Unbiased Estimates Research Articles

Unbiased Analysis of Knee Cartilage Thickness Change Over Three Years Post-Treatment with Sprifermin vs. Placebo – A Post-Hoc Analysis from the Phase II FORWARD Study

ObjectivePost-treatment cartilage morphometry in the FORWARD study was performed without blinding to MRI acquisition order, involving potential reader bias. Here we obtained unbiased estimates of cartilage change post-treatment, reading year (Y)2 and Y5 MRIs with blinding to time point. We studied whether post-treatment cartilage thickness change differed between sprifermin- and placebo-treated knees. MethodsFORWARD was a 5-year randomized control trial in 549 knee osteoarthritis patients. Here, Y2/Y5 images were analyzed with blinding to relative temporal order and treatment group. Cartilage change during Y2→Y5 was obtained in 337 participants: n=57 treated with placebo intra-articular injections every 6 months (q6M); n=69 with 30μg sprifermin every 12 months (q12M), n=67 with 30μg q6M, n=73 with 100μg q12M, and n=71 with 100μg q6M between baseline (BL) and 18M. Total femorotibial joint (TFTJ) cartilage thickness was the primary analytic focus. ResultsTFTJ cartilage thickness change during Y2→Y5 was -26μm (SD64; 95%CI -32,-19) across the cohort; no statistically significant difference (p=0.80) was observed between treatment or placebo arms (one-way ANOVA). All groups lost cartilage, but the treatment-related difference in cartilage thickness in Sprifermin arms relative to placebo at Y2 was maintained until Y5. Annualized cartilage change in placebo participants was -8.2μm (SD21; 95%CI -14,-2.5) during Y2→Y5 vs. -5.4μm (SD27; 95%CI -13,1.8) during BL→Y2; no significant difference was identified (t-test). ConclusionFORWARD is the first study evaluating post-treatment benefits of a potential disease modifying osteoarthritis drug. Cartilage thickness gained with 100μg sprifermin at Y2 is maintained to Y5 and thus appears viable and sustainable.This is a post-hoc analysis of the FORWARD trial: ClinicalTrials.gov Identifier: NCT01919164

Length of seminiferous tubules or epididymal duct estimated with profile counting or diameter measurement: a stereological comparison with transverse and isotropic sections

Transverse testicular sections are often used for histopathological studies and length of seminiferous tubules in the testis is often estimated with a stereological method of tubule diameter measurement, i.e. estimated by the tubule volume divided by the tubules’ cross-sectional area that is estimated with measurement of round or elliptical tubule profiles’ (short axial) diameters. But unbiased stereological length estimation is best based on counting of tubule profiles on isotropic sections. To determine the bias of length estimation with diameter measurement, (i) transverse sections (methacrylate embedded) were obtained from a testis of each normal mature male Sprague-Dawley rat (10 rats in total) and isotropic sections obtained (with the orientator) from the other testis of the rat, and (ii) the tubule length densities were estimated with both methods of profile counting and diameter measurement. The results demonstrated that the method of diameter measurement on transverse or isotropic sections overestimated the length density by 20%-25% compared with profile counting on isotropic sections, probably mainly because of underestimation of the tubules’ cross-sectional area estimated from the short-axial diameters. Length density of the epididymal duct in the epididymis was also estimated with profile counting in the present study, using transverse and isotropic sections for comparison, and significant statistical difference was not found between the results obtained with the transverse and isotropic sections.

On the posterior property of the Rician distribution

The Rician distribution, a well-known statistical distribution frequently encountered in fields like magnetic resonance imaging and wireless communications, is particularly useful for describing many real phenomena such as signal process data. In this paper, we introduce objective Bayesian inference for the Rician distribution parameters, specifically the Jeffreys rule and Jeffreys prior are derived. We proved that the obtained posterior for the first priors led to an improper posterior while the Jeffreys prior led to a proper distribution. To evaluate the effectiveness of our proposed Bayesian estimation method, we perform extensive numerical simulations and compare the results with those obtained from traditional moment-based and maximum likelihood estimators. Our simulations illustrate that the Bayesian estimators derived from the Jeffreys prior provide nearly unbiased estimates, showcasing the advantages of our approach over classical techniques. Additionally, our framework incorporates the S.A.F.E. principles – Sustainable, Accurate, Fair, and Explainable – ensuring robustness, fairness, and transparency in predictive modelling.

A Machine-Learning Approach to Mitigate Ground Clutter Effects in the GPM Combined Radar-Radiometer Algorithm (CORRA) Precipitation Estimates

Abstract In this study, a machine-learning based methodology is developed to mitigate the effects of ground clutter on precipitation estimates from the Global Precipitation Mission Combined Radar-Radiometer Algorithm. Ground clutter can corrupt and obscure precipitation echo in radar observations, leading to inaccuracies in precipitation estimates. To improve upon previous work, this study introduces a general machine learning (ML) approach that enables a systematic investigation and a better understanding of uncertainties in clutter mitigation. To allow for a less restrictive exploration of conditional relations between precipitation above the lowest clutter-free bin and surface precipitation, reflectivity observations above the clutter are included in a fixed-size set of predictors along with the precipitation type, surface type, and freezing level to estimate surface precipitation rates, and several ML-based estimation methods are investigated. A Neural Network Model (NN) is ultimately identified as the best candidate for systematic evaluations, as it is computationally fast to apply while effective in applications. The NN provides unbiased estimates; however, it does not significantly outperform a simple bias correction approach in reducing random errors in the estimates. The similar performance of other ML approaches suggests that the NN’s limited improvement beyond bias removal is due to indeterminacies in the data rather than limitations in the ML approach itself.

Robust Estimation of Lithium Battery State of Charge with Random Missing Current Measurement Data

The precise estimation of the state of charge (SOC) in lithium batteries is crucial for enhancing their operational lifespan. To address the issue of reduced accuracy in SOC estimation caused by the random missing values of lithium battery current measurements, a joint estimation method which combines recursive least squares with missing input data (MIDRLS) and the unscented Kalman filter (UKF) algorithm is proposed, called the MIDRLS-UKF algorithm. Firstly, the equivalent circuit model of a Thevenin battery is formulated. Then, the current imputation model is designed to interpolate the missing data, based on which the MIDRLS algorithm is derived by solving the unbiased estimation of the gradient of the objective function, thus realizing the online high-precision identification of the circuit model parameters. Furthermore, the proposed algorithm is combined with the UKF algorithm to facilitate the online precise estimation of SOC. The simulation results indicate a marked decrease in the SOC estimation error when employing the proposed joint algorithm, as opposed to the conventional forgetting factor recursive least squares (FFRLS) algorithm combined with the UKF joint estimation algorithm, which verifies the precision and effectiveness of the proposed joint algorithm.

A General Framework for the Multiple Nonparametric Behrens-Fisher Problem With Dependent Replicates.

In many trials and experiments, subjects are not only observed once but multiple times, resulting in a cluster of possibly correlated observations (e.g., brain regions per patient). Observations often do not fulfill model assumptions of mixed models and require the use of nonparametric methods. In this article, we develop and present a purely nonparametric rank-based procedure that flexibly allows the unbiased and consistent estimation of the Wilcoxon-Mann-Whitney effect in clustered data designs. Compared with existing methods, we allow flexible weights to be used in effect estimation. Additionally, we develop global and multiple contrast test procedures to test null hypotheses formulated regarding the generalized Mann-Whitney effects and for the computation of range-preserving simultaneous confidence intervals in a unified way. Extensive simulation studies show that these methods control the type-I error rate well and have reasonable power to detect alternatives in various situations.

Gill and Massar type bound for estimation of SU(2) channel

Abstract In the estimation for a parametric family of quantum state on a Hilbert space H, the Gill and Massar bound is known as a lower bound of weighted traces of covariances of unbiased estimators. The Gill and Massar bound is derived by considering the convexity of the set of classical Fisher information matrices, and the bound is locally achievable by using randomized strategies when H=C2. In this paper, we show that estimation for a parametric SU(2) unitary channel model has a similar convex structure as qubit state model, and a Gill and Massar type lower bound of weighted traces of covariances of unbiased estimators can be derived for any weight matrix. We show that the Gill and Massar type lower bound is achievable by using randomized strategies when certain conditions are satisfied. To derive a convex strucutre of the set of classical Fisher information matrices, we introduce a Fisher information matrix J(U) for a SU(2) unitary channel model, and we show a upper bound of inverse J(U) weighted trace of classical Fisher information matrix. The optimal randomized strategy we construct in this paper does not require ancilla systems in many cases.

The German Tank Problem with Multiple Factories

During the Second World War, estimates of the number of tanks deployed by Germany were critically needed. The Allies adopted a successful statistical approach to estimate this information: assuming that the tanks are sequentially numbered starting from 1, if we observe k tanks from an unknown total of N, then the best linear unbiased estimator for N is M(1+1/k)-1 where M is the maximum observed serial number. However, in many situations, the original German Tank Problem is insufficient, since typically there are l > 1 factories, and tanks produced by different factories may have serial numbers in disjoint ranges that are often far separated. Clark, Gonye and Miller presented an unbiased estimator for N when the minimum serial number is unknown. Provided one identifies which samples correspond to which factory, one can then estimate each factory's range and summing the sizes of these ranges yields an estimate for the rival's total productivity. We construct an efficient procedure to estimate the total productivity and prove that it is effective when log l/log k is sufficiently small. In the final section, we show that given information about the gaps, we can make an estimator that performs orders of magnitude better when we have a small number of samples.

Quantile adaptive lasso: variable selection for quantile treatment effect estimation

Quantile treatment effects (QTEs) characterize different causal effects of treatment on outcome distribution. Propensity score (PS) methods are commonly employed for estimating QTEs in non-randomized studies. Previous studies have shown that insufficient and unnecessary adjustment for covariates in PS models leads to bias and efficiency loss of treatment effects. Confounders may vary across different quantiles of outcome distribution. To allow for the selection of covariates related to outcome at interesting quantiles corresponding to QTE, we proposed quantile adaptive lasso (QAL) to select covariates that can provide unbiased and efficient QTE estimation. QAL utilized linear quantile regression models to construct penalty weights. Theoretical analyses showed that QAL enjoys several asymptotic properties. Simulations showed the superiority of QAL over existing methods in variable selection and QTE estimation. We applied QAL to datasets from the China Health and Retirement Longitudinal Study to explore the impact of smoking on the severity of depression.

Randomized iterative feedback tuning for fast MIMO feedback design of a mechatronic system

Iterative feedback tuning (IFT) enables the tuning of feedback controllers using only measured data to obtain the gradient of a cost criterion. The aim of this paper is to reduce the required number of experiments for MIMO IFT. It is shown that, through a randomization technique, an unbiased gradient estimate can be obtained from a single dedicated experiment, regardless of the size of the MIMO system. The gradient estimate is used in a stochastic gradient descent algorithm. The approach is experimentally validated on a mechatronic system, showing a significantly reduced number of experiments compared to standard IFT.

Unbiased and reproducible liver MRI-PDFF estimation using a scan protocol-informed deep learning method.

To estimate proton density fat fraction (PDFF) from chemical shift encoded (CSE) MR images using a deep learning (DL)-based method that is precise and robust to different MR scanners and acquisition echo times (TEs). Variable echo times neural network (VET-Net) is a two-stage framework that first estimates nonlinear variables of the CSE-MR signal model, to posteriorly estimate water/fat signal components using the least-squares method. VET-Net incorporates a vector with TEs as an auxiliary input, therefore enabling PDFF calculation with any TE setting. A single-site liver CSE-MRI dataset (188 subjects, 4146 axial slices) was considered, which was split into training (150 subjects), validation (18), and testing (20) subsets. Testing subjects were scanned using several protocols with different TEs, which we then used to measure the PDFF reproducibility coefficient (RDC) at two regions of interest (ROIs): the right posterior and left hepatic lobes. An open-source multi-site and multi-vendor fat-water phantom dataset was also used for PDFF bias assessment. VET-Net showed RDCs of 1.71% and 1.04% on the right posterior and left hepatic lobes, respectively, across different TEs, which was comparable to a reference graph cuts-based method (RDCs = 1.71% and 0.86%). VET-Net also showed a smaller PDFF bias (-0.55%) than graph cuts (0.93%) when tested on a multi-site phantom dataset. Reproducibility (1.94% and 1.59%) and bias (-2.04%) were negatively affected when the auxiliary TE input was not considered. VET-Net provided unbiased and precise PDFF estimations using CSE-MR images from different hardware vendors and different TEs, outperforming conventional DL approaches. Question Reproducibility of liver PDFF DL-based approaches on different scan protocols or manufacturers is not validated. Findings VET-Net showed a PDFF bias of -0.55% on a multi-site phantom dataset, and RDCs of 1.71% and 1.04% at two liver ROIs. Clinical relevance VET-Net provides efficient, in terms of scan and processing times, and unbiased PDFF estimations across different MR scanners and scan protocols, and therefore it can be leveraged to expand the use of MRI-based liver fat quantification to assess hepatic steatosis.

Advancing Interpretable Regression Analysis for Binary Data: A Novel Distributed Algorithm Approach.

Sparse data bias, where there is a lack of sufficient cases, is a common problem in data analysis, particularly when studying rare binary outcomes. Although a two-step meta-analysis approach may be used to lessen the bias by combining the summary statistics to increase the number of cases from multiple studies, this method does not completely eliminate bias in effect estimation. In this paper, we propose a one-shot distributed algorithm for estimating relative risk using a modified Poisson regression for binary data, named ODAP-B. We evaluate the performance of our method through both simulation studies and real-world case analyses of postacute sequelae of SARS-CoV-2 infection in children using data from 184 501 children across eight national academic medical centers. Compared with the meta-analysis method, our method provides closer estimates of the relative risk for all outcomes considered including syndromic and systemic outcomes. Our method is communication-efficient and privacy-preserving, requiring only aggregated data to obtain relatively unbiased effect estimates compared with two-step meta-analysis methods. Overall, ODAP-B is an effective distributed learning algorithm for Poisson regression to study rare binary outcomes. The method provides inference on adjusted relative risk with a robust variance estimator.

The number and distribution of proliferating cells in the rat’s rostral migratory stream as identified by means of two different proliferation markers

ABSTRACT In the brains of adult rodents, the cells arising in the subventricular zone of the lateral ventricles maintain the ability to divide when migrating to the olfactory bulb along the rostral migratory stream (RMS). Dividing cells in the RMS are most frequently revealed through immunohistochemical detection of an exogenous marker of proliferation, 5-Bromo-2-deoxyuridine (BrdU), which incorporates into DNA during the S-phase of mitosis. The more recently recognized antigen Ki-67 (also known as Kiel-67 and MKI67), an endogenous protein expressed in nuclei at all stages of mitosis, is also used for proliferation detection. BrdU and Ki-67 are often used as alternative methods, but they have not previously been compared in the RMS. We analyzed the numbers and distribution of cells labeled either with BrdU or Ki-67 within the RMS of adult rats. The first group of animals received a single i.p. dose of BrdU. In the second group, dividing cells were visualized by Ki-67 immunohistochemistry. Some sections from brains of BrdU-treated rats were also immunostained for Ki-67. Labeled cells were counted in the three anatomical parts of the RMS (vertical arm, elbow and horizontal arm) using a method for unbiased estimation of cell density. The distribution of proliferating cells was similar for both markers. Most BrdU and Ki-67 positive cells were located in the vertical arm and in the elbow, but a caudo-rostral reduction in cell divisions was more evident with Ki-67 labeling. The number of Ki-67 positive cells significantly exceeded the number of BrdU positive cells in all parts of the RMS. Our results indicate that BrdU and Ki-67 are not interchangeable markers for evaluation of proliferative activity in the RMS.

Probability density function based adaptive ensemble learning with global convergence for wind power prediction

Accurate wind power prediction is highly significant to the safety, stability, and economic operation of power grids. Currently, typical ensemble methods for wind power forecasting are widely designed based on the mean square error (MSE) loss, which are very suitable for the assumption that the error distribution obeys the Gaussian distribution. However, the complex nonlinear conversion of wind energy into wind power may change the statistical characteristics of errors, thus the prediction model based on the traditional MSE loss may lead to the performance degradation of the forecasting model. To address these problems, a probability density function based adaptive ensemble learning with global convergence is proposed for wind power prediction, which comprises three modules: a data preprocessing module, a prediction module, and a combination module. Firstly, an effective feature generation mechanism is employed to extract the multi-mode characteristics of wind data. Then, an auxiliary error based adaptive global convergence model is developed as benchmark predictor in the prediction module, where an adaptive updating algorithm is derived based on the Lyapunov approach to ensure the global convergence of the model weights. Moreover, considering the asymmetric characteristic of modeling error, a probability density function (PDF) based ensemble learning is created to integrate the results of benchmark predictors. Specifically, the ensemble model parameter updating is transformed into the shape control for the modeling error PDF, which can break through the limitation of MSE loss capturing only the second moment information, and emphasize the spatial distribution of errors to make an unbiased estimate of wind power. Experimental results show that the proposed ensemble model has significant advantages over other models involved in this study.

Instrumental variable analysis with categorical treatment.

Current instrumental variable methodology focuses mainly on estimating causal effects for a dichotomous or an ordinal treatment variable. Situations with more than two unordered treatments are less explored. The challenge is that assumptions needed to derive point-estimators become increasingly stronger with the number of relevant treatment alternatives. In this article, we aim at deriving causal point-estimators for head-to-head comparisons of the effect of multiple relevant treatments or interventions. We will achieve this with a set of plausible and well-defined rationality assumptions while only considering ordinal instruments. We demonstrate that our methodology provides asymptotically unbiased estimators in the presence of unobserved confounding effects in a simulation study. We then apply the method to compare the effectiveness of five anti-inflammatory drugs in the treatment of rheumatoid arthritis. For this, we use a clinical data set from an observational study in Norway, where price is the primary determinant of the preferred drug and can therefore be considered as an instrument. The developed methodology provides an important addition to the toolbox for causal inference when comparing more than two interventions influenced by an instrumental variable.

IPTW and DR Estimators: Advancing towards the Adoption of Methodologies for Estimating Causal Treatment Effects in Observational Studies

The propensity score refers to the likelihood of a subject being selected for intervention, given their observed baseline covariates. By assigning different weights to academic disciplines based on the reciprocal probability of receiving treatment, there will be a synthetic sample where the assignment of treatment is not influenced by baseline factors that were measured. Utilizing inverse probability of treatment weighting (IPTW) through the propensity score enables people to assemble unbiased or objective estimates regarding average treatment effects. In order to estimate the causal relationship between an exposure and an outcome, the second notion of doubly robust estimation combines an outcome regression with a propensity score model. Only when the statistical model is appropriately stated can unbiased estimates be obtained using the propensity score approach and outcome regression separately. However, even if only one of the two models is correctly specified, an unbiased estimator for the effect can be obtained by employing the doubly robust estimator. This introduction to inverse probability of treatment weighting and doubly robust estimators includes conceptual overviews, an application to students’ performance in high school, as well as discussions based on the project.

Pengaruh Pendapatan Asli Daerah (PAD), Dana Perimbangan, dan Pengeluaran Bidang kesehatan terhadap Indeks Pembangunan Manusia (IPM) Kabupaten Jombang Tahun 2007-2021

The aim of the research is to analyze the influence of a region's original income, balancing funds and health expenditure on the human development index (HDI) in Jombang Regency during the period 2007 - 2021. Using the multiple linear regression method by carrying out the Best Linear Unbiased Estimation (BLUE) test, and analysis hypothesis testing. The results of this research concluded that simultaneously the human development index (HDI) in Jombang Regency during the 2007-2021 period was influenced by local original income (PAD), balancing funds and expenditure in the health sector. However, only partial balancing funds can influence the human development index in Jombang Regency during the 2007-2021 period.

Phylogenetic Insights Into Canidae Trait Variation Across Continents

ABSTRACTAimUnderstanding the spatial structuring of ecological communities involves considering the interplay between evolutionary history and environmental factors. This study investigates how the phylogenetic structure of Canidae influences the geographical distribution and trait patterns of lineages globally, and how these patterns relate to Bergmann's and Rapoport's rules.LocationAmericas, Africa, Eurasia.Time Period12 million years ago—present.Major Taxa StudiedCanidae.MethodsUsing distribution data and phylogenetic information for 37 Canidae species, we analysed key ecological, functional and evolutionary variables. We applied phylogenetic fuzzy‐weighting via principal coordinates of phylogenetic structure (PCPS) and variance partitioning analysis (VPA) to assess the contributions of phylogenetic structure and environmental factors to trait variation among species.ResultsOur results revealed distinct global patterns in body size, body weight, range size, habitat use and evolutionary distinctiveness among lineages. We also identified the shared contributions of phylogenetic structure and temperature to trait variation using variance partitioning analysis. The PCPS axes highlighted the influence of phylogenetic relationships on Canidae assemblages, particularly in South America.Main ConclusionsImportantly, the study challenges the applicability of Bergmann's and Rapoport's rules across continents. The unique diversification history of Canidae in South America and Africa and their diverse environmental conditions likely contribute to the observed trait patterns that make both continents so distinguished when compared to North America and Eurasia. Our findings underscore the need to incorporate phylogenetic information in models assessing trait variation across geographic scales for unbiased estimates.

Exploring the Relationship Between Health and Out-of-Pocket Health Expenditures: Evidence for Middle-Aged and Older Adults in China.

With population aging, disability and chronic conditions are increasingly prevalent among middle-aged and older adults in China. Using panel data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018, this paper explores the effects of Activities of Daily Living limitations, Instrumental Activities of Daily Living limitations, and chronic conditions on Out-of-Pocket Expenditures (OOPEs) among middle-aged and older adults in China. A first-difference model and a system-generalized method of moment model (GMM) are used. The system-GMM model for the first time addresses unobserved heterogeneity and produces unbiased estimates of the effects of health and OOPEs. Additionally, this paper assesses the heterogeneity of the results across the demographic and socioeconomic groups. These findings can be used to inform policymakers on improving medical resource allocation and ensure better financial protection for those living with a disability and chronic diseases.

On the efficiency of paired ranked set sampling for estimating the population mean in the presence of non-response

Non-response is a common problem faced by surveyors while conducting surveys; this introduces a potential bias in the estimates of population parameters. One method of dealing with non-response is subsampling of the non-respondents, which increases precision in estimates by increasing the sample size. This study proposes an unbiased mean estimator in the presence of non-response using the Paired Ranked Set Sampling (PRSS) technique. The proposed estimator is based on a suggested strategy for adapting the subsampled units into the initial sample. Variance of the proposed estimator is derived, and conditions are provided for which the proposed estimator performs better than existing estimators. We conduct a simulation study to evaluate the precision of the proposed estimator in comparison with other existing estimators for estimating the population mean. In simulation, we assume populations based on normal distribution, exponential distribution, and real-life data on abalone. Simulation results show that the proposed mean estimator exhibits a higher probability of precisely estimating the finite population mean.