Sex differences in 4-tert-octylphenol toxicokinetics: Exploration of sex as an effective covariate through an in vivo modeling approach

4-tert-octylphenol (4-tert-OP) is a potentially harmful substance, which is found widely in the environment. Nevertheless, information on the in vivo toxicokinetics of 4-tert-OP is lacking, and quantitative risk assessment studies are urgently needed. Therefore, we aimed to quantitatively identify differences in the toxicokinetics of 4-tert-OP and its distribution among tissues between sexes. To this end, following exposure of male and female rats to 10 or 50 mg/kg 4-tert-OP orally and 4 or 8 mg/kg 4-tert-OP intravenously, we conducted a quantitative analysis of samples using ultra-high performance liquid chromatography-tandem mass spectrometry. The results revealed that the 4-tert-OP plasma concentration profiles differed between sexes; however, systemic absorption of 4-tert-OP through the gastrointestinal tract occurred within 0.5 h of exposure in both sexes. Although small, the excretion percentage of 4-tert-OP in urine and feces was lower in males than females (0.06-0.08% vs. 0.82-1.11% of exposure). Significant sex differences were also confirmed in the tissue distribution patterns of 4-tert-OP, and overall, the average tissue distribution in males was lower than that in females. The distribution of 4-tert-OP to liver, adipose, spleen, kidney, brain, and lung in both sexes was predominant. A covariate exploration modeling approach revealed that sex explained the differences in 4-tert-OP toxicokinetics between sexes. These significant differences in the toxicokinetics and tissue distribution of 4-tert-OP between sexes will be important for the scientific precision human risk assessment of 4-tert-OP.

Covariate inclusion in factor mixture modeling: Evaluating one-step and three-step approaches under model misspecification and overfitting

Factor mixture modeling (FMM) has been increasingly used in behavioral and social sciences to examine unobserved population heterogeneity. Covariates (e.g., gender, race) are often included in FMM to help understand the formation and characterization of latent subgroups or classes. This Monte Carlo simulation study evaluated the performance of one-step and three-step approaches to covariate inclusion across three scenarios, i.e., correct specification (study 1), model misspecification (study 2), and model overfitting (study 3), in terms of direct covariate effects on factors. Results showed that the performance of these two approaches was comparable when class separation was large and the specification of covariate effect was correct. However, one-step FMM had better class enumeration than the three-step approach when class separation was poor, and was more robust to the misspecification or overfitting concerning direct covariate effects. Recommendations regarding covariate inclusion approaches are provided herein depending on class separation and sample size. Large sample size (1000 or more) and the use of sample size-adjusted BIC (saBIC) in class enumeration are recommended.

Comparison of confound adjustment methods in the construction of gene co-expression networks

Adjustment for confounding sources of expression variation is an important preprocessing step in large gene expression studies, but the effect of confound adjustment on co-expression network analysis has not been well-characterized. Here, we demonstrate that the choice of confound adjustment method can have a considerable effect on the architecture of the resulting co-expression network. We compare standard and alternative confound adjustment methods and provide recommendations for their use in the construction of gene co-expression networks from bulk tissue RNA-seq datasets.

Identifying the most important confounders when assessing the association between low-grade systemic inflammation and musculoskeletal pain: A modified Delphi study

Objective

The association between low-grade systemic inflammation and musculoskeletal pain may be influenced by multiple factors. However, little is known about the relative importance of these factors, and few studies account for them. This Delphi study aimed to reach consensus on the most important confounders which influence the association between low-grade systemic inflammation and musculoskeletal pain.

Methods

The panel consisted of 48 experts. In Round 1, the experts proposed what they believed were important confounders. In Round 2, the experts indicated for each confounder whether they believed it was important (yes/no). At least 50% of experts had to indicate the confounder was important to be considered in the final round. In Round 3, the experts rated the importance of each confounder on a 7-point Likert scale. Consensus was reached if ≥75% of the experts considered the factor either extremely or moderately important.

Results

In Round 1, 120 confounders were proposed, which were synthesized into 38 distinct factors. In Round 2, 33 confounders met the criterion to be considered important. In Round 3, consensus was reached for 14 confounders: acute illness/trauma, immune disease, medication use, endocrine, nutritional, or metabolic disease, other musculoskeletal conditions, age, handling of blood samples, sex, cancer, body composition, pregnancy, cardiovascular disease, physical activity, and pain characteristics.

Conclusions

These findings provide insight in the complexity of the association between low-grade systemic inflammation and musculoskeletal pain. Some factors currently listed as confounders may be re-classified as moderators or mediators as insights progress.

Towards greater transparency in neurodevelopmental disorders research: use of a proposed workflow and propensity scores to facilitate selection of matched groups

BACKGROUND

Matching is one commonly utilized method in quasi-experimental designs involving individuals with neurodevelopmental disorders (NDD). This method ensures two or more groups (e.g., individuals with an NDD versus neurotypical individuals) are balanced on pre-existing covariates (e.g., IQ), enabling researchers to interpret performance on outcome measures as being attributed to group membership. While much attention has been paid to the statistical criteria of how to assess whether groups are well-matched, relatively little attention has been given to a crucial prior step: the selection of the individuals that are included in matched groups. The selection of individuals is often an undocumented process, which can invite unintentional, arbitrary, and biased decision-making. Limited documentation can result in findings that have limited reproducibility and replicability and thereby have poor potential for generalization to the broader population. Especially given the heterogeneity of individuals with NDDs, interpretation of research findings depends on minimizing bias at all stages of data collection and analysis.

RESULTS

In the spirit of open science, this tutorial demonstrates how a workflow can be used to provide a transparent, reproducible, and replicable process to select individuals for matched groups. Our workflow includes the following key steps: Assess data, Select covariates, Conduct matching, and Diagnose matching. Our sample dataset is from children with autism spectrum disorder (ASD; n = 25) and typically developing children (n = 43) but can be adapted to comparisons of any two groups in quasi-experimental designs. We work through this method to conduct and document matching using propensity scores implemented with the R package MatchIt. Data and code are publicly available, and a template for this workflow is provided in the Additional file 1 as well as on a public repository.

CONCLUSIONS

It is important to provide clear documentation regarding the selection process to establish matched groups. This documentation ensures better transparency in participant selection and data analysis in NDD research. We hope the adoption of such a workflow will ultimately advance our ability to replicate findings and help improve the lives of individuals with NDDs.

Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing

BACKGROUND

Epigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data.

RESULTS

In this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts.

CONCLUSIONS

Covariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended.

Performance of a mixture model by the degree of a missing categorical covariate when estimating clearance in NONMEM

The accuracy and predictability of mixture models in NONMEM® may change depending on the relative size of inter-individual differences and the size of the differences in the parameters between subpopulations. This study explored the accuracy of mixture models when dealing with missing a categorical covariate under various situations that may occur in reality. We generated simulation data under various scenarios where genotypes representing extensive metabolizers (EM) and poor metabolizers (PM) of drug-metabolizing enzymes affect the clearance of a drug by different degrees, and the inter-individual variations in clearance are different for each scenario. From each simulated datum, a specific proportion of the covariate (genotype information) was randomly removed. Based on these simulation data, the proportion of each individual subpopulation and the clearance were estimated using a mixture model. Overall, the clearance estimate was more accurate when the difference in clearance between subpopulations was large, and the inter-individual variations were small. In some scenarios that showed higher ETA or epsilon shrinkage, the clearance estimates were significantly biased. The mixture model made better predictions for individuals in the EM subpopulation than for individuals in the PM subpopulation. However, the estimated values were not significantly affected by the tested ratio, if the sample size was secured to some extent. The current simulation study suggests that when the coefficient of variation of inter-individual variations of clearance exceeds 40%, the mixture model should be used carefully, and it should be taken into account that shrinkage can bias the results.

Comparison of three classes of Marginal Risk Set Model in predicting infant mortality among newborn babies at Kigali University Teaching Hospital, Rwanda, 2016

BACKGROUND

The Infant Mortality Rate (IMR) in Sub-Saharan Africa (SSA) remains the highest relatively to the rest of the world. In the past decade, the policy on reducing infant mortality in SSA was reinforced and both infant mortality and parental death decreased critically for some countries of SSA. The analysis of risk to death or attracting chronic disease may be done for helping medical practitioners and decision makers and for better preventing the infant mortality.

METHODS

This study uses popular statistical methods of re-sampling and one selected model of multiple events analysis for measuring the survival outcomes for the infants born in 2016 at Kigali University Teaching Hospital (KUTH) in Rwanda, a country of SSA, amidst maternal and child's socio-economic and clinical covariates. Dataset comprises the newborns with correct information on the covariates of interest. The Bootstrap Marginal Risk Set Model (BMRSM) and Jackknife Marginal Risk Set Model (JMRSM) for the available maternal and child's socio-economic and clinical covariates were conducted and then compared to the outcome with Marginal Risk Set Model (MRSM). That was for measuring stability of the MRSM.

RESULTS

The 2117 newborns had the correct information on all the covariates, 82 babies died along the study time, 69 stillborn babies were observed while 1966 were censored. Both BMRSM JMRSM and MRSM displayed the close results for significant covariates. The BMRSM displayed in some instance, relatively higher standard errors for non-significant covariates and this emphasized their insignificance in MRSM. The models revealed that female babies survive better than male babies. The risk is higher for babies whose parents are under 20 years old parents as compared to other parents' age groups, the risk decreases as the APGAR increases, is lower for underweight babies than babies with normal weight and overweight and is lower for babies with normal circumference of head as compared to those with relatively small head.

CONCLUSION

The results of JMRSM were closer to MRSM than that of BMRSM. Newborns of mothers aged less than 20 years were at relatively higher risk of dying than those who their mothers were aged 20 years and above. Being abnormal in weight and head increased the risk of infant mortality. Avoidance of teenage pregnancy and provision of clinical care including an adequate dietary intake during pregnancy would reduce the IMR in Kigali.

The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables

Although covarying for potential confounds or nuisance variables is common in psychological research, relatively little is known about how the inclusion of covariates may influence the relations between psychological variables and indices of brain structure. In Part 1 of the current study, we conducted a descriptive review of relevant articles from the past two years of NeuroImage in order to identify the most commonly used covariates in work of this nature. Age, sex, and intracranial volume were found to be the most commonly used covariates, although the number of covariates used ranged from 0 to 14, with 37 different covariate sets across the 68 models tested. In Part 2, we used data from the Human Connectome Project to investigate the degree to which the addition of common covariates altered the relations between individual difference variables (i.e., personality traits, psychopathology, cognitive tasks) and regional gray matter volume (GMV), as well as the statistical significance of values associated with these effect sizes. Using traditional and random sampling approaches, our results varied widely, such that some covariate sets influenced the relations between the individual difference variables and GMV very little, while the addition of other covariate sets resulted in a substantially different pattern of results compared to models with no covariates. In sum, these results suggest that the use of covariates should be critically examined and discussed as part of the conversation on replicability in structural neuroimaging. We conclude by recommending that researchers pre-register their analytic strategy and present information on how relations differ based on the inclusion of covariates.

Demographic, sampling- and assay-related confounders of endogenous oxytocin concentrations: A systematic review and meta-analysis

Studies on endogenous oxytocin concentrations are often criticized for the debatable comparability between specimens and the variation in reported values. We performed meta-regressions on k = 229 studies (n = 12 741 participants), testing whether specimen, extraction, sex, age, time of day, or fasting instructions influenced oxytocin measurements. Predicted oxytocin concentrations differed depending on specimen and extraction: Measurements were extremely high in unextracted blood, compared to extracted blood and other specimens. Measurements were higher in samples with more female participants and higher age. Instructions not to smoke before sampling were correlated with higher oxytocin in unextracted samples. There was no impact of instructions to refrain from eating, drinking, consume caffeine, alcohol or exercising. Oxytocin concentrations increased from morning to afternoon. Our results showed that oxytocin is differentially reflected in blood, saliva, urine and cerebrospinal fluid. Extraction impacts oxytocin measurements, particularly in blood. Considering relevant confounders might increase comparability between studies.

Comparing different ways of calculating sample size for two independent means: A worked example

We discuss different methods of sample size calculation for two independent means, aiming to provide insight into the calculation of sample size at the design stage of a parallel two-arm randomised controlled trial (RCT). We compare different methods for sample size calculation, using published results from a previous RCT. We use variances and correlation coefficients to compare sample sizes using different methods, including

1. The choice of the primary outcome measure: post-intervention score vs. change from baseline score.

2. The choice of statistical methods: t-test without using correlation coefficients vs. analysis of covariance (ANCOVA).

We show that the required sample size will depend on whether the outcome measure is the post-intervention score, or the change from baseline score, with or without baseline score included as a covariate. We show that certain assumptions have to be met when using simplified sample size equations, and discuss their implications in sample size calculation when planning an RCT. We strongly recommend publishing the crucial result “mean change (SE, standard error)” in a study paper, because it allows (i) the calculation of the variance of the change score in each arm, and (ii) to pool the variances from both arms. It also enables us to calculate the correlation coefficient in each arm. This subsequently allows us to calculate sample size using change score as the outcome measure. We use simulation to demonstrate how sample sizes by different methods are influenced by the strength of the correlation.

Visual biofeedback training reduces quantitative drugs index scores associated with fall risk

OBJECTIVE

Drugs increase fall risk and decrease performance on balance and mobility tests. Conversely, whether biofeedback training to reduce fall risk also decreases scores on a published drug-based fall risk index has not been documented. Forty-eight community-dwelling older adults underwent either treadmill gait training plus visual feedback (+VFB), or walked on a treadmill without feedback. The Quantitative Drug Index (QDI) was derived from each participant's drug list and is based upon all cause drug-associated fall risk. Analysis of covariance assessed changes in the QDI during the study, and data is presented as mean ± standard error of the mean.

RESULTS

The QDI scores decreased significantly (p = 0.031) for participants receiving treadmill gait training +VFB (- 0.259 ± 0.207), compared to participants who walked on the treadmill without VFB (0.463 ± 0.246). Changes in participants QDI scores were dependent in part upon their age, which was a significant covariate (p = 0.007). These preliminary results demonstrate that rehabilitation to reduce fall risk may also decrease use of drugs associated with falls. Determination of which drugs or drug classes that contribute to the reduction in QDI scores for participants receiving treadmill gait training +VFB, compared to treadmill walking only, will require a larger participant investigation. Trial Registration ISRNCT01690611, ClinicalTrials.gov #366151-1, initial 9/24/2012, completed 4/21/2016.

Design of healthy hearts in the heartland (H3): A practice-randomized, comparative effectiveness study

BACKGROUND

The Healthy Hearts in the Heartland (H3) study is part of a nationwide effort, EvidenceNOW, seeking to better understand the ability of small primary care practices to improve "ABCS" clinical quality measures: appropriate Aspirin therapy, Blood pressure control, Cholesterol management, and Smoking cessation. H3 aimed to assess feasibility of implementing Point-of-Care (POC) or POC plus Population Management (POC + PM) quality improvement (QI) strategies to improve ABCS at practices in Illinois, Indiana, and Wisconsin. We describe the design and randomization of the H3 study.

METHODS

We conducted a two-arm (1:1, POC:POC + PM), practice-randomized, comparative effectiveness study in 226 primary care practices across four "waves" of randomization with a 12-month intervention period, followed by a six-month sustainability period. Randomization controlled imbalance in nine baseline variables through a modified constrained algorithm. Among others, we used initial, unverified estimates of baseline ABCS values.

RESULTS

We randomized 112 and 114 practices to POC and POC + PM arms, respectively. Randomization ensured baseline comparability for all nine key variables, including the ABCS measures indicating proportion of patients at the practice level meeting each quality measure. Median(Inner Quartile Range) values were A: 0.78(0.66-0.86) in POC arm vs. 0.77(0.63-0.86) in POC + PM arm, B: 0.64(0.53-0.73) vs. 0.64(0.53-0.75), C: 0.78(0.63-0.86) vs. 0.75(0.64-0.81), S: 0.80(0.65-0.81) vs. 0.79(0.61-0.91).

DISCUSSION

Surrogate estimates for the true ABCS at baseline coupled with the unique randomization logic achieved adequate baseline balance on these outcomes. Similar practice- or cluster-randomized trials may consider adaptations of this design. Final analyses on 12- and 18-month ABCS outcomes for the H3 study are forthcoming.

TRIAL REGISTRATION

This trial is registered on ClinicalTrials.gov (Initial post: 11/05/2015; identifier: NCT02598284; https://clinicaltrials.gov/ct2/show/NCT02598284?term=NCT02598284&rank=1).

Effects of different covariates and contrasts on classification of Parkinson's disease using structural MRI

Three-dimensional magnetic resonance imaging (3D-MRI) has been effectively used in the diagnosis of progressive neurodegenerative diseases including Parkinson's disease (PD). Pre-processing of 3D-MRI scans plays an important role for post-processing. In this paper, voxel-based morphometry (VBM) technique is used to compare morphological dierences of PDs versus healthy controls (HCs) in gray matter (GM) and white matter (WM). The effects of using different covariates (i.e. total intracranial volume (TIV), age, sex and combination of them) as well as two different hypotheses, t-contrast and f-contrast, on classification of PD from HCs have been studied. 3D masks for GM as well as WM tissues are obtained separately by utilizing local differences between PD and HC and using the two sample t-test method. PCA is used to perform dimensionality reduction and SVM is used for classification. The proposed method is evaluated on 40 PDs and 40 HCs obtained from the ppmi dataset. The classification results using f-contrast show a superior performance for GM, WM, and the combination of GM as well as WM compared to t-contrast. Furthermore, the experimental results indicate that using TIV as a covariate provides more robust results for PD classification compared to other covariate settings. The highest accuracies of distinguishing between PDs and HCs are obtained when TIV is used as a covariate and f-contrast is used for model building: 73.75%, 72.50%, and 93.7% for GM, WM, and the combination of them, respectively.

Multiple imputation of missing covariate values in multilevel models with random slopes: a cautionary note

Multiple imputation (MI) has become one of the main procedures used to treat missing data, but the guidelines from the methodological literature are not easily transferred to multilevel research. For models including random slopes, proper MI can be difficult, especially when the covariate values are partially missing. In the present article, we discuss applications of MI in multilevel random-coefficient models, theoretical challenges posed by slope variation, and the current limitations of standard MI software. Our findings from three simulation studies suggest that (a) MI is able to recover most parameters, but is currently not well suited to capture slope variation entirely when covariate values are missing; (b) MI offers reasonable estimates for most parameters, even in smaller samples or when its assumptions are not met; and

Temporal Prediction of Future State Occupation in a Multistate Model from High-Dimensional Baseline Covariates via Pseudo-Value Regression

In many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the state occupation at a future time t. With the advent of high throughput genomic and proteomic assays, a clinician may intent to use such high dimensional covariates in making better prediction of state occupation. In this article, we offer a practical solution to this problem by combining a useful technique, called pseudo value regression, with a latent factor or a penalized regression method such as the partial least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants. We explore the predictive performances of these combinations in various high dimensional settings via extensive simulation studies. Overall, this strategy works fairly well provided the models are tuned properly. Overall, the PLS turns out to be slightly better than LASSO in most settings investigated by us, for the purpose of temporal prediction of future state occupation. We illustrate the utility of these pseudo-value based high dimensional regression methods using a lung cancer data set where we use the patients' baseline gene expression values.

In vitro influence of light radiation on hair steroid concentrations

Hair cortisol concentrations (hairF) are considered to be relatively robust to various confounding influences. However, a potentially important covariate factor that has received little attention in this context is hair exposure to ultraviolet/sunlight radiation. We conducted a detailed experimental investigation to examine the effects of light exposure on hair cortisol. In study I, a hydrocortisone-containing solution was subjected to short-term artificial light irradiation for 1, 3, 5, 10, 15, or 30min to evaluate the stability of cortisol molecules due to radiant energy. In study II, hair samples (N=12) were subjected to single short-term artificial light irradiation for 0, 1, or 5h to examine light-induced effects in the hair matrix. In study III, hair samples (N=25) were subjected to long-term naturalistic sunlight radiation over a period of two months (during summer) with daily exposure times of 0, 1, 3, or 6h, respectively. Besides cortisol, studies II & III also examined concentrations of cortisone (hairE), dehydroepiandrosterone (hairDHEA) and progesterone (hairP) in hair, quantified using LC-MS/MS technology. Results across the three studies consistently revealed effects of light irradiation on hair steroid concentrations: Longer light exposure resulted in a decrease of dissolved hydrocortisone (study I) as well as of hairF and hairE (studies II and III). Conversely, hairDHEA and hairP increased with longer natural sunlight exposure times (study III), while this effect was not observed for short-term artificial light irradiation (study II). Combined, our findings imply sunlight exposure as a potential confound in hair steroid research. Given the experimental character of this investigation, the magnitude of this effect under real-life testing conditions is difficult to estimate. To support future investigation into this, we designed a 'sunlight-exposure' questionnaire to share with the research community. The assessment and statistical accounting for sunlight exposure-related effects in future hair steroid research (using this or a similar questionnaire) may help to reduce the potential influence of this unwanted error source and could thus lead to more valid and reliable results. In addition, our data strongly suggest that hair samples for steroid analyses need to be stored in a dark environment.

Effects of Long-Term Storage Time and Original Sampling Month on Biobank Plasma Protein Concentrations

The quality of clinical biobank samples is crucial to their value for life sciences research. A number of factors related to the collection and storage of samples may affect the biomolecular composition. We have studied the effect of long-time freezer storage, chronological age at sampling, season and month of the year and on the abundance levels of 108 proteins in 380 plasma samples collected from 106 Swedish women. Storage time affected 18 proteins and explained 4.8–34.9% of the observed variance. Chronological age at sample collection after adjustment for storage-time affected 70 proteins and explained 1.1–33.5% of the variance. Seasonal variation had an effect on 15 proteins and month (number of sun hours) affected 36 proteins and explained up to 4.5% of the variance after adjustment for storage-time and age. The results show that freezer storage time and collection date (month and season) exerted similar effect sizes as age on the protein abundance levels. This implies that information on the sample handling history, in particular storage time, should be regarded as equally prominent covariates as age or gender and need to be included in epidemiological studies involving protein levels.

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Storage time explains up to 35 % of plasma protein concentration variation in frozen biobank samples from healthy women.

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Storage time exert similar effect sizes as individual age and should be included as a covariate in epidemiological studies.

One basic requirement of life science research is the quality of samples. Proper handling and rigorous biobanking of clinical samples is crucial for collection of samples for rare diseases, for monitoring individual variation in longitudinal studies and for prospective studies of biomarkers and risk of developing for instance cardiovascular disease. We have studied the effect of long-time storage, individual age and sampling month and conclude that storage-time has similar impact on protein levels as age. The results emphasize the need to include sample parameters as covariates in future epidemiological studies, which may facilitate future discoveries of novel biomarkers for disease.

Splitting hair for cortisol? Associations of socio-economic status, ethnicity, hair color, gender and other child characteristics with hair cortisol and cortisone

The aim of this study was to examine associations of SES and ethnicity with hair cortisol and cortisone and to identify potential child and family characteristics that can assist in choosing covariates and potential confounders for analyses involving hair cortisol and cortisone concentrations. Hair samples were collected in 2484 6-year-old children from the Generation R Study, a prospective cohort in Rotterdam, the Netherlands. Measurements for cortisol and cortisone were used as the outcome in regression analyses. Predictors were SES, ethnicity, hair color and child characteristics such as birthweight, gestational age at birth, BMI, disease, allergy, and medication use. Lower family income, more children to be supported by this income, higher BMI and darker hair color were associated with higher hair cortisol and cortisone levels. Boys also showed higher levels. Ethnicity (Dutch and North European descent) was related to lower levels. High amounts of sun in the month of hair collection was related to higher levels of cortisone only. More recent hair washing was related to lower levels of cortisol and cortisone. Gestational age at birth, birth weight, age, medication use, hair washing frequency, educational level of the mother, marital status of the mother, disease and allergy were not associated with cortisol or cortisone levels. Our results serve as a starting point for choosing covariates and confounders in studies of substantive predictors or outcomes. Gender, BMI, income, the number of persons in a household, ethnicity, hair color and recency of hair washing are strongly suggested to take into account.

Comparisons of physical fitness and body composition among Sasang types with and without body mass index as a covariate

Background

The purpose of this study was to examine the difference of physical fitness and body composition among Sasang types with BMI as covariate, which is reported to have influence on physical fitness and body composition.

Methods

We measured the physical fitness and body composition of 930 korean female college students, and compared the differences among Sasang type groups with or without considering Body Mass Index (BMI). We evaluated muscle strength, agility, muscle endurance, power and flexibility for the physical fitness, and total body water, protein, muscle mass, mineral, lean body mass and fat mass for the body composition.

Results

We got 352 So-Yang (SY), 385 So-Eum (SE), and 193 Tae-Eum (TE) Sasang types, and there were significant differences among Sasang types in height, weight and BMI. The significant differences among TE and SY types were disappeared in muscle strength, total body water, protein when BMI is used as a covariate. In ANOVA, there were significant differences that TE was higher on the mineral and fat mass compared to the SY type and SE type. However it disappeared when we introduced BMI as covariate.

Conclusion

The results demonstrated that the BMI should be considered as an important element for studying physical characteristics of Sasang typology.

Covariate effect on constancy assumption in noninferiority clinical trials

Noninferiority (NI) clinical trials are getting a lot of attention of late due to their direct application in biosimilar studies. Because of the missing placebo arm, NI is an indirect approach to demonstrate efficacy of a test treatment. One of the key assumptions in the NI test is the constancy assumption, that is, that the effect of the reference treatment is the same in current NI trials as in historical superiority trials. However, if a covariate interacts with the treatment arms, then changes in distribution of this covariate will likely result in violation of constancy assumption. In this article, we propose four new NI methods and compare them with two existing methods to evaluate the change of background constancy assumption on the performance of these six methods. To achieve this goal, we study the impact of three elements-(1) strength of covariate, (2) degree of interaction between covariate and treatment, and (3) differences in distribution of the covariate between historical and current trials-on both the type I error rate and power using three different measures of association: difference, log relative risk, and log odds ratio. Based on this research, we recommend using a modified covariate-adjustment fixed margin method.

Generalizing the spatial relative risk function

The spatial relative risk function is defined as the ratio of densities describing respectively the spatial distribution of cases and controls. It has proven to be an effective tool for visualizing spatial variation in risk in many epidemiological applications over the past 20 years. We discuss the generalization of this function to spatio-temporal case-control data, and also to situations where there are covariates available that may affect the spatial patterns of disease. We examine estimation of the generalized relative risk functions using kernel smoothing, including asymptotic theory and data-driven bandwidth selection. We also consider construction of tolerance contours. Our methods are illustrated on spatio-temporal data describing the 2001 outbreak of foot-and-mouth disease in the United Kingdom, with farm size as a covariate.