Candidate Models Research Articles

On Optimality of Mallows Model Averaging

In the past decades, model averaging (MA) has attracted much attention as it has emerged as an alternative tool to the model selection (MS) statistical approach. Hansen introduced a Mallows model averaging (MMA) method with model weights selected by minimizing a Mallows’ C p criterion. The main theoretical justification for MMA is an asymptotic optimality (AOP), which states that the risk/loss of the resulting MA estimator is asymptotically equivalent to that of the best but infeasible averaged model. MMA’s AOP is proved in the literature by either constraining weights in a special discrete weight set or limiting the number of candidate models. In this work, it is first shown that under these restrictions, however, the optimal risk of MA becomes an unreachable target, and MMA may converge more slowly than MS. In this background, a foundational issue that has not been addressed is: When a suitably large set of candidate models is considered, and the model weights are not harmfully constrained, can the MMA estimator perform asymptotically as well as the optimal convex combination of the candidate models? We answer this question in both nested and non-nested settings. In the nested setting, we provide finite sample inequalities for the risk of MMA and show that without unnatural restrictions on the candidate models, MMA’s AOP holds in a general continuous weight set under certain mild conditions. In the non-nested setting, a sufficient condition and a negative result are established for the achievability of the optimal MA risk. Implications on minimax adaptivity are given as well. The results from simulations and real data analysis back up our theoretical findings. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Improving Tensor Regression by Optimal Model Averaging

Tensors have broad applications in neuroimaging, data mining, digital marketing, etc. CANDECOMP/PARAFAC (CP) tensor decomposition can effectively reduce the number of parameters to gain dimensionality-reduction and thus plays a key role in tensor regression. However, in CP decomposition, there is uncertainty about which rank to use. In this article, we develop a model averaging method to handle this uncertainty by weighting the estimators from candidate tensor regression models with different ranks. When all candidate models are misspecified, we prove that the model averaging estimator is asymptotically optimal. When correct models are included in the set of candidate models, we prove the consistency of parameters and the convergence of the model averaging weight. Simulations and empirical studies illustrate that the proposed method has superiority over the competition methods and has promising applications. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Intelligent Diagnosis of Hypopigmented Dermatoses and Intelligent Evaluation of Vitiligo Severity on the Basis of Deep Learning.

There is a lack of objective, accurate, and convenient methods for classification diagnostic hypopigmented dermatoses (HD) and severity evaluation of vitiligo. To achieve an accurate and intelligent classification diagnostic model of HD and severity evaluation model of vitiligo using a deep learning-based method. A total of 11,483 images from 4744 patients with HD were included in this study. An optimal diagnostic model was constructed by merging the squeeze-and-excitation (SE) module with the candidate model, its diagnostic efficiency was compared with that of 98 dermatologists. An objective severity evaluation indicator was proposed through weighting method and combined with a segmentation model to form a severity evaluation model, which was then compared with the assessments conducted by three experienced dermatologists using the naked eye. The improved diagnosis model SE_ResNet-18 outperformed the other 11 classic models with an accuracy of 0.9389, macro-specificity of 0.9878, and macro-f1 score of 0.9395, and outperformed the different categories of 98 dermatologists (P < 0.001). The weighted Kappa test indicated medium consistency between the Indicatorv and the VASIchange (K = 0.567, P < 0.05). The optimal segmented model, HR-Net, had 0.8421 mIOU. The model-based severity evaluation results were not significantly different among the three experienced dermatologists. This study proposes an objective, accurate, and convenient hybrid model for diagnosing HD and evaluating the severity of vitiligo, providing a method for dermatologists especially in grassroots hospitals, and provides a foundation for telemedicine.

Modeling of FAK-PROTAC candidates from GSK2256098 Analogues for Targeted Protein Degradation

Modeling of FAK-PROTAC candidates from GSK2256098 Analogues for Targeted Protein Degradation

Considering the Costs: Resin 3D Printing for a Temporal Bone Dissection Course.

Significant costs associated with obtaining cadaveric temporal bones (TBs) have led many to seek more cost-effective alternatives for TB surgical simulation. Multiple studies support the face validity of resin 3-dimensional (3D)-printed TBs as high-fidelity, useful alternatives for simulating TB dissection. However, a paucity of literature describes the cost or time associated with in-house manufacturing of resin TBs at scale. This paper reviews the hardware and manufacturing costs, and time required for in-house development of resin TB models for an annual dissection course. An open-source library of TB models was queried for a candidate model which was edited for optimal printing on a recently developed resin 3D printer. In the described workflow, we were able to 3D-print 60 TB models at $6.40 each, for a total material cost of $384.10, less than the price of a single cadaveric TB specimen (∼$400-$700).

Integrating Factor Models Using Bayesian Model Averaging: Evidence from Korea

This study applies the Bayesian model averaging approach to the Korean stock market data to yield an integrated factor model that can address model uncertainty. During the period from 2004 to 2022, we choose 14 factors and 10 macro predictors and generate a total of 10,485,760 candidate factor models by considering all possible combinations of each variable and whether to allow for model mispricing. The integrated model estimates the expected return and covariance matrix by combining the predictions of all the candidate models using Bayesian posterior probabilities as weights. In the Korean stock market, there is no clear winner model with dominant posterior probabilities, implying that model uncertainty is substantial. The optimal portfolio of the integrated model has a higher out-of-sample Sharpe ratio and lower downside risk than benchmark factor models. Model uncertainty has a significant impact on the estimation of the covariance matrix of the integrated model, and model disagreement about expected returns is particularly acute during periods of sharp market declines. In the presence of model uncertainty, Bayesian investors perceive equities to be riskier than historical volatility.

Bayesian Ensembles of Exponentially Smoothed Life-Cycle Forecasts

Problem definition: We study the problem of forecasting an entire demand distribution for a new product before and after its launch. Firms need accurate distributional forecasts of demand to make operational decisions about capacity, inventory, and marketing expenditures. We introduce a unified, robust, and interpretable approach to producing these pre- and postlaunch distributional forecasts. Methodology/results: Our approach is inspired by Bayesian model averaging. Each candidate model in our ensemble is a life-cycle model fitted to the completed life cycle of a comparable product. A prelaunch forecast is an ensemble with equal weights on the candidate models’ forecasts, whereas a postlaunch forecast is an ensemble with weights that evolve according to Bayesian updating. Our approach is part frequentist and part Bayesian, resulting in a novel approach tailored to the demand forecasting challenge. We also introduce a new type of life-cycle or product diffusion model with states that can be updated using exponential smoothing. The trend in this model follows the density of an exponentially tilted Gompertz random variable. For postlaunch forecasting, this model is attractive because it can adapt itself to the most recent changes in a product’s life cycle. We provide closed-form distributional forecasts from our model. In two empirical studies, we show that when the ensemble’s candidate models are all in our new type of exponential smoothing model, this version of the ensemble outperforms several leading approaches in both point and quantile forecasting. Managerial implications: In a data-driven operations environment, our model can produce accurate forecasts frequently and at scale. When quantile forecasts are needed, our model has the potential to provide meaningful economic benefits. In addition, our model’s interpretability should be attractive to managers who already use exponential smoothing and ensemble methods for other forecasting purposes. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0359 .

A novel digital health approach to improving global pediatric sepsis care in Bangladesh using wearable technology and machine learning.

Sepsis is the leading cause of child death globally with low- and middle-income countries (LMICs) bearing a disproportionate burden of pediatric sepsis deaths. Limited diagnostic and critical care capacity and health worker shortages contribute to delayed recognition of advanced sepsis (severe sepsis, septic shock, and/or multiple organ dysfunction) in LMICs. The aims of this study were to 1) assess the feasibility of a wearable device for physiologic monitoring of septic children in a LMIC setting and 2) develop machine learning models that utilize readily available wearable and clinical data to predict advanced sepsis in children. This was a prospective observational study of children with sepsis admitted to an intensive care unit in Dhaka, Bangladesh. A wireless, wearable device linked to a smartphone was used to collect continuous recordings of physiologic data for the duration of each patient's admission. The correlation between wearable device-collected vital signs (heart rate [HR], respiratory rate [RR], temperature [T]) and manually collected vital signs was assessed using Pearson's correlation coefficients and agreement was assessed using Bland-Altman plots. Clinical and laboratory data were used to calculate twice daily pediatric Sequential Organ Failure Assessment (pSOFA) scores. Ridge regression was used to develop three candidate models for advanced sepsis (pSOFA > 8) using combinations of clinical and wearable device data. In addition, the lead time between the models' detection of advanced sepsis and physicians' documentation was compared. 100 children were enrolled of whom 41% were female with a mean age of 15.4 (SD 29.6) months. In-hospital mortality rate was 24%. Patients were monitored for an average of 2.2 days, with > 99% data capture from the wearable device during this period. Pearson's r was 0.93 and 0.94 for HR and RR, respectively) with r = 0.72 for core T). Mean difference (limits of agreement) was 0.04 (-14.26, 14.34) for HR, 0.29 (-5.91, 6.48) for RR, and -0.0004 (-1.48, 1.47) for core T. Model B, which included two manually measured variables (mean arterial pressure and SpO2:FiO2) and wearable device data had excellent discrimination, with an area under the Receiver-Operating Curve (AUC) of 0.86. Model C, which consisted of only wearable device features, also performed well, with an AUC of 0.78. Model B was able to predict the development of advanced sepsis more than 2.5 hours earlier compared to clinical documentation. A wireless, wearable device was feasible for continuous, remote physiologic monitoring among children with sepsis in a LMIC setting. Additionally, machine-learning models using wearable device data could discriminate cases of advanced sepsis without any laboratory tests and minimal or no clinician inputs. Future research will develop this technology into a smartphone-based system which can serve as both a low-cost telemetry monitor and an early warning clinical alert system, providing the potential for high-quality critical care capacity for pediatric sepsis in resource-limited settings.

Development and validation of a risk prediction model for worsening renal function in patients with incident heart failure

Abstract Background Worsening renal function (WRF) is one of the strongest predictors of outcome in patients with heart failure (HF). The presence of WRF often influences the decision to start, up-titrate, or discontinue disease modifying HF therapies and is one of the key determinants of suboptimal guideline-directed medical therapy. Existing WRF risk scores were developed in hospitalised HF patients to predict in-hospital WRF. Their performance among incident HF is far from ideal. It is therefore important to develop a new WRF risk prediction model in people with newly diagnosed HF. Purpose This study was to develop and validate a clinical risk prediction model for 180-day WRF post diagnosis of incident HF using data from a population-based longitudinal cohort. Methods We developed and validated a multivariable logistic regression model for WRF in the 180 days post diagnosis of incident HF. Data for individuals with incident HF between 2016 to 2021 were extracted from the NELSON study conducted in Tayside, Scotland. WRF was defined as a composite event of (i) having two consecutive eGFRs declined by 40% or greater; (ii) having an eGFR &amp;lt; 15mL/min/1.73m2; (iii) initiation of sustained dialysis; (iv) development of end-stage kidney disease; (v) receiving kidney transplantation; or (vi) kidney related death. Four candidate models included one model using literature knowledge; one multivariable fractional polynomial (MFP) model; and two stepwise selected models based on either Akaike or Bayesian information criterion (AIC or BIC). Calibration and discrimination were assessed, and performance stability were evaluated using optimism corrected performance via 1000 times bootstrapping. Results 4076 individuals (mean age 72.8 ± 13.2; 58% male; 1081 HF with reduced ejection fraction (HFrEF), 646 with HF with mildly reduced ejection fraction (HFmrEF), 1348 HF with preserved ejection fraction (HFpEF), and 1001 HF with unknown ejection fraction (EF)) who were WRF-free when diagnosed with HF were identified. Of these, 2095 (51.4%) were in-patients, and 1981 (48.6%) were out-patients. 285 (7%) patients developed WRF within 180 days post HF diagnosis. The selected MFP model (comprising 12 predictors, Table 1) showed good calibration (slope = 1.00 [0.86, 1.14]; intercept = 0.0 [-0.13, 0.13]) discrimination (C-statistic = 0.69 [95% CI: 0.65, 0.72]), and stability (optimism corrected: calibration slope = 0.94 [0.80, 1.07]; calibration intercept = -0.14 [-0.50, 0.20]; C-statistic = 0.67 [0.64, 0.71]). Discrimination of the developed model significantly outperformed two existing risk scores (Forman and Basel, both with a C-statistic = 0.62 [0.59 – 0.65]). Conclusions We have shown that the developed model performed better in predicting WRF in individuals with newly diagnosed HF. This could have utility in identifying at risk patients with HF early in the disease trajectory for therapies that can reduce renal decline.Calibration of predicted riskModel estimates for predicting 180d WRF

Transdiagnostic modeling of clinician-rated symptoms in affective and nonaffective psychotic disorders.

Prevailing factor models of psychosis are centered on schizophrenia-related disorders defined by the Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases, restricting generalizability to other clinical presentations featuring psychosis, even though affective psychoses are more common. This study aims to bridge this gap by conducting exploratory and confirmatory factor analyses, utilizing clinical ratings collected from patients with either affective or nonaffective psychoses (n = 1,042). Drawing from established clinical instruments, such as the Positive and Negative Syndrome Scale, Young Mania Rating Scale, and Montgomery-Åsberg Depression Rating Scale, a broad spectrum of core psychotic symptoms was considered for the model development. Among the candidate models considered, including correlated factors and multifactor models, a model with seven correlated factors encompassing positive symptoms, negative symptoms, depression, mania, disorganization, hostility, and anxiety was most interpretable with acceptable fit. The seven factors exhibited expected associations with external validators, were replicable through cross-validation, and were generalizable across affective and nonaffective psychoses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prognostic score-based model averaging approach for propensity score estimation

BackgroundPropensity scores (PS) are typically evaluated using balance metrics that focus on covariate balance, often without considering their predictive power for the outcome. This approach may not always result in optimal bias reduction in the treatment effect estimate. To address this issue, evaluating covariate balance through prognostic scores, which account for the relationship between covariates and the outcome, has been proposed. Similarly, using a typical model averaging approach for PS estimation that minimizes prediction error for treatment status and covariate imbalance does not necessarily optimize PS-based confounding adjustment. As an alternative approach, using the averaged PS model that minimizes inter-group differences in the prognostic score may further reduce bias in the treatment effect estimate. Moreover, since the prognostic score is also an estimated quantity, model averaging in the prognostic scores can help identify a better prognostic score model. Utilizing the model-averaged prognostic scores as the balance metric for constructing the averaged PS model can contribute to further decreasing bias in treatment effect estimates. This paper demonstrates the effectiveness of the PS model averaging approach based on prognostic score balance and proposes a method that uses the model-averaged prognostic score as a balance metric, evaluating its performance through simulations and empirical analysis.MethodsWe conduct a series of simulations alongside an analysis of empirical observational data to compare the performances of weighted treatment effect estimates using the proposed and existing approaches. In our examination, we separately provid four candidate estimates for the PS and prognostic score models using traditional regression and machine learning methods. The model averaging of PS based on these candidate estimators is performed to either maximize the prediction accuracy of the treatment or to minimize intergroup differences in covariate distributions or prognostic scores. We also utilize not only the prognostic scores from each candidate model but also an averaged score that best predicted the outcome, for the balance assessment.ResultsThe simulation and empirical data analysis reveal that our proposed model-averaging approaches for PS estimation consistently yield lower bias and less variability in treatment effect estimates across various scenarios compared to existing methods. Specifically, using the optimally averaged prognostic scores as a balance metric significantly improves the robustness of the weighted treatment effect estimates.DiscussionThe prognostic score-based model averaging approach for estimating PS can outperform existing model averaging methods. In particular, the estimator using the model averaging prognostic score as a balance metric can produce more robust estimates. Since our results are obtained under relatively simple conditions, applying them to real data analysis requires adjustments to obtain accurate estimates according to the complexity and dimensionality of the data.ConclusionsUsing the prognostic score as the balance metric for the PS model averaging enhances the performance of the treatment effect estimator, which can be recommended for a wide variety of situations. When applying the proposed method to real-world data, it is important to use it in conjunction with techniques that mitigate issues arising from the complexity and high dimensionality of the data.

Multiplicative noise induced bistability and stochastic resonance

Abstract Stochastic resonance is a well established phenomenon, which proves relevant for a wide range of applications, of broad trans-disciplinary breath. Consider a one dimensional bistable stochastic system, characterized by a deterministic double well potential and shaken by an additive noise source. When subject to an external periodic drive, and for a proper choice of the noise strength, the system swings regularly between the two existing deterministic fixed points, with just one switch for each oscillation of the imposed forcing term. This resonant condition can be exploited to unravel weak periodic signals, otherwise inaccessible to conventional detectors. Here, we will set to revisit the stochastic resonance concept by operating in a modified framework where bistability is induced by the nonlinear nature of the multiplicative noise. A candidate model is in particular introduced which fulfils the above requirements while allowing for analytical progress to be made. Working with reference to this case study, we elaborate on the conditions for the onset of the generalized stochastic resonance mechanism. As a byproduct of the analysis, a novel resonant regime is also identified which displays no lower bound for the frequencies that can be resolved, at variance with the traditional setting.

Robust model averaging approach by Mallows-type criterion.

Model averaging is an important tool for treating uncertainty from model selection process and fusing information from different models, and has been widely used in various fields. However, the most existing model averaging criteria are proposed based on the methods of ordinary least squares or maximum likelihood, which possess high sensitivity to outliers or violation of certain model assumption. For the mean regression, no optimal robust methods are developed. To fill this gap, in our paper, we propose an outlier-robust model averaging approach by Mallows-type criterion. The idea is that we first construct a generalized M (GM) estimator for each candidate model, and then build robust weighting schemes by the asymptotic expansion of the final prediction error based on the GM-type loss function. So, we can still achieve a trustworthy result even if the dataset is contaminated by outliers in response and/or covariates. Asymptotic properties of the proposed robust model averaging estimators are established under some regularity conditions. The consistency of our weight estimators tending to the theoretically optimal weight vectors is also derived. We prove that our model averaging estimator is robust in terms of having bounded influence function. Further, we define the empirical prediction influence function to evaluate the quantitative robustness of the model averaging estimator. A simulation study and a real data analysis are conducted to demonstrate the finite sample performance of our estimators and compare them with other commonly used model selection and averaging methods.

Predicting implementation of response to intervention in math using elastic net logistic regression.

The primary objective of this study was to identify variables that significantly influence the implementation of math Response to Intervention (RTI) at the school level, utilizing the ECLS-K: 2011 dataset. Due to missing values in the original dataset, a Random Forest algorithm was employed for data imputation, generating a total of 10 imputed datasets. Elastic net logistic regression, combined with nested cross-validation, was applied to each imputed dataset, potentially resulting in 10 models with different variables. Variables for the models derived from the imputed datasets were selected using four methods, leading to four candidate models for final selection. These models were assessed based on their performance of prediction accuracy, culminating in the selection of the final model that outperformed the others. Method50 and Methodcoef emerged as the most effective, achieving a balanced accuracy of 0.852. The ultimate model selected relevant variables that effectively predicted RTI. The predictive accuracy of the final model was also demonstrated by the receiver operating characteristic (ROC) plot and the corresponding area under the curve (AUC) value, indicating its ability to accurately forecast math RTI implementation in schools for the following year.

Temperature-Dependent Thermal Conductivity Identification by Bayesian Inference

The identification of temperature-dependent thermal conductivity in aerogel material, which is commonly used as insulation in thermal protection structures of high-speed aircraft, faces the challenge of selecting the appropriate model in engineering practice. Considering the uncertainties in the selection process of an appropriate functional model, a novel Bayesian probability method computational framework based on response data is established to improve the accuracy of thermal conductivity identification. Three implementation steps are presented: 1) the database of candidate models is established; 2) the reconstructed signals can be calculated by a heat transfer analysis model; and 3) the posterior probability of each candidate model is estimated to obtain the optimal thermal conductivity model and determine the characteristic coefficients. Numerical simulations of a theoretical one-dimensional heat transfer model and a curved thermal protection structure are performed to verify the proposed method. Then, a heating experimental investigation of the curved thermal protection structure is conducted to identify the temperature-dependent thermal conductivity of aerogel material. The results indicate that the temperature-varying thermal conductivity can be accurately identified by the proposed method, which can be applied to the heat transfer analysis and design of aerogel materials in high-speed aircraft.

A Robust [18F]-PSMA-1007 Radiomics Ensemble Model for Prostate Cancer Risk Stratification.

The aim of this study is to investigate the role of [18F]-PSMA-1007 PET in differentiating high- and low-risk prostate cancer (PCa) through a robust radiomics ensemble model. This retrospective study included 143 PCa patients who underwent [18F]-PSMA-1007 PET/CT imaging. PCa areas were manually contoured on PET images and 1781 image biomarker standardization initiative (IBSI)-compliant radiomics features were extracted. A 30 times iterated preliminary analysis pipeline, comprising of the least absolute shrinkage and selection operator (LASSO) for feature selection and fivefold cross-validation for model optimization, was adopted to identify the most robust features to dataset variations, select candidate models for ensemble modelling, and optimize hyperparameters. Thirteen subsets of selected features, 11 generated from the preliminary analysis plus two additional subsets, the first based on the combination of robust and fine-tuning features, and the second only on fine-tuning features were used to train the model ensemble. Accuracy, area under curve (AUC), sensitivity, specificity, precision, and f-score values were calculated to provide models' performance. Friedman test, followed by post hoc tests corrected with Dunn-Sidak correction for multiple comparisons, was used to verify if statistically significant differences were found in the different ensemble models over the 30 iterations. The model ensemble trained with the combination of robust and fine-tuning features obtained the highest average accuracy (79.52%), AUC (85.75%), specificity (84.29%), precision (82.85%), and f-score (78.26%). Statistically significant differences (p < 0.05) were found for some performance metrics. These findings support the role of [18F]-PSMA-1007 PET radiomics in improving risk stratification for PCa, by reducing dependence on biopsies.

Incorporating Digital Footprints into Credit-Scoring Models through Model Averaging

Digital footprints provide crucial insights into individuals’ behaviors and preferences. Their role in credit scoring is becoming increasingly significant. Therefore, it is crucial to combine digital footprint data with traditional data for personal credit scoring. This paper proposes a novel credit-scoring model. First, lasso-logistic regression is used to select key variables that significantly impact the prediction results. Then, digital footprint variables are categorized based on business understanding, and candidate models are constructed from various combinations of these groups. Finally, the optimal weight is selected by minimizing the Kullback–Leibler loss. Subsequently, the final prediction model is constructed. Empirical analysis validates the advantages and feasibility of the proposed method in variable selection, coefficient estimation, and predictive accuracy. Furthermore, the model-averaging method provides the weights for each candidate model, providing managerial implications to identify beneficial variable combinations for credit scoring.

Novel multitarget analgesic candidate SZV-1287 demonstrates potential disease-modifying effects in the monoiodoacetate-induced osteoarthritis mouse model.

Monoiodoacetate (MIA)-induced osteoarthritis (OA) is the most commonly used rodent model for testing anti-OA drug candidates. Herein, we investigated the effects of our patented multitarget drug candidate SZV-1287 (3-(4,5-diphenyl-1,3-oxazol-2-yl) propanal oxime) that is currently under clinical development for neuropathic pain and characterized the mouse model through complex functional, in vivo imaging, and morphological techniques. Knee OA was induced by intraarticular MIA injection (0.5 and 0.8mg). Spontaneous pain was assessed based on weight distribution, referred pain by paw mechanonociception (esthesiometry), edema by caliper, neutrophil myeloperoxidase activity by luminescence, matrix metalloproteinase activity, vascular leakage and bone remodeling by fluorescence imaging, bone morphology by micro-CT, histopathological alterations by semiquantitative scoring, and glia activation by immunohistochemistry. Then, SZV-1287 (20mg/kg/day) or its vehicle was injected intraperitoneally over a 21-day period. MIA induced remarkably decreased thresholds of weight bearing and paw withdrawal, alterations in the tibial and femoral structures (reactive sclerosis, increased trabeculation, and cortical erosions), histopathological damage (disorganized cartilage structure, hypocellularity, decreased matrix staining and tidemark integrity, and increased synovial hyperplasia and osteophyte formation), and changes in the astrocyte and microglia density in the lumbar spinal cord. There were no major differences between the two MIA doses in most outcome measures. SZV-1287 inhibited MIA-induced weight bearing reduction, hyperalgesia, edema, myeloperoxidase activity, histopathological damage, and astrocyte and microglia density. SZV-1287 may have disease-modifying potential through analgesic, anti-inflammatory, and chondroprotective effects. The MIA mouse model is valuable for investigating OA-related mechanisms and testing compounds in mice at an optimal dose of 0.5mg.

Data transformation and model selection in bivariate allometry.

Students of biological allometry have used the logarithmic transformation for over a century to linearize bivariate distributions that are curvilinear on the arithmetic scale. When the distribution is linear, the equation for a straight line fitted to the distribution can be back-transformed to form a two-parameter power function for describing the original observations. However, many of the data in contemporary studies of allometry fail to meet the requirement for log-linearity, thereby precluding the use of the aforementioned protocol. Even when data are linear in logarithmic form, the two-parameter power equation estimated by back-transformation may yield a misleading or erroneous perception of pattern in the original distribution. A better approach to bivariate allometry would be to forego transformation altogether and to fit multiple models to untransformed observations by nonlinear regression, thereby creating a pool of candidate models with different functional form and different assumptions regarding random error. The best model in the pool of candidate models could then be identified by a selection procedure based on maximum likelihood. Two examples are presented to illustrate the power and versatility of newer methods for studying allometric variation. It always is better to examine the original data when it is possible to do so.

53 Relationship of Angus yearling bull pulmonary arterial pressure estimates with birth weight, gestation length and growth traits

Abstract Pulmonary arterial pressure (PAP), determines the susceptibility to High Altitude Disease (HAD) of an animal, which causes pulmonary hypertension due to hypoxia. Cattle that reside in elevations greater than 1,500 m are more susceptible to HAD. PAP measures the resistance of blood flow through the lungs and can be used as an indicator of the susceptibility of an animal to HAD. It is estimated that 1.5 million head of cattle are raised in high altitude environments (above 1,500 m) and HAD accounts for 3 to 5% of calf death loss yearly. The objectives of this study were to model the relationship of PAP estimates with birth weight, gestation length and growth traits of developing bulls. PAP estimates were collected from Angus yearling bulls ranging in age from 12 to 18-mo of age from a Montana-based Angus operation (&amp;gt; 1,600 m elevation, 5,400 yearlings/18-mo-old bulls from 2016 – 2023). We hypothesized that the relationship between PAP and performance traits could be described with linear or asymptotic models. We used Akaike’s Information Criterion adjusted for small sample sizes (AICc) to evaluate support for competing models reflecting hypotheses about the effects of bull age at time of testing and performance traits. Models containing an asymptotic relationship between PAP and all measured variables were the best supported from the candidate model sets. Bull age at time of PAP testing was not supported in model selection for any of the performance traits measured. PAP was greater for bulls with greater birth weight (P &amp;lt; 0.01; β = 2.86 ± 0.81; R2marginal = 0.25 %; R2conditional = 0.77 %). PAP was greater for bulls that had greater gestation lengths (P &amp;lt; 0.01; β = 33.74 ± 11.25; R2marginal = 1.40 %; R2conditional = 27.37 %). There were no observed relationships between bull PAP and weaning weight (P = 0.26). PAP was less for bulls that had greater yearling weights (P = 0.04; β = -2.16 ± 1.05; R2marginal = 0.09 %; R2conditional = 0.65 %). In addition, PAP was less for bulls that had greater weaning to yearling gains (P = 0.02; β = -1.21 ± 0.54; R2marginal = 0.12 %; R2conditional = 0.79 %). In summary, PAP was related to birth weight, gestation length, yearling weight, and growth between weaning and yearling; however, these relationships explain very little of the variation of PAP estimates.