Tag: nevin manimala

Biom J. 2023 Apr 17:e2100368. doi: 10.1002/bimj.202100368. Online ahead of print.

ABSTRACT

We propose a semiparametric mean residual life mixture cure model for right-censored survival data with a cured fraction. The model employs the proportional mean residual life model to describe the effects of covariates on the mean residual time of uncured subjects and the logistic regression model to describe the effects of covariates on the cure rate. We develop estimating equations to estimate the proposed cure model for the right-censored data with and without length-biased sampling, the latter is often found in prevalent cohort studies. In particular, we propose two estimating equations to estimate the effects of covariates in the cure rate and a method to combine them to improve the estimation efficiency. The consistency and asymptotic normality of the proposed estimates are established. The finite sample performance of the estimates is confirmed with simulations. The proposed estimation methods are applied to a clinical trial study on melanoma and a prevalent cohort study on early-onset type 2 diabetes mellitus.

PMID:37068192 | DOI:10.1002/bimj.202100368

Biom J. 2023 Apr 17:e2200153. doi: 10.1002/bimj.202200153. Online ahead of print.

ABSTRACT

Buckley-James (BJ) model is a typical semiparametric accelerated failure time model, which is closely related to the ordinary least squares method and easy to be constructed. However, traditional BJ model built on linearity assumption only captures simple linear relationships, while it has difficulty in processing nonlinear problems. To overcome this difficulty, in this paper, we develop a novel regression model for right-censored survival data within the learning framework of BJ model, basing on random survival forests (RSF), extreme learning machine (ELM), and L₂ boosting algorithm. The proposed method, referred to as ELM-based BJ boosting model, employs RSF for covariates imputation first, then develops a new ensemble of ELMs-ELM-based boosting algorithm for regression by ensemble scheme of L₂ boosting, and finally, uses the output function of the proposed ELM-based boosting model to replace the linear combination of covariates in BJ model. Due to fitting the logarithm of survival time with covariates by the nonparametric ELM-based boosting method instead of the least square method, the ELM-based BJ boosting model can capture both linear covariate effects and nonlinear covariate effects. In both simulation studies and real data applications, in terms of concordance index and integrated Brier sore, the proposed ELM-based BJ boosting model can outperform traditional BJ model, two kinds of BJ boosting models proposed by Wang et al., RSF, and Cox proportional hazards model.

PMID:37068191 | DOI:10.1002/bimj.202200153

Biom J. 2023 Apr 17:e2200092. doi: 10.1002/bimj.202200092. Online ahead of print.

ABSTRACT

Quantifying drug potency, which requires an accurate estimation of dose-response relationship, is essential for drug development in biomedical research and life sciences. However, the standard estimation procedure of the median-effect equation to describe the dose-response curve is vulnerable to extreme observations in common experimental data. To facilitate appropriate statistical inference, many powerful estimation tools have been developed in R, including various dose-response packages based on the nonlinear least squares method with different optimization strategies. Recently, beta regression-based methods have also been introduced in estimation of the median-effect equation. In theory, they can overcome nonnormality, heteroscedasticity, and asymmetry and accommodate flexible robust frameworks and coefficients penalization. To identify a reliable estimation method(s) to estimate dose-response curves even with extreme observations, we conducted a comparative study to review 14 different tools in R and examine their robustness and efficiency via Monte Carlo simulation under a list of comprehensive scenarios. The simulation results demonstrate that penalized beta regression using the mgcv package outperforms other methods in terms of stable, accurate estimation, and reliable uncertainty quantification.

PMID:37068189 | DOI:10.1002/bimj.202200092

Biom J. 2023 Apr 17:e2200112. doi: 10.1002/bimj.202200112. Online ahead of print.

ABSTRACT

When observing data on a patient-reported outcome measure in, for example, clinical trials, the variables observed are often correlated and intended to measure a latent variable. In addition, such data are also often characterized by a hierarchical structure, meaning that the outcome is repeatedly measured within patients. To analyze such data, it is important to use an appropriate statistical model, such as structural equation modeling (SEM). However, researchers may rely on simpler statistical models that are applied to an aggregated data structure. For example, correlated variables are combined into one sum score that approximates a latent variable. This may have implications when, for example, the sum score consists of indicators that relate differently to the latent variable being measured. This study compares three models that can be applied to analyze such data: the multilevel multiple indicators multiple causes (ML-MIMIC) model, a univariate multilevel model, and a mixed analysis of variance (ANOVA) model. The focus is on the estimation of a cross-level interaction effect that presents the difference over time on the patient-reported outcome between two treatment groups. The ML-MIMIC model is an SEM-type model that considers the relationship between the indicators and the latent variable in a multilevel setting, whereas the univariate multilevel and mixed ANOVA model rely on sum scores to approximate the latent variable. In addition, the mixed ANOVA model uses aggregated second-level means as outcome. This study showed that the ML-MIMIC model produced unbiased cross-level interaction effect estimates when the relationships between the indicators and the latent variable being measured varied across indicators. In contrast, under similar conditions, the univariate multilevel and mixed ANOVA model underestimated the cross-level interaction effect.

PMID:37068180 | DOI:10.1002/bimj.202200112

Kidney360. 2023 Apr 17. doi: 10.34067/KID.0000000000000129. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic kidney disease of a nontraditional etiology (CKDnt) is responsible for high mortality in Central America, although its causes remain unclear. Evidence of kidney dysfunction has been observed among youth, suggesting that early kidney damage contributing to CKDnt may initiate in childhood.

METHODS: Urine specimens of Nicaraguan adolescents 12-23 years without CKDnt (n=136) were analyzed by proton nuclear magnetic resonance (1H-NMR) spectroscopy for 50 metabolites associated with kidney dysfunction. Urinary metabolite levels were compared by CKiD U25 estimated glomerular filtration rate (eGFR), regional CKDnt prevalence, sex, age, and family history of CKDnt using supervised statistical methods and pathway analysis in MetaboAnalyst. Magnitude of associations and changes over time were assessed through multivariable linear regression.

RESULTS: In adjusted analyses, glycine concentrations were higher among youth from high-risk regions (β=0.82, (95% CI: 0.16, 1.85); p=0.01). Pyruvate concentrations were lower among youth with low eGFR (β= -0.36; (95% CI:-0.57, -0.04); p=0.03) and concentrations of other citric acid (TCA) cycle metabolites differed by key risk factors. Over four years, participants with low eGFR experienced greater declines in 1-methylnicotinamide and 2-oxoglutarate and greater increases in citrate and guanidinoacetate concentrations.

CONCLUSION: Urinary concentration of glycine, a molecule associated with thermoregulation and kidney function preservation, was higher among youth in high-risk CKDnt regions, suggestive of greater heat exposure or renal stress. Lower pyruvate concentrations were associated with low eGFR, and citric acid cycle metabolites like pyruvate likely relate to mitochondrial respiration rates in the kidneys. Participants with low eGFR experienced longitudinal declines in concentrations of 1-methylnicotinamide, an anti-inflammatory metabolite associated with anti-fibrosis in tubule cells. These findings merit further consideration in research on the origins of CKDnt.

PMID:37068179 | DOI:10.34067/KID.0000000000000129

Curr Protoc. 2023 Apr;3(4):e734. doi: 10.1002/cpz1.734.

ABSTRACT

Prior to the development of genome-wide arrays and whole genome sequencing technologies, heritability estimation mainly relied on the study of related individuals. Over the past decade, various approaches have been developed to estimate SNP-based narrow-sense heritability ( $h_{\mathrm{SNP}}^2$ ) in unrelated individuals. These latter approaches use either individual-level genetic variations or summary results from genome-wide association studies (GWAS). Recently, several studies compared these approaches using extensive simulations and empirical datasets. However, sparse information on hands-on training necessitates revisiting these approaches from the perspective of a stepwise guide for practical applications. Here, we provide an overview of the commonly used SNP-heritability estimation approaches utilizing genome-wide array, imputed or whole genome data from unrelated individuals, or summary results. We not only discuss these approaches based on their statistical concepts, utility, advantages, and limitations, but also provide step-by-step protocols to apply these approaches. For illustration purposes, we estimate $h_{\mathrm{SNP}}^2$ of height and BMI utilizing individual-level data from The Northern Finland Birth Cohort (NFBC) and summary results from the Genetic Investigation of ANthropometric Traits (GIANT;) consortium. We present this review as a template for the researchers who estimate and use heritability in their studies and as a reference for geneticists who develop or extend heritability estimation approaches. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: GREML (GCTA) Alternate Protocol 1: Stratified GREML Basic Protocol 2: LDAK Alternate Protocol 2: Stratified LDAK Basic Protocol 3: Threshold GREML Basic Protocol 4: LD score (LDSC) regression Basic Protocol 5: SumHer.

PMID:37068172 | DOI:10.1002/cpz1.734

Pharmacoepidemiol Drug Saf. 2023 Apr 17. doi: 10.1002/pds.5631. Online ahead of print.

ABSTRACT

PURPOSE: Heterogeneous results from multi-database studies have been observed, for example in the context of generating background incidence rates (IR) for adverse events of special interest for SARS-CoV-2 vaccines. In this study, we aimed to explore different between-database sources of heterogeneity influencing the estimated background IR of venous thromboembolism (VTE).

METHODS: Through forest plots and random-effects models, we performed a qualitative and quantitative assessment of heterogeneity of VTE background IR derived from 11 databases from six European countries, using age and gender stratified background IR for the years 2017-2019 estimated in two studies. Sensitivity analyses were performed to assess the impact of selection criteria on the variability of the reported IR.

RESULTS: A total of 54,257,284 subjects were included in this study. Age-gender pooled VTE IR varied from 5 to 421/100,000 person-years and IR increased with increasing age for both genders. Wide confidence intervals demonstrated considerable within-data-source heterogeneity. Selecting databases with similar characteristics had only a minor impact on the variability as shown in forest plots and the magnitude of the I² statistic, which remained large. Solely including databases with primary care and hospital data resulted in a noticeable decrease in heterogeneity.

CONCLUSIONS: Large variability in IR between data sources and within age group and gender strata warrants the need for stratification and limits the feasibility of a meaningful pooled estimate. A more detailed knowledge of the data characteristics, operationalisation of case definitions and cohort population might support an informed choice of the adequate databases to calculate reliable estimates. This article is protected by copyright. All rights reserved.

PMID:37068170 | DOI:10.1002/pds.5631

Acc Chem Res. 2023 Apr 17. doi: 10.1021/acs.accounts.3c00030. Online ahead of print.

ABSTRACT

ConspectusThere has been a recent surge of advances in biomolecular assays based on the measurement of discrete molecular targets as opposed to signals averaged across molecular ensembles. Many of these “digital” assay designs derive from now-mature technologies involving single-molecule imaging and microfluidics and provide an assortment of new modalities to quantify nucleic acids and proteins in biospecimens such as blood and tissue homogenates. A primary new benefit is the robust detection of trace analytes at attomolar to femtomolar concentrations for which many ensemble assays cannot distinguish signals above noise levels. In addition, multiple biomolecules can be differentiated within a mixture using optical barcodes, with much faster and simpler readouts compared with sequencing methods. In ideal digital assays, signals should, in theory, further represent absolute molecular counts, rather than relative levels, eliminating the need for calibration standards that are the mainstay of typical assays. Several digital assay platforms have now been commercialized but challenges hinder the adoption and diversification of these new formats, as there are broad needs to balance sensitivity and dynamic range of detection, increase analyte multiplexing, improve sample throughput, and reduce cost. Our lab and others have developed technologies to address these challenges by redesigning molecular probes and labels, improving molecular transport within detection focal volumes, and applying solution-based readout methods in flow.This Account describes the principles, formats, and design constraints of digital biomolecular assays that apply optical labels toward the goal of simple and routine target counting that may ultimately approach absolute readout standards. The primary challenges can be understood from fundamental concepts in thermodynamics and kinetics of association reactions, mass transport, and discrete statistics. Major advances include (1) new inorganic nanocrystal probes for more robust counting compared with dyes, (2) diverse molecular amplification tools that endow attachment of numerous labels to single targets, (3) specialized surfaces with patterned features for electromagnetic coupling to labels for signal amplification, (4) surface capture enhancement methods to concentrate targets through disruption of diffusion depletion zones, and (5) flow counting in which analytes are rapidly counted in solution without pull-down to a surface. Further progress and integration of these tools for biomolecular counting could improve the precision of laboratory measurements in life sciences research and benefit clinical diagnostic assays for low abundance biomarkers in limiting biospecimen volumes that are out of reach of traditional ensemble-level bioassays.

PMID:37068158 | DOI:10.1021/acs.accounts.3c00030

Can Urol Assoc J. 2023 Apr 11. doi: 10.5489/cuaj.8136. Online ahead of print.

ABSTRACT

INTRODUCTION: Renal colic is a common emergency department (ED) presentation. Variations in assessment and management of suspected renal colic may have significant implications on patient and hospital outcomes. We developed a clinical practice guideline to standardize the assessment and management of renal colic in the ED. We subsequently compared outcomes before and after guideline implementation.

METHODS: The guidelines standardized the analgesia regimen, urology consult criteria, imaging modality, patient education, and followup instructions. This is a single-center, observational cohort study of patients presenting to the ED with renal colic prospectively collected after guideline implementation (December 2018 to May 2019), compared to a control group retrospectively collected before guideline implementation (December 2017 to May 2018). A total of 528 patients (pre-guideline n=283, post-guideline n=245) were included. Statistical analysis was performed with SPSS using multivariate linear regression.

RESULTS: ED length of stay (LOS) was significantly shorter after guideline implementation (preguideline 295.82±178.8 minutes vs. post-guideline 253.2±118.2 minutes, p=0.017). The number of computed tomography (CT) scans patients received was significantly less after guideline implementation (pre guideline 1.35±1.34 vs. post-guideline 1.00±0.68, p=0.034). Patients discharged for conservative management had a lower re-presentation rate in the post-guideline group (12.6%) than the pre-guideline group (17.2%); however, this did not reach statistical significance (p=0.18).

CONCLUSIONS: Implementation of a clinical practice guideline for ureteric stones reduces the ED LOS and the total number of CT scan in patients who present with renal colic. Standardizing assessment and management of ureteric stones can potentially improve patient and hospital outcomes without compromising the quality of care.

PMID:37068151 | DOI:10.5489/cuaj.8136

Can Urol Assoc J. 2023 Apr 11. doi: 10.5489/cuaj.8222. Online ahead of print.

ABSTRACT

INTRODUCTION: Patients with chronic scrotal content pain (CSCP) lack effective, non-invasive treatment options. We aimed to determine the local and systemic safety, tolerability, pharmacokinetics (PK), and efficacy of a long-lasting local anesthetic in patients with CSCP.

METHODS: This was a prospective, single-center, open-label, single-arm, phase 1 dose-escalating trial completed between October 2019 and March 2021. Twelve patients ≥19 years old with unilateral scrotal pain lasting ≥3 months reporting an average maximum pain score over seven days of ≥4 on a 0-10 numerical rating scale (NRS) were included. Patients underwent a test spermatic cord block and those reporting a decrease of ≥2 points were included. The investigational drug, ST-01 (sustained-release lidocaine polymer solution), is a long-acting injection of lidocaine around the spermatic cord. Subjects were provided a NRS dairy and recorded their NRS score until day 28. The Chronic Epididymitis Symptom Index (CESI) was completed on days 0, 7, 14, and 28. All patients underwent an examination and assessment for adverse events (AE) on days 0, 1, 7, 14, and 28. Exploratory statistical hypothesis testing was planned for this study due to its investigative nature.

RESULTS: There were no serious adverse events (SAEs) reported. All subjects reported at least one treatment-emergent adverse event (TEAE); 83% of related AEs were injection-site reactions consisting of swelling and bruising. NRS was reduced across all cohorts between baseline and end of study.

CONCLUSIONS: This study provides evidence that the novel ST-01 treatment is safe and well-tolerated.

PMID:37068147 | DOI:10.5489/cuaj.8222