Valid instrumental variable selection method using negative control outcomes and constructing efficient estimator.

In observational studies, instrumental variable (IV) methods are commonly applied when there are unmeasured covariates. In Mendelian randomization, constructing an allele score using many single nucleotide polymorphisms is often implemented; however, estimating biased causal effects by including some invalid IVs poses some risks. Invalid IVs are those IV candidates that are associated with unobserved variables. To solve this problem, we developed a novel strategy using negative control outcomes (NCOs) as auxiliary variables. Using NCOs, we are able to select only valid IVs and exclude invalid IVs without knowing which of the instruments are invalid. We also developed a new two-step estimation procedure and proved the semiparametric efficiency of our estimator. The performance of our proposed method was superior to some previous methods through simulations. Subsequently, we applied the proposed method to the UK Biobank dataset. Our results demonstrate that the use of an auxiliary variable, such as an NCO, enables the selection of valid IVs with assumptions different from those used in previous methods.

Sequential covariate-adjusted randomization via hierarchically minimizing Mahalanobis distance and marginal imbalance.

In comparative studies, covariate balance and sequential allocation schemes have attracted growing academic interest. Although many theoretically justified adaptive randomization methods achieve the covariate balance, they often allocate patients in pairs or groups. To better meet the practical requirements where the clinicians cannot wait for other participants to assign the current patient for some economic or ethical reasons, we propose a method that randomizes patients individually and sequentially. The proposed method conceptually separates the covariate imbalance, measured by the newly proposed modified Mahalanobis distance, and the marginal imbalance, that is the sample size difference between the 2 groups, and it minimizes them with an explicit priority order. Compared with the existing sequential randomization methods, the proposed method achieves the best possible covariate balance while maintaining the marginal balance directly, offering us more control of the randomization process. We demonstrate the superior performance of the proposed method through a wide range of simulation studies and real data analysis, and also establish theoretical guarantees for the proposed method in terms of both the convergence of the imbalance measure and the subsequent treatment effect estimation.

Ocular Biometric Components in Hyperopic Children and a Machine Learning-Based Model to Predict Axial Length.

Purpose: The purpose of this study was to investigate the development of optical biometric components in children with hyperopia, and apply a machine-learning model to predict axial length. Methods: Children with hyperopia (+1 diopters [D] to +10 D) in 3 age groups: 3 to 5 years (n = 74), 6 to 8 years (n = 102), and 9 to 11 years (n = 36) were included. Axial length, anterior chamber depth, lens thickness, central corneal thickness, and corneal power were measured; all participants had cycloplegic refraction within 6 months. Spherical equivalent (SEQ) was calculated. A mixed-effects model was used to compare sex and age groups and adjust for interocular correlation. A classification and regression tree (CART) analysis was used to predict axial length and compared with the linear regression. Results: Mean SEQ for all 3 age groups were similar but the 9 to 11 year old group had 0.49 D less hyperopia than the 3 to 5 year old group (P < 0.001). With the exception of corneal thickness, all other ocular components had a significant sex difference (P < 0.05). The 3 to 5 year group had significantly shorter axial length and anterior chamber depth and higher corneal power than older groups (P < 0.001). Using SEQ, age, and sex, axial length can be predicted with a CART model, resulting in lower mean absolute error of 0.60 than the linear regression model (0.76). Conclusions: Despite similar values of refractive errors, ocular biometric parameters changed with age in hyperopic children, whereby axial length growth is offset by reductions in corneal power. Translational Relevance: We provide references for optical components in children with hyperopia, and a machine-learning model for convenient axial length estimation based on SEQ, age, and sex.

A method for determining groups in cumulative incidence curves in competing risk data.

The cumulative incidence function is the standard method for estimating the marginal probability of a given event in the presence of competing risks. One basic but important goal in the analysis of competing risk data is the comparison of these curves, for which limited literature exists. We proposed a new procedure that lets us not only test the equality of these curves but also group them if they are not equal. The proposed method allows determining the composition of the groups as well as an automatic selection of their number. Simulation studies show the good numerical behavior of the proposed methods for finite sample size. The applicability of the proposed method is illustrated using real data.

Regression models for average hazard.

Limitations of using the traditional Cox's hazard ratio for summarizing the magnitude of the treatment effect on time-to-event outcomes have been widely discussed, and alternative measures that do not have such limitations are gaining attention. One of the alternative methods recently proposed, in a simple 2-sample comparison setting, uses the average hazard with survival weight (AH), which can be interpreted as the general censoring-free person-time incidence rate on a given time window. In this paper, we propose a new regression analysis approach for the AH with a truncation time τ. We investigate 3 versions of AH regression analysis, assuming (1) independent censoring, (2) group-specific censoring, and (3) covariate-dependent censoring. The proposed AH regression methods are closely related to robust Poisson regression. While the new approach needs to require a truncation time τ explicitly, it can be more robust than Poisson regression in the presence of censoring. With the AH regression approach, one can summarize the between-group treatment difference in both absolute difference and relative terms, adjusting for covariates that are associated with the outcome. This property will increase the likelihood that the treatment effect magnitude is correctly interpreted. The AH regression approach can be a useful alternative to the traditional Cox's hazard ratio approach for estimating and reporting the magnitude of the treatment effect on time-to-event outcomes.

Predicting class switch recombination in B-cells from antibody repertoire data.

Statistical and machine learning methods have proved useful in many areas of immunology. In this paper, we address for the first time the problem of predicting the occurrence of class switch recombination (CSR) in B-cells, a problem of interest in understanding antibody response under immunological challenges. We propose a framework to analyze antibody repertoire data, based on clonal (CG) group representation in a way that allows us to predict CSR events using CG level features as input. We assess and compare the performance of several predicting models (logistic regression, LASSO logistic regression, random forest, and support vector machine) in carrying out this task. The proposed approach can obtain an unweighted average recall of 71 % $71\%$ with models based on variable region descriptors and measures of CG diversity during an immune challenge and, most notably, before an immune challenge.

A nonparametric proportional risk model to assess a treatment effect in time-to-event data.

Time-to-event analysis often relies on prior parametric assumptions, or, if a semiparametric approach is chosen, Cox's model. This is inherently tied to the assumption of proportional hazards, with the analysis potentially invalidated if this assumption is not fulfilled. In addition, most interpretations focus on the hazard ratio, that is often misinterpreted as the relative risk (RR), the ratio of the cumulative distribution functions. In this paper, we introduce an alternative to current methodology for assessing a treatment effect in a two-group situation, not relying on the proportional hazards assumption but assuming proportional risks. Precisely, we propose a new nonparametric model to directly estimate the RR of two groups to experience an event under the assumption that the risk ratio is constant over time. In addition to this relative measure, our model allows for calculating the number needed to treat as an absolute measure, providing the possibility of an easy and holistic interpretation of the data. We demonstrate the validity of the approach by means of a simulation study and present an application to data from a large randomized controlled trial investigating the effect of dapagliflozin on all-cause mortality.

The reliability of a Biometrics device as a tool for assessing hand grip and pinch strength, in a Polish cohort-A prospective observational study.

The aim of the study was to assess the external and internal compatibility of the Biometrics E-LINK EP9 evaluation system device in the area of hand grip and pinch strength in the Polish population. The testing of hand grip and pinch strength was carried out among 122 healthy students. Two examiners performed hand grip and pinch strength measurements with a Biometrics E-LINK EP9 evaluation system device. Measurements were made for the right and left hands. The same people were tested again two weeks later, under the same conditions. The scores of one rater on the first and second tests were compared for reproducibility, and the scores of the two raters were compared to assess the reliability of the instrument. The measurements were found to be highly consistent both between the investigators and between the tests in the hand grip dynamometer test. The findings show high values of the Pearson's correlation coefficient equal or close to 1, as well as the interclass correlation coefficient (ICC) >0.9. Analysis of pinch strength measurements performed using the pinchmeter also found high values of the Pearson's correlation coefficient close to 1, as well as the interclass correlation coefficient >0.9; this reflects high agreement between the measurements performed by two investigators as well as assessments performed by one investigator at time intervals. These findings were confirmed by analyses performed using Bland-Altman plots. The measurements made with the Biometrics E-link EP9 evaluation system show high internal and external consistency in hand grip and pinch strength assessment. Biometrics E-link EP9 can be recommended for daily clinical practice.

Modeling tropical tuna shifts: An inflated power logit regression approach.

We introduce a new class of zero-or-one inflated power logit (IPL) regression models, which serve as a versatile tool for analyzing bounded continuous data with observations at a boundary. These models are applied to explore the effects of climate changes on the distribution of tropical tuna within the North Atlantic Ocean. Our findings suggest that our modeling approach is adequate and capable of handling the outliers in the data. It exhibited superior performance compared to rival models in both diagnostic analysis and regarding the inference robustness. We offer a user-friendly method for fitting IPL regression models in practical applications.

Changes in ocular biometrics following cycloplegic refraction in strabismic and amblyopic children.

This study was aimed to analyze ocular biometric changes following cycloplegia in pediatric patients with strabismus and amblyopia. Cycloplegia is routinely used to measure refractive error accurately by paralyzing accommodation. However, effects on axial length (AL), anterior chamber depth (ACD), keratometry (Km), and white-to-white distance (WTW) are not well studied in this population. This retrospective study examined 797 patients (1566 eyes) undergoing cycloplegic refraction at a Samsung Kangbuk hospital pediatric ophthalmology clinic from 2010 to 2023. Ocular biometry was measured before and after instilling 1% cyclopentolate and 0.5% phenylephrine/0.5% tropicamide. Patients were categorized by strabismus diagnosis, age, refractive error and amblyopia status. Differences in AL, ACD, Km, WTW, and refractive error pre- and post-cycloplegia were analyzed using paired t tests. ACD (3.44 ±â€…0.33 vs 3.58 ±â€…0.29 mm, P < .05) and WTW (12.09 ±â€…0.42 vs 12.30 ±â€…0.60 mm, P < .05) increased significantly after cycloplegia in all groups except other strabismus subgroup (Cs) in both parameters and youngest subgroup (G1) in ACD. Refractive error demonstrated a hyperopic shift from -0.48 ±â€…3.00 D to -0.06 ±â€…3.32 D (P < .05) in overall and a myopic shift from -6.97 ±â€…4.27 to -8.10 ±â€…2.26 in high myopia (HM). Also, AL and Km did not change significantly. In conclusion, cycloplegia impacts ocular biometrics in children with strabismus and amblyopia, significantly increasing ACD and WTW. Refractive error shifts hyperopically in esotropia subgroup (ET) and myopically in high myopia subgroup (HM), eldest subgroup (G3) relating more to anterior segment changes than AL/Km. Understanding cycloplegic effects on biometry is important for optimizing refractive correction in these patients.

[Changes in intraocular pressure and biometric parameters of the anterior segment of the eye after intravitreal injections]. / Izmeneniya urovnya vnutriglaznogo davleniya i biometricheskikh pokazatelei perednego segmenta glaza posle intravitreal'nykh in"ektsii.

PURPOSE: This study compares the changes in the parameters of the anterior chamber of the eye using anterior segment optical coherence tomography (AS-OCT) in patients with a natural and artificial lens after treatment of neovascular age-related macular degeneration (nAMD) by multiple intravitreal injections (IVI) of anti-VEGF drugs. MATERIAL AND METHODS: The patients were divided into 2 groups: group 1 (control) included 30 patients (30 eyes) with a natural lens, group 2 - 30 patients (30 eyes) with an intraocular lens (IOL). AS-OCT was performed using the Revo NX tomograph (Optopol, Poland) to analyze anterior chamber depth (ACD) and the parameters of anterior chamber angle (ACA). Intraocular pressure (IOP) was measured with a contact tonometer ICare Pro. RESULTS: In patients with an IOL, the IOP level 1 minute after intravitreal injection (IVI) of an anti-VEGF drug was statistically lower than in the control group, on average by 17.8% during the first IVI and by 28.7% after 1 year of observation (p<0.001). ACD before treatment was statistically significantly higher in patients with IOL compared to patients of group 1 by an average of 39.3% (p<0.001). ACA from the nasal and temporal sides in the meridian 0°-180° before the start of treatment was statistically significantly wider in phakic patients than in the control group, by an average of 15.9±9.3° (p<0.001) and 16.9±8.2° (p<0.001), respectively. According to AS-OCT, there was no shift of the iris-lens diaphragm in patients with an IOL after multiple IVI of an anti-VEGF drug, in contrast to the control group. CONCLUSIONS: AS-OCT was used to determine for the first time the changes in the parameters of the anterior chamber of the eye in patients with a natural and artificial lens after multiple injections of an anti-VEGF drug in the treatment of nAMD.

Enhancing Resilience in Biometric Research: Generation of 3D Synthetic Face Data Using Advanced 3D Character Creation Techniques from High-Fidelity Video Games and Animation.

Biometric authentication plays a vital role in various everyday applications with increasing demands for reliability and security. However, the use of real biometric data for research raises privacy concerns and data scarcity issues. A promising approach using synthetic biometric data to address the resulting unbalanced representation and bias, as well as the limited availability of diverse datasets for the development and evaluation of biometric systems, has emerged. Methods for a parameterized generation of highly realistic synthetic data are emerging and the necessary quality metrics to prove that synthetic data can compare to real data are open research tasks. The generation of 3D synthetic face data using game engines' capabilities of generating varied realistic virtual characters is explored as a possible alternative for generating synthetic face data while maintaining reproducibility and ground truth, as opposed to other creation methods. While synthetic data offer several benefits, including improved resilience against data privacy concerns, the limitations and challenges associated with their usage are addressed. Our work shows concurrent behavior in comparing semi-synthetic data as a digital representation of a real identity with their real datasets. Despite slight asymmetrical performance in comparison with a larger database of real samples, a promising performance in face data authentication is shown, which lays the foundation for further investigations with digital avatars and the creation and analysis of fully synthetic data. Future directions for improving synthetic biometric data generation and their impact on advancing biometrics research are discussed.

Causal inference for time-to-event data with a cured subpopulation.

When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.

Identifying temporal pathways using biomarkers in the presence of latent non-Gaussian components.

Time-series data collected from a network of random variables are useful for identifying temporal pathways among the network nodes. Observed measurements may contain multiple sources of signals and noises, including Gaussian signals of interest and non-Gaussian noises, including artifacts, structured noise, and other unobserved factors (eg, genetic risk factors, disease susceptibility). Existing methods, including vector autoregression (VAR) and dynamic causal modeling do not account for unobserved non-Gaussian components. Furthermore, existing methods cannot effectively distinguish contemporaneous relationships from temporal relations. In this work, we propose a novel method to identify latent temporal pathways using time-series biomarker data collected from multiple subjects. The model adjusts for the non-Gaussian components and separates the temporal network from the contemporaneous network. Specifically, an independent component analysis (ICA) is used to extract the unobserved non-Gaussian components, and residuals are used to estimate the contemporaneous and temporal networks among the node variables based on method of moments. The algorithm is fast and can easily scale up. We derive the identifiability and the asymptotic properties of the temporal and contemporaneous networks. We demonstrate superior performance of our method by extensive simulations and an application to a study of attention-deficit/hyperactivity disorder (ADHD), where we analyze the temporal relationships between brain regional biomarkers. We find that temporal network edges were across different brain regions, while most contemporaneous network edges were bilateral between the same regions and belong to a subset of the functional connectivity network.

Comparative review of novel model-assisted designs for phase I/II clinical trials.

In recent years, both model-based and model-assisted designs have emerged to efficiently determine the optimal biological dose (OBD) in phase I/II trials for immunotherapy and targeted cellular agents. Model-based designs necessitate repeated model fitting and computationally intensive posterior sampling for each dose-assignment decision, limiting their practical application in real trials. On the other hand, model-assisted designs employ simple statistical models and facilitate the precalculation of a decision table for use throughout the trial, eliminating the need for repeated model fitting. Due to their simplicity and transparency, model-assisted designs are often preferred in phase I/II trials. In this paper, we systematically evaluate and compare the operating characteristics of several recent model-assisted phase I/II designs, including TEPI, PRINTE, Joint i3+3, BOIN-ET, STEIN, uTPI, and BOIN12, in addition to the well-known model-based EffTox design, using comprehensive numerical simulations. To ensure an unbiased comparison, we generated 10,000 dosing scenarios using a random scenario generation algorithm for each predetermined OBD location. We thoroughly assess various performance metrics, such as the selection percentages, average patient allocation to OBD, and overdose percentages across the eight designs. Based on these assessments, we offer design recommendations tailored to different objectives, sample sizes, and starting dose locations.

Integrating randomized and observational studies to estimate optimal dynamic treatment regimes.

Sequential multiple assignment randomized trials (SMARTs) are the gold standard for estimating optimal dynamic treatment regimes (DTRs), but are costly and require a large sample size. We introduce the multi-stage augmented Q-learning estimator (MAQE) to improve efficiency of estimation of optimal DTRs by augmenting SMART data with observational data. Our motivating example comes from the Back Pain Consortium, where one of the overarching aims is to learn how to tailor treatments for chronic low back pain to individual patient phenotypes, knowledge which is lacking clinically. The Consortium-wide collaborative SMART and observational studies within the Consortium collect data on the same participant phenotypes, treatments, and outcomes at multiple time points, which can easily be integrated. Previously published single-stage augmentation methods for integration of trial and observational study (OS) data were adapted to estimate optimal DTRs from SMARTs using Q-learning. Simulation studies show the MAQE, which integrates phenotype, treatment, and outcome information from multiple studies over multiple time points, more accurately estimates the optimal DTR, and has a higher average value than a comparable Q-learning estimator without augmentation. We demonstrate this improvement is robust to a wide range of trial and OS sample sizes, addition of noise variables, and effect sizes.

Efficient data integration under prior probability shift.

Conventional supervised learning usually operates under the premise that data are collected from the same underlying population. However, challenges may arise when integrating new data from different populations, resulting in a phenomenon known as dataset shift. This paper focuses on prior probability shift, where the distribution of the outcome varies across datasets but the conditional distribution of features given the outcome remains the same. To tackle the challenges posed by such shift, we propose an estimation algorithm that can efficiently combine information from multiple sources. Unlike existing methods that are restricted to discrete outcomes, the proposed approach accommodates both discrete and continuous outcomes. It also handles high-dimensional covariate vectors through variable selection using an adaptive least absolute shrinkage and selection operator penalty, producing efficient estimates that possess the oracle property. Moreover, a novel semiparametric likelihood ratio test is proposed to check the validity of prior probability shift assumptions by embedding the null conditional density function into Neyman's smooth alternatives (Neyman, 1937) and testing study-specific parameters. We demonstrate the effectiveness of our proposed method through extensive simulations and a real data example. The proposed methods serve as a useful addition to the repertoire of tools for dealing dataset shifts.

[Evaluation of the accuracy of modern intraocular lens calculation formulas when optical biometry is not possible]. / Otsenka tochnosti sovremennykh formul rascheta intraokulyarnykh linz pri nevozmozhnosti vypolneniya opticheskoi biometrii.

PURPOSE: This study evaluates the accuracy of modern intraocular lens (IOL) calculation formulas using axial length (AL) data obtained by ultrasound biometry (UBM) compared to the third-generation SRK/T calculator. MATERIAL AND METHODS: The study included 230 patients (267 eyes) with severe lens opacities that prevented optical biometry, who underwent phacoemulsification (PE) with IOL implantation. IOL power calculation according to the SRK/T formula was based on AL and anterior chamber depth obtained by UBM (Tomey Biometer Al-100) and keratometry on the Topcon KR 8800 autorefractometer. To adapt AL for new generation calculators - Barrett Universal II (BUII), Hill RBF ver. 3.0 (RBF), Kane and Ladas Super Formula (LSF) - the retinal thickness (0.20 mm) was added to the axial length determined by UBM, and then the optical power of the artificial lens was calculated. The mean error and its modulus value were used as criteria for the accuracy of IOL calculation. RESULTS: A significant difference (p=0.008) in the mean IOL calculation error was found between the formulas. Pairwise analysis revealed differences between SRK/T (-0.32±0.58 D) and other formulas - BUII (-0.16±0.52 D; p=0.014), RBF (-0.17±0.51 D; p=0.024), Kane (-0.17±0.52 D; p=0.029), but not with the LSF calculator (-0.19±0.53 D; p=0.071). No significant differences between the formulas were found in terms of mean error modulus (p=0.238). New generation calculators showed a more frequent success in hitting target refraction (within ±1.00 D in more than 95% of cases) than the SRK/T formula (86%). CONCLUSION: The proposed method of adding 0.20 mm to the AL determined by UBM allows using this parameter in modern IOL calculation formulas and improving the refractive results of PE, especially in eyes with non-standard anterior segment structure.