess the confidence interval coverage and efficiency properties of the method, compared to methods that do not use the continuous tumour size, in a simulation study and the real study. The coverage is close to nominal when model assumptions are met, although can be below nominal when the model is misspecified. Compared to methods that treat response as binary, the method has confidence intervals with 30-50% narrower widths. The method adds considerable efficiency but care must be taken that the model assumptions are reasonable.

| S-EPMC6986906 | biostudies-literature

A patient-centered composite endpoint weighting technique for orthopaedic trauma research.

Project description:BACKGROUND:This study aimed to address the current limitations of the use of composite endpoints in orthopaedic trauma research by quantifying the relative importance of clinical outcomes common to orthopaedic trauma patients and use those values to develop a patient-centered composite endpoint weighting technique. METHODS:A Best-Worst Scaling choice experiment was administered to 396 adult surgically-treated fracture patients. Respondents were presented with ten choice sets, each consisting of three out of ten plausible clinical outcomes. Hierarchical Bayesian modeling was used to determine the utilities associated with the outcomes. RESULTS:Death was the outcome of greatest importance (mean utility?=?-?8.91), followed by above knee amputation (-?7.66), below knee amputation (-?6.97), severe pain (-?5.90), deep surgical site infection (SSI) (-?5.69), bone healing complications (-?5.20), and moderate pain (-?4.59). Mild pain (-?3.30) and superficial SSI (-?3.29), on the other hand, were the outcomes of least importance to respondents. CONCLUSION:This study revealed that patients' relative importance towards clinical outcomes followed a logical gradient, with distinct and quantifiable preferences for each possible component outcome. These findings were incorporated into a novel composite endpoint weighting technique.

| S-EPMC6933647 | biostudies-literature

Low Serum Uric Acid Predicts Risk of a Composite Disease Endpoint.

Project description:Background and objectives: Mortality may increase in hypouricemia as well as inhyperuricemia. We assessed the predictive value of low serum uric acid (SUA) levels on the risk of overall mortality or a composite endpoint of death and nonfatal events. Materials and Methods: In 1013 community-based middle-aged adults, free of uncontrolled diabetes and coronary heart disease at baseline, the association of sex-specific SUA tertiles with defined outcomes was evaluated prospectively by logistic regression, stratified to gender and presence of type-2 diabetes, using recent criteria. Results: Totally, 43 deaths and additional incident nonfatal events in 157 cases were recorded at a median 3.4 years' follow-up. Multivariable linear regression disclosed SUA to be significantly associated among non-diabetic individuals positively with creatinine, triglycerides, and body mass index in women further with fasted glucose. In multivariable-adjusted logistic regression analysis, sex-specifically dichotomized baseline uric acid (<5.1 and <4.1 mg/dL vs. higher values) significantly predicted the non-fatal events in the whole sample (relative risk (RR) 1.51 [95% confidence interval (CI) 1.02; 2.26]), as well as in men, while composite endpoint in the whole sample tended to rise (RR 1.38). Compared with the intermediate one, the top and bottom SUA tertiles combined tended to confer mortality risk (RR 2.40 [95% CI 0.89; 6.51]). Adverse outcomes in diabetic women were predicted by tertiles 2 and 3. Conclusions: Inverse association of SUA with adverse outcomes, especially in men, is consistent with the involvement of uric acid mass in autoimmune activation. The positive association of uric acid with adverse outcomes in diabetic women is likely mediated by concomitant high-density lipoprotein dysfunction.

| S-EPMC8068308 | biostudies-literature

Latent variable modeling for the microbiome.

Project description:The human microbiome is a complex ecological system, and describing its structure and function under different environmental conditions is important from both basic scientific and medical perspectives. Viewed through a biostatistical lens, many microbiome analysis goals can be formulated as latent variable modeling problems. However, although probabilistic latent variable models are a cornerstone of modern unsupervised learning, they are rarely applied in the context of microbiome data analysis, in spite of the evolutionary, temporal, and count structure that could be directly incorporated through such models. We explore the application of probabilistic latent variable models to microbiome data, with a focus on Latent Dirichlet allocation, Non-negative matrix factorization, and Dynamic Unigram models. To develop guidelines for when different methods are appropriate, we perform a simulation study. We further illustrate and compare these techniques using the data of Dethlefsen and Relman (2011, Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proceedings of the National Academy of Sciences108, 4554-4561), a study on the effects of antibiotics on bacterial community composition. Code and data for all simulations and case studies are available publicly.

| S-EPMC6797058 | biostudies-literature

Validation analysis of a composite real-world mortality endpoint for patients with cancer in the United States.

Project description:ObjectiveWe expanded the previous assessment of a mortality variable suited for real-world evidence-focused oncology research.Data sourceWe used a nationwide electronic health record (EHR)-derived de-identified database.Data collectionWe included patients with at least 1 of 18 cancer types between January 1, 2011 and December 31, 2017. Patient-level structured data (EHRs, obituaries, and Social Security Death Index) and unstructured EHR data (abstracted) were linked to generate a composite mortality variable.Study designWe benchmarked sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and ±15-day agreement against the National Death Index (NDI). Real-world overall survival (rwOS) was estimated using the Kaplan-Meier method. We performed sensitivity analyses using a smaller patient cohort that underwent next-generation sequencing testing.Principal findingsCompared with the NDI across 18 cancer types (overall N = 160 436): sensitivity, 83.9%-91.5% (17/18 cancer types had sensitivity ≥85.0%); specificity, 93.5%-99.7%; PPV, 96.3%-98.3%; NPV, 75.0%-98.7%; ±15-day agreement, 95.6%-97.6%; and median rwOS estimates ranging from 2.8% to 12.7% greater. Sensitivity analysis results (n = 17 540) were consistent with the main analysis.ConclusionsAcross all cancer types analyzed, this composite mortality variable showed high sensitivity, specificity, PPV, NPV, and ±15-day agreement, and yielded median rwOS values modestly overestimated when compared to NDI-based results.

| S-EPMC8586476 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data