UNASSIGNED: To investigate the impact of back-to-front corneal radius ratio (B/F ratio) and posterior keratometry (PK) on the accuracy of intraocular lens power calculation formulas in eyes after myopic laser in situ keratomileusis (LASIK)/photorefractive keratectomy (PRK) surgery.
UNASSIGNED: A retrospective, consecutive case series study included 101 patients (132 eyes) with cataract after myopic LASIK/PRK. Mean prediction error (PE), mean absolute PE (MAE), median absolute error (MedAE), and the percentage of eyes within ±0.25, ±0.50, and ±1.00 diopters (D) of PE were determined.
UNASSIGNED: The Barrett True K-TK formula exhibited the lowest MAE (0.59 D) and MedAE (0.48 D) and the highest percentage of eyes within ±0.50 D of PE (54.55%) in total. In eyes with a B/F ratio of 0.70 or less and PK of -5.70 D or greater, the Potvin-Hill formula displayed the lowest MAE (0.46 to 0.67 D).
UNASSIGNED: The Barrett True-TK exhibited the highest prediction accuracy in eyes after myopic LASIK/PRK overall. However, for eyes with a low B/F ratio and flat PK, the Potvin-Hill performed best. [J Refract Surg. 2024;40(9):e635-e644.].

The US Food and Drug Administration launched Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development, calling for the paradigm shift from finding the maximum tolerated dose to the identification of optimal biological dose (OBD). Motivated by a real-world drug development program, we propose a master-protocol-based platform trial design to simultaneously identify OBDs of a new drug, combined with standards of care or other novel agents, in multiple indications. We propose a Bayesian latent subgroup model to accommodate the treatment heterogeneity across indications, and employ Bayesian hierarchical models to borrow information within subgroups. At each interim analysis, we update the subgroup membership and dose-toxicity and -efficacy estimates, as well as the estimate of the utility for risk-benefit tradeoff, based on the observed data across treatment arms to inform the arm-specific decision of dose escalation and de-escalation and identify the OBD for each arm of a combination partner and an indication. The simulation study shows that the proposed design has desirable operating characteristics, providing a highly flexible and efficient way for dose optimization. The design has great potential to shorten the drug development timeline, save costs by reducing overlapping infrastructure, and speed up regulatory approval.

Meta-analysis is a powerful tool to synthesize findings from multiple studies. The normal-normal random-effects model is widely used to account for between-study heterogeneity. However, meta-analyses of sparse data, which may arise when the event rate is low for binary or count outcomes, pose a challenge to the normal-normal random-effects model in the accuracy and stability in inference since the normal approximation in the within-study model may not be good. To reduce bias arising from data sparsity, the generalized linear mixed model can be used by replacing the approximate normal within-study model with an exact model. Publication bias is one of the most serious threats in meta-analysis. Several quantitative sensitivity analysis methods for evaluating the potential impacts of selective publication are available for the normal-normal random-effects model. We propose a sensitivity analysis method by extending the likelihood-based sensitivity analysis with the $t$-statistic selection function of Copas to several generalized linear mixed-effects models. Through applications of our proposed method to several real-world meta-analyses and simulation studies, the proposed method was proven to outperform the likelihood-based sensitivity analysis based on the normal-normal model. The proposed method would give useful guidance to address publication bias in the meta-analysis of sparse data.

UNASSIGNED: To compare the refractive predictability of Argos measurements with Barrett Universal II (BUII) and Barrett True Axial Length (BTAL) formulas in a large sample of long, medium, and short axial length (AL) eyes.
UNASSIGNED: A retrospective chart review identified 445 eyes of 247 patients for inclusion. The Argos was used for preoperative biometry, and BUII formula for intraocular lens (IOL) power calculations. Back calculations were performed using data from the Argos for the BTAL formula. Data were collected for postoperative absolute prediction error (APE), refractive outcomes, and monocular uncorrected and distance corrected visual acuities at distance (UDVA, CDVA).
UNASSIGNED: Overall, mean APE was 0.36 ± 0.33 D for BUII and for 0.34 ± 0.32 D BTAL (p = 0.04). In short AL eyes, mean APE was 0.45 ± 0.37 D for BUII and for 0.37 ± 0.31 D BTAL (p < 0.001). No significant differences between BUII and BTAL were identified for long AL or medium AL eyes. The percentages of eyes with APE of 0.5 D or less in long, medium, and short eyes were 79%, 79% and 51%, respectively, for BUII and 82%, 78% and 69%, respectively, for BTAL.
UNASSIGNED: The prediction accuracies were high with both the BUII and BTAL formulas in long, medium, and short eyes, leading to excellent refractive outcomes. The BTAL formula may have lower absolute prediction error in short eyes compared to BUII.
When the natural lens inside the eye becomes opaque, it can be replaced during cataract surgery with a clear artificial intraocular lens (IOL). It is critical for good postoperative outcomes that the optimal power for the IOL is implanted. Biometers are devices used to measure the eye and typically have built-in formulas to calculate the most appropriate IOL power for implantation. However, it is challenging to select the optimal power in eyes that are longer or shorter than average. The purpose of this study was to compare the refractive predictability of a novel biometer using two IOL power calculation formulas in a large sample of long, medium, and short eyes. The results of this study suggest that predictability was high with both formulas in long, medium, and short eyes, leading to excellent refractive outcomes.

UNASSIGNED: The purpose of this study was to define the normal range of peripapillary retinal nerve fiber layer (pRNFL), macular ganglion cell layer (mGCL), and macular inner plexiform layer (mIPL) thickness in cynomolgus macaques, and explore their inter-relationship and correlation with age, refractive errors, and axial length (AL).
UNASSIGNED: In this cross-sectional study, we measured biometric and refractive parameters, and pRNFL/mGCL/mIPL thickness in 357 healthy cynomolgus macaques. Monkeys were divided into groups by age and spherical equivalent (SE). Correlation and regression analyses were used to explore the relationship between pRNFL and mGCL/mIPL thickness, and their correlation with the above parameters.
UNASSIGNED: The mean age, SE, and AL were 14.46 ± 6.70 years, -0.96 ± 3.23 diopters (D), and 18.39 ± 1.02 mm, respectively. The mean global pRNFL thickness was 95.06 ± 9.42 µm (range = 54-116 µm), with highest values in the inferior quadrant, followed by the superior, temporal, and nasal quadrants (P < 0.001). Temporal pRNFL thickness correlated positively with age (r = 0.218, P < 0.001) and AL (r = 0.364, P < 0.001), and negatively with SE (r = -0.270, P < 0.001). In other quadrants, pRNFL thickness correlated negatively with age and AL, but positively with SE. In the multivariable linear regression model, adjusted for sex and AL, age (β = -0.350, P < 0.001), and SE (β = 0.206, P < 0.001) showed significant associations with global pRNFL thickness. After adjusting for age, sex, SE, and AL, pRNFL thickness positively correlated with mGCL (β = 0.433, P < 0.001) and mIPL thickness (β = 0.465, P < 0.001).
UNASSIGNED: The pRNFL/mGCL/mIPL thickness distribution and relationship with age, AL, and SE in cynomolgus macaques were highly comparable to those in humans, suggesting that cynomolgus monkeys are valuable animal models in ophthalmic research.

We present a new method for constructing valid covariance functions of Gaussian processes for spatial analysis in irregular, non-convex domains such as bodies of water. Standard covariance functions based on geodesic distances are not guaranteed to be positive definite on such domains, while existing non-Euclidean approaches fail to respect the partially Euclidean nature of these domains where the geodesic distance agrees with the Euclidean distances for some pairs of points. Using a visibility graph on the domain, we propose a class of covariance functions that preserve Euclidean-based covariances between points that are connected in the domain while incorporating the non-convex geometry of the domain via conditional independence relationships. We show that the proposed method preserves the partially Euclidean nature of the intrinsic geometry on the domain while maintaining validity (positive definiteness) and marginal stationarity of the covariance function over the entire parameter space, properties which are not always fulfilled by existing approaches to construct covariance functions on non-convex domains. We provide useful approximations to improve computational efficiency, resulting in a scalable algorithm. We compare the performance of our method with those of competing state-of-the-art methods using simulation studies on synthetic non-convex domains. The method is applied to data regarding acidity levels in the Chesapeake Bay, showing its potential for ecological monitoring in real-world spatial applications on irregular domains.

The geometric median, which is applicable to high-dimensional data, can be viewed as a generalization of the univariate median used in 1-dimensional data. It can be used as a robust estimator for identifying the location of multi-dimensional data and has a wide range of applications in real-world scenarios. This paper explores the problem of high-dimensional multivariate analysis of variance (MANOVA) using the geometric median. A maximum-type statistic that relies on the differences between the geometric medians among various groups is introduced. The distribution of the new test statistic is derived under the null hypothesis using Gaussian approximations, and its consistency under the alternative hypothesis is established. To approximate the distribution of the new statistic in high dimensions, a wild bootstrap algorithm is proposed and theoretically justified. Through simulation studies conducted across a variety of dimensions, sample sizes, and data-generating models, we demonstrate the finite-sample performance of our geometric median-based MANOVA method. Additionally, we implement the proposed approach to analyze a breast cancer gene expression dataset.

Recent years have witnessed a rise in the popularity of information integration without sharing of raw data. By leveraging and incorporating summary information from external sources, internal studies can achieve enhanced estimation efficiency and prediction accuracy. However, a noteworthy challenge in utilizing summary-level information is accommodating the inherent heterogeneity across diverse data sources. In this study, we delve into the issue of prior probability shift between two cohorts, wherein the difference of two data distributions depends on the outcome. We introduce a novel semi-parametric constrained optimization-based approach to integrate information within this framework, which has not been extensively explored in existing literature. Our proposed method tackles the prior probability shift by introducing the outcome-dependent selection function and effectively addresses the estimation uncertainty associated with summary information from the external source. Our approach facilitates valid inference even in the absence of a known variance-covariance estimate from the external source. Through extensive simulation studies, we observe the superiority of our method over existing ones, showcasing minimal estimation bias and reduced variance for both binary and continuous outcomes. We further demonstrate the utility of our method through its application in investigating risk factors related to essential hypertension, where the reduced estimation variability is observed after integrating summary information from an external data.

Prior distributions, which represent one\'s belief in the distributions of unknown parameters before observing the data, impact Bayesian inference in a critical and fundamental way. With the ability to incorporate external information from expert opinions or historical datasets, the priors, if specified appropriately, can improve the statistical efficiency of Bayesian inference. In survival analysis, based on the concept of unit information (UI) under parametric models, we propose the unit information Dirichlet process (UIDP) as a new class of nonparametric priors for the underlying distribution of time-to-event data. By deriving the Fisher information in terms of the differential of the cumulative hazard function, the UIDP prior is formulated to match its prior UI with the weighted average of UI in historical datasets and thus can utilize both parametric and nonparametric information provided by historical datasets. With a Markov chain Monte Carlo algorithm, simulations and real data analysis demonstrate that the UIDP prior can adaptively borrow historical information and improve statistical efficiency in survival analysis.

UNASSIGNED: We aimed to examine the normative profile of crystalline lens power (LP) and its associations with ocular biometric parameters including age, axial length (AL), spherical equivalent refraction (SE), corneal radius (CR), lens thickness, anterior chamber depth, and AL/CR ratio among a cynomolgus monkey colony.
UNASSIGNED: This population-based cross-sectional Non-human Primate Eye Study recruited middle-aged subjects in South China. All included macaques underwent a detailed ophthalmic examination. LP was calculated using the modified Bennett\'s formula, with biometry data from an autorefractometer and A-scan. SPSS version 25.0 was used for statistical analysis.
UNASSIGNED: A total of 301 macaques with an average age of 18.75 ± 2.95 years were collected in this study. The mean LP was 25.40 ± 2.96 D. Greater LP was independently associated with younger age, longer AL, and lower SE (P = 0.028, P = 0.025, and P = 0.034, respectively). LP showed a positive correlation with age, SE, CR, AL, lens thickness, and anterior chamber depth, whereas no correlation was observed between LP and AL/CR ratio.
UNASSIGNED: Our results suggested the LP distribution in the nonhuman primate colony and indicated that AL and SE strongly influenced the rate of LP. Therefore, this study contributed to a deeper understanding of the relative significance of the LP on the optics of the crystalline lens study.