Covariate Effects Research Articles

Effect of the Earth’s declination variation on characteristics of Jovian decametric radio emissions

Context. The variation in the Jovicentric sub-latitude (declination, DE) of a radio observer of Jupiter has long been known to affect the observation of Jupiter’s decametric (DAM) radio emissions due to these emissions’ anisotropic nature (through cyclotron maser instability beaming cones centered on Jovian magnetic field lines). The effect of the DE variation, however, is still not clearly understood. For ground-based observations of Jupiter, the DE variation, from −4° to +4°, occurs concomitantly with the cyclic variation in the distance to Jupiter, R, and Jupiter’s elongation angle, γ, which also affect the emission observation. Those covariant effects must be removed, then, for an analysis of the pure effect of DE. Aims. The aim of this study is to investigate the pure effect of the Earth’s DE variation on the maximum frequency, duration, average Io phase, and average longitude of Io-induced DAM emissions observed with the Nançay Decameter Array (NDA). Methods. For this purpose, we selected from the NDA/Routine digital catalog Jovian DAM emissions with an intensity (distance-corrected) above or equal to 8.8 dB and a maximum frequency above or equal to 20 MHz (25 MHz) for southern (northern) emissions. Distinct maximum frequency thresholds were adopted because of the typical discrepancy in the emissions’ frequency due to the high amplitude anomaly in the Jovian magnetic field at Jupiter’s northern hemisphere. The selected emissions comprise a new “unbiased” catalog. After analyzing the tenuous variation in the characteristics of the unbiased set of Io-DAM emissions with DE, we compared them with those of matching Io-DAM simulations obtained with the Exoplanetary and Planetary Radio Emissions Simulator (ExPRES). Results. From both the NDA data and the ExPRES simulations, it is observed that the pure DE effect on the Io-DAM emissions characteristics is minor, yet a clear proportionality of the maximum frequency and duration of the northern Io-DAM emissions with DE is noticed. Conclusions. The northern Io-DAM emissions seem to be more strongly affected by the DE variation than the southern emissions. Additionally, ExPRES can predict Io-DAM emissions consistently, from which we conclude that the current understanding of emission generation and propagation is reasonable. This study may be extended for broader ranges of DE, such as Juno’s.

Structure Induced by a Multiple Membership Transformation on the Conditional Autoregressive Model

The objective of disease mapping is to model data aggregated at the areal level. In some contexts, however, (e.g. residential histories, general practitioner catchment areas) when data is arising from a variety of sources, not necessarily at the same spatial scale, it is possible to specify spatial random effects, or covariate effects, at the areal level, by using a multiple membership principle (MM) (Petrof et al., 2020; Gramatica et al., 2021). A weighted average of conditional autoregressive (CAR) spatial random effects embeds spatial information for a spatially-misaligned outcome and estimate relative risk for both frameworks (areas and memberships). In this paper we investigate the theoretical underpinnings of these application of the multiple membership principle to the CAR prior, in particular with regard to parameterisation, properness and identifiability. We carry out simulations involving different numbers of memberships as compared to number of areas and assess impact of this on estimating parameters of interest. Both analytical and simulation study results show under which conditions parameters of interest are identifiable, so that we can offer actionable recommendations to researchers. Finally, we present the results of an application of the multiple membership model to diabetes prevalence data in South London, together with strategic implications for public health considerations.

Association between time to treatment and bladder cancer survival: a population-based analysis.

Cancer treatment delay is a global health system issue. However, data concerning the impact of treatment delays on survival in bladder cancer remain controversial. This study sought to evaluate the impact of time from diagnosis to treatment on survival outcomes of bladder cancer patients in the US Surveillance, Epidemiology, and End Results (SEER) database. The SEER was searched from 2000 to 2020 for bladder cancer patients. Logistical regression was used to explore potential factors related to treatment delay. Kaplan-Meier curves were generated to investigate the overall and cancer-specific survival. Multivariate Cox proportional hazards regression models were used to evaluate the effects of covariables on survival outcomes in bladder cancer with treatment delay. There were 12,686 eligible patients included in this study. A total of 2,379 patients experienced an initial treatment delay. Initial treatment delay was related to worse survival. Sex, age, pathological grade, clinical stage, and surgery were associated with increased odds of initial treatment delay. In the patients with initial treatment delay, age, advanced stage, lymph node involvement, high pathological grades and metastasis were independent predictors of poor overall survival and cancer-specific survival, while marital status at diagnosis, surgery, chemotherapy, and radiotherapy were found to improve both overall survival and cancer-specific survival. Significant disparities in pathological/clinical variables could contribute to treatment delay. Surgery, chemotherapy, and radiotherapy benefited the survival of patients with treatment delays.

Predictors of pathological complete response to neoadjuvant treatment in invasive breast cancer with different human epidermal growth factor receptor 2 (HER2) subcategories.

Among human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who receive anti-HER2 treatment, a noteworthy correlation between pathological complete response (pCR) and longer survival has been observed. The rate of pCR varies with the tumor's degree of HER2 protein expression. The aim of this study was to assess the correlations between clinicopathological characteristics, magnetic resonance imaging (MRI) parameters, and pCR in breast cancer with different HER2 subcategories. A total of 281 invasive breast cancer patients diagnosed with HER2-positivity were included. HER2-positive translated to immunohistochemistry (IHC) 3+ or IHC 2+/fluorescence in situ hybridization (FISH)(+). All enrolled patients underwent baseline MRI examination and received neoadjuvant chemotherapy, dual anti-HER2 therapy, and subsequent therapeutic surgery from January 2021 to May 2022. A logistic regression model was used to evaluate the effects of covariates on pCR. Compared to the IHC 2+/FISH(+) group, patients with IHC 3+ tumors had a higher pCR rate (58.1% vs. 26.7%, P<0.001), clinical stage (58.6% vs. 40%, P=0.038), apparent diffusion coefficient (ADC) value (0.96 vs. 0.88 mm2/s, P=0.004), and were more likely to be estrogen receptor (ER) negative (55.9% vs. 31.1%, P=0.002) and progesterone receptor (PR) negative (72.5% vs. 46.7%, P=0.001). In both groups, univariate analysis showed that the pCR group more often had ER-negative and PR-negative status than the non-pCR group (P<0.001). The final multivariable analysis showed that ER-negativity was associated with pCR in the IHC 2+/FISH(+) group (P=0.004). ER-negativity and the longest diameter were two independent predictors of pCR in the IHC 3+ group (P<0.001 for ER, P=0.026 for longest diameter). The IHC 3+ group had a higher pCR rate than the IHC 2+/FISH(+) group. Along with clinicopathological characteristics, MRI parameters were supplemental predictors of pCR, particularly in IHC 3+ patients.

Exploring time-to-event analysis models: A study of accelerated failure time and weighted least square estimation

Background: This study explores time-to-event analysis models, focusing on the comparison between accelerated failure time (AFT) and weighted least square estimation (WLSE) methods in analyzing survival data, emphasizing the challenge of heteroscedasticity in real-world datasets. Methods: Using the North Central Cancer Treatment Group (NCCTG) Lung Cancer dataset, five parametric AFT distributions (exponential, Weibull, log-logistic, log-normal, and generalized gamma) were evaluated. The log-likelihood method was utilized to calculate the AIC and BIC values for each distribution to determine the best fit model. Results: Among the different distributions tested, the Weibull distribution was identified as the best fit model based with the lowest AIC and BIC values of 2311.70 and 2318.56, respectively. This distribution played a crucial role in determining factors associated with the cancer data. Specifically, the variablecalories consumed at a meal (meal.cal) was associated with cancer patient recovery, with an estimate of 0.00004 and a standard error of 0.0002. Results indicated homoscedastic data through the Breusch-Pagan test. Moreover, the results demonstrated that when dealing with homoscedastic data, the Weibull AFT model outperformed WLSE in producing statistically significant effects and precise estimates. Conclusion: For data exhibiting homoscedasticity, the AFT method yielded superior results compared to WLSE in terms of statistical significance and precision. Therefore, it is advisable to employ AFT for homoscedastic data and WLSE for heteroscedastic data to enhance the accuracy of covariate effect estimates in survival analysis.

SCAT Symptom Evolution in the Acute Concussion Phase: Findings From the NCAA-DoD CARE Consortium.

The Sport Concussion Assessment Tool (SCAT) is the most widely used tool following sport-related concussion (SRC). Initial SCAT symptom burden is a strong predictor of recovery in collegiate athletes; however, it is unknown if symptom presentation varies within the acute (<48 hours) post-SRC phase. The purpose of this cohort study was to examine acute SRC symptom presentation among National Collegiate Athletic Association (NCAA) athletes. Concussed NCAA varsity athletes (n=1,780) from thirty universities across the United States that participated in the Concussion Assessment, Research, and Education (CARE) Consortium were included. Time of injury occurrence and SCAT administration data were recorded, from which time-to-SCAT (hours, continuous) was calculated. The main outcome was SCAT total symptom severity [(TSS), 0-126]. Multivariable negative binomial regression was used to examine the association between time (hours) since injury and TSS. Covariates included sex, previous concussion, sport contact level, amnesia/loss of consciousness, immediate reporting of injury, and injury situation. A random effect (person level) accounted for multiple assessments. TSS score ratios (SR) with associated 95% confidence intervals (CI) were provided. The SCAT was administered an average of 14 (25th-75th percentile: 1.2-24) hours post-SRC, and average TSS was 27.35±21.28 across all participants. Time-to-SCAT was associated with a 1% decrease in TSS after adjusting for covariate effects (SR: 0.99, 95% CI: 0.99-0.99, p<0.001). Overall, we observed a small, but significant decrease in TSS with each hour post-SRC. Assessing a concussed athlete once in the acute phase will likely provide a sufficient sense of their symptomatic well-being, as measures did not fluctuate dramatically. Future research should aim to examine how acute symptom evolution influences recovery metrics.

Development and application of a population pharmacokinetic model repository for caffeine dose tailoring in preterm infants

ABSTRACT Background Considerable interindividual variability for the pharmacokinetics of caffeine in preterm infants has been demonstrated, emphasizing the importance of personalized dosing. This study aimed to develop and apply a repository of currently published population pharmacokinetic (PopPK) models of caffeine in preterm infants to facilitate model-informed precision dosing (MIPD). Research design and methods Literature search was conducted using PubMed, Embase, Scopus, and Web of Science databases. Relevant publications were screened, and their quality was assessed. PopPK models were reestablished to develop the model repository. Covariate effects were evaluated and the concentration–time profiles were simulated. An online simulation and calculation tool was developed as an instance. Results Twelve PopPK models were finally included in the repository. Preterm infants’ age and body size, especially the postnatal age and current weight, were identified as the most clinically critical covariates. Simulated blood concentration–time profiles across these models were comparable. Caffeine citrate-dose regimen should be adjusted according to the age and body size of preterm infants. The developed online tool can be used to facilitate clinical decision-making. Conclusions The first developed repository of PopPK models for caffeine in preterm infants has a wide range of potential applications in the MIPD of caffeine.

Population pharmacokinetics and exposure-response analyses of safety (ARIA-E and isolated ARIA-H) of lecanemab in subjects with early Alzheimer's disease.

Lecanemab (Leqembi®) was recently approved by health authorities in the United States, Japan, and China to treat early Alzheimer's disease (AD), including patients with mild cognitive impairment (MCI) or mild dementia due to Alzheimer's disease upon confirmation of amyloid beta pathology. Extensively and sparsely sampled PK profiles from 1619 AD subjects and 21,929 serum lecanemab observations from two phase I, one phase II, and one phase III studies were well characterized using a two-compartment model with first-order elimination. The final PK model quantified covariate effects of body weight and sex on clearance and central volume of distribution, ADA-positive status, and albumin on clearance, and of Japanese ethnicity on central and peripheral volumes of distribution. Exposure to lecanemab was comparable between two lecanemab-manufacturing processes. However, none of the identified covariates in the model had a clinically relevant impact on model-predicted lecanemab Cmax or AUC at steady state following 10 mg/kg bi-weekly. Importantly, age, a well-recognized risk factor for AD, was not found to significantly affect lecanemab PK. The incidence of ARIA-E as a function of lecanemab exposure was modeled using a logit function with data pooled from 2641 subjects from the phase II and phase III studies, in which a total of 177 incidences of ARIA-E were observed. The probability of ARIA-E was significantly correlated with model-predicted Cmax and predicted to be higher in subjects homozygous for APOE4. The incidence of isolated ARIA-H was not associated with lecanemab exposure and was similar between placebo and lecanemab-treated subjects.

Shoulder balloon spacer for massive irreparable rotator cuff tears results in significant improvements.

The aim of the present study was to assess the effectiveness of balloon implantation in patients with irreparable supraspinatus tears alone or in combination withother rotator cuff (RC) tendon tearsand the effect of several covariables, such as age, gender, status of the long head biceps, with or without tendon repair and regardless the number of tendon involved. Patients enrolled from 'San Carlo' Hospital of Potenza (Italy, IT), from January 2012 to September 2014, underwent arthroscopic implantation of shoulder balloon by a single surgeon, and followed for 3 years. The American Shoulder and Elbow Surgeons (ASES) and Constant score (CS) were administered pre-, post-operatively at 12 months, and then annually. Patients were classified on the basis of the number of tendons involved in the tears and treatment performed, considering the reparability of the tendons themselves. Gleno-humeral joint osteoarthrosis (OA) was evaluated through shoulder radiographs and classified according to the Samilson-Prieto classification, at the first examination and at the final follow-up. Statistical improvements were evaluated using a variance model (least-squares means)and a T distribution test for the evaluation between different treatment groups. A total of 61 procedures were performed, and eight patients were lost during follow-up. The mean baseline CS was 30.2 ± 15.4 with statistically significant improvement, respectively, at 1-, 2- and 3-year follow-up to 69.3 ± 4.2, 74.6 ± 3.6 and 69.7 ± 5.1 respectively. ASES score at baseline was 22.5 ± 10.9, with a statistically significant improvement to 69.7 ± 9.2, 68 ± 17.8 and 71.2 ± 16.6 at 1-, 2- and 3-year follow-up, respectively. Tenotomy or absence of long head biceps at presentation did not influence results (n.s.), with no difference according to gender and age. At final follow-up, 24 patients (43.9%) showed progression of glenohumeral OA. One patient required secondary surgery for shoulder replacement after 18 months for persistent pain and one patient required implant removal following post-operative laser treatment. Arthroscopic rotator cuff tears repair with subacromial spacer balloon implantation showed statistically significant clinical and functional improvement at 3-year follow-up. Patients treated with combined partial repair and subacromial spacer balloon implantation experienced good results independent of gender, age, type of tear and long-head biceps tendon status. The risks related to this procedure appear to be minimal. Level IV.

Evaluation of Obesity-Related Physiological Changes on Pantoprazole Clearance in Children Using a Population Pharmacokinetic Approach.

Pediatric obesity is a growing health concern, affecting millions of children worldwide. While pharmacokinetic (PK) changes in numerous commonly prescribed medications have been linked to obesity, the physiological mechanisms driving these alterations and their implications for drug dosing remain poorly understood. The objective of this study was to evaluate previously reported observations of reduced pantoprazole clearance (CL) in children with obesity, investigate obesity-related characteristics in liver physiology as explanatory causes for these observations, and evaluate the clinical relevance of obesity on drug dosing. A prospective, comparative PK study, enrolling participants 6-21 years of age, with and without obesity, was conducted to evaluate the association between obesity-related characteristics and pantoprazole CL. A nonlinear mixed-effects modeling approach was used to identify sources of interindividual variability in pantoprazole PK. Monte Carlo simulations were performed to assess pantoprazole exposure in children and evaluate the association between obesity and pantoprazole exposure. The study population consisted of 39 pediatric participants: 31% without obesity and 69% with obesity. A two-compartment PK model with covariate effects of total body weight (TBW), CYP2C19 metabolizer phenotype, and obesity status adequately described the PK data. After accounting for differences due to TBW and CYP2C19 metabolizer phenotype, obesity was associated with an estimated 18% reduction in pantoprazole CL (comparable to the reduction estimated for a CYP2C19 loss of function allele). Further research is warranted to evaluate the physiological mechanisms associated with reduced drug CL in children with increased body size and the implications for drug dosing.

Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis.

Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.

Estimation of change-point in the covariate effects on restricted mean survival time

The restricted mean survival time (RMST) emerges as a crucial summary metric in survival analysis. In some cases, certain covariates may have a varying impact on RMST at one specific change-point. For instance, consider studies focusing on women’s health, where a notable shift in a patient’s lifespan might occur when the age at menopause surpasses a particular cutoff value. To tackle this complex phenomenon, we consider a regression analysis of RMST with change-point in the covariate effects. This involves building a generalized linear model that directly captures the relationship between RMST and associated covariates that may have a change-point. Using the smoothed estimating equation with pseudo-observations, we obtain parameter estimators and establish the large sample properties. Finally, we assess the finite sample performance of the proposed method through simulation studies and apply it to real data examples from primary biliary cirrhosis and rectal cancer studies.

Racial and Ethnic Differences in Postoperative Nausea and Vomiting Care.

Racial and ethnic differences in health care may result in significant morbidity. The objective of this study was to determine whether there was an association between a patient's race or ethnicity and the receipt of an antiemetic agent preoperatively, during surgery, and in the recovery room. A single-institution retrospective study of adult patients (>18 years) who had undergone cancer-related operating room procedures under anesthesia between March 2016 and August 2021 was conducted. A multivariable logistic regression model was fitted to estimate the effects of covariates on antiemetic administration. Of the 60,595 patients included in the study, 3053 (5.0%) self-identified as Asian, 5376 (8.9%) as Black, 8431 (13.9%) as Hispanic or Latino, 42,533 (70.2%) as White, and 1202 (2.0%) as belonging to another racial or ethnic group. Multivariable analyses showed significant associations between a patient's race or ethnicity and the receipt of antiemetics in the preoperative holding area, operating room, and recovery room (all P < .001). In the preoperative holding area, White patients (8962 of 42,533 [21.1%]; odds ratio [OR], 1.188; 95% confidence interval [CI], 1.100-1.283; P < .001) had higher odds of receiving an antiemetic than Black patients (1006 of 5376 [18.7%]). Intraoperatively, the odds were significantly greater for Hispanic or Latino (7323 of 8431 [86.9%]; OR, 1.175; 95% CI, 1.065-1.297; P = .001) and patients who identified as belonging to another race (1078 of 1202 [89.7%]; OR, 1.582; 95% CI, 1.290-1.941; P < .001) than for Black patients (4468 of 5376 [83.1%]). In the recovery room, Asian (499 of 3053 [16.3%]; OR, 1.328; 95% CI: 1.127-1.561; P < .001), Hispanic or Latino (1335 of 8431 [15.8%]; OR, 1.208; 95% CI, 1.060-1.377; P < .005), and White patients (6533 of 42,533 [15.4%]; OR, 1.276; 95% CI, 1.140-1.427; P < .001) had significantly higher odds of receiving antiemetics than Black patients (646 of 5376 [12%]). This retrospective study suggests significant differences between the administrations of antiemetics to patients of different races or ethnicities, with Black patients often being less likely to receive an antiemetic than patients belonging to all other races or ethnicities.

Semiparametric Additive Modeling of the Restricted Mean Survival Time.

Analysis of the restricted mean survival time (RMST) has become increasingly common in biomedical studies during the last decade as a means of estimating treatment or covariate effects on survival. Advantages of RMST over the hazard ratio (HR) include increased interpretability and lack of reliance on the often tenuous proportional hazards assumption. Some authors have argued that RMST regression should generally be the frontline analysis as opposed to methods based on counting process increments. However, in order for the use of the RMST to be more mainstream, it is necessary to broaden the range of data structures to which pertinent methods can be applied. In this report, we address this issue from two angles. First, most of existing methodological development for directly modeling RMST has focused on multiplicative models. An additive model may be preferred due to goodness of fit and/or parameter interpretation. Second, many settings encountered nowadays feature high-dimensional categorical (nuisance) covariates, for which parameter estimation is best avoided. Motivated by these considerations, we propose stratified additive models for direct RMST analysis. The proposed methods feature additive covariate effects. Moreover, nuisance factors can be factored out of the estimation, akin to stratification in Cox regression, such that focus can be appropriately awarded to the parameters of chief interest. Large-sample properties of the proposed estimators are derived, and a simulation study is performed to assess finite-sample performance. In addition, we provide techniques for evaluating a fitted model with respect to risk discrimination and predictive accuracy. The proposed methods are then applied to liver transplant data to estimate the effects of donor characteristics on posttransplant survivaltime.

Nonstationary longitudinal autoregressive mixed model for count data with measurement error in covariates: Estimation and asymptotics

Several methods have been proposed in the literature for computing unbiased and efficient estimates of the parameters of generalized linear models when the covariates are measured with error. However, to our knowledge, no documented research on computational techniques for parameter estimation currently exist in the literature when the data is a longitudinal count data influenced by an unobservable latent variable and observable covariates that are measured with error. In this paper, we propose a nonstationary conditionally Poisson mixed model for such data and develop unbiased estimating equations with iterative methods for computing estimates of the effect of the covariates, variance of the latent variable, and the correlation index parameter. The performance of the iterative methods is examined through extensive simulation studies. The results show that the methods performed well when the magnitude of the measurement error is not so large as to dominate or mask the effect of the true covariates. Using observed longitudinal count data on the number of patents awarded to 168 firms in the United States from 1974 to 1979 along with associated covariate information on the type of firm, log of the book value of capital in 1972 and research and development (R &amp; D) expenditures we have demonstrated how the methods proposed in this paper can be applied to a real data. In addition, we derive the influence function of the estimator of the covariate effect and discuss the asymptotic properties of the estimator. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 151-180.

Longitudinal analysis of serum urate in prediabetic phase.

Despite the well-established association between prediabetes and hyperuricaemia, knowledge about serum urate (SU) trends during the prediabetic phase is limited. Therefore, we aimed to assess the longitudinal changes of SU in individuals with prediabetes. Individuals with prediabetes, defined by initial haemoglobin A1c (HbA1c) levels between 5.7% and 6.4%, were identified using electronic health records from an academic health system (2007-2022). We required at least one SU test before and after the prediabetes diagnosis. The primary outcome was the longitudinal SU trends during the follow-up period, estimated with a multivariable mixed-effects model. Patients were censored at diabetes onset. Marginal effects of covariates on SU changes were estimated. Subsequent analyses examined SU variations in subgroups stratified by age, sex, body mass index (BMI), HbA1c, estimated glomerular filtration rate (eGFR) and metformin use. Out of 25 526 individuals with prediabetes, 1,521 met the SU cohort requirements, contributing to 6,832 SU observations. At baseline, median age was 63 years and 40% were female. Median values were SU 6.3 mg/dl, HbA1c 5.9% and BMI 30 kg/m2. Median follow-up was 7.4 years. Older age, male sex, greater BMI, and higher HbA1c were significant predictors of increased longitudinal SU levels. Individuals with a BMI ≥30 kg/m2 exhibited higher SU levels compared with those with lower BMI values. Among individuals with prediabetes, several baseline variables were significant predictors of increased SU levels over time. These longitudinal trends in SU, support the potential for early intervention during the prediabetic phase, possibly reducing the risk of gout.

Protocol for a randomised trial of a self-directed digital pain management intervention (Empowered Relief) tailored to adults with chronic pain and prescription opioid misuse/disorder: the MOBILE Relief study

IntroductionChronic pain increases the risk of prescription opioid misuse or opioid use disorder (OUD). Non-pharmacological treatments are needed to dually address pain and opioid risks. The purpose of the Mobile...

Incidence of mortality and its predictors among preterm neonates in nigist eleni mohammed memmorial comprehensive specialized hospital, Hossana, Ethiopia: a prospective follow-up study

BackgroundPreterm birth is the leading cause of neonatal mortality accounting for 35% of all neonatal deaths worldwide, and the second most frequent cause of death for under five children. Despite different efforts, preterm neonatal mortality is still persistently high in Ethiopia. Little is known about death and its predictors among preterm neonates in the study area.ObjectiveThis study is aimed at estimating the incidence of mortality and its predictors among preterm neonates admitted to the NICU of NEMMCSH.Methods and materials: A hospital-based prospective follow-up study was conducted from January to November 2022. A total of 197 preterm neonates were selected consecutively and followed. The Kaplan-Meier survival and failure curves were used to describe the proportion of deaths over time and to compare groups. The independent effects of covariates on the hazard of death were analyzed using a multivariable Cox proportional hazard model.ResultsPreterm neonates were followed for 1840 person-days. The mean time to death was 5.68 days (SD = 5.54). The incidence of mortality was 26.08 (95% CI: 19.65, 34.61) per 1000 person days. Preterm neonates of mothers with eclamsia (AHR = 3.03), preterm neonates who have not received KMC (AHR = 2.26), and preterm neonates who have not exclusively breastfed (AHR = 4.4) had higher hazards of death as compared to their counterparts.Conclusion and recommendation: The mean time to death was 5.68 days (SD = 5.54). The incidence of mortality was 26.08 per 1000 person days. Eclamsia, KMC, and exclusive breastfeeding were significant predictors of death among preterm neonates. The role of KMC in reducing mortality rates and improving outcomes has to be emphasized for mothers and families. Caregivers have to ensure that mothers and families receive adequate support and resources to facilitate KMC, including access to lactation support, counseling, and assistance with practical aspects of caregiving. Counseling and practical support to enhance exclusive breastfeeding initiation and continuation have to be strengthened. Special attention has to be given to the preterm neonates of mothers with eclampsia.

Inference for restricted mean survival time as a function of restriction time under length-biased sampling.

The restricted mean survival time (RMST) is often of direct interest in clinical studies involving censored survival outcomes. It describes the area under the survival curve from time zero to a specified time point. When data are subject to length-biased sampling, as is frequently encountered in observational cohort studies, existing methods cannot estimate the RMST for various restriction times through a single model. In this article, we model the RMST as a continuous function of the restriction time under the setting of length-biased sampling. Two approaches based on estimating equations are proposed to estimate the time-varying effects of covariates. Finally, we establish the asymptotic properties for the proposed estimators. Simulation studies are performed to demonstrate the finite sample performance. Two real-data examples are analyzed by our procedures.

DeepResBat: deep residual batch harmonization accounting for covariate distribution differences.

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.