When remediating one artifact results in another: control, confounders, and correction

Scientists aim to remediate artifacts in their experimental datasets. However, the remediation of one artifact can result in another. Why might this happen, and what does this consequence tell us about how we should account for artifacts and their control? In this paper, I explore a case in functional neuroimaging where remediation appears to have caused this problem. I argue that remediation amounts to a change to an experimental arrangement. These changes need not be surgical, and the arrangement need not satisfy the criterion of causal modularity. Thus, remediation can affect more than just the factor responsible for the artifact. However, if researchers can determine the consequences of their remediation, they can make adjustments that control for the present artifact as well as for previously controlled ones. Current philosophical accounts of artifacts and the factors responsible for them cannot adequately address this issue, as they do not account for what is needed for artifact remediation (and specifically correction). I support my argument by paralleling it with ongoing concerns regarding the transparency of complex computational systems, as near future remediation across the experimental life sciences will likely make greater use of AI tools to correct for artifacts.

Adjusting for Confounders in Outcome Studies Using the Korea National Health Insurance Claim Database: A Review of Methods and Applications

OBJECTIVES

Adjusting for potential confounders is crucial for producing valuable evidence in outcome studies. Although numerous studies have been published using the Korea National Health Insurance Claim Database, no study has critically reviewed the methods used to adjust for confounders. This study aimed to review these studies and suggest methods and applications to adjust for confounders.

METHODS

We conducted a literature search of electronic databases, including PubMed and Embase, from January 1, 2021 to December 31, 2022. In total, 278 studies were retrieved. Eligibility criteria were published in English and outcome studies. A literature search and article screening were independently performed by 2 authors and finally, 173 of 278 studies were included.

RESULTS

Thirty-nine studies used matching at the study design stage, and 171 adjusted for confounders using regression analysis or propensity scores at the analysis stage. Of these, 125 conducted regression analyses based on the study questions. Propensity score matching was the most common method involving propensity scores. A total of 171 studies included age and/or sex as confounders. Comorbidities and healthcare utilization, including medications and procedures, were used as confounders in 146 and 82 studies, respectively.

CONCLUSIONS

This is the first review to address the methods and applications used to adjust for confounders in recently published studies. Our results indicate that all studies adjusted for confounders with appropriate study designs and statistical methodologies; however, a thorough understanding and careful application of confounding variables are required to avoid erroneous results.

Covariate selection in causal learning under non-Gaussianity

Understanding causal mechanisms is a central goal in the behavioral, developmental, and social sciences. When estimating and probing causal effects using observational data, covariate adjustment is a crucial element to remove dependencies between focal predictors and the error term. Covariate selection, however, constitutes a challenging task because availability alone is not an adequate criterion to decide whether a covariate should be included in the statistical model. The present study introduces a non-Gaussian method for covariate selection and provides a forward selection algorithm for linear models (i.e., non-Gaussian forward selection; nGFS) to select appropriate covariates from a set of potential control variables to avoid inconsistent and biased estimators of the causal effect of interest. Further, we demonstrate that the forward selection algorithm has properties compatible with principles of direction of dependence, i.e., probing whether the causal target model is correctly specified with respect to the causal direction of effects. Results of a Monte Carlo simulation study suggest that the selection algorithm performs well, in particular when sample sizes are large (i.e., n ≥ 250) and data strongly deviate from Gaussianity (e.g., distributions with skewness beyond 1.5). An empirical example is given for illustrative purposes.

Evaluation of instrumental variable method using Cox proportional hazard model in epidemiological studies

The instrumental variable (IV) method with a Cox proportional hazard (PH) model has been used to evaluate treatment effects in epidemiological studies involving survival data. The effectiveness of the IV methods in these circumstances has yet to be fully understood, though. The study aimed to evaluate the performance of IV methods using a Cox model. We evaluated the validity of treatment effects estimated by two-stage IV models using simulated scenarios with varying confounder strengths and baseline hazards. Our simulation demonstrated that when observed confounders were not taken into account in the IV models, and the confounder strength was moderate, the treatment effects based on the two-stage IV models were similar to the true value. However, the effect estimates diverged from the true value when observed confounders were taken into account in the IV models. In the case of a null treatment effect (i.e., hazard ratio=1), the estimates from the unadjusted and adjusted IV models (only two-stage) were close to the true value. The implication of our study findings is that the treatment effects obtained through IV analyses using the Cox PH model remain valid if the estimates are reported from unadjusted IV models with moderate confounding effects or if the treatment does not impact the outcome.•For every simulation, we utilized a sample size of 10,000 and performed 1,000 replications.•The true treatment effects (HR) of 3, 2, and 1 (null effect) were evaluated.•The 95% confidence intervals (CI) were calculated as the range between the 2.5 and 97.5 percentiles of the 1000 estimates.

Causal modelling of heavy-tailed variables and confounders with application to river flow

Confounding variables are a recurrent challenge for causal discovery and inference. In many situations, complex causal mechanisms only manifest themselves in extreme events, or take simpler forms in the extremes. Stimulated by data on extreme river flows and precipitation, we introduce a new causal discovery methodology for heavy-tailed variables that allows the effect of a known potential confounder to be almost entirely removed when the variables have comparable tails, and also decreases it sufficiently to enable correct causal inference when the confounder has a heavier tail. We also introduce a new parametric estimator for the existing causal tail coefficient and a permutation test. Simulations show that the methods work well and the ideas are applied to the motivating dataset.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10687-022-00456-4.

Validity evaluation of indirect adjustment method for multiple unmeasured confounders: A simulation and empirical study

BACKGROUND

An indirect adjustment method was developed to control for unmeasured confounders in a large administrative cohort study. A previous study that proposed the indirect adjustment method assessed the validity of the method by simulations but did not consider the direction of bias and scenarios with multiple missing confounders. In this study, we evaluated the direction and the magnitude of bias of the indirect adjustment method with multiple correlated unmeasured confounders using simulation and empirical datasets.

METHODS

A simulation study was conducted to compare the bias of the indirect adjustment by varying the number of confounders, magnitude of correlation between confounders, and the number of adjustment variables. An empirical study was conducted by applying the indirect adjustment method to the association between PM₁₀ and mortality using the Korea National Health and Nutrition Examination Survey linked Cause of Death data for 2007-2016.

RESULTS

The simulations of the present study demonstrated that 1) when a confounder is positively associated with both exposure and outcome, indirect adjustment might bias the effect size downward; 2) the magnitude of bias might depend on the correlation between unmeasured confounders; and 3) indirect adjustment for multiple missing confounders at once could result in a higher bias than that for some of the missing confounders. Empirical analyses also showed consistent results, but the bias of indirectly adjusted effect estimates was sometimes larger than that of unadjusted effect estimates.

CONCLUSIONS

The indirect adjustment method is a promising technique to reduce the bias from unmeasured confounding; however, it should be implemented carefully, particularly when there are multiple correlated unmeasured confounders of the same direction.

Real-world data from Europe and Africa suggest that accuracy of systems for self-monitoring of blood glucose is frequently impaired by low hematocrit

AIMS

Certain systems for self-monitoring of blood glucose (SMBG) demonstrate inaccuracy at low and high hematocrit (HCT). Manufacturers define HCT ranges for accurate performance. Our objective was to assess the frequency of HCT values that can lead to clinically relevant errors.

METHODS

In this cross-sectional study, we collected real-world data representing over 360,000 outpatients from the Netherlands (NL), the Czech Republic (CZ), and South Africa (ZA). These were subsequently stratified by sex and age and compared to commonly specified HCT range limits, reference intervals, and data from 1780 healthy Czech subjects.

RESULTS

HCT values were comparably distributed in NL and CZ. Outpatients had a higher dispersion of values than healthy subjects. Low HCT values in Europe were common in age groups with a high prevalence of diabetes. All ZA age groups showed a higher prevalence of low HCT than in Europe.

CONCLUSIONS

Real-world data indicate that SMBG systems specified to perform only within the frequently used 30-55% HCT range would leave 3% of outpatients in Europe and 18% in South Africa at risk of false SMBG results, with individual age strata being substantially higher. This could affect their diabetes management. Adequate SMBG systems should thus be chosen.

A Unification of Mediator, Confounder, and Collider Effects

Third-variable effects, such as mediation and confounding, are core concepts in prevention science, providing the theoretical basis for investigating how risk factors affect behavior and how interventions change behavior. Another third variable, the collider, is not commonly considered but is also important for prevention science. This paper describes the importance of the collider effect as well as the similarities and differences between these three third-variable effects. The single mediator model in which the third variable (T) is a mediator of the independent variable (X) to dependent variable (Y) effect is used to demonstrate how to estimate each third-variable effect. We provide difference in coefficients and product of coefficients estimators of the effects and demonstrate how to calculate these values with real data. Suppression effects are defined for each type of third-variable effect. Future directions and implications of these results are discussed.

Can statistical adjustment guided by causal inference improve the accuracy of effect estimation? A simulation and empirical research based on meta-analyses of case-control studies

BACKGROUND

Statistical adjustment is often considered to control confounding bias in observational studies, especially case-control studies. However, different adjustment strategies may affect the estimation of odds ratios (ORs), and in turn affect the results of their pooled analyses. Our study is aimed to investigate how to deal with the statistical adjustment in case-control studies to improve the validity of meta-analyses.

METHODS

Three types of adjustment strategies were evaluated including insufficient adjustment (not all preset confounders were adjusted), full adjustment (all confounders were adjusted under the guidance of causal inference), and improper adjustment (covariates other than confounders were adjusted). We carried out a series of Monte Carlo simulation experiments based on predesigned scenarios, and assessed the accuracy of effect estimations from meta-analyses of case-control studies by combining ORs calculated according to different adjustment strategies. Then we used the data from an empirical review to illustrate the replicability of the simulation results.

RESULTS

For all scenarios with different strength of causal relations, combining ORs that were comprehensively adjusted for confounders would get the most precise effect estimation. By contrast, combining ORs that were not sufficiently adjusted for confounders or improperly adjusted for mediators or colliders would easily introduce bias in causal interpretation, especially when the true effect of exposure on outcome was weak or none. The findings of the simulation experiments were further verified by the empirical research.

CONCLUSIONS

Statistical adjustment guided by causal inference are recommended for effect estimation. Therefore, when conducting meta-analyses of case-control studies, the causal relationship formulated by exposure, outcome, and covariates should be firstly understood through a directed acyclic graph, and then reasonable original ORs could be extracted and combined by suitable methods.

The effect of intervention in nickel concentrations on benthic macroinvertebrates: A case study of statistical causal inference in ecotoxicology

Field survey-based ecological risk assessments for trace metals are conducted to examine the necessity and/or effectiveness of management intervention, such as setting of environmental quality standards. Observational datasets often involve confounders that may bias estimation of the effects of intervention (e.g., reduction of trace-metal concentrations through regulation). The field of ecotoxicology lags behind some other research fields in understanding proper analytical procedures for causal inference from observational datasets; there are only a few field survey-based ecotoxicological studies that have explicitly controlled for confounders in their statistical analyses. In the present study, we estimated the effect of intervention in nickel concentrations on Ephemeroptera, Plecoptera, and Trichoptera richness in rivers in Japan. We also provide detailed explanations for the backgrounds of spurious associations derived from confounders and on proper analytical procedures for obtaining an unbiased estimate of the targeted intervention effect by using regression analysis. We constructed a multiple regression model based on a causal diagram for aquatic insects and environmental factors, and on "the backdoor criterion," that enabled us to determine the set of covariates required to obtain an unbiased estimate of the targeted intervention effect from regression coefficients. We found that management intervention in nickel concentrations may be ineffective compared to intervention in organic pollution, and that analysis ignoring the confounders overestimated the effect of intervention in nickel concentrations. Our results highlight the fact that confounders can lead to misjudging the necessity for management of anthropogenic chemical substances. Confounders should be explicitly specified and statistically controlled to achieve a comprehensive assessment of ecological risks for various substances.

Influence of hydration status on cardiovascular magnetic resonance myocardial T1 and T2 relaxation time assessment: an intraindividual study in healthy subjects

BACKGROUND

Myocardial native T1 and T2 relaxation time mapping are sensitive to pathological increase of myocardial water content (e.g. myocardial edema). However, the influence of physiological hydration changes as a possible confounder of relaxation time assessment has not been studied. The purpose of this study was to evaluate, whether changes in myocardial water content due to dehydration and hydration might alter myocardial relaxation times in healthy subjects.

METHODS

A total of 36 cardiovascular magnetic resonance (CMR) scans were performed in 12 healthy subjects (5 men, 25.8 ± 3.2 years). Subjects underwent three successive CMR scans: (1) baseline scan, (2) dehydration scan after 12 h of fasting (no food or water), (3) hydration scan after hydration. CMR scans were performed for the assessment of myocardial native T1 and T2 relaxation times and cardiac function. For multiple comparisons, repeated measures ANOVA or the Friedman test was used.

RESULTS

There was no change in systolic blood pressure or left ventricular ejection fraction between CMR scans (P > 0.05, respectively). T1 relaxation times were significantly reduced with dehydration (987 ± 27 ms [baseline] vs. 968 ± 29 ms [dehydration] vs. 986 ± 28 ms [hydration]; P = 0.006). Similar results were observed for T2 relaxation times (52.9 ± 1.8 ms [baseline] vs. 51.5 ± 2.0 ms [dehydration] vs. 52.2 ± 1.9 ms [hydration]; P = 0.020).

CONCLUSIONS

Dehydration may lead to significant alterations in relaxation times and thereby may influence precise, repeatable and comparable assessment of native T1 and T2 relaxation times. Hydration status should be recognized as new potential confounder of native T1 and T2 relaxation time assessment in clinical routine.

Confounder detection in linear mediation models: Performance of kernel-based tests of independence

It is well-known that the identification of direct and indirect effects in mediation analysis requires strong unconfoundedness assumptions. Even when the predictor is under experimental control, unconfoundedness assumptions must be imposed on the mediator-outcome relation in order to guarantee valid indirect-effect identification. Researchers are therefore advised to test for unconfoundedness when estimating mediation effects. Significance tests to evaluate unconfoundedness usually rely on an instrumental variable (IV)-that is, a variable that is nonindependent of the explanatory variable and, at the same time, independent of all exogenous factors that affect the outcome when the explanatory variable is held constant. Because IVs may be hard to come by, the present study shows that confounders of the mediator-outcome relation can be detected without making use of IVs when variables are nonnormal. We show that kernel-based tests of independence are able to detect confounding under nonnormality. Results from a simulation study are presented that suggest that these tests perform well in terms of Type I error protection and statistical power, independent of the distribution or measurement level of the confounder. A real-world data example from the Job Search Intervention Study (JOBS II) illustrates how the presented approach can be used to minimize the risk of obtaining biased indirect-effect estimates. The data requirements and role of unconfoundedness tests as diagnostic tools are discussed. A Monte Carlo-based power analysis tool for sample size planning is also provided.

Antimicrobials administration time in patients with suspected sepsis: faster is better for severe patients

In a recent report, Ascuntar et al. describes the impossibility to demonstrate a significant association between early antibiotic therapy administration and mortality in sepsis patients. Nevertheless, we believe that some methodological issues deserve their conclusions. First, the objective of the authors of an early antibiotic therapy may be ambitious considering practical daily emergency department limitation. Second, most of patients included in the study appear to suffer from sepsis and not from septic shock, which limits the impact of an early and aggressive management. At last, more than a single intervention such as antibiotic therapy, sepsis treatment is now considered as based on a “bundle of care.”

[Application of directed acyclic graphs in identifying and controlling confounding bias]

Observational study has been viewed as the most convenient method in designing etiological studies. However, the presence of confounders always challenge the researchers in study design, since unadjusted confounders may lead to biased results. The traditional definition of a confounder is not intuitional in application and sometimes leading to inappropriate adjustment of nonexistent "confounders" which might induce new bias to merge. The use of directed acyclic graphs (DAGs) may identify confounders easier and more intuitional, as well as avoiding superfluous adjustment. It can also contribute to the identification of adjustment methods, and be useful in causal inference of observational studies.

[How to adjust confounders in studies on observational comparative effectiveness: (3) approaches on sensitivity analysis for confounder adjustment]

Confounders are difficult to avoid in studies on observational comparative effectiveness. It is often unclear whether the confounders have been completely eliminated after controlling the measured or unmeasured potential confounding effects or if sensitivity analysis is needed when using the specific statistical methods, under given circumstances. This manuscript summarizes and evaluates the confounding sensitivity analysis methods. Based on different studies, sensitivity analyses need to use different approaches. The traditional sensitivity analysis can be applied for the measured confounders. Currently, the relatively systematic sensitivity analyses for unmeasured confounders would include confounding function, bounding factor and propensity score calibration. Additionally, more investigations are associated with Monte Carlo and Bayesian sensitivity analysis. Reliability of the research conclusion thus may largely be improved when the sensitivity analysis results are consistent with the main analysis.

Pragmatic trials: ignoring a mediator and adjusting for confounding

OBJECTIVES

In pragmatic trials, the new treatment is compared with usual care (heterogeneous control arm) that makes the comparison of the new treatment with each treatment within the control arm more difficult. The usual assumption is that we can fully capture the relations between different quantities. In this paper we use simulation to assess the performance of statistical methods that adjust for confounding when the assumed relations are not true. The true relations contain a mediator and heterogeneity with or without confounding, but the assumption is that there is no mediator and that confounding and heterogeneity are fully captured. The statistical methods that are compared include multivariable logistic regression, propensity score, disease risk score, inverse probability weighting, doubly robust inverse probability weighting and standardisation.

RESULTS

The misconception that there is no mediator can cause to misleading comparative effectiveness of individual treatments when a method that estimates the conditional causal effect is used. Using a method that estimates the marginal causal effect is a better approach, but not for all scenarios.

Principles of confounder selection

Selecting an appropriate set of confounders for which to control is critical for reliable causal inference. Recent theoretical and methodological developments have helped clarify a number of principles of confounder selection. When complete knowledge of a causal diagram relating all covariates to each other is available, graphical rules can be used to make decisions about covariate control. Unfortunately, such complete knowledge is often unavailable. This paper puts forward a practical approach to confounder selection decisions when the somewhat less stringent assumption is made that knowledge is available for each covariate whether it is a cause of the exposure, and whether it is a cause of the outcome. Based on recent theoretically justified developments in the causal inference literature, the following proposal is made for covariate control decisions: control for each covariate that is a cause of the exposure, or of the outcome, or of both; exclude from this set any variable known to be an instrumental variable; and include as a covariate any proxy for an unmeasured variable that is a common cause of both the exposure and the outcome. Various principles of confounder selection are then further related to statistical covariate selection methods.

The use of hair cortisol for the assessment of stress in animals

The hair cortisol concentration (HCC) is assumed to be a retrospective marker of integrated cortisol secretion and stress over longer periods of time. Its quantification is increasingly used in psychoneuroendocrinological studies in humans, but also in animal stress and welfare research. The measurement of HCCs for the assessment of stress offers many considerable benefits for use in domesticated and wild animals, especially due to the easy and minimally invasive sampling procedure and the representation of longer time periods in one sample. This review aims to outline the different fields of application and to assess the applicability and validity of HCC as an indicator for chronic stress or long-term activity of the hypothalamic-pituitary-adrenal axis in wild and domesticated animals. Specific hair characteristics are presented and the advantages and limitations of using HCC are discussed. An overview of findings on the impact of stress- and health-related factors on HCCs and of diverse influencing factors causing variation in hair cortisol levels in different species is given. Recommendations for the use of hair cortisol analysis are proposed and potential fields of future research are pointed out. The studies indicate an effect of age and pregnancy on HCCs, and cortisol incorporation into hair was also found to depend on hair colour, body region, sex and season of year, but these results are less consistent. Furthermore, the results in animals show that a wide array of stressors and pathological conditions alters the cortisol concentrations in hair and that HCC thereby provides a reliable and valid reflection of long-term cortisol secretion in many species. However, more research is necessary to investigate the underlying mechanisms of cortisol incorporation into the hair and to explore the hair growth characteristics in the species of interest. To overcome confounding influences, the use of standardized sampling protocols is strongly advised.

Retrospectively reported childhood adversity is associated with asthma and chronic bronchitis, independent of mental health

Several researchers have raised the concern that the cross-sectional association of retrospectively reported childhood adversity with self-reported onset of asthma and chronic bronchitis in adulthood may be confounded, as well as mediated by an individual's mental health. The aim of this study was to assess the effect of retrospectively reported childhood adversity on self-reported onset of asthma and chronic bronchitis in adulthood, independent of potential confounding and mediating variables (including respondent's mental health). We used data collected in 2007-2008 within the framework of the Tromsø Study (N = 12,981), a representative study of adult men and women in Norway. The associations of childhood adversity with asthma and chronic bronchitis were assessed with Poisson regression models. Relative risks (RR) and 95% confidence intervals (CI) were estimated with bias-corrected bootstrapping. Childhood adversity was associated with a 9% increased risk of asthma (RR = 1.09, 95% CI: 1.02, 1.16) and a 14% increased risk chronic bronchitis (RR = 1.14, 95% CI: 1.03, 1.26) in adulthood, independent of age, sex, parental history of psychiatric problems/asthma/dementia, education, smoking, social support, and respondent's mental health. Controlling for indicators of respondent's mental health reduced the strength of associations of childhood adversity with asthma and chronic bronchitis; however, the associations were still present in the same direction (p < .05). These findings suggest that the association of retrospectively reported childhood adversity with asthma and chronic bronchitis is independent of respondent's mental health. We recommend controlling for indicators of the respondent's mental health to assess an unbiased association of retrospectively measured childhood adversity with self-reported asthma and chronic bronchitis.

Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder

We consider a causal effect that is confounded by an unobserved variable, but with observed proxy variables of the confounder. We show that, with at least two independent proxy variables satisfying a certain rank condition, the causal effect is nonparametrically identified, even if the measurement error mechanism, i.e., the conditional distribution of the proxies given the confounder, may not be identified. Our result generalizes the identification strategy of Kuroki & Pearl (2014) that rests on identification of the measurement error mechanism. When only one proxy for the confounder is available, or the required rank condition is not met, we develop a strategy to test the null hypothesis of no causal effect.

Acute and chronic effect of physical activity on cognition and behaviour in young people with ADHD: A systematic review of intervention studies

BACKGROUND

Young people with attention deficit hyperactivity disorder (ADHD) often have learning and behavioral control difficulties.

AIM

The aim of this review is analyse the acute and chronic effect of physical activity (PA) on the cognition and behaviour of children and adolescents with ADHD.

METHODS

Studies were identified in five databases (PubMed, SPORTDiscus, ProQuest, Web of Science, and SCOPUS), from January 2000 through to January 2017. A total of 16 interventional studies met the inclusion criteria.

RESULTS/CONCLUSIONS

PA practice of 20-30 min (intensity 40-75%) produces a positive acute effect on processing speed, working memory, planning and problem solving in young people with ADHD. However, these effects on behaviour are contradictory and vary depending on age. Chronic PA practice (≥30 min per day, ≥40% intensity, ≥three days per week, ≥five weeks) further improves attention, inhibition, emotional control, behaviour and motor control. The results must be treated with caution, because only 25% of the studies used confounders.

IMPLICATION

More research is needed to justify the causes of these effects. It is necessary to establish programs with regard to the duration, intensity, kind of exercise, and time of PA to improve cognition and behaviour in young people with ADHD taking into account potential confounders.

Determinants of hair cortisol concentration in children: A systematic review

BACKGROUND

Several factors are known contribute to hair cortisol concentration (HCC) in adults. However, there is less research on determinants of HCC in children and adolescents. HCC is a valuable tool for medical research pertaining to the hypothalamic-pituitary-adrenal (HPA) axis. This review aims to assess the extent to which established determinants of HCC in adults have been consistently reported in children (birth - 18 years) and to identify determinants of HCC specific to this age group.

METHODS

Eligible studies were identified, selected and appraised as per PRISMA-P guidelines and as detailed in our systematic review protocol, registered on PROSPERO (registration number CRD42017056220). In view of contrasting methods and measures, a meta-analysis could not be done but a qualitative synthesis was performed.

RESULTS

Thirty-six studies were included in the analysis. Higher HCC is associated with male sex and anthropometry, particularly increased body mass index and waist circumference. There is preliminary evidence to suggest that socio-economic status is inversely related to child HCC, particularly with reference to caregiver education and income. Of note, most of the studies analysing socio-economic variables were performed in relatively equal societies. Hair wash frequency and use of hair products and treatments do not affect HCC when proximal segments of hair are used. There is conflicting evidence regarding the relationship between HCC and age in children and adolescents. Further investigation is required to better delineate if and how the following are associated with HCC in children: hair colour, hair type, exposure to trauma and stressors, psychiatric illness, atopic illness, steroid use (including topical and inhaled steroids) and perinatal variables.

CONCLUSIONS

Sex and anthropometry are potential confounders and should be considered for adjustment in hair cortisol research. Hair wash frequency and use of hair products and treatments are not important confounders when proximal hair segments are used. A better understanding of HCC in children in relation to exposure to trauma and stressors is required before it can be used as a biomarker, particularly in terms of vulnerable developmental stages, definition and measurement of stress, and temporal relationship to stressors. Age, SES and other correlates also warrant further investigation.

Stress-related and basic determinants of hair cortisol in humans: A meta-analysis

The analysis of hair cortisol concentrations (HCC) is a relatively new strategy to measure long-term cumulative cortisol levels, which is increasingly used in psychoneuroendocrinological research. Here, we conduct a first comprehensive meta-analysis of HCC research based on aggregated data from a total of 124 (sub)samples (66 independent studies; total N=10,289). We seek to answer two central questions: (i) Which covariates and basic features of HCC need to be considered in future research? (ii) What are the main determinants of HCC in terms of chronic stress exposure and mental health? Concerning basic characteristics, our findings identify several covariates to be considered (age, sex, hair washing frequency, hair treatment, oral contraceptive use), confirm a decline of HCC from the first to the second proximal 3cm hair segment, and show positive associations between HCC and short-term salivary cortisol measures. Regarding chronic stress, we show that stress-exposed groups on a whole exhibit 22% increased HCC. This long-term cortisol hypersecretion emerges particularly when stress is still ongoing at the time of study (+43% HCC) but is not present in conditions of past/absent stress (-9% HCC, n.s.). We also report evidence for 17%-reduced HCC in anxiety disorders, such as PTSD. Interestingly, no consistent associations with mood disorders and self-reports of perceived stress, depressiveness or social support are found. However, our findings reveal positive associations of HCC with stress-related anthropometric (body mass index, waist-to-hip ratio) and hemodynamic measures (systolic blood pressure). These meta-analytic results are discussed in the light of their practical implications and important areas for future inquiry are outlined.

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.

A causal network analysis in an observational study identifies metabolomics pathways influencing plasma triglyceride levels

INTRODUCTION

Plasma triglyceride levels are a risk factor for coronary heart disease. Triglyceride metabolism is well characterized, but challenges remain to identify novel paths to lower levels. A metabolomics analysis may help identify such novel pathways and, therefore, provide hints about new drug targets.

OBJECTIVES

In an observational study, causal relationships in the metabolomics level of granularity are taken into account to distinguish metabolites and pathways having a direct effect on plasma triglyceride levels from those which are only associated with or have indirect effect on triglyceride.

METHOD

The analysis began by leveraging near-complete information from the genome level of granularity using the GDAG algorithm to identify a robust causal network over 122 metabolites in an upper level of granularity. Knowing the metabolomics causal relationships, we enter the triglyceride variable in the model to identify metabolites with direct effect on plasma triglyceride levels. We carried out the same analysis on triglycerides measured over five different visits spanning 24 years.

RESULT

Nine metabolites out of 122 metabolites under consideration influenced directly plasma triglyceride levels. Given these nine metabolites, the rest of metabolites in the study do not have a significant effect on triglyceride levels at significance level alpha = 0.001. Therefore, for the further analysis and interpretations about triglyceride levels, the focus should be on these nine metabolites out of 122 metabolites in the study. The metabolites with the strongest effects at the baseline visit were arachidonate and carnitine, followed by 9-hydroxy-octadecadenoic acid and palmitoylglycerophosphoinositol. The influence of arachidonate on triglyceride levels remained significant even at the fourth visit, which was 10 years after the baseline visit.

CONCLUSION

These results demonstrate the utility of integrating multi-omics data in a granularity framework to identify novel candidate pathways to lower risk factor levels.

[Gap between postulated and real outcome quality of radical prostatectomy]

BACKGROUND

Certified Prostate Centers proclaim congruent process and outcome quality results for treatment of prostate carcinoma. Therapy in accordance with the guidelines after presentation of the patient in an interdisciplinary conference and regular documented follow up are not in themselves a guarantee for good quality results (complication free, continence, erectile function, negative surgical margins, biochemical recurrence free), and are significantly influenced by factors not contained within the certification framework.

DISCUSSION

An association between exceeding the minimum number of operations and quality assurance exists, if at all, only vaguely and on no account justifies the absolute numbers necessary for certification. Although good measuring instruments for a Pentafecta analysis are available, the gathering of quality results for a center are limited to questionnaires for functional quality results and in the non-differentiated request for a pT2R1 rate of under 10 % for oncological quality results.

CONCLUSIONS

The reasons for this systematic ignoring of the for the patient so important quality results with a simultaneous excessive regard for standardizing organizational procedure processes are manifold. They comprise valid verifiability of process quality, the unclear effects of standardized treatment pathways on actual operation quality and the capitulation to statistical and patient determined problems with sufficient acquisition of comparable functional OP results. Whereas the outcome quality is more important than the process quality for patients with prostate carcinoma, the certified centers conduct themselves in exactly the opposite manner, thus creating a virtually insoluble dilemma.

Strategies for comparative analyses of registry data

The present paper is a description and summary of methods used in non-randomised cohort data where the comparability of the study groups usually is not granted. Such study groups are formed by a diagnostic or therapeutic intervention, or by other characteristics of the patient or the treatment environment. This is a typical situation in the analysis of registry data. The methods are presented together with an illustrative example of whole-body computed tomography in the early phase of treatment of severe trauma cases. The following approaches are considered: (i) unadjusted direct comparisons; (ii) parallelisation; (iii) subgroup analysis; (iv) matched-pairs analysis; (v) outcome adjustment; and (vi) propensity score analysis. All these approaches have in common that they try to separate, or limit, the influence of confounding variables, which are unevenly distributed among the study groups, but also influence the outcome of interest. They differ in the number of confounders being considered, as well as the number of patients regarded. The more sophisticated the approach, the more effectively such confounding factors could be reduced. However, any method used for the reduction of bias depends on the quality and completeness of recorded confounders. Factors which are difficult or even impossible to be measured could thus not be adjusted for. This is a general limitation of retrospective analyses of cohort data.

Silicone implants--a possible confounder for urinary platinum background concentrations?

INTRODUCTION

Urinary platinum excretion from occupationally unexposed population is very low. Up to now, in Germany, dental noble metal alloys and a platinum based chemotherapy have been identified as reason for elevated urine concentrations. As fabrication of silicone involves platinum as catalyst, this study examines the potential release of platinum from silicone breast implants by quantifying urinary platinum concentration.

METHODS AND RESULTS

Platinum release from three different types of silicone implants into saline solution was measured in a laboratory experiment. It showed a strong increase of platinum concentration during the first 30 min and high platinum concentrations even after 60 h. In the following field study urinary platinum concentrations were determined from 30 women with dental gold alloy restorations and 28 women without such dental inlays. Median platinum concentrations were 5.2 ng/l urine (21.2 ng/g creatinine) for the women with dental gold inlays and 6.0 ng/l urine (5.4 ng/g creatinine) for those without. Compared with the urinary platinum concentrations provided by the German Environmental Survey (GerES) for the general female population the urinary platinum levels of women with silicone implants of the presented study were significantly higher, both for the study groups with and without dental gold alloy inlays.

CONCLUSIONS

Silicone breast implants must be considered as a new confounder and as a further contributor to elevated urinary platinum concentrations in human platinum background reference values of women.

Is the superiority of apoB over non-HDL-C as a marker of cardiovascular risk in the INTERHEART study due to confounding by related variables?

BACKGROUND

Patients with increased numbers of cholesterol-depleted apolipoprotein B (apoB) particles frequently have multiple other abnormalities, which might confound the comparison of apoB and non-high-density-lipoprotein-cholesterol (non-HDL-C) as markers of cardiovascular risk.

OBJECTIVE

We wanted to determine whether the superiority of apoB over non-HDL-C as a marker of cardiovascular risk in the INTERHEART study is due to such variables that act as confounders of the primary comparison.

METHODS

To test for confounding, cases and controls were first separated into 3 groups on the basis of the percentile levels within the study of non-HDL-C and apoB with discordance defined as a difference of 5 percentile points. Logistic regression was used to compute odds ratio of myocardial infarction (as an outcome) for different categories, assuming concordance as reference adjusted for other confounders.

RESULTS

Plasma triglyceride and non-HDL-C levels were highest in the discordant group with lowest risk and lowest in the discordant group with highest risk, whereas apoB was highest in the discordant group with the highest risk and lowest in the discordant group with the lowest group. Moreover, no significant change was found in the odds ratio for either discordant group when adjusted for the effect of any of the variables examined, evidence that none confounded the primary comparison.

CONCLUSION

Factors such as hypertriglyceridemia do not confound the comparison of apoB and non-HDL-C, further evidence that apoB is superior to non-HDL-C as a marker of the importance of the apoB atherogenic lipoproteins in cardiovascular risk.