Vomeronasal organ volume increases with body size and is dissociated with the loss of a visual signal in Sceloporus lizards.

Many organisms communicate using signals in different sensory modalities (multicomponent or multimodal). When one signal or component is lost over evolutionary time, it may be indicative of changes in other characteristics of the signalling system, including the sensory organs used to perceive and process signals. Sceloporus lizards predominantly use chemical and visual signals to communicate, yet some species have lost the ancestral ventral colour patch used in male-male agonistic interactions and exhibit increased chemosensory behaviour. Here, we asked whether evolutionary loss of this sexual signal is associated with larger vomeronasal organ (VNO) volumes (an organ that detects chemical scents) compared with species that have retained the colour patch. We measured VNO coronal section areas of 7-8 adult males from each of 11 Sceloporus species (4 that lost and 7 that retained the colour patch), estimated sensory and total epithelium volume, and compared volumes using phylogenetic analysis of covariance, controlling for body size. Contrary to expectations, we found that species retaining the ventral patch had similar relative VNO volumes as did species that had lost the ancestral patch, and that body size explains VNO epithelium volume. Visual signal loss may be sufficiently compensated for by increased chemosensory behaviour, and the allometric pattern may indicate sensory system trade-offs for large-bodied species.

Robustness of response-adaptive randomization.

Doubly adaptive biased coin design (DBCD), a response-adaptive randomization scheme, aims to skew subject assignment probabilities based on accrued responses for ethical considerations. Recent years have seen substantial advances in understanding DBCD's theoretical properties, assuming correct model specification for the responses. However, concerns have been raised about the impact of model misspecification on its design and analysis. In this paper, we assess the robustness to both design model misspecification and analysis model misspecification under DBCD. On one hand, we confirm that the consistency and asymptotic normality of the allocation proportions can be preserved, even when the responses follow a distribution other than the one imposed by the design model during the implementation of DBCD. On the other hand, we extensively investigate three commonly used linear regression models for estimating and inferring the treatment effect, namely difference-in-means, analysis of covariance (ANCOVA) I, and ANCOVA II. By allowing these regression models to be arbitrarily misspecified, thereby not reflecting the true data generating process, we derive the consistency and asymptotic normality of the treatment effect estimators evaluated from the three models. The asymptotic properties show that the ANCOVA II model, which takes covariate-by-treatment interaction terms into account, yields the most efficient estimator. These results can provide theoretical support for using DBCD in scenarios involving model misspecification, thereby promoting the widespread application of this randomization procedure.

Relationship between flightlessness and brain morphology among Rallidae.

Studies have suggested that the brain morphology and flight ability of Aves are interrelated; however, such a relationship has not been thoroughly investigated. This study aimed to examine whether flight ability, volant or flightless, affects brain morphology (size and shape) in the Rallidae, which has independently evolved to adapt secondary flightlessness multiple times within a single taxonomic group. Brain endocasts were extracted from computed tomography images of the crania, measured by 3D geometric morphometrics, and were analyzed using principal component analysis. The results of phylogenetic ANCOVA showed that flightless rails have brain sizes and shapes that are significantly larger than and different from those of volant rails, even after considering the effects of body mass and brain size respectively. Flightless rails tended to have a wider telencephalon and more inferiorly positioned foramen magnum than volant rails. Although the brain is an organ that requires a large amount of metabolic energy, reduced selective pressure for a lower body weight may have allowed flightless rails to have larger brains. The evolution of flightlessness may have changed the position of the foramen magnum downward, which would have allowed the support of the heavier cranium. The larger brain may have facilitated the acquisition of cognitively advanced behavior, such as tool-using behavior, among rails.

Regression analysis for covariate-adaptive randomization: A robust and efficient inference perspective.

Linear regression is arguably the most fundamental statistical model; however, the validity of its use in randomized clinical trials, despite being common practice, has never been crystal clear, particularly when stratified or covariate-adaptive randomization is used. In this article, we investigate several of the most intuitive and commonly used regression models for estimating and inferring the treatment effect in randomized clinical trials. By allowing the regression model to be arbitrarily misspecified, we demonstrate that all these regression-based estimators robustly estimate the treatment effect, albeit with possibly different efficiency. We also propose consistent non-parametric variance estimators and compare their performances to those of the model-based variance estimators that are readily available in standard statistical software. Based on the results and taking into account both theoretical efficiency and practical feasibility, we make recommendations for the effective use of regression under various scenarios. For equal allocation, it suffices to use the regression adjustment for the stratum covariates and additional baseline covariates, if available, with the usual ordinary-least-squares variance estimator. For unequal allocation, regression with treatment-by-covariate interactions should be used, together with our proposed variance estimators. These recommendations apply to simple and stratified randomization, and minimization, among others. We hope this work helps to clarify and promote the usage of regression in randomized clinical trials.

Adjusting for covariates in analysis based on restricted mean survival times.

We summarize extensions to the analysis of restricted mean survival time (RMST) in the context of time-to-event outcomes. The RMST estimate and its inference are based on the classical Kaplan-Meier curves. When covariate effects are considered, an adjusted RMST (ARMST) estimate can be derived analogously based on adjusted Kaplan-Meier curves. The adjusted Kaplan-Meier Estimator (AKME) was developed to reduce confounding by the method of inverse probability of treatment weighting. We will show how the ARMST method combines the concepts of the RMST and AKME to make inferences. Two regression based methods to adjust for potential covariate effect on the RMST estimates will be compared with the ARMST approach. Simulation studies are performed to compare the different methods with and without covariate adjustments. In addition, we will summarize the extension of RMST and ARMST to the setting with competing risks. The restricted mean time lost (RMTL) and adjusted RMTL (ARMTL) are estimates of interest from cumulative incidence curves. A phase 3 oncology clinical trial example is provided to demonstrate the applications of these methods.

Analysis of covariance under variance heteroscedasticity in general factorial designs.

Adjusting for baseline values and covariates is a recurrent statistical problem in medical science. In particular, variance heteroscedasticity is non-negligible in experimental designs and ignoring it might result in false conclusions. Approximate inference methods are developed to test null hypotheses formulated in terms of adjusted treatment effects and regression parameters in general analysis of covariance designs with arbitrary numbers of factors. Variance homoscedasticity is not assumed. The distributions of the test statistics are approximated using Box-type approximation methods. Extensive simulation studies show that the procedures are particularly suitable when sample sizes are rather small. A real data set illustrates the application of the methods.

A powerful test for the maximum treatment effect in thorough QT/QTc studies.

Parallel-group thorough QT/QTc studies focus on the change of QT/QTc values at several time-matched points from a pretreatment day (baseline) to a posttreatment day for different groups of treatment. The International Council for Harmonisation E14 stresses that QTc prolongation beyond a threshold represents high cardiac risk and calls for a test on the largest time-matched treatment effect (QTc prolongation). QT/QTc analysis usually assumes a jointly multivariate normal (MVN) distribution of pretreatment and posttreatment QT/QTc values, with a blocked compound symmetry covariance matrix. Existing methods use an analysis of covariance (ANCOVA) model including day-averaged baseline as a covariate to deal with the MVN model. However, the ANCOVA model tends to underestimate the variation of the estimator for treatment effects, resulting in the inflation of empirical type I error rate when testing whether the largest QTc prolongation is beyond a threshold. In this article, we propose two new methods to estimate the time-matched treatment effects under the MVN model, including maximum likelihood estimation and ordinary-least-square-based two-stage estimation. These two methods take advantage of the covariance structure and are asymptotically efficient. Based on these estimators, powerful tests for QT/QTc prolongation are constructed. Simulation shows that the proposed estimators have smaller mean square error, and the tests can control the type I error rate with high power. The proposed methods are applied on testing the carryover effect of diltiazem to inhibit dofetilide in a randomized phase 1 trial.

Statistical analysis of two arm randomized pre-post designs with one post-treatment measurement.

BACKGROUND: Randomized pre-post designs, with outcomes measured at baseline and after treatment, have been commonly used to compare the clinical effectiveness of two competing treatments. There are vast, but often conflicting, amount of information in current literature about the best analytic methods for pre-post designs. It is challenging for applied researchers to make an informed choice. METHODS: We discuss six methods commonly used in literature: one way analysis of variance ("ANOVA"), analysis of covariance main effect and interaction models on the post-treatment score ("ANCOVAI" and "ANCOVAII"), ANOVA on the change score between the baseline and post-treatment scores ("ANOVA-Change"), repeated measures ("RM") and constrained repeated measures ("cRM") models on the baseline and post-treatment scores as joint outcomes. We review a number of study endpoints in randomized pre-post designs and identify the mean difference in the post-treatment score as the common treatment effect that all six methods target. We delineate the underlying differences and connections between these competing methods in homogeneous and heterogeneous study populations. RESULTS: ANCOVA and cRM outperform other alternative methods because their treatment effect estimators have the smallest variances. cRM has comparable performance to ANCOVAI in the homogeneous scenario and to ANCOVAII in the heterogeneous scenario. In spite of that, ANCOVA has several advantages over cRM: i) the baseline score is adjusted as covariate because it is not an outcome by definition; ii) it is very convenient to incorporate other baseline variables and easy to handle complex heteroscedasticity patterns in a linear regression framework. CONCLUSIONS: ANCOVA is a simple and the most efficient approach for analyzing pre-post randomized designs.

Secular trends in muscular fitness from 1983 to 2014 among Slovenian children and adolescents.

Low physical fitness has been found to be associated with many chronic diseases and medical conditions. Knowledge of secular trends in physical fitness is important to initiate countermeasures for addressing negative trends. The aim of this study was to analyze secular trends in health-related muscular fitness in Slovenian children and adolescents between 1983 and 2014. Data were collected as part of "The Analysis of Children's Development in Slovenia (ACDSi)" study in 1983, 1993/94, 2003/04, and 2013/14. Anthropometry (body weight, height, BMI, and triceps skinfold) and muscular fitness (standing long jump, bent-arm hang, and sit-ups 60 s test) of 18730 (9168 female) students from primary and secondary schools were recorded. The secular trend was analyzed considering anthropometry. The results showed that anthropometric measures had an increasing trend and overall muscular fitness had a decreasing trend. Leg muscle power decreased over the decades in all age groups (relative difference between -1.5% and -2.6%), being more pronounced in boys. Arm muscle strength decreased in two younger age groups (range -21.1% to -42.7%, 6-10, and 11-14 years), but not in the oldest group (15-19 years), where the increase occurred in both genders (0.4% to 9.3%). In terms of decades, the largest negative changes (-30.1%) occurred from 1993/94 to 2003/04 and the smallest changes (-4.2%) from 2003/04 to 2013/14. The overall trend in repetitive core strength surprisingly increased (1.1% to 18.3%). There is a need to promote healthy lifestyles, raise parental awareness and use all government resources to redirect the negative trend in physical fitness.

Robustness of ANCOVA in randomized trials with unequal randomization.

Randomized trials with continuous outcomes are often analyzed using analysis of covariance (ANCOVA), with adjustment for prognostic baseline covariates. The ANCOVA estimator of the treatment effect is consistent under arbitrary model misspecification. In an article recently published in the journal, Wang et al proved the model-based variance estimator for the treatment effect is also consistent under outcome model misspecification, assuming the probability of randomization to each treatment is 1/2. In this reader reaction, we derive explicit expressions which show that when randomization is unequal, the model-based variance estimator can be biased upwards or downwards. In contrast, robust sandwich variance estimators can provide asymptotically valid inferences under arbitrary misspecification, even when randomization probabilities are not equal.

Dual-centered ANCOVA: Resolving contradictory results from Lord's paradox with implications for reducing bias in longitudinal analyses.

INTRODUCTION: Valid causal inferences are necessary to use developmental research to improve adolescent outcomes. What type of change should be analyzed to approximate causal inferences from longitudinal data? Difference-score and ANCOVA-type analyses often produce contradictory results, a problem known as Lord's paradox. This study investigates 2-group, 2-wave difference-score analyses and ANCOVA, and introduces a method that produces consistent results, namely dual-centered ANCOVA, which is compared to pretest matching. METHODS: These methods are tested first on two datasets simulated to fit each of Lord's contrasting results. The methods are then applied to data investigating the longitudinal associations of parent-adolescent discussions about sexual risks on subsequent unprotected sexual behaviors in 4753 American adolescents (62.2% whites). RESULTS: The results replicate Lord's contradictory results for all datasets. Dual-centered ANCOVA and pretest matching both produce consistent results, but dual-centered ANCOVA replicates the original results for difference-score analyses, whereas pretest matching replicates the original ANCOVA results. Thus, the two sets of consistent results differ from each other as much as the original discrepancy rather than reducing bias. CONCLUSION: The least biased analysis is the one whose null hypothesis best approximates a plausible change pattern to represent a no-treatment effect. When difference-score analyses are thought to approximate valid causal inferences as closely as ANCOVA-type analyses, dual-centered ANCOVA estimates the difference-score effect while retaining the advantages of ANCOVA in statistical power and covariate inclusion. These findings are widely applicable to longitudinal analyses that incorporate one or both of these basic methods to analyze change.

Robust statistical methods in R using the WRS2 package.

This paper introduces the R package WRS2 that implements various robust statistical methods. It elaborates on the basics of robust statistics by introducing robust location, dispersion, and correlation measures. The location and dispersion measures are then used in robust variants of independent and dependent samples t tests and ANOVA, including between-within subject designs and quantile ANOVA. Further, robust ANCOVA as well as robust mediation models are introduced. The paper targets applied researchers; it is therefore kept rather non-technical and written in a tutorial style. Special emphasis is placed on applications in the social and behavioral sciences and illustrations of how to perform corresponding robust analyses in R. The R code for reproducing the results in the paper is given in the Supplementary Materials.

Comparisons of statistical techniques to assess age-related skeletal markers in bioarchaeology.

OBJECTIVES: Many authors argue that inconsistencies between studies of skeletal markers are based on different data collection protocols, especially when comparing age-related markers such as osteoarthritis. Less attention is given to the choice of statistical techniques that are used to test the hypotheses associated with the data. This paper addresses how different statistical techniques compare the prevalence of age-related skeletal indicators, specifically osteoarthritis. MATERIALS AND METHODS: Osteoarthritis prevalence was scored in eight postcranial joints in 243 adult individuals from seven prehistoric archaeological sites in Central California, and data was compared between three time periods [Early (4800-2800 BP), Middle (2800-1200 BP), and Late (1200-250 BP)] using commonly used statistical tests: chi-square, Fisher's exact, and odds ratios. In addition, we analyzed the data with tests that are able to take into consideration the effect of age on osteoarthritis prevalence: ANCOVA and Factorial ANOVA. Finally, bootstraps were applied to the data to investigate how fluctuating frequencies, sample size, and age-at-death distributions affected the interpretations resulting from each test. RESULTS: The results demonstrate that the tests that consider age as a covariate (ANCOVA and Factorial ANOVA) are more efficient in rejecting the null hypothesis when smaller magnitudes of difference are observed between samples, irrespective of sample size, even though osteoarthritis prevalence fails to meet assumptions of normal distribution and homoscedasticity. DISCUSSION: ANCOVAs or Factorial ANOVAs that incorporate age as a covariate should be considered more often in studies that test different prevalences of age-related osteological markers among past populations.

Detection of superior genotype of fatty acid synthase in Korean native cattle by an environment-adjusted statistical model.

OBJECTIVE: This study examines the genetic factors influencing the phenotypes (four economic traits:oleic acid [C18:1], monounsaturated fatty acids, carcass weight, and marbling score) of Hanwoo. METHODS: To enhance the accuracy of the genetic analysis, the study proposes a new statistical model that excludes environmental factors. A statistically adjusted, analysis of covariance model of environmental and genetic factors was developed, and estimated environmental effects (covariate effects of age and effects of calving farms) were excluded from the model. RESULTS: The accuracy was compared before and after adjustment. The accuracy of the best single nucleotide polymorphism (SNP) in C18:1 increased from 60.16% to 74.26%, and that of the two-factor interaction increased from 58.69% to 87.19%. Also, superior SNPs and SNP interactions were identified using the multifactor dimensionality reduction method in Table 1 to 4. Finally, high- and low-risk genotypes were compared based on their mean scores for each trait. CONCLUSION: The proposed method significantly improved the analysis accuracy and identified superior gene-gene interactions and genotypes for each of the four economic traits of Hanwoo.

Robustness of ordinary least squares in randomized clinical trials.

There has been a series of occasional papers in this journal about semiparametric methods for robust covariate control in the analysis of clinical trials. These methods are fairly easy to apply on currently available computers, but standard software packages do not yet support these methods with easy option selections. Moreover, these methods can be difficult to explain to practitioners who have only a basic statistical education. There is also a somewhat neglected history demonstrating that ordinary least squares (OLS) is very robust to the types of outcome distribution features that have motivated the newer methods for robust covariate control. We review these two strands of literature and report on some new simulations that demonstrate the robustness of OLS to more extreme normality violations than previously explored. The new simulations involve two strongly leptokurtic outcomes: near-zero binary outcomes and zero-inflated gamma outcomes. Potential examples of such outcomes include, respectively, 5-year survival rates for stage IV cancer and healthcare claim amounts for rare conditions. We find that traditional OLS methods work very well down to very small sample sizes for such outcomes. Under some circumstances, OLS with robust standard errors work well with even smaller sample sizes. Given this literature review and our new simulations, we think that most researchers may comfortably continue using standard OLS software, preferably with the robust standard errors.

A clinical perspective on the analysis and presentation of the number of incontinence episodes following treatment for OAB.

AIMS: To provide a clinical view and interpretation on the methods for analysis of incontinence in patients with overactive bladder. METHODS: Results are analyzed using the total number of incontinence episodes in a 3-day diary period, using fixed and random effect Poisson regression models to calculate ratio of event rates and 95% confidence interval (CI) together with P-values and are compared with the analysis of the mean number of incontinence episodes/24 hr using analysis of covariance models to calculate P-values and 95% CI for the difference between treatments. RESULTS: Using random effects Poisson regression models demonstrated that the number of incontinence episodes was reduced by 26% more with mirabegron 50 mg than with placebo. For solifenacin 5 and 10 mg, treatment resulted in a 43% (41%) greater decrease in the number of incontinence episodes compared with placebo. CONCLUSION: Instead of providing a fixed number of incontinence episodes/24 hr that reflects the mean effect, the estimate using Poisson methodology provides an efficacy estimate that can be interpreted in the context of, and relative to, the patient's baseline (severity). Using the total number of incontinence episodes in the diary period, and expressing this as percent decrease in the number of episodes, may be easier to interpret; for example, because this results in a relative measure of effect that provides an alternative understanding of a patient's improvement at end of treatment compared with the comparator arm. Also, it is based on statistical methods that are more suitable for the analysis of count data. Neurourol. Urodynam. 35:728-732, 2016. © 2015 Wiley Periodicals, Inc.

Genetic association analyses of fast plasma glucose level in pre-menopausal Chinese women: potential interaction between osteocalcin and oestrogen receptor α.

BACKGROUND: Fasting plasma glucose (FPG) levels are usually tightly regulated within a narrow physiologic range. Variation of FPG levels is clinically important and is strongly heritable. Several lines of evidence suggest the importance of the oestrogen receptor α (ER-α) and osteocalcin (also known as BGP, for bone Gla protein) in determining FPG; however, whether their polymorphisms are associated with FPG variation is not well understood. AIM: To investigate whether ER-a PvuII and BGP HindIII genetic polymorphisms and their potential interaction are associated with FPG variation. SUBJECTS AND METHODS: The study subjects were 328 unrelated pre-menopausal Chinese women aged 21 years and over (mean age ± SD, 33.2 ± 5.9 years), with an average FPG of 4.92 (SD = 0.81). All subjects were genotyped at the ER-α PvuII and BGP HindIII loci using polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP). RESULTS: The ER-α PvuII genotypes were significantly associated with FPG (p = 0.007). In addition, a significant interaction was observed of the ER-α PvuII polymorphism with BGP HindIII polymorphism on FPG variation (p = 0.013), although the BGP HindIII polymorphism was not shown to be individually associated with FPG. CONCLUSION: The PvuII polymorphism of the ER-α gene and its potential interaction with the HindIII polymorphism of the BGP gene were associated with FPG in pre-menopausal Chinese women.

An evaluation of trauma outcomes related to insurance status in patients requiring prehospital helicopter transport.

INTRODUCTION: Disparities in access to medical care and outcomes of medical treatment related to insurance status are documented. However, little attention has been given to the effect of health care funding status on outcomes in trauma patients. Hypothesis/Problem This study evaluated if adult trauma patients who arrived by air transport to a trauma center had different clinical outcomes based on their health insurance status. METHODS: A retrospective analysis was performed of all adult trauma patients arriving by prehospital flight services to a Level I Trauma Center over a 5-year period. Patients were classified as unfunded or funded based on health insurance status. Injury severity scores (ISS) were compared, while the end points evaluated in the study included duration of stay in the intensive care unit (ICU), duration of hospitalization, and mortality. RESULTS: A total of 1,877 adult patients met inclusion criteria for the study, with 14% (n = 259) classified as unfunded and 86% (n = 1,618) classified as funded. Unfunded patients compared to funded patients had a significantly lower average ISS (12.82 vs 15.56; P < .001) but a significantly higher mortality rate (16.6% vs 10.7%; P < .01) and a 1.54 relative risk of death (95% CI, 1.136-2.098). Neither mean ICU stay (3.44 days vs 4.98 days; P = .264) nor duration of hospitalization (11.18 days vs 13.34 days; P = .382) was significantly different when controlling for ISS. CONCLUSION: Unfunded health insurance status is associated with worse outcomes following less significant injury. Further investigation of baseline health disparities for identification and early intervention may improve outcomes. Additionally, these findings may have implications for the health systems of other countries that lack universal health care coverage.

Refining knowledge, attitude and practice of evidence-based medicine (EBM) among pharmacy students for professional challenges.

Practicing evidence based medicine (EBM) is a professional need for the future clinical pharmacist in UAE and around the world. An attempt was made to evaluate pharmacy student's knowledge, attitude and proficiency in the practice of EBM. A within-subject study design with pre and post survey and skill test were conducted using case based practice of EBM through a validated questionnaire. The results were tabulated and there was a statistically significant increase in pharmacy students' perceived ability to go through steps of EBM, namely: formulating PICO questions (95.3%), searching for evidence (97%), appraising the evidence (81%), understanding statistics (78.1%), and applying evidence at point of care (81.2%). In this study, workshops and (Problem Based Learning) PBLs were used as a module of EBM teaching and practices, which has been shown to be an effective educational method in terms of improving students' skills, knowledge and attitude toward EBM. Incorporating hands on experience, PBLs will become an impetus for developing EBM skills and critical appraisal of research evidence alongside routine clinical practice. This integration would constitute the cornerstone in lifting EBM in UAE up to the needed standards and would enable pharmacy students to become efficient pharmacists that rely on evidence in their health practice.

The effect of music training on students' mathematics and physics development at middle schools in China: A longitudinal study.

As a descriptive-inferential study, this research aimed at revealing the relationship between music training and academic development with the Chinese middle school students' academic performance of mathematics and physics skills. The participants of this study consisted of the students from two different middle schools located at two cities in Shandong province, China. From each school 250 students were selected, and the statistics was used to analyze both the academic performance of the students and the data obtained from the scale designed by the authors. The research results show that the non-music students outperformed music students on both mathematics and physics development. In addition, music training did not contribute to the academic achievement independently but rather integrated with several factors like parents' education and out-of-school engagement. The findings suggest the positive influence on non-musical cognitive learning, and it has potential implications for the Chinese middle school education.