Evaluation of embryo-foetal biometry and its correlation with parturition date in Toy Poodle bitches.

Estimating the parturition date in dogs is challenging due to their reproductive peculiarities that. Ultrasonographic examination serves as a tool for studying embryo/foetal biometry and estimating the time of parturition by measuring foetal and extra-foetal structures. However, due to reproductive differences among various dog breeds, such estimates may have a non-significant pattern, representing inaccuracies in the estimated date of birth. This study aimed to monitor pregnant Toy Poodle bitches and establish relationships between ultrasonographically measured foetal and extra-foetal dimensions and the remaining time until parturition. Eighteen pregnant Toy Poodle bitches were subjected to weekly ultrasonographic evaluations and measurements of the inner chorionic cavity diameter, craniocaudal length (CCL), biparietal diameter (BPD), diameter of the deep portion of diencephalo-telencephalic vesicle (DPTV), abdominal diameter, thorax diameter (TXD), placental thickness and the renal diameter (REND). These parameters were retrospectively correlated with the date of parturition and linear regressions were established between gestational measurements and days before parturition (DBP). All analyses were conducted using the Statistical Package for Social Sciences (IBM® SPSS®) program at a 5% significance level. The foetal measurements that showed a high correlation (r) and reliability (R2) with DBP were BPD [(DBP = [15.538 × BPD] - 39.756), r = .97 and R2 = .93], TXD [(DBP = [8.933 × TXD] - 32.487), r = .94 and R2 = .89], DPTV [(DBP = [34.580 × DPTV] - 39.403), r = .93 and R2 = .86] and REND [(DBP = [13.735 × REND] - 28.937), r = .91 and R2 = .82]. This statistically validates the application of these specific formulas to estimate the parturition date in Toy Poodle bitches.

On exact randomization-based covariate-adjusted confidence intervals.

Randomization-based inference using the Fisher randomization test allows for the computation of Fisher-exact P-values, making it an attractive option for the analysis of small, randomized experiments with non-normal outcomes. Two common test statistics used to perform Fisher randomization tests are the difference-in-means between the treatment and control groups and the covariate-adjusted version of the difference-in-means using analysis of covariance. Modern computing allows for fast computation of the Fisher-exact P-value, but confidence intervals have typically been obtained by inverting the Fisher randomization test over a range of possible effect sizes. The test inversion procedure is computationally expensive, limiting the usage of randomization-based inference in applied work. A recent paper by Zhu and Liu developed a closed form expression for the randomization-based confidence interval using the difference-in-means statistic. We develop an important extension of Zhu and Liu to obtain a closed form expression for the randomization-based covariate-adjusted confidence interval and give practitioners a sufficiency condition that can be checked using observed data and that guarantees that these confidence intervals have correct coverage. Simulations show that our procedure generates randomization-based covariate-adjusted confidence intervals that are robust to non-normality and that can be calculated in nearly the same time as it takes to calculate the Fisher-exact P-value, thus removing the computational barrier to performing randomization-based inference when adjusting for covariates. We also demonstrate our method on a re-analysis of phase I clinical trial data.

Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes.

In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the "gold-standard" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The "causal excursion effect," a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data.

A New Method to Minimize the Standard Deviation and Root Mean Square of the Prediction Error of Single-Optimized IOL Power Formulas.

Purpose: The purpose of this study was to develop a simplified method to approximate constants minimizing the standard deviation (SD) and the root mean square (RMS) of the prediction error in single-optimized intraocular lens (IOL) power calculation formulas. Methods: The study introduces analytical formulas to determine the optimal constant value for minimizing SD and RMS in single-optimized IOL power calculation formulas. These formulas were tested against various datasets containing biometric measurements from cataractous populations and included 10,330 eyes and 4 different IOL models. The study evaluated the effectiveness of the proposed method by comparing the outcomes with those obtained using traditional reference methods. Results: In optimizing IOL constants, minor differences between reference and estimated A-constants were found, with the maximum deviation at -0.086 (SD, SRK/T, and Vivinex) and -0.003 (RMS, PEARL DGS, and Vivinex). The largest discrepancy for third-generation formulas was -0.027 mm (SD, Haigis, and Vivinex) and 0.002 mm (RMS, Hoffer Q, and PCB00/SN60WF). Maximum RMS differences were -0.021 and +0.021, both involving Hoffer Q. Post-minimization, the largest mean prediction error was 0.726 diopters (D; SD) and 0.043 D (RMS), with the highest SD and RMS after adjustments at 0.529 D and 0.875 D, respectively, indicating effective minimization strategies. Conclusions: The study simplifies the process of minimizing SD and RMS in single-optimized IOL power predictions, offering a valuable tool for clinicians. However, it also underscores the complexity of achieving balanced optimization and suggests the need for further research in this area. Translational Relevance: The study presents a novel, clinically practical approach for optimizing IOL power calculations.

Causal inference in the absence of positivity: The role of overlap weights.

How to analyze data when there is violation of the positivity assumption? Several possible solutions exist in the literature. In this paper, we consider propensity score (PS) methods that are commonly used in observational studies to assess causal treatment effects in the context where the positivity assumption is violated. We focus on and examine four specific alternative solutions to the inverse probability weighting (IPW) trimming and truncation: matching weight (MW), Shannon's entropy weight (EW), overlap weight (OW), and beta weight (BW) estimators. We first specify their target population, the population of patients for whom clinical equipoise, that is, where we have sufficient PS overlap. Then, we establish the nexus among the different corresponding weights (and estimators); this allows us to highlight the shared properties and theoretical implications of these estimators. Finally, we introduce their augmented estimators that take advantage of estimating both the propensity score and outcome regression models to enhance the treatment effect estimators in terms of bias and efficiency. We also elucidate the role of the OW estimator as the flagship of all these methods that target the overlap population. Our analytic results demonstrate that OW, MW, and EW are preferable to IPW and some cases of BW when there is a moderate or extreme (stochastic or structural) violation of the positivity assumption. We then evaluate, compare, and confirm the finite-sample performance of the aforementioned estimators via Monte Carlo simulations. Finally, we illustrate these methods using two real-world data examples marked by violations of the positivity assumption.

Accuracy of 12 Intraocular Lens Power Formulas After Corneal Myopic Refractive Surgery.

PURPOSE: To assess the predictive accuracy of new-generation online intraocular lens (IOL) power formulas in eyes with previous myopic laser refractive surgery (LRS) and to evaluate the influence of corneal asphericity on the predictive accuracy. METHODS: The authors retrospectively evaluated 52 patients (78 eyes) with a history of laser in situ keratomileusis (LASIK) or photorefractive keratectomy (PRK) who subsequently underwent cataract surgery. Refractive prediction errors were calculated for 12 no-history new online formulas: 8 formulas with post-LRS versions (Barrett True-K, EVO 2.0, Hoffer QST, and Pearl DGS) using keratometry and posterior/total keratometry measured by IOLMaster 700 and 4 formulas without post-LRS versions (Cooke K6 and Kane) using keratometry and total keratometry. The refractive prediction error, mean absolute error (MAE), and percentages of eyes with prediction errors of ±0.25, ±0.50, ±0.75, ±1.00, and ±1.50 diopters (D) were compared. RESULTS: The MAEs of the 12 formulas were significantly different (F = 83.66, P < .001). The MAEs ranged from 0.62 to 0.94 D and from 1.07 to 1.84 D in the formulas with and without post-LRS versions, respectively. The EVO formula produced the lowest MAE (0.60) and MedAE (0.47), followed by the Barrett True-K (0.69 and 0.50, respectively). Each percentage of eyes with refractive prediction error was also significantly different among the 12 formulas (P < .001). CONCLUSIONS: The EVO and Barrett True-K formulas demonstrate comparable performance to the other existing formulas in eyes with a history of myopic LASIK/PRK. Surgeons should use these formulas with post-LRS versions and input keratometric values whenever possible. [J Refract Surg. 2024;40(6):e354-e361.].

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.

Refraction and ocular biometric parameters in 3-to 6-year-old preschool children : a large-scale population-based study in Chengdu, China.

PURPOSE: To understand the ocular biometric parameters characteristics and refractive errors in 3-to 6-year-old preschool children in Chengdu, China, and to investigate the prevalence of refractive errors. METHOD: A school-based cross-sectional study was conducted in Chengdu from 2020 to2022 with a total of 666 kindergartens. All children were measured by non-cycloplegic autorefraction and uncorrected visual acuity (UCVA) and ocular biometric parameters. Finally, univariate linear regression models were used to analyze the relationship between ocular biometric parameters and refraction. RESULTS: A total of 108,578 preschool children aged 3-6 underwent examinations, revealing a myopia prevalence of 6.1%. The mean axial length (AL), keratometry (K), corneal radius (CR), axial length/corneal radius (AL/CR) Ratio, central corneal thickness (CCT), anterior chamber depth (ACD), lens thickness (LT), and vitreous chamber depth (VCD) were 22.35 ± 0.69 mm, 43.35 ± 1.58 D, 7.80 ± 0.28 mm, 2.87 ± 0.08, 533.31 ± 32.51 µm, 2.70 ± 0.28 mm, 3.91 ± 0.27 mm, and 15.20 ± 0.68 mm, respectively. With increasing age, AL, CR, AL/CR ratio, CCT, ACD, LT, and VCD also increased. Regardless of age, males consistently exhibited longer AL, flatter corneal curvature, shallower ACD, thicker CCT, thinner LT, and longer VCD compared to females. AL, K, CR, LT, and VCD all showed significant linear relationships with SE (all P < 0.001) in univariate linear regression analysis after adjusting for gender and age. CONCLUSION: The prevalence of myopia among preschool children aged 3-6 in Chengdu is relatively low. Ocular biometric parameters affecting refractive errors include AL, K, CR, LT, and VCD. The preschool period serves as a critical phase for myopia prevention and control.

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.

[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.

Direct and indirect treatment effects in the presence of semicompeting risks.

Semicompeting risks refer to the phenomenon that the terminal event (such as death) can censor the nonterminal event (such as disease progression) but not vice versa. The treatment effect on the terminal event can be delivered either directly following the treatment or indirectly through the nonterminal event. We consider 2 strategies to decompose the total effect into a direct effect and an indirect effect under the framework of mediation analysis in completely randomized experiments by adjusting the prevalence and hazard of nonterminal events, respectively. They require slightly different assumptions on cross-world quantities to achieve identifiability. We establish asymptotic properties for the estimated counterfactual cumulative incidences and decomposed treatment effects. We illustrate the subtle difference between these 2 decompositions through simulation studies and two real-data applications in the Supplementary Materials.

Testing conditional quantile independence with functional covariate.

We propose a new non-parametric conditional independence test for a scalar response and a functional covariate over a continuum of quantile levels. We build a Cramer-von Mises type test statistic based on an empirical process indexed by random projections of the functional covariate, effectively avoiding the "curse of dimensionality" under the projected hypothesis, which is almost surely equivalent to the null hypothesis. The asymptotic null distribution of the proposed test statistic is obtained under some mild assumptions. The asymptotic global and local power properties of our test statistic are then investigated. We specifically demonstrate that the statistic is able to detect a broad class of local alternatives converging to the null at the parametric rate. Additionally, we recommend a simple multiplier bootstrap approach for estimating the critical values. The finite-sample performance of our statistic is examined through several Monte Carlo simulation experiments. Finally, an analysis of an EEG data set is used to show the utility and versatility of our proposed test statistic.

Sparse Group Penalties for bi-level variable selection.

Many data sets exhibit a natural group structure due to contextual similarities or high correlations of variables, such as lipid markers that are interrelated based on biochemical principles. Knowledge of such groupings can be used through bi-level selection methods to identify relevant feature groups and highlight their predictive members. One of the best known approaches of this kind combines the classical Least Absolute Shrinkage and Selection Operator (LASSO) with the Group LASSO, resulting in the Sparse Group LASSO. We propose the Sparse Group Penalty (SGP) framework, which allows for a flexible combination of different SGL-style shrinkage conditions. Analogous to SGL, we investigated the combination of the Smoothly Clipped Absolute Deviation (SCAD), the Minimax Concave Penalty (MCP) and the Exponential Penalty (EP) with their group versions, resulting in the Sparse Group SCAD, the Sparse Group MCP, and the novel Sparse Group EP (SGE). Those shrinkage operators provide refined control of the effect of group formation on the selection process through a tuning parameter. In simulation studies, SGPs were compared with other bi-level selection methods (Group Bridge, composite MCP, and Group Exponential LASSO) for variable and group selection evaluated with the Matthews correlation coefficient. We demonstrated the advantages of the new SGE in identifying parsimonious models, but also identified scenarios that highlight the limitations of the approach. The performance of the techniques was further investigated in a real-world use case for the selection of regulated lipids in a randomized clinical trial.

Potassium silicate and vinasse enhance biometric characteristics of perennial sweet pepper (Capsicum annuum) under greenhouse conditions.

An effective strategy for enhancing fruit production continuity during extended sweet pepper season involves adopting innovative biostimulants such as potassium silicate (PS) and vinasse. Adjusting PS and vinasse concentrations are crucial for maintaining the balance between vegetative and fruit growth, particularly in sweet pepper with a shallow root system, to sustain fruiting over prolonged season. However, the interaction between PS and vinasse and the underlying physiological mechanisms that extend the sweet pepper season under greenhouse conditions remain unclear. This study aimed to investigate the impact of PS and vinasse treatments on the yield and biochemical constituents of perennial pepper plants cultivated under greenhouse conditions. For two consecutive seasons [2018/2019 and 2019/2020], pepper plants were sprayed with PS (0, 0.5, and 1 g/l) and drenched with vinasse (0, 1, 2, and 3 l/m3). To estimate the impact of PS and vinasse on the growth, yield, and biochemical constituents of pepper plants, fresh and dry biomass, potential fruit yield, and some biochemical constituents were evaluated. Results revealed that PS (0.5 g/l) coupled with vinasse (3 l/m3) generated the most remarkable enhancement, in terms of plant biomass, total leaf area, total yield, and fruit weight during both growing seasons. The implementation of vinasse at 3 l/m3 with PS at 0.5 and 1 g/l demonstrated the most pronounced augmentation in leaf contents (chlorophyll index, nitrogen and potassium), alongside improved fruit quality, including total soluble solid and ascorbic acid contents, of extended sweet pepper season. By implementing the optimal combination of PS and vinasse, growers can significantly enhance the biomass production while maintaining a balance in fruiting, thereby maximizing the prolonged fruit production of superior sweet pepper under greenhouse conditions.

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.

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.