The effect of cycloplegia in the accuracy of autorefraction, keratometry and axial length using the Myopia Master.

BACKGROUND: Assessing refractive errors under cycloplegia is recommended for paediatric patients; however, this may not always be feasible. In these situations, refraction has to rely on measurements made under active accommodation which may increase measurements variability and error. Therefore, evaluating the accuracy and precision of non-cycloplegic refraction and biometric measurements is clinically relevant. The Myopia Master, a novel instrument combining autorefraction and biometry, is designed for monitoring refractive error and ocular biometry in myopia management. This study assessed its repeatability and agreement for autorefraction and biometric measurements pre- and post-cycloplegia. METHODS: A prospective cross-sectional study evaluated a cohort of 96 paediatric patients that underwent ophthalmologic examination. An optometrist performed two repeated measurements of autorefraction and biometry pre- and post-cycloplegia. Test-retest repeatability (TRT) was assessed as differences between consecutive measurements and agreement as differences between post- and pre-cycloplegia measurements, for spherical equivalent (SE), refractive and keratometric J0/J45 astigmatic components, mean keratometry (Km) and axial length (AL). RESULTS: Cycloplegia significantly improved the SE repeatability (TRT, pre-cyclo: 0.65 D, post-cyclo: 0.31 D). SE measurements were more repeatable in myopes and emmetropes compared to hyperopes. Keratometry (Km) repeatability did not change with cycloplegia (TRT, pre-cyclo: 0.25 D, post-cyclo:0.27 D) and AL repeatability improved marginally (TRT, pre-cyclo: 0.14 mm, post-cyclo: 0.09 mm). Regarding pre- and post-cycloplegia agreement, SE became more positive by + 0.79 D, varying with refractive error. Myopic eyes showed a mean difference of + 0.31 D, while hyperopes differed by + 1.57 D. Mean keratometry, refractive and keratometric J0/J45 and AL showed no clinically significant differences. CONCLUSIONS: Refractive error measurements, using the Myopia Master were 2.5x less precise pre-cycloplegia than post-cycloplegia. Accuracy of pre-cycloplegic refractive error measurements was often larger than the clinically significant threshold (0.25 D) and was refractive error dependent. The higher precision compared to autorefraction measurements, pre- and post-cycloplegia agreement and refractive error independence of AL measurements emphasize the superiority of AL in refractive error monitoring.

Factor-Analytic Variance-Covariance Structures for Prediction Into a Target Population of Environments.

Finlay-Wilkinson regression is a popular method for modeling genotype-environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor-analytic variance-covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi-environment trials for the purpose of making predictions in a target population of environments. We investigate the implication of random versus fixed effects assumptions, starting from basic analysis-of-variance models, then moving on to factor-analytic models and considering the transition to models involving observable environmental covariates, which promise to provide more accurate and targeted predictions than models with latent environmental variables.

Sample Size Calculation for an Individual Stepped-Wedge Randomized Trial.

In the individual stepped-wedge randomized trial (ISW-RT), subjects are allocated to sequences, each sequence being defined by a control period followed by an experimental period. The total follow-up time is the same for all sequences, but the duration of the control and experimental periods varies among sequences. To our knowledge, there is no validated sample size calculation formula for ISW-RTs unlike stepped-wedge cluster randomized trials (SW-CRTs). The objective of this study was to adapt the formula used for SW-CRTs to the case of individual randomization and to validate this adaptation using a Monte Carlo simulation study. The proposed sample size calculation formula for an ISW-RT design yielded satisfactory empirical power for most scenarios except scenarios with operating characteristic values near the boundary (i.e., smallest possible number of periods, very high or very low autocorrelation coefficient). Overall, the results provide useful insights into the sample size calculation for ISW-RTs.

Biostatistical Aspects of Whole Genome Sequencing Studies: Preprocessing and Quality Control.

Rapid advances in high-throughput DNA sequencing technologies have enabled large-scale whole genome sequencing (WGS) studies. Before performing association analysis between phenotypes and genotypes, preprocessing and quality control (QC) of the raw sequence data need to be performed. Because many biostatisticians have not been working with WGS data so far, we first sketch Illumina's short-read sequencing technology. Second, we explain the general preprocessing pipeline for WGS studies. Third, we provide an overview of important QC metrics, which are applied to WGS data: on the raw data, after mapping and alignment, after variant calling, and after multisample variant calling. Fourth, we illustrate the QC with the data from the GENEtic SequencIng Study Hamburg-Davos (GENESIS-HD), a study involving more than 9000 human whole genomes. All samples were sequenced on an Illumina NovaSeq 6000 with an average coverage of 35× using a PCR-free protocol. For QC, one genome in a bottle (GIAB) trio was sequenced in four replicates, and one GIAB sample was successfully sequenced 70 times in different runs. Fifth, we provide empirical data on the compression of raw data using the DRAGEN original read archive (ORA). The most important quality metrics in the application were genetic similarity, sample cross-contamination, deviations from the expected Het/Hom ratio, relatedness, and coverage. The compression ratio of the raw files using DRAGEN ORA was 5.6:1, and compression time was linear by genome coverage. In summary, the preprocessing, joint calling, and QC of large WGS studies are feasible within a reasonable time, and efficient QC procedures are readily available.

A Shared-Frailty Spatial Scan Statistic Model for Time-to-Event Data.

Spatial scan statistics are well-known methods widely used to detect spatial clusters of events. Furthermore, several spatial scan statistics models have been applied to the spatial analysis of time-to-event data. However, these models do not take account of potential correlations between the observations of individuals within the same spatial unit or potential spatial dependence between spatial units. To overcome this problem, we have developed a scan statistic based on a Cox model with shared frailty and that takes account of the spatial dependence between spatial units. In simulation studies, we found that (i) conventional models of spatial scan statistics for time-to-event data fail to maintain the type I error in the presence of a correlation between the observations of individuals within the same spatial unit and (ii) our model performed well in the presence of such correlation and spatial dependence. We have applied our method to epidemiological data and the detection of spatial clusters of mortality in patients with end-stage renal disease in northern France.

Functional Multivariable Logistic Regression With an Application to HIV Viral Suppression Prediction.

Motivated by improving the prediction of the human immunodeficiency virus (HIV) suppression status using electronic health records (EHR) data, we propose a functional multivariable logistic regression model, which accounts for the longitudinal binary process and continuous process simultaneously. Specifically, the longitudinal measurements for either binary or continuous variables are modeled by functional principal components analysis, and their corresponding functional principal component scores are used to build a logistic regression model for prediction. The longitudinal binary data are linked to underlying Gaussian processes. The estimation is done using penalized spline for the longitudinal continuous and binary data. Group-lasso is used to select longitudinal processes, and the multivariate functional principal components analysis is proposed to revise functional principal component scores with the correlation. The method is evaluated via comprehensive simulation studies and then applied to predict viral suppression using EHR data for people living with HIV in South Carolina.

Multiply robust estimation of marginal structural models in observational studies subject to covariate-driven observations.

Electronic health records and other sources of observational data are increasingly used for drawing causal inferences. The estimation of a causal effect using these data not meant for research purposes is subject to confounding and irregularly-spaced covariate-driven observation times affecting the inference. A doubly-weighted estimator accounting for these features has previously been proposed that relies on the correct specification of two nuisance models used for the weights. In this work, we propose a novel consistent multiply robust estimator and demonstrate analytically and in comprehensive simulation studies that it is more flexible and more efficient than the only alternative estimator proposed for the same setting. It is further applied to data from the Add Health study in the United States to estimate the causal effect of therapy counseling on alcohol consumption in American adolescents.

Propensity weighting plus adjustment in proportional hazards model is not doubly robust.

Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doubly robust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model or the propensity score model is correctly specified. This combination does not, in general, produce a doubly robust estimator, even after regression standardization, when there is truly a causal effect. We demonstrate via simulation this lack of double robustness for the semiparametric Cox model, the Weibull proportional hazards model, and a simple proportional hazards flexible parametric model, with both the latter models fit via maximum likelihood. We provide a novel proof that the combination of propensity score weighting and a proportional hazards survival model, fit either via full or partial likelihood, is consistent under the null of no causal effect of the exposure on the outcome under particular censoring mechanisms if either the propensity score or the outcome model is correctly specified and contains all confounders. Given our results suggesting that double robustness only exists under the null, we outline 2 simple alternative estimators that are doubly robust for the survival difference at a given time point (in the above sense), provided the censoring mechanism can be correctly modeled, and one doubly robust method of estimation for the full survival curve. We provide R code to use these estimators for estimation and inference in the supporting information.

Optimal refinement of strata to balance covariates.

What is the best way to split one stratum into two to maximally reduce the within-stratum imbalance in many covariates? We formulate this as an integer program and approximate the solution by randomized rounding of a linear program. A linear program may assign a fraction of a person to each refined stratum. Randomized rounding views fractional people as probabilities, assigning intact people to strata using biased coins. Randomized rounding is a well-studied theoretical technique for approximating the optimal solution of certain insoluble integer programs. When the number of people in a stratum is large relative to the number of covariates, we prove the following new results: (i) randomized rounding to split a stratum does very little randomizing, so it closely resembles the linear programming relaxation without splitting intact people; (ii) the linear relaxation and the randomly rounded solution place lower and upper bounds on the unattainable integer programming solution; and because of (i), these bounds are often close, thereby ratifying the usable randomly rounded solution. We illustrate using an observational study that balanced many covariates by forming matched pairs composed of 2016 patients selected from 5735 using a propensity score. Instead, we form 5 propensity score strata and refine them into 10 strata, obtaining excellent covariate balance while retaining all patients. An R package optrefine at CRAN implements the method. Supplementary materials are available online.

Absolute risk from double nested case-control designs: cause-specific proportional hazards models with and without augmented estimating equations.

We estimate relative hazards and absolute risks (or cumulative incidence or crude risk) under cause-specific proportional hazards models for competing risks from double nested case-control (DNCC) data. In the DNCC design, controls are time-matched not only to cases from the cause of primary interest, but also to cases from competing risks (the phase-two sample). Complete covariate data are available in the phase-two sample, but other cohort members only have information on survival outcomes and some covariates. Design-weighted estimators use inverse sampling probabilities computed from Samuelsen-type calculations for DNCC. To take advantage of additional information available on all cohort members, we augment the estimating equations with a term that is unbiased for zero but improves the efficiency of estimates from the cause-specific proportional hazards model. We establish the asymptotic properties of the proposed estimators, including the estimator of absolute risk, and derive consistent variance estimators. We show that augmented design-weighted estimators are more efficient than design-weighted estimators. Through simulations, we show that the proposed asymptotic methods yield nominal operating characteristics in practical sample sizes. We illustrate the methods using prostate cancer mortality data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Study of the National Cancer Institute.

Comparison of axial length and anterior segment parameters of patients with myopia measured using 2 fourier-domain optical coherent biometry devices.

BACKGROUND: This study assessed the agreement of ocular parameters of patients with myopia measured using Colombo intraocular lens (IOL) 2 and IOLMaster 700. METHODS: Eighty patients (male, 22; average age, 29.14 ± 7.36 years) with myopia (159 eyes) were included in this study in May 2023. The participants' axial length (AXL), central corneal thickness (CCT), lens thickness (LT), white-to-white distance (WTW), front flat (K1), steep (K2), mean (Km) corneal keratometry, astigmatism (Astig), J0 vector, and J45 vector were measured using the IOLMaster 700 and Colombo IOL 2. The measurements from both devices were compared using the generalized estimating equation, correlation analysis, and Bland-Altman plots. RESULTS: With the Colombo IOL 2, lower values for K2 and J0 (odds ratio [OR] = 0.587, p = 0.033; OR = 0.779, p < 0.0001, respectively), and larger values for WTW, Astig, and J45 (OR = 1.277, OR = 1.482, OR = 1.1, all p < 0.0001) were obtained. All ocular measurements by both instruments showed positive correlations, with AXL demonstrating the strongest correlation (r = 0.9996, p < 0.0001). The intraclass correlation coefficients for AXL and CCT measured by both instruments was 0.999 and 0.988 (both p < 0.0001), and Bland-Altman plot showed 95% limits of agreement (LoA) of -0.078 to 0.11 mm and - 9.989 to 13.486 µm, respectively. The maximum absolute 95% LoA for LT, WTW, K1, K2, and J0 were relatively high, achieving 0.829 mm, 0.717 mm, 0.983 D, 0.948 D, and 0.632 D, respectively. CONCLUSIONS: In young patients with myopia, CCT and AXL measurements obtained with the Colombo IOL 2 and IOLMaster 700 were comparable. However, WTW, LT, corneal refractive power, and astigmatism values could not be used interchangeably in clinical practice.

Evaluation of Selected Biometric Parameters in Cataract Patients-A Comparison between Argos® and IOLMaster 700®: Two Swept-Source Optical Coherence Tomography-Based Biometers.

Background and Objectives: To compare the biometry of eyes obtained with two swept-source optical coherence tomography-based biometers-Argos (A), using an individual refractive index, and IOLMaster 700 (IM), using an equivalent refractive index-for all structures. Materials and Methods: The biometry of 105 eyes of 105 patients before cataracts were analyzed in this study. Parameters such as axial length (AL), anterior chamber depth (ACD), and lens thickness (LT) were compared from both devices. According to the axial length measurements, patients were divided into three groups, as follows: group 1-short eyes (AL < 22.5 mm), group 2-average eyes (22.5 ≤ AL ≤ 26.0 mm), and group 3-long eyes (AL > 26.0 mm). Results: The correlation coefficiency among all compared parameters varies from R = 0.92 to R = 1.00, indicating excellent reliability of IM and A. A statistical significance in axial length was indicated in the group of short eyes (n = 26)-mean AL (A) 21.90 mm (±0.59 mm) vs. AL (IM) 21.8 mm ± (0.61 mm) (p < 0.001)-and in the group of long eyes (n = 5)-mean AL (A) 27.95 mm (±2.62 mm) vs. mean AL (IM) 28.10 mm (±2.64) (p < 0.05). In the group of average eyes (n = 74), outcomes were similar-mean AL (A) 23.56 mm (±0.70 mm) vs. mean AL (IM) 23,56 mm (±0.71 mm) (p > 0.05). The anterior chamber depth measurements were higher when obtained with Argos than with IOLMaster 700-mean ACD (A) 3.06 mm (±0.48 mm) vs. mean ACD (IM) 2.92 mm (±0.46) p < 0.001. There was no statistical significance in mean LT-mean LT (A) 4.75 mm (±0.46 mm) vs. mean LT (IM) 4.72 mm (±0.44 mm) (p = 0.054). The biometry of one eye with dense cataracts could be measured only with Argos, using the Enhanced Retinal Visualization mode. Conclusions: Axial length measurements from both devices were different in the groups of short and long eyes, but were comparable in the group of average eyes. The anterior chamber depth values obtained with Argos were higher than the measurements acquired with IOLMaster 700. These differences may be particularly important when selecting IOLs for patients with extreme AL values.

Bayesian inference for multivariate probit model with latent envelope.

The response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear regression model. It improves estimation efficiency by identifying material and immaterial parts of responses and removing the immaterial variation. The response envelope model has been investigated only for continuous response variables. In this paper, we propose the multivariate probit model with latent envelope, in short, the probit envelope model, as a response envelope model for multivariate binary response variables. The probit envelope model takes into account relations between Gaussian latent variables of the multivariate probit model by using the idea of the response envelope model. We address the identifiability of the probit envelope model by employing the essential identifiability concept and suggest a Bayesian method for the parameter estimation. We illustrate the probit envelope model via simulation studies and real-data analysis. The simulation studies show that the probit envelope model has the potential to gain efficiency in estimation compared to the multivariate probit model. The real data analysis shows that the probit envelope model is useful for multi-label classification.

Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach.

In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step p $p$ -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National Toxicology Program.

Combining Partial True Discovery Guarantee Procedures.

Closed testing has recently been shown to be optimal for simultaneous true discovery proportion control. It is, however, challenging to construct true discovery guarantee procedures in such a way that it focuses power on some feature sets chosen by users based on their specific interest or expertise. We propose a procedure that allows users to target power on prespecified feature sets, that is, "focus sets." Still, the method also allows inference for feature sets chosen post hoc, that is, "nonfocus sets," for which we deduce a true discovery lower confidence bound by interpolation. Our procedure is built from partial true discovery guarantee procedures combined with Holm's procedure and is a conservative shortcut to the closed testing procedure. A simulation study confirms that the statistical power of our method is relatively high for focus sets, at the cost of power for nonfocus sets, as desired. In addition, we investigate its power property for sets with specific structures, for example, trees and directed acyclic graphs. We also compare our method with AdaFilter in the context of replicability analysis. The application of our method is illustrated with a gene ontology analysis in gene expression data.

Years of Life Lost to COVID-19 and Related Mortality Indicators: An Illustration in 30 Countries.

The concept of (potential) years of life lost is a measure of premature mortality that can be used to compare the impacts of different specific causes of death. However, interpreting a given number of years of life lost at face value is more problematic because of the lack of a sensible reference value. In this paper, we propose three denominators to divide an excess years of life lost, thus obtaining three indicators, called average life lost, increase of life lost, and proportion of life lost, which should facilitate interpretation and comparisons. We study the links between these three indicators and classical mortality indicators, such as life expectancy and standardized mortality rate, introduce the concept of weighted standardized mortality rate, and calculate them in 30 countries to assess the impact of COVID-19 on mortality in the year 2020. Using any of the three indicators, a significant excess loss is found for both genders in 18 of the 30 countries.

A Marginalized Zero-Inflated Negative Binomial Model for Spatial Data: Modeling COVID-19 Deaths in Georgia.

Spatial count data with an abundance of zeros arise commonly in disease mapping studies. Typically, these data are analyzed using zero-inflated models, which comprise a mixture of a point mass at zero and an ordinary count distribution, such as the Poisson or negative binomial. However, due to their mixture representation, conventional zero-inflated models are challenging to explain in practice because the parameter estimates have conditional latent-class interpretations. As an alternative, several authors have proposed marginalized zero-inflated models that simultaneously model the excess zeros and the marginal mean, leading to a parameterization that more closely aligns with ordinary count models. Motivated by a study examining predictors of COVID-19 death rates, we develop a spatiotemporal marginalized zero-inflated negative binomial model that directly models the marginal mean, thus extending marginalized zero-inflated models to the spatial setting. To capture the spatiotemporal heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects to model both the excess zeros and the marginal mean. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis-Hastings steps. We investigate features of the model and use the model to identify key predictors of COVID-19 deaths in the US state of Georgia during the 2021 calendar year.

Robust Regression Techniques for Multiple Method Comparison and Transformation.

A generalization of Passing-Bablok regression is proposed for comparing multiple measurement methods simultaneously. Possible applications include assay migration studies or interlaboratory trials. When comparing only two methods, the method boils down to the usual Passing-Bablok estimator. It is close in spirit to reduced major axis regression, which is, however, not robust. To obtain a robust estimator, the major axis is replaced by the (hyper-)spherical median axis. This technique has been applied to compare SARS-CoV-2 serological tests, bilirubin in neonates, and an in vitro diagnostic test using different instruments, sample preparations, and reagent lots. In addition, plots similar to the well-known Bland-Altman plots have been developed to represent the variance structure.

Repeatability of Automatic Measurements by an Anterior Segment Swept-Source OCT Biometer in Patients With Keratoconus.

PURPOSE: To evaluate the repeatability of automatic measurements of a new anterior segment optical coherence tomographer (ANTERION; Heidelberg Engineering) and their agreement with an anterior segment optical coherence tomography device combined with Placido disc corneal topography (MS-39; CSO) in patients affected by keratoconus. METHODS: Fifty-four consecutive patients were included. Three measurements were performed with the ANTERION and one with the MS-39. Repeatability was assessed by means of within-subject standard deviation, coefficient of variation (CoV), and intraclass correlation coefficient (ICC). Agreement was investigated with the 95% limits of agreement. The paired t-test and Wilcoxon matched-pairs test were performed to compare the measurements of the different devices. RESULTS: Repeatability of ANTERION measurements was high, with an ICC greater than 0.98 for all parameters. Many parameters revealed a CoV of less than 1% and a CoV within 5% was obtained for astigmatism measurements. The ANTERION measured a significantly higher corneal power and the MS-39 more negative posterior keratometric values. These differences were mirrored by a moderate agreement for mean simulated keratometry and poor agreement for total corneal power and posterior keratometry. CONCLUSIONS: The ANTERION revealed high repeatability of automatic measurements and good agreement with the MS-39 for many parameters in patients affected by keratoconus, but for most parameters the two instruments cannot be considered interchangeable. [J Refract Surg. 2024;40(7):e445-e452.].

Joint structure learning and causal effect estimation for categorical graphical models.

The scope of this paper is a multivariate setting involving categorical variables. Following an external manipulation of one variable, the goal is to evaluate the causal effect on an outcome of interest. A typical scenario involves a system of variables representing lifestyle, physical and mental features, symptoms, and risk factors, with the outcome being the presence or absence of a disease. These variables are interconnected in complex ways, allowing the effect of an intervention to propagate through multiple paths. A distinctive feature of our approach is the estimation of causal effects while accounting for uncertainty in both the dependence structure, which we represent through a directed acyclic graph (DAG), and the DAG-model parameters. Specifically, we propose a Markov chain Monte Carlo algorithm that targets the joint posterior over DAGs and parameters, based on an efficient reversible-jump proposal scheme. We validate our method through extensive simulation studies and demonstrate that it outperforms current state-of-the-art procedures in terms of estimation accuracy. Finally, we apply our methodology to analyze a dataset on depression and anxiety in undergraduate students.