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.

Communication training for general practitioners aimed at improving antibiotic prescribing: a controlled before-after study in multicultural Dutch cities

Introduction

Suboptimal doctor-patient communication drives inappropriate prescribing of antibiotics. We evaluated a communication intervention for general practitioners (GPs) in multicultural Dutch cities to improve antibiotic prescribing for respiratory tract infections (RTI).

Methods

This was a non-randomized controlled before-after study. The study period was pre-intervention November 2019 – April 2020 and post-intervention November 2021 – April 2022. The intervention consisted of a live training (organized between September and November 2021), an E-learning, and patient material on antibiotics and antibiotic resistance in multiple languages. The primary outcome was the absolute number of prescribed antibiotic courses indicated for RTIs per GP; the secondary outcome was all prescribed antibiotics per GP. We compared the post-intervention differences in the mean number of prescribed antibiotics between the intervention (N = 25) and the control group (N = 110) by using an analysis of covariance (ANCOVA) test, while adjusting for the pre-intervention number of prescribed antibiotics. Additionally, intervention GPs rated the training and their knowledge and skills before the intervention and 3 months thereafter.

Results

There was no statistically significant difference in the mean number of prescribed antibiotics for RTI between the intervention and the control group, nor for mean number of overall prescribed antibiotics. The intervention GPs rated the usefulness of the training for daily practice a 7.3 (on a scale from 1–10) and there was a statistically significant difference between pre- and post-intervention on four out of nine items related to knowledge and skills.

Discussion

There was no change in GPs prescription behavior between the intervention and control group. However, GPs found the intervention useful and showed some improvement on self-rated knowledge and communication skills.

Improving the mixed model for repeated measures to robustly increase precision in randomized trials

In randomized trials, repeated measures of the outcome are routinely collected. The mixed model for repeated measures (MMRM) leverages the information from these repeated outcome measures, and is often used for the primary analysis to estimate the average treatment effect at the primary endpoint. MMRM, however, can suffer from bias and precision loss when it models intermediate outcomes incorrectly, and hence fails to use the post-randomization information harmlessly. This paper proposes an extension of the commonly used MMRM, called IMMRM, that improves the robustness and optimizes the precision gain from covariate adjustment, stratified randomization, and adjustment for intermediate outcome measures. Under regularity conditions and missing completely at random, we prove that the IMMRM estimator for the average treatment effect is robust to arbitrary model misspecification and is asymptotically equal or more precise than the analysis of covariance (ANCOVA) estimator and the MMRM estimator. Under missing at random, IMMRM is less likely to be misspecified than MMRM, and we demonstrate via simulation studies that IMMRM continues to have less bias and smaller variance. Our results are further supported by a re-analysis of a randomized trial for the treatment of diabetes.

A comparison of covariate adjustment approaches under model misspecification in individually randomized trials

Adjustment for baseline covariates in randomized trials has been shown to lead to gains in power and can protect against chance imbalances in covariates. For continuous covariates, there is a risk that the the form of the relationship between the covariate and outcome is misspecified when taking an adjusted approach. Using a simulation study focusing on individually randomized trials with small sample sizes, we explore whether a range of adjustment methods are robust to misspecification, either in the covariate-outcome relationship or through an omitted covariate-treatment interaction. Specifically, we aim to identify potential settings where G-computation, inverse probability of treatment weighting (IPTW), augmented inverse probability of treatment weighting (AIPTW) and targeted maximum likelihood estimation (TMLE) offer improvement over the commonly used analysis of covariance (ANCOVA). Our simulations show that all adjustment methods are generally robust to model misspecification if adjusting for a few covariates, sample size is 100 or larger, and there are no covariate-treatment interactions. When there is a non-linear interaction of treatment with a skewed covariate and sample size is small, all adjustment methods can suffer from bias; however, methods that allow for interactions (such as G-computation with interaction and IPTW) show improved results compared to ANCOVA. When there are a high number of covariates to adjust for, ANCOVA retains good properties while other methods suffer from under- or over-coverage. An outstanding issue for G-computation, IPTW and AIPTW in small samples is that standard errors are underestimated; they should be used with caution without the availability of small-sample corrections, development of which is needed. These findings are relevant for covariate adjustment in interim analyses of larger trials.

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.

MA-cont:pre/post effect size: An interactive tool for the meta-analysis of continuous outcomes using R Shiny

Meta‐analysis is a widely used methodology to combine evidence from different sources examining a common research phenomenon, to obtain a quantitative summary of the studied phenomenon. In the medical field, multiple studies investigate the effectiveness of new treatments and meta‐analysis is largely performed to generate the summary (average) treatment effect. In the meta‐analysis of aggregate continuous outcomes measured in a pretest‐posttest design using differences in means as the effect measure, a plethora of methods exist: analysis of final (follow‐up) scores, analysis of change scores and analysis of covariance. Specialised and general‐purpose statistical software is used to apply the various methods, yet, often the choice among them depends on data availability and statistical affinity. We present a new web‐based tool, MA‐cont:pre/post effect size, to conduct meta‐analysis of continuous data assessed pre‐ and post‐treatment using the aforementioned approaches on aggregate data and a more flexible approach of generating and analysing pseudo individual participant data. The interactive web environment, available by R Shiny, is used to create this free‐to‐use statistical tool, requiring no programming skills by the users. A basic statistical understanding of the methods running in the background is a prerequisite and we encourage the users to seek advice from technical experts when necessary.

What Is the Numerical Nature of Pain Relief?

Pain relief, or a decrease in self-reported pain intensity, is frequently the primary outcome of pain clinical trials. Investigators commonly report pain relief in one of two ways: using raw units (additive) or using percentage units (multiplicative). However, additive and multiplicative scales have different assumptions and are incompatible with one another. In this work, we describe the assumptions and corollaries of additive and multiplicative models of pain relief to illuminate the issue from statistical and clinical perspectives. First, we explain the math underlying each model and illustrate these points using simulations, for which readers are assumed to have an understanding of linear regression. Next, we connect this math to clinical interpretations, stressing the importance of statistical models that accurately represent the underlying data; for example, how using percent pain relief can mislead clinicians if the data are actually additive. These theoretical discussions are supported by empirical data from four longitudinal studies of patients with subacute and chronic pain. Finally, we discuss self-reported pain intensity as a measurement construct, including its philosophical limitations and how clinical pain differs from acute pain measured during psychophysics experiments. This work has broad implications for clinical pain research, ranging from statistical modeling of trial data to the use of minimal clinically important differences and patient-clinician communication.

Pharmacogenomics variants are associated with BMI differences between individuals with bipolar and other psychiatric disorders

Aim: Regardless of the plethora of next-generation sequencing studies in the field of pharmacogenomics (PGx), the potential effect of covariate variables on PGx response within deeply phenotyped cohorts remains unexplored. Materials & methods: We explored with advanced statistical methods the potential influence of BMI, as a covariate variable, on PGx response in a Greek cohort with psychiatric disorders. Results: Nine PGx variants within UGT1A6, SLC22A4, GSTP1, CYP4B1, CES1, SLC29A3 and DPYD were associated with altered BMI in different psychiatric disorder groups. Carriers of rs2070959 (UGT1A6), rs199861210 (SLC29A3) and rs2297595 (DPYD) were also characterized by significant changes in the mean BMI, depending on the presence of psychiatric disorders. Conclusion: Specific PGx variants are significantly associated with BMI in a Greek cohort with psychiatric disorders.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01323-9.

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.

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.

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.

Differences in the parasitic effects of a bopyrid isopod and rhizocephalan barnacle on the portunid crab, Charybdis bimaculata

Bopyrid isopods and rhizocephalan barnacles are obligate parasite crustaceans which harm their decapod hosts. However, to the best of our knowledge, studies have not compared which of these parasites has a greater parasitic effect on its hosts. Here, the parasitic effect of the bopyrid isopod, Allokepon hendersoni, and an unidentified sacculinid rhizocephalan species, infesting the same population of portunid crabs, Charybdis bimaculata, was investigated and compared for the first time. Samples were collected from the bycatch of a trawl fishery in Tosa Bay, Japan. A total of 2601 crabs were collected, of which 14 (0.55%) were parasitized by the bopyrid and 21 (0.82%) by the rhizocephalan. One of the two female crabs parasitized by the bopyrid was ovigerous (with much fewer eggs than unparasitized females). No ovigerous crab was found from the eight females parasitized by the rhizocephalan. Because only two female crabs were parasitized by the bopyrid, the following analyses were made using the male crabs. Both parasites reduced the wet weight (crab condition) and the cheliped size (secondary growth) of C. bimaculata, but the impact of the parasitism did not differ between the parasite species. The size of the abdominal flap of male hosts was reduced by the bopyrid infestation; however, rhizocephalan infestation caused enlargement of the abdominal flap, which is an indication of feminization. The present study provides information on how the effect of these two parasitic castrators on the same host crab varies. A moderate decrease in crab condition and cheliped development was common among the parasites, suggesting that the degree of impact might be favorable for the survival of the two parasites species.

Meta-analysis of continuous outcomes: Using pseudo IPD created from aggregate data to adjust for baseline imbalance and assess treatment-by-baseline modification

Meta‐analysis of individual participant data (IPD) is considered the “gold‐standard” for synthesizing clinical study evidence. However, gaining access to IPD can be a laborious task (if possible at all) and in practice only summary (aggregate) data are commonly available. In this work we focus on meta‐analytic approaches of comparative studies where aggregate data are available for continuous outcomes measured at baseline (pre‐treatment) and follow‐up (post‐treatment). We propose a method for constructing pseudo individual baselines and outcomes based on the aggregate data. These pseudo IPD can be subsequently analysed using standard analysis of covariance (ANCOVA) methods. Pseudo IPD for continuous outcomes reported at two timepoints can be generated using the sufficient statistics of an ANCOVA model, i.e., the mean and standard deviation at baseline and follow‐up per group, together with the correlation of the baseline and follow‐up measurements. Applying the ANCOVA approach, which crucially adjusts for baseline imbalances and accounts for the correlation between baseline and change scores, to the pseudo IPD, results in identical estimates to the ones obtained by an ANCOVA on the true IPD. In addition, an interaction term between baseline and treatment effect can be added. There are several modeling options available under this approach, which makes it very flexible. Methods are exemplified using reported data of a previously published IPD meta‐analysis of 10 trials investigating the effect of antihypertensive treatments on systolic blood pressure, leading to identical results compared with the true IPD analysis and of a meta‐analysis of fewer trials, where baseline imbalance occurred.

Statistical Issues and Group Classification in Plasma MicroRNA Studies With Data Application

The analysis of plasma microRNAs (miRNAs) has been widely used as a method for finding potential biomarkers for human diseases, especially those with a link to cancer. Methods of analyzing plasma miRNA have been thoroughly discussed from sample extraction to data modeling. However, some issues exist within the process that have rarely been talked about. Rice et al. discussed some issues in plasma miRNA studies, such as the lack of standard methodology including the use of different cycle threshold, time to plasma extraction, among others. These issues can lead to inconsistent data, and thus impact the result and assay reproducibility. Other external issues, such as batch effect and operator effect, may also indirectly impact the statistical analysis. Here, we discuss issues in plasma miRNA studies from a statistical point of view. The interaction effect of different ways of calculating fold-change, the choice of housekeeping genes, and methods of normalization are among the issues we discuss, with data demonstrations. P values are calculated and compared to determine the effect of those issues on statistical conclusions. Statistical methods such as analysis of variance and analysis of covariance are crucial in the analysis of miRNA but investigators are often confused about them; therefore, a brief explanation of these statistical methods is also included. In addition, 3-group classification is discussed, as it is often challenging, compared with 2-group classification.

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.

Sample size estimation for randomised controlled trials with repeated assessment of patient-reported outcomes: what correlation between baseline and follow-up outcomes should we assume?

Background

Patient-reported outcome measures (PROMs) are now frequently used in randomised controlled trials (RCTs) as primary endpoints. RCTs are longitudinal, and many have a baseline (PRE) assessment of the outcome and one or more post-randomisation assessments of outcome (POST). With such pre-test post-test RCT designs there are several ways of estimating the sample size and analysing the outcome data: analysis of post-randomisation treatment means (POST); analysis of mean changes from pre- to post-randomisation (CHANGE); analysis of covariance (ANCOVA).

Sample size estimation using the CHANGE and ANCOVA methods requires specification of the correlation between the baseline and follow-up measurements. Other parameters in the sample size estimation method being unchanged, an assumed correlation of 0.70 (between baseline and follow-up outcomes) means that we can halve the required sample size at the study design stage if we used an ANCOVA method compared to a comparison of POST treatment means method. So what correlation (between baseline and follow-up outcomes) should be assumed and used in the sample size calculation? The aim of this paper is to estimate the correlations between baseline and follow-up PROMs in RCTs.

Methods

The Pearson correlation coefficients between the baseline and repeated PROM assessments from 20 RCTs (with 7173 participants at baseline) were calculated and summarised.

Results

The 20 reviewed RCTs had sample sizes, at baseline, ranging from 49 to 2659 participants. The time points for the post-randomisation follow-up assessments ranged from 7 days to 24 months; 464 correlations, between baseline and follow-up, were estimated; the mean correlation was 0.50 (median 0.51; standard deviation 0.15; range − 0.13 to 0.91).

Conclusions

There is a general consistency in the correlations between the repeated PROMs, with the majority being in the range of 0.4 to –0.6. The implications are that we can reduce the sample size in an RCT by 25% if we use an ANCOVA model, with a correlation of 0.50, for the design and analysis. There is a decline in correlation amongst more distant pairs of time points.

Flipped classroom as an effective approach in enhancing student learning of a pharmacy course with a historically low student pass rate

BACKGROUND AND PURPOSE

The flipped classroom has not been fully exploited to improve tertiary education in Malaysia. A transformation in pharmacy education using flipped classrooms will be pivotal to resolve poor academic performance in certain courses. This study aimed to investigate the effectiveness of the flipped classroom in improving student learning and academic performance in a course with a historically low pass rate.

EDUCATIONAL ACTIVITY AND SETTING

A quasi-experimental pre- and posttest control group design was employed. The experimental group experienced the flipped classroom for selected topics while the control group learned in a traditional classroom. Analysis of covariance was utilized to compare the performance on the final exam using the grade point of a pre-requisite course as the covariate. Students' perceptions of their experience in the flipped classroom were gauged through a web-based survey.

FINDINGS

Student performance on the final exam was significantly higher in the flipped classroom group. The lowest-scoring students benefitted the most in terms of academic performance. More than two-thirds of students responded positively to the use of the flipped classroom and felt more confident while participating in classes and tests.

SUMMARY

The flipped classroom is academically beneficial in a challenging course with a historically low pass rate; it was also effective in stimulating learning interest. The current study identified that for the flipped classroom to be successful, the role of educators, the feasibility of the approach, and the acceptance of students were important.

Compensation and Amplification of Attenuation Bias in Causal Effect Estimates

Covariate-adjusted treatment effects are commonly estimated in non-randomized studies. It has been shown that measurement error in covariates can bias treatment effect estimates when not appropriately accounted for. So far, these delineations primarily assumed a true data generating model that included just one single covariate. It is, however, more plausible that the true model consists of more than one covariate. We evaluate when a further covariate may reduce bias due to measurement error in another covariate and in which cases it is not recommended to include a further covariate. We analytically derive the amount of bias related to the fallible covariate's reliability and systematically disentangle bias compensation and amplification due to an additional covariate. With a fallible covariate, it is not always beneficial to include an additional covariate for adjustment, as the additional covariate can extensively increase the bias. The mechanisms for an increased bias due to an additional covariate can be complex, even in a simple setting of just two covariates. A high reliability of the fallible covariate or a high correlation between the covariates cannot in general prevent from substantial bias. We show distorting effects of a fallible covariate in an empirical example and discuss adjustment for latent covariates as a possible solution.

Analyzing pre-post designs using the analysis of covariance models with and without the interaction term in a heterogeneous study population

Pre-post parallel group randomized designs have been frequently used to compare the effectiveness of competing treatment strategies and the ordinary least squares (OLS)-based analysis of covariance model (ANCOVA) is a routine analytic approach. In many scenarios, the associations between the baseline and the post-randomization scores could differ between the treatment and control arms, which justifies the inclusion of the treatment by baseline score interaction in ANCOVA. This heterogeneity may also cause heteroscedastic errors in ANCOVA. In this study, we compared the performances of the ANCOVA models with and without the interaction term in estimating the marginal treatment effect in a heterogeneous two-arm pre-post design. We explored the relationship between the two nested ANCOVA models from the perspective of an omitted variable bias problem and further revealed the reasons why the usual ANCOVA may fail in heterogeneous scenario through the discussion of the three types of variances associated with the ANCOVA estimators of the marginal treatment effect: the target unconditional variance, the conditional variance allowing unequal error variances, and the OLS conditional variance derived under the assumption of constant error variance. We demonstrated analytically and with simulations that the proposed heteroscadastic-consistent variance estimators provide valid unconditional inference for ANCOVA, and the ANCOVA interaction model is more powerful than the ANCOVA main effect model when a design is unbalanced.

Oligopeptidases activity in bipolar disorder: Ndel1 and angiotensin I converting enzyme

BACKGROUND

Abnormal activity of two enzymes relevant to neurodevelopment, namely nuclear-distribution element-like 1 (Ndel1) and angiotensin I-converting enzyme (ACE), was reported in individuals with schizophrenia; to our knowledge, these oligopeptidases were never measured in bipolar disorder (BD).

AIMS

Evaluate the enzyme activity of Ndel1 and ACE in euthymic individuals with BD type 1 which was compare to healthy control (HC) group.

METHODS

Ndel1 and ACE activities were assessed in the serum of individuals with BD type 1 according to DSM-IV criteria (n = 70) and a HC group (n = 34). The possible differences between BD type 1 and HC groups were evaluated using Analysis of Covariance (ANCOVA), and the results were adjusted for age, gender and body mass index.

RESULTS

We observed a positive correlation between Ndel1 activity and the total YMRS score in BD group (p = 0.030) and a positive correlation between ACE activity and Ham-D score (p = 0.047). ANCOVA analysis showed lower Ndel1 activity in BDs compared to HCs. Interestingly, we did not observe between-groups differences in ACE activity, despite the recognized correlation of ACE activity levels with cognitive functions, also described to be worsened in psychiatric patients.

CONCLUSION

Oligopeptidases, especially Ndel1, which has been strongly correlated with neurodevelopment and brain formation, are potentially a good new target in the study of the neurobiology of BD.

LIMITATIONS

The relatively small sample size did not permit to examine the cause-effect relationship of clinical dimensions of BD and the enzymatic activity.

Comparison of three meta-analytic methods using data from digital interventions on type 2 diabetes

AIMS

Pooling the effect sizes of randomized controlled trials (RCTs) from continuous outcomes, such as glycated hemoglobin level (HbA1c), is an important method in evidence syntheses. However, due to challenges related to baseline imbalances and pre/post correlations, simple analysis of change scores (SACS) and simple analysis of final values (SAFV) meta-analyses result in under- or overestimation of effect estimates. This study was aimed to compare pooled effect sizes estimated by Analysis of Covariance (ANCOVA), SACS, and SAFV meta-analyses, using the example of RCTs of digital interventions with HbA1c as the main outcome.

MATERIALS AND METHODS

Three databases were systematically searched for RCTs published from 1993 through June 2017. Two reviewers independently assessed titles and abstracts using predefined eligibility criteria, assessed study quality, and extracted data, with disagreements resolved by arbitration from a third reviewer.

RESULTS

ANCOVA, SACS, and SAFV resulted in pooled HbA1c mean differences of -0.39% (95% CI: [-0.51, -0.26]), -0.39% (95% CI: [-0.51, -0.26]), and -0.34% (95% CI: [-0.48-0.19]), respectively. Removing studies with both high baseline imbalance (≥±0.2%) and pre/post correlation of ≥±0.6 resulted in a mean difference of -0.39% (95% CI: [-0.53, -0.26]), -0.40% (95% CI: [-0.54, -0.26]), and -0.33% (95% CI: [-0.48, -0.18]) with ANCOVA, SACS, and SAFV meta-analyses, respectively. Substantial heterogeneity was noted. Egger's test for funnel plot symmetry did not indicate evidence of publication bias for all methods.

CONCLUSION

By all meta-analytic methods, digital interventions appear effective in reducing HbA1c in type 2 diabetes. The effort to adjust for baseline imbalance and pre/post correlation using ANCOVA relies on the level of detail reported from individual studies. Reporting detailed summary data and, ideally, access to individual patient data of intervention trials are essential.

CalR: A Web-Based Analysis Tool for Indirect Calorimetry Experiments

We report a web-based tool for analysis of experiments using indirect calorimetry to measure physiological energy balance. CalR simplifies the process to import raw data files, generate plots, and determine the most appropriate statistical tests for interpretation. Analysis using the generalized linear model (which includes ANOVA and ANCOVA) allows for flexibility in interpreting diverse experimental designs, including those of obesity and thermogenesis. Users also may produce standardized output files for an experiment that can be shared and subsequently re-evaluated using CalR. This framework will provide the transparency necessary to enhance consistency, rigor, and reproducibility. The CalR analysis software will greatly increase the speed and efficiency with which metabolic experiments can be organized, analyzed per accepted norms, and reproduced and will likely become a standard tool for the field. CalR is accessible at https://CalRapp.org/.

Nitrate and phosphorus transport through subsurface drains under free and controlled drainage

Controlled drainage (CD) is a structural conservation practice in which the drainage outlet is managed in order to reduce drain flow volume and nutrient loads to water bodies. The goal of this study was to evaluate the potential of CD to improve water quality for two different seasons and levels of outlet control, using ten years of data collected from an agricultural drained field in eastern Indiana with two sets of paired plots. The Rank Sum test was used to quantify the impact of CD on cumulative annual drain flow and nitrate-N and phosphorus loads. CD plots had a statistically significant (at 5% level) lower annual drain flow (eastern pair: 39%; western pair: 25%) and nitrate load (eastern pair: 43%; western pair: 26%) compared to free draining (FD) plots, while annual soluble reactive phosphorus (SRP) and total phosphorus (TP) loads were not significantly different. An ANCOVA model was used to evaluate the impact of CD on daily drain flow, nitrate-N, SRP and TP concentrations and loads during the two different periods of control. The average percent reduction of daily drain flow was 68% in the eastern pair and 58% in the western pair during controlled drainage at the higher outlet level (winter) and 64% and 58% at the lower outlet level (summer) in the eastern and western pairs, respectively. Nitrate load reduction was similar to drain flow reduction, while the effect of CD on SRP and TP loads was not significant except for the increase in SRP in one pair. These results from a decade-long field monitoring and two different statistical methods enhance our knowledge about water quality impacts of CD system and support this management practice as a reliable system for reducing nitrate loss through subsurface drains, mainly caused by flow reduction.

Comparison of intent-to-treat analysis strategies for pre-post studies with loss to follow-up

In pre-post studies when all outcomes are completely observed, previous studies have shown that analysis of covariance (ANCOVA) is more powerful than a change-score analysis in testing the treatment effect. However, there have been few studies comparing power under missing post-test values. This paper was motivated by the Behavior and Exercise for Physical Health Intervention (BePHIT) Study, a pre-post study designed to compare two interventions on postmenopausal women's walk time. The goal of this study was to compare the power of two methods which adhere to the intent-to-treat (ITT) principle when post-test data are missing: ANCOVA after multiple imputation (MI) and the mixed model applied to all-available data (AA). We also compared the two ITT analysis strategies to two methods which do not adhere to ITT principles: complete-case (CC) ANCOVA and the CC mixed model. Comparisons were made through analyses of the BePHIT data and simulation studies conducted under various sample sizes, missingness rates, and missingness scenarios. In the analysis of the BePHIT data, ANCOVA after MI had the smallest p-value for the test of the treatment effect of the four methods. Simulation results demonstrated that the AA mixed model was usually more powerful than ANCOVA after MI. The power of ANCOVA after MI dropped the fastest as the missingness rate increased; in most simulated scenarios, ANCOVA after MI had the smallest power when 50% of the post-test outcomes were missing.

Analyzing pre-post randomized studies with one post-randomization score using repeated measures and ANCOVA models

The analysis of covariance (ANCOVA) or repeated measures (RM) models are often used to compare the treatment effect between different arms in pre-post randomized studies. ANCOVA adjusts the baseline score as a covariate in regression models. RM treats both the baseline and post-randomization scores as outcome variables. We aim to establish the underlying connections between ANCOVA and a constrained RM ("cRM"). We start with the interrelated concepts in a pre-post randomized designs: homogeneous vs. heterogeneous study populations, the marginal vs. the conditional treatment effect, and homogeneity vs. heterogeneity of treatment effect. We then demonstrate the asymptotic equivalence between the ANCOVA and cRM estimators for the marginal treatment effect and discuss the conditions under which ANCOVA needs to include a baseline score by treatment interaction term. In particular, an ANCOVA interaction model with a mean centered baseline score can assess both the marginal treatment effect and the heterogeneity in the conditional treatment effect. However, the ordinary least squares (OLS)-based inference is not valid for unconditional inference because this interaction model typically has heteroskedastic errors, and ordinary least squares treats the sample mean of the baseline score as a known parameter. We propose a bootstrap and a heteroskedasticity consistent variance estimator for heteroskedastic ANCOVA. Our simulation studies demonstrate that the proposed methods provide valid inferences for testing both the marginal treatment effect and the heterogeneity of treatment effect using an ANCOVA interaction model. We used an acupuncture headache trial to elucidate the proposed approaches.

Indoor radon concentration in Korea residential environments

The purpose of this study is to provide basic data for the evaluation and management of health effects with respect to exposure to radon within residential environments in South Korea. It is part of a case-control study to develop a management plan based on indoor radon exposure levels and assess their impact on health. To investigate the long-term cumulative concentration levels of radon, 599 patients who have respiratory diseases were recruited in South Korea, and alpha track detectors were installed in their residences for a period of 3 months from mid-2015 to late 2016. A survey was then conducted to determine the factors affecting the radon concentration. The radon concentration levels were analyzed in conjunction with the survey results. The results show that the arithmetic mean of the radon concentrations in domestic residences was in the range of 70.8 ± 65.2 Bq/m³. An analysis of covariance (ANCOVA) was performed to identify the environmental factors affecting the radon concentration and contributing to variations in the residential radon concentration based on the height of the residence. The results show that the contribution of the local environmental factor to the variation in radon concentration (p < 0.05) was greater than that of other environmental factors. Although no statistically significant difference was found with regard to the construction year of the building before the control (p > 0.05), the same was found with regard to the construction year after the control (p < 0.05).

Carbon isotopes of C3 herbs correlate with temperature on removing the influence of precipitation across a temperature transect in the agro-pastoral ecotone of northern China

Plant δ¹³C-temperature (δ-T) relation has been established in many systems and is often used as paleotemperature transfer function. However, it is still unclear about the exact contributions of temperature variation to plant ¹³C discrimination because of covariation between temperature and precipitation (aridity), which reduces confidence in reconstruction of paleoclimate. In this study, we measured carbon isotope composition (δ¹³C) of 173 samples of C3 perennial herbs from 22 sites across a temperature gradient along the 400 mm isohyet in the farming-pastoral zone of North China. The results showed that precipitation obviously affected the correlations of temperatures and foliar δ¹³C. After removing the influence of precipitation by analysis of covariance (ANCOVA), a more strongly positive relationship was obtained between site-mean foliar δ¹³C and annual mean temperature (AMT), with a regression coefficient of 0.1636‰/°C (p = .0024). For widespread species, Artemisia lavandulaefolia and Artemisia capillaries, the slopes (or coefficients) of foliar δ¹³C and AMT were significantly steeper (larger) than those of foliar δ¹³C and AMT where the precipitation influence was not excluded, whereas the δ-T coefficients of Polygonum persicaria and Leymus chinensis showed little change across the transect after deducting the precipitation effect. Moreover, the positive relationship between temperature and δ¹³C over the transect could be explained by soil moisture availability related to temperature. Our results may afford new opportunities for investigating the nature of past climate variability.

QKI6B mRNA levels are upregulated in schizophrenia and predict GFAP expression

Schizophrenia is a highly heritable disorder with a heterogeneous symptomatology. Research increasingly indicates the importance of the crucial and often overlooked glial perturbations within schizophrenia. Within this study, we examined an isoform of quaking (a gene encoding an RNA-binding protein that is exclusively expressed in glial cells), known as QKI6B, and a prototypical astrocyte marker, glial fibrillary acidic protein (GFAP), postulated to be under the regulation of QKI. The expression levels of these genes were quantified across post-mortem brain samples from 55 schizophrenic individuals, and 55 healthy controls, using real-time PCR. We report, through an analysis of covariance (ANCOVA) model, an upregulation of both QKI6B, and GFAP in the prefrontal cortex of brain samples of schizophrenic individuals, as compared to control samples. Previous research has suggested that the QKI protein directly regulates the expression of several genes through interaction with a motif in the target's sequence, termed the Quaking Response Element (QRE). We therefore examined if QKI6B expression can predict the outcome of GFAP, and several oligodendrocyte-related genes, using a multiple linear regression approach. We found that QKI6B significantly predicts the expression of GFAP, but does not predict oligodendrocyte-related gene outcome, as previously seen with other QKI isoforms.

To condition or not condition? Analysing 'change' in longitudinal randomised controlled trials

OBJECTIVE

The statistical analysis for a 2-arm randomised controlled trial (RCT) with a baseline outcome followed by a few assessments at fixed follow-up times typically invokes traditional analytic methods (eg, analysis of covariance (ANCOVA), longitudinal data analysis (LDA)). 'Constrained' longitudinal data analysis (cLDA) is a well-established unconditional technique that constrains means of baseline to be equal between arms. We use an analysis of fasting lipid profiles from the Group Medical Clinics (GMC) longitudinal RCT on patients with diabetes to illustrate applications of ANCOVA, LDA and cLDA to demonstrate theoretical concepts of these methods including the impact of missing data.

METHODS

For the analysis of the illustrated example, all models were fit using linear mixed models to participants with only complete data and to participants using all available data.

RESULTS

With complete data (n=195), 95% CI coverage are equivalent for ANCOVA and cLDA with an estimated 11.2 mg/dL (95% CI -19.2 to -3.3; p=0.006) lower mean low-density lipoprotein (LDL) cholesterol in GMC compared with usual care. With all available data (n=233), applying the cLDA model yielded an LDL improvement of 8.9 mg/dL (95% CI -16.7 to -1.0; p=0.03) for GMC compared with usual care. The less efficient, LDA analysis yielded an LDL improvement of 7.2 mg/dL (95% CI -17.2 to 2.8; p=0.15) for GMC compared with usual care.

CONCLUSIONS

Under reasonable missing data assumptions, cLDA will yield efficient treatment effect estimates and robust inferential statistics. It may be regarded as the method of choice over ANCOVA and LDA.

Impact of analysing continuous outcomes using final values, change scores and analysis of covariance on the performance of meta-analytic methods: a simulation study

When meta-analysing intervention effects calculated from continuous outcomes, meta-analysts often encounter few trials, with potentially a small number of participants, and a variety of trial analytical methods. It is important to know how these factors affect the performance of inverse-variance fixed and DerSimonian and Laird random effects meta-analytical methods. We examined this performance using a simulation study. Meta-analysing estimates of intervention effect from final values, change scores, ANCOVA or a random mix of the three yielded unbiased estimates of pooled intervention effect. The impact of trial analytical method on the meta-analytic performance measures was important when there was no or little heterogeneity, but was of little relevance as heterogeneity increased. On the basis of larger than nominal type I error rates and poor coverage, the inverse-variance fixed effect method should not be used when there are few small trials. When there are few small trials, random effects meta-analysis is preferable to fixed effect meta-analysis. Meta-analytic estimates need to be cautiously interpreted; type I error rates will be larger than nominal, and confidence intervals will be too narrow. Use of trial analytical methods that are more efficient in these circumstances may have the unintended consequence of further exacerbating these issues. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

A New SEYHAN's Approach in Case of Heterogeneity of Regression Slopes in ANCOVA

In this study, when the assumptions of linearity and homogeneity of regression slopes of conventional ANCOVA are not met, a new approach named as SEYHAN has been suggested to use conventional ANCOVA instead of robust or nonlinear ANCOVA. The proposed SEYHAN's approach involves transformation of continuous covariate into categorical structure when the relationship between covariate and dependent variable is nonlinear and the regression slopes are not homogenous. A simulated data set was used to explain SEYHAN's approach. In this approach, we performed conventional ANCOVA in each subgroup which is constituted according to knot values and analysis of variance with two-factor model after MARS method was used for categorization of covariate. The first model is a simpler model than the second model that includes interaction term. Since the model with interaction effect has more subjects, the power of test also increases and the existing significant difference is revealed better. We can say that linearity and homogeneity of regression slopes are not problem for data analysis by conventional linear ANCOVA model by helping this approach. It can be used fast and efficiently for the presence of one or more covariates.

Does Exercise Improve Cognitive Performance? A Conservative Message from Lord's Paradox

Although extant meta-analyses support the notion that exercise results in cognitive performance enhancement, methodology shortcomings are noted among primary evidence. The present study examined relevant randomized controlled trials (RCTs) published in the past 20 years (1996-2015) for methodological concerns arise from Lord's paradox. Our analysis revealed that RCTs supporting the positive effect of exercise on cognition are likely to include Type I Error(s). This result can be attributed to the use of gain score analysis on pretest-posttest data as well as the presence of control group superiority over the exercise group on baseline cognitive measures. To improve accuracy of causal inferences in this area, analysis of covariance on pretest-posttest data is recommended under the assumption of group equivalence. Important experimental procedures are discussed to maintain group equivalence.

Caretaker Quality of Life in Rett Syndrome: Disorder Features and Psychological Predictors

OBJECTIVE

Rett syndrome is a severe neurodevelopmental disorder affecting approximately one in 10,000 female births. The clinical features of Rett syndrome are known to impact both patients' and caretakers' quality of life in Rett syndrome. We hypothesized that more severe clinical features would negatively impact caretaker physical quality of life but would positively impact caretaker mental quality of life.

METHODS

Participants were individuals enrolled in the Rett Natural History Study with a diagnosis of classic Rett syndrome. Demographic data, clinical disease features, caretaker quality of life, and measures of family function were assessed during clinic visits. The Optum SF-36v2 Health Survey was used to assess caretaker physical and mental quality of life (higher scores indicate better quality of life). Descriptive, univariate, and multivariate analyses were used to characterize relationships between child and caretaker characteristics and caretaker quality of life.

RESULTS

Caretaker physical component scores (PCS) were higher than mental component scores (MCS): 52.8 (9.7) vs 44.5 (12.1). No differences were demonstrated between the baseline and 5-year follow-up. In univariate analyses, disease severity was associated with poorer PCS (P = 0.006) and improved MCS (P = 0.003). Feeding problems were associated with poorer PCS (P = 0.007) and poorer MCS (P = 0.018). In multivariate analyses, limitations in caretaker personal time and home conflict adversely affected PCS. Feeding problems adversely impacted MCS.

CONCLUSIONS

Caretaker quality of life in Rett syndrome is similar to that for caretakers in other chronic diseases. Disease characteristics significantly impact quality of life, and feeding difficulties may represent an important clinical target for improving both child and caretaker quality of life. The stability of quality-of-life scores between baseline and five years adds important value.

A Simulation-Based Comparison of Covariate Adjustment Methods for the Analysis of Randomized Controlled Trials

Covariate adjustment methods are frequently used when baseline covariate information is available for randomized controlled trials. Using a simulation study, we compared the analysis of covariance (ANCOVA) with three nonparametric covariate adjustment methods with respect to point and interval estimation for the difference between means. The three alternative methods were based on important members of the generalized empirical likelihood (GEL) family, specifically on the empirical likelihood (EL) method, the exponential tilting (ET) method, and the continuous updated estimator (CUE) method. Two criteria were considered for the comparison of the four statistical methods: the root mean squared error and the empirical coverage of the nominal 95% confidence intervals for the difference between means. Based on the results of the simulation study, for sensitivity analysis purposes, we recommend the use of ANCOVA (with robust standard errors when heteroscedasticity is present) together with the CUE-based covariate adjustment method.

Baseline adjustment increases accurate interpretation of posturographic sway scores

Postural steadiness may be quantified using posturographic sway measures. These measures are commonly used to differentiate between a person's baseline balance and balance related to some physiological condition. However, the difference in sway scores between the two conditions may be difficult to detect due to large inter-subject variation. We compared detection accuracy provided by three models that linearly regress a sway measure (mean distance, velocity, or frequency) on the effect of eye closure on balance (eyes open (EO) vs. eyes closed (EC)). In Model 1 the dependent variable is a single sway score (EO or EC), whereas in Models 2 and 3 it is a change score (EO-EO or EC-EO). The independent variable is always the group (group=0: EO or group=1: EC). Model 3 also accounts for the regression to the mean effect (RTM), by considering the baseline value (EO) as a covariate. When differentiating between EO and EC conditions, 94% accuracy can be achieved when using mean velocity as sway measure and either Model 2 or 3. Thus by adjusting for baseline score one increases the accurate interpretation of posturographic sway scores.

A cautionary note on the use of the Analysis of Covariance (ANCOVA) in classification designs with and without within-subject factors

A number of statistical textbooks recommend using an analysis of covariance (ANCOVA) to control for the effects of extraneous factors that might influence the dependent measure of interest. However, it is not generally recognized that serious problems of interpretation can arise when the design contains comparisons of participants sampled from different populations (classification designs). Designs that include a comparison of younger and older adults, or a comparison of musicians and non-musicians are examples of classification designs. In such cases, estimates of differences among groups can be contaminated by differences in the covariate population means across groups. A second problem of interpretation will arise if the experimenter fails to center the covariate measures (subtracting the mean covariate score from each covariate score) whenever the design contains within-subject factors. Unless the covariate measures on the participants are centered, estimates of within-subject factors are distorted, and significant increases in Type I error rates, and/or losses in power can occur when evaluating the effects of within-subject factors. This paper: (1) alerts potential users of ANCOVA of the need to center the covariate measures when the design contains within-subject factors, and (2) indicates how they can avoid biases when one cannot assume that the expected value of the covariate measure is the same for all of the groups in a classification design.

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.

Empirical comparison of four baseline covariate adjustment methods in analysis of continuous outcomes in randomized controlled trials

Background

Although seemingly straightforward, the statistical comparison of a continuous variable in a randomized controlled trial that has both a pre- and posttreatment score presents an interesting challenge for trialists. We present here empirical application of four statistical methods (posttreatment scores with analysis of variance, analysis of covariance, change in scores, and percent change in scores), using data from a randomized controlled trial of postoperative pain in patients following total joint arthroplasty (the Morphine COnsumption in Joint Replacement Patients, With and Without GaBapentin Treatment, a RandomIzed ControlLEd Study [MOBILE] trials).

Methods

Analysis of covariance (ANCOVA) was used to adjust for baseline measures and to provide an unbiased estimate of the mean group difference of the 1-year postoperative knee flexion scores in knee arthroplasty patients. Robustness tests were done by comparing ANCOVA with three comparative methods: the posttreatment scores, change in scores, and percentage change from baseline.

Results

All four methods showed similar direction of effect; however, ANCOVA (−3.9; 95% confidence interval [CI]: −9.5, 1.6; P=0.15) and the posttreatment score (−4.3; 95% CI: −9.8, 1.2; P=0.12) method provided the highest precision of estimate compared with the change score (−3.0; 95% CI: −9.9, 3.8; P=0.38) and percent change (−0.019; 95% CI: −0.087, 0.050; P=0.58).

Conclusion

ANCOVA, through both simulation and empirical studies, provides the best statistical estimation for analyzing continuous outcomes requiring covariate adjustment. Our empirical findings support the use of ANCOVA as an optimal method in both design and analysis of trials with a continuous primary outcome.

Catechol-o-methyltransferase gene and executive function in children with ADHD

OBJECTIVE

To examine the association between functional haplotypes in the catechol-o-methyltransferase (COMT) gene and ADHD diagnosis, and executive function (EF) in children with ADHD.

METHOD

COMT single nucleotide polymorphism (SNPs; rs6269, rs4633, rs4818, and rs4680) were genotyped in 445 ADHD children. EF was assessed using Wisconsin Card Sorting Test (WCST), Tower of London, and self-ordered pointing task. COMT haplotypes were tested for association using family-based association testing (fBAT) and quantitative trait analyses.

RESULTS

fBAT analysis showed no association between COMT alleles/haplotypes and ADHD diagnosis and EF parameters. Using ANCOVA in the Caucasian only sample, significant associations between COMT haplotypes, and WCST indices were observed. However, after correction for multipletesting, the only significant effect observed was between rs6269 and the number of categories completed (a measure of concept formation ability) on the WCST, F(1,285) = 8.92, p = .003.

CONCLUSION

These results tentatively implicate COMT gene in modulating EF in children with ADHD.

Oxytocin administration, salivary testosterone, and father-infant social behavior

The growing involvement of fathers in childcare is followed by an increased interest in the neurobiology of fatherhood; yet, experimental work on the neuroendocrine basis of paternal care in humans is limited. The steroid Testosterone (T) and the neuropeptide Oxytocin (OT) have each been implicated in complex social behavior including parenting. However, no study to date explored the interaction between these two hormones in the context of fathering. In the current study we first test the relationship between father's basal salivary T and father and infant's social behaviors during parent-child interaction. Second, we examine the effects of intranasal OT administration on father's T production, and, finally, address the relations between OT-induced change in father's T with father-infant social behavior. Thirty-five fathers and their infants participated in a double-blind, placebo-controlled, within-subject study. Father-infant interaction was micro-coded for paternal and infant social behavior and synchrony was measured as the coordination between their gaze, affect, and vocalizations. Father's salivary T levels were measured at baseline and three times after administration. Results indicate that lower baseline T correlated with more optimal father and infant's behaviors. OT administration altered T production in fathers, relative to the pattern of T in the placebo condition. Finally, OT-induced change in T levels correlated with parent-child social behaviors, including positive affect, social gaze, touch, and vocal synchrony. Findings support the view that neuroendocrine systems in human males evolved to support committed parenting and are the first to describe the dynamic interactions between OT and T within a bio-behavioral synchrony model.

Inflammatory cytokines and chemokines, skeletal muscle and polycystic ovary syndrome: effects of pioglitazone and metformin treatment

OBJECTIVE

Chronic low-grade inflammation is a common feature of insulin resistant states, including obesity and type 2 diabetes. Less is known about inflammation in Polycystic Ovary Syndrome (PCOS). Thus we evaluated the impact of PCOS on circulating cytokine levels and the effects of anti-diabetic therapies on insulin action, cytokine and chemokine levels and inflammatory signaling in skeletal muscle.

METHODS

Twenty subjects with PCOS and 12 healthy normal cycling (NC) subjects of similar body mass index were studied. PCOS subjects received oral placebo or pioglitazone, 45 mg/d, for 6 months. All PCOS subjects then had metformin, 2 g/day, added to their treatment. Circulating levels of cytokines, chemokines, and adiponectin, skeletal muscle markers of inflammation and phosphorylation of signaling proteins, insulin action evaluated by the hyperinsulinemic/euglycemic clamp procedure and Homeostasis Model Assessment of Insulin Resistance were measured.

RESULTS

Circulating levels of a number of cytokines and chemokines were generally similar between PCOS and NC subjects. Levels in PCOS subjects were not altered by pioglitazone or metformin treatment, even though whole body insulin action and adiponectin levels increased with pioglitazone. In spite of the lack of change in levels of cytokines and chemokines, several markers of inflammation in skeletal muscle were improved with Pio treatment.

CONCLUSIONS

PCOS may represent a state of elevated sensitivity of inflammatory cells in skeletal muscle to cytokines and chemokines, a property that could be reversed by pioglitazone treatment together with improved insulin action.

Olfactory cells via nasal biopsy reflect the developing brain in gene expression profiles: utility and limitation of the surrogate tissues in research for brain disorders

Human olfactory cells obtained by rapid nasal biopsy have been suggested to be a good surrogate system to address brain disease-associated molecular changes. Nonetheless, whether use of this experimental strategy is justified remains unclear. Here we compared expression profiles of olfactory cells systematically with those from the brain tissues and other cells. Principal component analysis indicated that the expression profiles of olfactory cells are very different from those of blood cells, but are closer to those of stem cells, in particular mesenchymal stem cells, that can be differentiated into the cells of the central nervous system.

Acute prenatal exposure to a moderate dose of valproic acid increases social behavior and alters gene expression in rats

Prenatal exposure to moderate doses of valproic acid (VPA) produces brainstem abnormalities, while higher doses of this teratogen elicit social deficits in the rat. In this pilot study, we examined effects of prenatal exposure to a moderate dose of VPA on behavior and on transcriptomic expression in three brain regions that mediate social behavior. Pregnant Long Evans rats were injected with 350 mg/kg VPA or saline on gestational day 13. A modified social interaction test was used to assess social behavior and social preference/avoidance during early and late adolescence and in adulthood. VPA-exposed animals demonstrated more social investigation and play fighting than control animals. Social investigation, play fighting, and contact behavior also differed as a function of age; the frequency of these behaviors increased in late adolescence. Social preference and locomotor activity under social circumstances were unaffected by treatment or age. Thus, a moderate prenatal dose of VPA produces behavioral alterations that are substantially different from the outcomes that occur following exposure to a higher dose. At adulthood, VPA-exposed subjects exhibited transcriptomic abnormalities in three brain regions: anterior amygdala, cerebellar vermis, and orbitofrontal cortex. A common feature among the proteins encoded by the dysregulated genes was their ability to be modulated by acetylation. Analysis of the expression of individual exons also revealed that genes involved in post-translational modification and epigenetic regulation had particular isoforms that were ubiquitously dysregulated across brain regions. The vulnerability of these genes to the epigenetic effects of VPA may highlight potential mechanisms by which prenatal VPA exposure alters the development of social behavior.

Iron deficiency alters expression of dopamine-related genes in the ventral midbrain in mice

A clear link exists between iron deficiency (ID) and nigrostriatal dopamine malfunction. This link appears to play an important role in at least restless legs syndrome (RLS) if not several other neurological diseases. Yet, the underlying mechanisms remain unclear. The effects of ID on gene expression in the brain have not been studied extensively. Here, to better understand how exactly ID alters dopamine functioning, we investigated the effects of ID on gene expression in the brain, seeking to identify any potential transcription-based mechanisms. We used six strains of recombinant inbred mice (BXD type) known to differ in susceptibility to ID in the brain. Upon weaning, we subjected mice from each strain to either an iron-deficient or iron-adequate diet. After 100 days of dietary treatment, we measured the effects of ID on gene expression in the ventral midbrain, a region containing the substantia nigra. The substantia nigra is the base of the nigrostriatal dopamine pathway and a region particularly affected by iron loss in RLS. We screened for ID-induced changes in expression, including changes in that of both iron-regulating and dopamine-related genes. Results revealed a number of expression changes occurring in ID, with large strain-dependent differences in the genes involved and number of expression changes occurring. In terms of dopamine-related genes, results revealed ID-induced expression changes in three genes with direct ties to nigrostriatal dopamine functioning, two of which have never before been implicated in an iron-dopamine pathway. These were stromal cell-derived factor 1 (Cxcl12, or SDF-1), a ferritin regulator and potent dopamine neuromodulator, and hemoglobin, beta adult chain 1 (Hbb-b1), a gene recently shown to play a functional role in dopaminergic neurons. The extent of up-regulation of these genes varied by strain. This work not only demonstrates a wide genetic variation in the transcriptional response to ID in the brain, but also reveals two novel biochemical pathways by which iron may potentially alter dopamine function.

Gender and body mass index modify the effect of increasing amounts of caffeinated coffee on postprandial glucose and insulin concentrations; a randomized, controlled, clinical trial

OBJECTIVE

To examine the effects of different coffee amounts on blood glucose and insulin concentrations of healthy volunteers, and to assess potential effect modification by sex and body mass index category.

MATERIALS/METHODS

Thirty-three volunteers [16 ♀/17 ♂, 16 normal-weight and 17 overweight/obese, 27.3 ± 7.2 (19-44) y] took part in this randomized, crossover study. Ιn the morning of each experimental day volunteers received a standardized meal along with 200 mL of water or instant coffee containing either 3 or 6 mg of caffeine/kg body weight. Blood samples were obtained and analyzed for glucose and insulin concentrations in the fasting state, immediately after meal/drink consumption and at standard time points for the next 3h thereafter.

RESULTS

Coffee delayed the rise of insulin in response to the standardized meal and the fall of glucose concentrations from its maximum levels in the entire study sample. Glucose incremental area under the curve (IAUC) was significantly different between interventions (P=.009) with both coffee amounts inducing a greater area compared to water. Secondary, subgroup analysis at the nominal level showed that this might be more evident among females (PIAUC=.05) and overweight/obese participants (PIAUC=.03). Furthermore, coffee, mainly the 6 mg dose, could be lowering insulin concentrations the first 30 min after its consumption compared to water in men and overweight/obese participants.

CONCLUSIONS

Coffee exerts an acute effect on postprandial glucose and insulin concentrations. This effect may be modified by sex and overweight/obese status. Future research is necessary to elucidate underlying mechanisms.

Objectively quantified physical activity in persons with multiple sclerosis

OBJECTIVES

To investigate levels of moderate-to-vigorous physical activity (MVPA) in a large sample of persons with multiple sclerosis (MS) and controls using accelerometry as a measure of physical activity, and to compare the rates of meeting public health guidelines for MVPA (ie, 30min/d) between persons with MS and controls.

DESIGN

Secondary analysis of a combined data set of persons with MS and healthy controls from 13 previous investigations of physical activity over a 8-year period (2005-2013).

SETTING

University.

PARTICIPANTS

Participants with MS (n=800) were recruited primarily within Illinois through multiple sources, including print and e-mail flyers and an online advertisement on the National Multiple Sclerosis Society website. Healthy controls (n=137) were recruited via public e-mail postings delivered across the university community.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Levels of MVPA and meeting public health guidelines for MVPA between persons with MS and controls.

RESULTS

After controlling for covariates (ie, age, sex, education, race, income), there was a moderate (d=.68) and statistically significant (F=47.2, P<.001) difference of 13.1 minutes of MVPA per day (95% confidence interval, 9.4-16.8) between MS and controls. There was a difference in the rates of meeting public health guidelines for MVPA (χ(2)=50.7, P<.001) between MS patients (20%) and controls (47%). Among those with MS, minutes of MVPA significantly differed as a function of education, employment status, clinical course, disease duration, and disability status.

CONCLUSIONS

We provide data using an objective physical activity measure and a large sample to indicate that only a small proportion of persons with MS are achieving adequate amounts of daily MVPA.

Statistical approaches to assessing single and multiple outcome measures in dry eye therapy and diagnosis

Dry eye is a multifactorial disease which would require a broad spectrum of test measures in the monitoring of its treatment and diagnosis. However, studies have typically reported improvements in individual measures with treatment. Alternative approaches involve multiple, combined outcomes being assessed by different statistical analyses. In order to assess the effect of various statistical approaches to the use of single and combined test measures in dry eye, this review reanalyzed measures from two previous studies (osmolarity, evaporation, tear turnover rate, and lipid film quality). These analyses assessed the measures as single variables within groups, pre- and post-intervention with a lubricant supplement, by creating combinations of these variables and by validating these combinations with the combined sample of data from all groups of dry eye subjects. The effectiveness of single measures and combinations in diagnosis of dry eye was also considered.

Open-label, randomized study of the effect of rivaroxaban with or without acetylsalicylic acid on thrombus formation in a perfusion chamber

INTRODUCTION

Rivaroxaban, a direct factor Xa inhibitor, has demonstrated effectiveness for the management of both venous and arterial thrombosis. This study was designed to investigate the antithrombotic effect of rivaroxaban, with or without acetylsalicylic acid (ASA), in an ex vivo perfusion chamber at both low and high shear rates.

MATERIALS AND METHODS

Healthy subjects (N=51) were enrolled in a randomized, crossover (rivaroxaban 5, 10 or 20mg with or without ASA), and parallel-group (compared with ASA plus clopidogrel) study. Thrombi formed on pig aorta strips were measured after a 5-minute perfusion at low and high shear rates with blood from the subjects by measuring D-dimer concentration (for fibrin deposition) and P-selectin content (for platelet deposition).

RESULTS

ASA alone had no impact on thrombus D-dimer levels, whereas rivaroxaban alone at peak concentrations decreased D-dimer levels by 9%, 84% and 65% at low shear rate and 37%, 73% and 74% at high shear rate after doses of 5, 10 and 20mg, respectively. Steady-state ASA plus rivaroxaban 5mg caused a greater reduction in D-dimer levels (63%) than monotherapy at low shear rate. Co-administration of ASA with clopidogrel was associated with a 30% decrease in D-dimer levels at low shear rate and a 14% decrease at high shear rate. No conclusive effect on P-selectin content was observed across the treatment groups.

CONCLUSIONS

Rivaroxaban dose-dependently inhibited ex vivo thrombus formation under low and high shear rates. Co-administration of ASA had an additional effect on the antithrombotic action of low-dose rivaroxaban.