Estimated Regression Coefficients Research Articles

Bayesian ridge estimators based on a vine copula-based prior in Poisson and negative binomial regression models

Poisson and negative binomial regressions are popular methods for modelling the relationship between a count variable and explanatory variables. In the presence of multicollinearity, ridge regression is an alternative to the maximum likelihood estimation for regression coefficients. Furthermore, ridge estimators are interpreted as the Bayesian posterior mean (or mode) when the regression coefficients follow a multivariate normal prior. However, using the multivariate normal prior may not effectively estimate regression coefficients, especially in the presence of interaction terms. This study proposes vine copula-based priors for Bayesian ridge estimators in Poisson and negative binomial regression models. The simulations and data analysis results indicate that for the Poisson model with equidispersion and the negative binomial model with overdispersion, the Clayton and Gumbel copula priors of the Archimedean family achieve superior performance than the multivariate normal prior and Gaussian copula prior.

NLRP3 inflammasome-induced pyroptosis and serum ASC specks are increased in patients with cardiogenic shock.

Cardiogenic shock (CS) is characterized by impaired cardiac function, very high mortality, and limited treatment options. The proinflammatory signaling during different phases of CS is incompletely understood. We collected serum and plasma (n = 44) as well as freshly isolated peripheral blood mononuclear cells (PBMCs, n = 7) of patients with CS complicating acute myocardial infarction on admission and after revascularization (24, 48, and 72 h) and of healthy controls (serum and plasma, n = 75; PBMCs, n = 12). PBMCs of patients with CS had increased gene expression of NLRP3, CASP1, PYCARD, IL1B, and IL18 and showed increased rates of pyroptosis (control, 4.7 ± 0.3 vs. 9.9 ± 1.7% in patients with CS, P = 0.02). Serum interleukin (IL)-1β levels were increased after revascularization. IL-18 and IL-6 were higher in patients with CS than in healthy controls but comparable before and after revascularization. Proinflammatory apoptosis-associated speck-like proteins containing CARD (ASC) specks were elevated in the serum of patients with CS on admission and increased after revascularization (admission, 11.1 ± 4.4 specks/µL; after 24 h, 19.0 ± 3.9, P = 0.02). ASC specks showed a significant association with 30-day mortality in patients with CS (P < 0.05). The estimated regression coefficients and odds ratios indicated a positive relationship between ASC specks and mortality (odds ratio: 1.029, 95% confidence interval, 1.000 to 1.072; P = 0.02). Pyroptosis and circulating ASC specks are increased in patients with CS and are particularly induced after reperfusion. This underscores their potential role as a biomarker for poor outcomes in patients with CS. ASC specks represent promising new therapeutic targets for patients with CS with high inflammatory burden.NEW & NOTEWORTHY The expression of NLR family pyrin domain containing-3 (NLRP3) inflammasome-related genes and the rate of pyroptosis are increased in PBMCs from patients with CS. Furthermore, patients with CS are characterized by higher serum concentrations of ASC specks and IL-1β, IL-6, and IL-18. This current study adds circulating ASC specks to the portfolio of biomarkers for the identification of patients with a high inflammatory burden paving the way for precision medicine approaches to improve clinical outcomes.

An Econometric Evaluation of the Variation effect of the Nigerian Rate of Exchange on the Economic Progression

This research work investigated an econometric evaluation of the variation effect of the Nigerian rate of exchange on the economic progression between the years 1986-2017. A secondary source of data was employed in this work, which was extracted from the National Bureau Statistical Bulletin published by the Central Bank of Nigeria. Maximum Likelihood estimation of the R-software was carried out to evaluate the parameter estimate of the multiple regression coefficients and analyzed the influence of the explanatory variables (Gross Domestic Product, Unemployment and Inflation on the rate of Exchange). It was observed that the Gross Domestic Product and Unemployment rate influenced negatively on the Exchange rate but significant with (β = -1.646, t = -2.409, sig.val = 0.0228) and (β = - 0.8602, t = -3.302, sig.val = 0.00263) respectively. Nevertheless Inflation rate had positive influence on the rate of Exchange but not significant with (β = 1.311, t = 0.852, sig.val = 0.40122). The Coefficient of determination (R2 = 82.7) implies that the variation of the Nigerian rate of exchange has significant effect on the economic progression between the years 1986-2017. However, the paper recommends the following; a control of the sequential rising of inflation so as to have a stabilized rate of exchange and a promising economy for the citizens, adequate security, effective fiscal and monetary policies, as well as the provision of infrastructural facilities so that foreign investors will be attracted to invest in Nigeria.

Nonparametric Test for Logistic Regression with Application to Italian Enterprises’ Propensity for Innovation

In this work, a nonparametric method is proposed to jointly test the significance of the regression coefficient estimates in a logistic regression model and identify which explanatory variables are effective in predicting the binary response. The motivating example is related to the factors affecting the propensity of Italian Small Medium Enterprises (SMEs) to innovate. The explanatory variables of the model represent firms’ characteristics, such as size and age, and the possible effect of the sector of economic activity is taken into account by including a set of binary variables as control factors. The dependent variable indicates whether a company, in the period under study, introduced at least one product or process innovation. Therefore, it is also dichotomous, and the logistic regression model is appropriate for representing the relationship between explanatory variables and dependent variables. Specifically, the logit transformation of the firm’s propensity to innovate, i.e., the probability that a company randomly chosen from the population of Italian SMEs has introduced an innovation or, equivalently, the proportion of innovative companies among the Italian SMEs, is expressed as a linear function of the predictors (explanatory and control variables). The proposed test is based on the permutation approach and satisfies important statistical properties, proved in a simulation study. The test is more flexible and robust than the classic parametric approach, and is preferable to typical stepwise regression procedures for the selection of a parsimonious and effective model.

Decreases in serum PFAS are associated with decreases in serum lipids: A longitudinal study on a highly exposed population

IntroductionPerfluoroalkyl substances (PFAS) are widely used, ubiquitous and highly persistent man-made chemicals. Previous cross-sectional studies have consistently linked PFAS exposure to alterations in lipid profiles. However, longitudinal investigations are preferred to mitigate issues related to reverse causation and confounding. Hence, we aimed to investigate the association between changes in serum PFAS and changes in serum lipids, while shedding light on potential modifiers of the examined relationships. MethodsWe used data from a health surveillance program offered to residents of a vast area of the Veneto Region (North-Eastern Italy), who had been exposed to PFAS via contaminated drinking water until 2013. We included subjects aged ≥20 years who provided two blood samples over an average 4-year interval (n = 8101). We examined the relationships between changes in PFOA, PFOS and PFHxS and changes in total cholesterol (TC), high-density lipoprotein cholesterol (HDLC) and low-density lipoprotein cholesterol (LDL-C). Linear models were fitted for change in the natural logarithm (ln) of each lipid in relation to the change in the ln of PFAS. From the estimated regression coefficients, we calculated the predicted percentage change in the response for a ln-decrease in PFAS serum concentrations. ResultsOverall concentrations of PFOA, PFOS, PFHxS fell by 62.1 %, 24.4 % and 35.4 % from baseline, while small increases in lipids were observed. Declines in PFAS concentrations were associated with decreases in all lipids. For a ln-decrease in PFOA HDL-C decreased by 1.99 % (95 % CI: 1.28, 2.70), TC by 1.49 % (95 % CI: 0.88, 2.10), and LDL-C by 1.40 % (95 % CI: 0.45, 2.37). ConclusionsWe found a positive association between changes in PFAS concentrations and changes in cholesterol levels, observing the most marked contrasts across sexes and age groups. Our findings support the reversibility of the associations identified in cross-sectional analyses, emphasizing the importance of water treatment measures in mitigating adverse health effects.

Statistical physical view of statistical inference in Bayesian linear regression model.

This paper considers similarities between statistical physics and Bayes inference through the Bayesian linear regression model. Some similarities have been discussed previously, such as the analogy between the marginal likelihood in Bayes inference and the partition function in statistical mechanics. In particular, this paper considers the proposal to associate discrete sample size with inverse temperature [C. H. LaMont and P. A. Wiggins, Phys. Rev. E 99, 052140 (2019)2470-004510.1103/PhysRevE.99.052140]. The previous study suggested that incorporating this similarity motivates the derivation of analogs of thermodynamic functions such as energy and entropy. The study also anticipated that those analogous functions have potential to describe Bayes estimation from physical points of view and to provide physical insights into mechanisms of estimation. This paper incorporates a macroscopic perspective as an asymptotics similar to the thermodynamic limit into the previous suggestion. Its motivation stems from the statistical mechanical concept of deriving thermodynamic functions that characterize macroscopic properties of macroscopic systems. This incorporation not only allows analogs of macroscopic thermodynamic functions to be considered but also suggests a candidate for an analog of inverse temperature with continuity, which is partly consistent with the previous proposal to associate the discrete sample size with inverse temperature. On the basis of this suggestion, we analyze analogs of macroscopic thermodynamic functions for a Bayesian linear regression model which is the basis of various machine learning models. We further investigate, through the behavior of these functions, how Bayes estimation is described from the perspective of physics and what kind of physical insight is obtained. As a result, the estimation of regression coefficients, which is the primary task of regression, appears to be described by the physical picture of balance between decreasing energy and increasing entropy as in equilibrium states of thermodynamic systems. More specifically we observe the physical view of Bayes inference as follows: the estimation succeeds where the effect of decreasing energy is dominant at low temperature. On the other hand, the estimation fails where the effect of increasing entropy is dominant at high temperature.

Nested case–control sampling without replacement

Nested case–control design (NCC) is a cost-effective outcome-dependent design in epidemiology that collects all cases and a fixed number of controls at the time of case diagnosis from a large cohort. Due to inefficiency relative to full cohort studies, previous research developed various estimation methodologies but changing designs in the formulation of risk sets was considered only in view of potential bias in the partial likelihood estimation. In this paper, we study a modified design that excludes previously selected controls from risk sets in view of efficiency improvement as well as bias. To this end, we extend the inverse probability weighting method of Samuelsen which was shown to outperform the partial likelihood estimator in the standard setting. We develop its asymptotic theory and a variance estimation of both regression coefficients and the cumulative baseline hazard function that takes account of the complex feature of the modified sampling design. In addition to good finite sample performance of variance estimation, simulation studies show that the modified design with the proposed estimator is more efficient than the standard design. Examples are provided using data from NIH-AARP Diet and Health Cohort Study.

Group Inverse-Gamma Gamma Shrinkage for Sparse Linear Models with Block-Correlated Regressors

Heavy-tailed continuous shrinkage priors, such as the horseshoe prior, are widely used for sparse estimation problems. However, there is limited work extending these priors to predictors with grouping structures. Of particular interest in this article, is regression coefficient estimation where pockets of high collinearity in the covariate space are contained within known covariate groupings. To assuage variance inflation due to multicollinearity we propose the group inverse-gamma gamma (GIGG) prior, a heavy-tailed prior that can trade-off between local and group shrinkage in a data adaptive fashion. A special case of the GIGG prior is the group horseshoe prior, whose shrinkage profile is correlated within-group such that the regression coefficients marginally have exact horseshoe regularization. We show posterior consistency for regression coefficients in linear regression models and posterior concentration results for mean parameters in sparse normal means models. The full conditional distributions corresponding to GIGG regression can be derived in closed form, leading to straightforward posterior computation. We show that GIGG regression results in low mean-squared error across a wide range of correlation structures and within-group signal densities via simulation. We apply GIGG regression to data from the National Health and Nutrition Examination Survey for associating environmental exposures with liver functionality.

Fractal geometry of culprit coronary plaque images within optical coherence tomography in patients with acute coronary syndrome vs stable angina pectoris.

The main cause of acute coronary syndrome (ACS) is plaque rupture and thrombus formation. However, it has not been fairly successful to identify vulnerable plaque to rupture using conventional parameters of intravascular imaging modalities. Fractal analysis is one of the mathematical models to examine geometrical features of picture image using a specific parameter called as fractal dimension (FD) which suggests geometric complexity of the image. This study examined FD of the optical coherence tomography (OCT)-derived images of the culprit plaque in patients with ACS vs stable angina pectoris (SAP) to evaluate the feasibility of FD for identifying vulnerable coronary plaques prone to provoke ACS distinguished from stable plaques only provoking SAP. We examined 65 cases (34 ACS patients, 31 SAP patients) in which the culprit lesion was imaged by OCT before percutaneous coronary intervention in patients with ACS and SAP. The culprit plaque lesion in the ACS group had a significantly larger mean lipid arc (203.8 ± 39.4° vs 152.3 ± 34.5°, p < 0.001) and a larger lipid plaque length (12.6 ± 5.1mm vs 7.7 ± 2.7mm, p < 0.001) and a thinner fibrous cap thickness (75.3 ± 22.3μm vs 134.8 ± 53.2μm, p < 0.001) than those in the SAP group. The prevalence of OCT-derived macrophage infiltration (Mph) in the entire culprit coronary vessel as well as that of the OCT-derived thin-cap fibroatheroma (TCFA) at the culprit lesion were significantly greater in the ACS group than those in the SAP group, respectively (Mph: 61.8% vs 35.5%, p = 0.048; TCFA: 44.1% vs 6.4%, p < 0.001). The FD of culprit plaque in the ACS group was significantly greater than in the SAP group (2.401 ± 0.073 vs 2.341 ± 0.051, p < 0.001). In multivariate regression analysis, the presence of Mph was a significant determinant of FD (regression coefficient estimate 0.049, CI 0.018-0.079, p = 0.002). The FD of OCT-derived image of culprit coronary plaque in the ACS group was significantly greater than that in the SAP group, indicating that the culprit plaque in ACS were structurally more complex. Therefore, fractal analysis of coronary OCT images might be clinically useful for identifying coronary plaques prone to provoke ACS.

Genotype and X-chromosome inactivation are associated with disease severity in females with X-linked Alport syndrome.

Male patients with X-linked Alport syndrome (XLAS) generally develop end-stage kidney disease in early or middle adulthood and show distinct genotype-phenotype correlations. Female patients, however, show various phenotypes ranging from asymptomatic to severe with no genotype-phenotype correlations. However, the factors affecting the severity of XLAS in female patients are unclear. Since X-chromosome inactivation (XCI) affects the severity of certain female X-linked diseases, we investigated whether genotype and XCI were associated with XLAS severity in female patients in a large Japanese cohort. Among 139 female patients with genetically diagnosed XLAS at our institution, we conducted XCI analysis on peripheral blood leukocytes using the human androgen receptor assay method and analyzed two cohorts. In 74 adult female patients, we evaluated the correlation between kidney function (creatinine-estimated glomerular filtration rate [Cr-eGFR] optimized for Japanese individuals) and genotype/XCI using multivariable linear regression analysis, and in 65 pediatric female patients, we evaluated the correlation between kidney function (Cr-eGFR optimized for Japanese individuals) and genotype/XCI using multivariable linear regression analysis. We also investigated the correlation between the development of proteinuria (urine protein-to-creatinine ratio above normal for the patient's age) and genotype/XCI using multivariable Cox proportional hazard analysis. In adult female patients, XCI pattern was significantly associated with Cr-eGFR (regression coefficient estimate = -0.53, P=0.004), whereas genotype was not (P=0.892). In pediatric female patients, both genotype and XCI pattern were significant independent risk factors for the development of proteinuria (hazard ratio [HR], 3.702; 95% confidence interval [CI], 1.681-8.150; P=0.001 and HR, 1.043; 95% CI, 1.061-1.070; P=0.001, respectively), whereas both genotype and XCI pattern were not associated with Cr-eGFR (P=0.20, P=0.67, respectively). Genotype and XCI are factors associated with the severity in females with XLAS.

Tensor-based statistical learning methods for diagnosing product quality defects in multistage manufacturing processes

Multistage manufacturing processes with identical stages provide three-dimensional process data in which the first dimension represents the process (control/sensing) variable, the second is the stage, and the third is the measurement/sampling/data acquisition time point. Diagnosing quality faults in such processes often requires the simultaneous identification of crucial process variables and stages associated with product quality anomalies. Most existing diagnosis methods convert 3D data into a 2D matrix, resulting in loss of information and reduced diagnostic accuracy and stability. To address this challenge, we propose a penalized tensor regression model that regresses the product quality index against its 3D process data. For the estimation of high-dimensional regression coefficients with the limited amount of historical data, we apply the CANDECOMP/PARAFAC and Tucker decompositions to the coefficient tensor, which significantly reduces the number of parameters to be estimated. Based on the decompositions, a new regularization term is designed to enable the joint identification of critical process variables and stages. To estimate the parameters, we develop the block coordinate proximal descent algorithm and provide its convergence guarantee. Numerical studies demonstrate that the proposed methods can enhance diagnostic stability and on average improve the diagnostic accuracy by around 20% over existing benchmarks.

Bayesian regression modeling and inference of energy efficiency data: the effect of collinearity and sensitivity analysis

The majority of research predicted heating demand using linear regression models, but they did not give current building features enough context. Model problems such as Multicollinearity need to be checked and appropriate features must be chosen based on their significance to produce accurate load predictions and inferences. Numerous building energy efficiency features correlate with each other and with heating load in the energy efficiency dataset. The standard Ordinary Least Square regression has a problem when the dataset shows Multicollinearity. Bayesian supervised machine learning is a popular method for parameter estimation and inference when frequentist statistical assumptions fail. The prediction of the heating load as the energy efficiency output with Bayesian inference in multiple regression with a collinearity problem needs careful data analysis. The parameter estimates and hypothesis tests were significantly impacted by the Multicollinearity problem that occurred among the features in the building energy efficiency dataset. This study demonstrated several shrinkage and informative priors on likelihood in the Bayesian framework as alternative solutions or remedies to reduce the collinearity problem in multiple regression analysis. This manuscript tried to model the standard Ordinary Least Square regression and four distinct Bayesian regression models with several prior distributions using the Hamiltonian Monte Carlo algorithm in Bayesian Regression Modeling using Stan and the package used to fit linear models. Several model comparison and assessment methods were used to select the best-fit regression model for the dataset. The Bayesian regression model with weakly informative prior is the best-fitted model compared to the standard Ordinary Least Squares regression and other Bayesian regression models with shrinkage priors for collinear energy efficiency data. The numerical findings of collinearity were checked using variance inflation factor, estimates of regression coefficient and standard errors, and sensitivity of priors and likelihoods. It is suggested that applied research in science, engineering, agriculture, health, and other disciplines needs to check the Multicollinearity effect for regression modeling for better estimation and inference.

Estimating inter-regional trade: the radiation model versus the gravity model

The estimation of interregional trade flows within a country is confined to the context of multi-regional input–output models. Despite the remarkable relevance of these trade flows for regional incomes, most countries have almost no official data, with Korea being an exception used here. In addition to survey-based estimation methods, there are non-survey methods, some based on gravity models, which can be used to estimate inter-regional trade flows. This paper compares the gravity model with the novel radiation model. Preliminary results show that the accuracy of the radiation model’s estimation is superior to that of the gravity model, with the invaluable benefit of not needing to estimate regression coefficients, unlike the gravity model. This finding provides a generalisation of the radiation model for estimating interregional trade in contexts where there is no prior multi-regional input–output table without restrictions derived from coefficient estimation as in the gravity model.

Temporal recovery and prognostic factors for dysphagia following cardiovascular surgery: Retrospective analysis and development of predictive score

ObjectiveDysphagia is a common complication associated with cardiovascular surgery (CVS). This study sought to better understand recovery timelines, identify risk factors, and create a prognostic model for oral intake restoration. MethodsThis retrospective study included 134 patients who had CVS between April 2022 and March 2024. We assessed swallowing ability through fiberoptic endoscopic evaluation of swallowing (FEES). We randomly divided the patients’ data into a training dataset and a test dataset in a ratio of 70/30 and Kaplan–Meier analyses and Cox regression were used to assess predictors of total oral intake. We also created a scoring system using the estimated regression coefficients. ResultsMost patients with CVS achieved total oral intake in 7–11 days after extubation. Over 168 h of intubation, the presence of penetration or aspiration, a poor FEES score (score > 6), and perioperative complications were significant risk factors for delayed total oral intake. The predicting score was calculated by adding the scores for each individual factor, including FEES score, penetration/aspiration, and preoperative complications. Scores ranged 0–8, categorizing patients into 0–2, 3–5, and 6–8 groups, clearly demonstrating that the higher the predicting score, the longer the time to total oral intake in both the training and the test dataset. ConclusionsAll risk factors for unsuccessful or delayed total oral intake were intubation for more than a week, poor swallowing ability, and the presence of perioperative complications. The scoring system accurately predicts the restoration of oral intake following CVS.

On the prediction of rare events when sampling from large data

When modeling rare events using logistic regression, independent samples of event occurrence (ones) and nonoccurrence (zeros) are commonly taken from large datasets in order to fit models efficiently. A deterministic offset may then be included in the model to compensate for that sampling method. We propose a more complex sampling approach using stratified sampling within the sets of ones and zeros to ensure that we may sample more zeros from strata of interest. This design may avoid situations in which a random sample of zeros fails to capture the range of a key covariate. We employ sampling weights along with stratum-specific intercepts to obtain unbiased estimates of the logistic regression coefficients (including the intercept) and their standard errors. We use simulation to show that this method provides unbiased parameter estimates comparable with those of maximum likelihood. We also illustrate an application of this method to wildland fire occurrence prediction in a study area in northwestern Ontario, Canada.

Statistical analysis of self-reported health conditions in cohort studies: handling of missing onset age

ObjectivesThis paper discusses methodological challenges in epidemiological association analysis of a time-to-event outcome and hypothesized risk factors, where age/time at the onset of the outcome may be missing in some cases, a condition commonly encountered when the outcome is self-reported. Study Design and SettingA cohort study with long-term follow-up for outcome ascertainment such as the Childhood Cancer Survivor Study (CCSS), a large cohort study of 5-year survivors of childhood cancer diagnosed in 1970–1999 in which occurrences and age at onset of various chronic health conditions (CHCs) are self-reported in surveys. Simple methods for handling missing onset age and their potential bias in the exposure-outcome association inference are discussed. The interval-censored method is discussed as a remedy for handling this problem. The finite sample performance of these approaches is compared through Monte Carlo simulations. Examples from the CCSS include four CHCs (diabetes, myocardial infarction, osteoporosis/osteopenia, and growth hormone deficiency). ResultsThe interval-censored method is useable in practice using the standard statistical software. The simulation study showed that the regression coefficient estimates from the ‘Interval censored’ method consistently displayed reduced bias and, in most cases, smaller standard deviations, resulting in smaller mean square errors, compared to those from the simple approaches, regardless of the proportion of subjects with an event of interest, the proportion of missing onset age, and the sample size. ConclusionThe interval-censored method is a statistically valid and practical approach to the association analysis of self-reported time-to-event data when onset age may be missing. While the simpler approaches that force such data into complete data may enable the standard analytic methods to be applicable, there is considerable loss in both accuracy and precision relative to the interval-censored method.

Leveraging independence in high-dimensional mixed linear regression.

We address the challenge of estimating regression coefficients and selecting relevant predictors in the context of mixed linear regression in high dimensions, where the number of predictors greatly exceeds the sample size. Recent advancements in this field have centered on incorporating sparsity-inducing penalties into the expectation-maximization (EM) algorithm, which seeks to maximize the conditional likelihood of the response given the predictors. However, existing procedures often treat predictors as fixed or overlook their inherent variability. In this paper, we leverage the independence between the predictor and the latent indicator variable of mixtures to facilitate efficient computation and also achieve synergistic variable selection across all mixture components. We establish the non-asymptotic convergence rate of the proposed fast group-penalized EM estimator to the true regression parameters. The effectiveness of our method is demonstrated through extensive simulations and an application to the Cancer Cell Line Encyclopedia dataset for the prediction of anticancer drug sensitivity.

Detection of earnings manipulations during the corporate life cycle in Central European countries

Research background: The growing trend of financial distress risk worldwide increases the need for effective tools to detect accounting manipulation by stakeholders (debt holders, shareholders, suppliers, and customers). However, the well-known models of accrual earnings management do not reflect the financial specificity of individual life cycle stages and their cash flow patterns. Purpose of the article: The study examines the impact of the corporate life cycle on the reliability of accrual earnings management models and identifies appropriate models for detecting accounting manipulation in Central European countries. Methods: Seven accrual earnings management models were used. They were evaluated based on five criteria: explanatory power (adjusted coefficient of determination), overall significance of the regression model, significance of the regression coefficients, predicted sign of the regression coefficients, and standard errors of the estimated regression coefficients. Finally, a comprehensive scoring model was used to determine the overall quality of the models examined. The study was conducted on a sample of more than 30,000 enterprises from four Central European countries (the Czech Republic, Hungary, Poland, and Slovakia), with data covering the period 2017–2021. The Dickinson non-sequential life cycle model was used to distinguish life cycle stages according to cash flow patterns. Findings &amp; value added: The results of the study suggest that, firstly, discretionary accruals vary across countries as well as over the firm's life cycle; earnings manipulations have an inverted U-shape with mature firms tending to reduce their accounting profit. The reliability of the models examined was poor in the case of start-ups. This reflects the need to distinguish between life cycle stages in the detection of earnings manipulation.

Amyloid and tau burden relate to longitudinal changes in the performance of complex everyday activities among cognitively unimpaired older adults: results from the performance-based Harvard Automated Phone Task.

Changes in everyday functioning constitute a clinically meaningful outcome, even in the early stages of Alzheimer's disease. Performance-based assessments of everyday functioning might help uncover these early changes. We aimed to investigate how changes over time in everyday functioning relate to tau and amyloid in cognitively unimpaired older adults. Seventy-six cognitively unimpaired participants (72 ± 6 years old, 61% female) completed multiple Harvard Automated Phone Task (APT) assessments over 2.0 ± 0.9 years. The Harvard APT consists of three tasks, performed through an automated phone system, in which participants refill a prescription (APT-Script), select a new primary care physician (APT-PCP), and transfer money to pay a bill (APT-Bank). Participants underwent Pittsburgh compound-B and flortaucipir positron emission tomography scans at baseline. We computed distribution volume ratios for a cortical amyloid aggregate and standardized uptake volume ratios for medial temporal and neocortical tau regions. In separate linear mixed models, baseline amyloid by time and tau by time interactions were used to predict longitudinal changes in performance on the Harvard APT tasks. Three-way amyloid by tau by time interactions were also investigated. Lastly, we examined associations between tau and change in Harvard APT scores in exploratory voxel-wise whole-brain analyses. All models were adjusted for age, sex, and education. Amyloid [unstandardized partial regression coefficient estimate (β) = -0.007, 95% confidence interval (95% CI) = (-0.013, -0.001)], and medial temporal tau [β = -0.013, 95% CI = (-0.022, -0.004)] were associated with change over time in years on APT-PCP only, i.e., higher baseline amyloid and higher baseline tau were associated with steeper rate of decline of APT-PCP. Voxel-wise analyses showed widespread associations between tau and change in APT-PCP scores over time. Even among cognitively unimpaired older adults, changes over time in the performance of cognitively complex everyday activities relate to cortical amyloid and widespread cerebral tau burden at baseline. These findings support the link between Alzheimer's disease pathology and function and highlight the importance of measuring everyday functioning in preclinical disease stages.

CLINICAL OUTCOMES OF AUTOLOGOUS OSTEOCHONDRAL TRANSPLANTATION FOR OSTEOCHONDRAL LESIONS OF THE TALUS: AN AGE-BASED MULTIVARIABLE ANALYSIS

IntroductionThe purpose of this study was to examine trends in patient characteristics and clinical outcomes that occur with age as a statistical variable when performing autologous osteochondral transplantation (AOT) for the treatment of osteochondral lesions of the talus (OLT).MethodsA retrospective cohort study for AOT procedures on 78 patients from 2006 to 2019. was conducted Clinical outcomes were evaluated via FAOS scores. A multivariable linear regression was used to assess the independent factors predictive of the first post-operative FAOS after AOT. The independent variables included pre-operative FAOS, age, defect size, shoulder lesion, cystic lesion, prior traumatic injury, and history of microfracture surgery. A p-value &lt;.05 was considered significant and 95% confidence limits (95% CL) for regression coefficient estimates (est.) were calculated.Results78 patients were included with a mean age of 35.5 ± 13.6years at a mean follow-up was 54.4 ± 18.9 months. The mean pre-operative FAOS was 54.3 ± 19.4 and the mean post-operative FAOS was 83.4 ± 13.6. The mean defect size was 109.3±62.4 mm2. The multivariable linear regression showed that the pre-operative FAOS was associated with a higher post-operative FAOS (est., 95% CL: 0.16, 0.012 – 0.307; p=0.034). Defect size (est., 95% CL: −0.05, −0.097 – −0.003; p=0.0358), having a shoulder lesion (est., 95% CL: −9.068, −15.448 – −2.688; p=0.006), or having a prior microfracture surgery (est., 95% CL: −7.07, −13.118 – −1.021; p=0.0226) were associated with a lower post-operative FAOS.ConclusionPatient age was not an independent risk factor for inferior clinical outcomes after AOT for OLT. Additionally, cystic lesions, or lesions with a traumatic aetiology were not significantly associated with post-operative FAOS. Having a shoulder lesion had the largest marginal effect on post-operative FAOS. These findings provide important information for providers when counseling and selecting patients for AOT procedure for treatment of OLT.