Construction of Split-Plot Designs with General Minimum Lower Order Confounding

Fractional factorial split-plot design has been widely used in many fields due to its advantage of saving experimental cost. The general minimum lower order confounding criterion is usually used as one of the attractive design criterion for selecting fractional factorial split-plot design. In this paper, we are interested in the theoretical construction methods of the optimal fractional factorial split-plot designs under the general minimum lower order confounding criterion. We present the theoretical construction methods of optimal fractional factorial split-plot designs under general minimum lower order confounding criterion under several conditions.

Confounding Effect in Clinical Research of Otolaryngology and Its Control

Confounding effect is a critical issue in clinical research of otolaryngology because it can distort the research's conclusion. In this review, we introduce the definition of confounding effect, the methods of verifying and controlling the effect. Confounding effect can be prevented by research's design, and adjusted by data analysis. Clinicians would be aware and cautious about confounding effect in their research. They would be able to set up a research's design in which appropriate methods have been applied to prevent this effect.They would know how to adjust confounding effect after data collection. It is important to remember that sometimes it is impossible to eliminate confounding effect completely, and statistical method is not a master key. Solid research knowledge and critical thinking of our brain are the most important in controlling confounding effect.

Relations among homogeneity, collapsibility and nonconfounding in distribution effects

In this paper, the concept of distribution effect is proposed without the causal diagram. Following the notation of Stone [11], we assume that the exposure treatment X is an unknown deterministic function of the confounder set Pa（X） and a random error ε. We discuss sufficient and necessary conditions for homogeneity, collapsibility and nonconfounding for distribution effects and discuss relations among them.

Admission hemoglobin level and prognosis of type 2 diabetes mellitus and possible confounding factors:Correspondence

This letter to editor discusses on the publication on admission hemoglobin level and prognosis of type 2 diabetes mellitus.A comment on published article is raised.The specific confounding conditions on the hemoglobin level are mentioned.Concerns on clinal application are raised and discussed.

Confounding of Three Binary-Variable Counterfactual Model with DAG

Confounding of three binary-variable counterfactual model with directed acyclic graph (DAG) is discussed in this paper. According to the effect between the control variable and the covariate variable, we investigate three causal counterfactual models: the control variable is independent of the covariate variable, the control variable has the effect on the covariate variable and the covariate variable affects the control variable. Using the ancillary information based on conditional independence hypotheses and ignorability, the sufficient conditions to determine whether the covariate variable is an irrelevant factor or whether there is no confounding in each counterfactual model are obtained.

Estimation of Treatment Effects in Nonlinear Models with Unobserved Confounding

Estimation of treatment effects is one of the crucial mainstays in economics and sociology studies.The problem will become more serious and complicated if the treatment variable is endogenous for the presence of unobserved confounding.The estimation and conclusion are likely to be biased and misleading if the endogeny of treatment variable is ignored.In this article,we propose the pseudo maximum likelihood method to estimate treatment effects in nonlinear models.The proposed method allows the unobserved confounding and random error terms to exist in an arbitrary relationship(such as,add or multiply),and the unobserved confounding have different influence directions on treatment variables and outcome variables.The proposed estimator is consistent and asymptotically normally distributed.Simulation studies show that the proposed estimator performs better than the special regression estimator,and the proposed method is stable for various distribution of error terms.Finally,the proposed method is applied to the real data that studies the influence of individuals have health insurance on an individual’s decision to visit a doctor.

Postrandomization Confounding Challenges the Applicability of Randomized Clinical Trials in Comparative Effectiveness Research

Different from trials for regulatory approvals of new interventions aimed to test the efficacy, comparative effectiveness research （CER） is the direct comparison of existing health-care interventions （compared with active controls） to examine which treatment works best, for whom,

Design of Experiments (DOE)—A Valuable Multi-Purpose Methodology

The DOE methodology is an effective tool for upgrading the level of measurement and assessment. In any design, planning or control problem the designer is faced with many alternatives. He/she is challenged to develop design approaches that can meet both quality and cost criteria. The way experiments are designed greatly affects the effective use of the experimental resources and the easiness with which the measured results can be analyzed. This paper does not present new evidence based on designed experiments. Its objective is solely to show how useful application of multifactor experiments is in a variety of circumstances and decision making scenarios. The paper reviews three published examples where this method was used in different contexts: quality control, flexible manufacturing systems (FMS) and logistics systems. The physical experiment has been carried out to improve the quality of a special type of batteries. The simulation experiment has been carried out to investigate the impact of several flexibility factors in a flexible manufacturing system. The numerical value of a complex analytical expression representing a customer oriented logistics performance measure has been calculated for different values of its parameters, i.e. the given numerical values of the investigated factors. It enabled a methodical examination of all factor effects and especially their interactions, thus shedding light on complex aspects of the logistics decision problem. In these examples, cases from different contexts were presented, enabling to view design of experiments as a powerful ingredient for improving decision making in a variety of circumstances.

Improving the conduct of meta-analyses of observational studies

The author, who has published numerous meta-analyses of epidemiological studies, particularly on tobacco, comments on various aspects of their content. While such meta-analyses, even when well conducted, are more difficult to draw inferences from than are meta-analyses of clinical trials, they allow greater insight into an association than do simple qualitative reviews. This editorial starts with a discussion of some problems relating to hypothesis definition. These include the definition of the outcome, the exposure and the population to be considered, as well as the study inclusion and exclusion criteria. Under literature searching, the author argues against restriction to studies published in peer-reviewed journals, emphasising the fact that relevant data may be available from other sources. Problems of identifying studies and double counting are discussed, as are various issues in regard to data entry. The need to check published effect estimates is emphasised, and techniques to calculate estimates from material provided in the source publication are described. Once the data have been collected and an overall effect estimate obtained, tests for heterogeneity should be conducted in relation to different study characteristics. Though some meta-analysts recommend classifying studies by an overall index of study quality, the author prefers to separately investigate heterogeneity by those factors which contribute to the assessment of quality. Reasons why an association may not actually reflect a true causal relationship are also discussed, with the editorial describing techniques for investigating the relevance of confounding, and referring to problems resulting from misclassification of key variables. Misclassification of disease, exposure and confounding variables can all produce a spurious association, as can misclassification of the variable used to determine whether an individual can enter the study, and the author points to techniques to adjust for this. Issues relating to publication bias and the interpretation of "statistically significant" results are also discussed. The editorial should give the reader insight into the difficulties of producing a good meta-analysis.

Effect of Skin Pigmentation on Near Infrared Spectroscopy

The purpose of this study was to determine the effects of skin pigmentation regarding Near Infrared Spectroscopy (NIRS) tissue oxygen saturation values (StO2). The study examined NIRS values in individuals with varying skin pigmentation on the anterior compartment of the lower leg and volar forearm to determine if correlation exists among three NIRS devices, the EQUANOX, Casmed, and INVOS. Skin pigmentation was measured on the anterior lower leg (AL) and volar forearm (VF) of participants using a noninvasive colorimeter that employed reflective spectroscopy to produce a quantitative value for erythema (skin “redness”) and melanin (skin pigment). Muscle oxygenation was measured using three oximetry devices with sensors placed in the same areas. The EQUANOX device showed no significant correlation with skin pigmentation, while the Casmed and INVOS devices showed moderate and significant correlation with skin pigmentation, respectively. Different devices have different abilities to remove confounding variables, such as skin pigmentation and erythema, which may affect clinical decision-making, and affect the use of NIRS technology.

响应面优化超声波辅助提取荠菜维生素C工艺

采用超声波辅助提取荠菜中维生素C,使用混合分式析因设计(Confounded Fractional Factorial)实验选出荠菜维生素C提取的主要因素,采用Box-Behnken响应面法优化提取工艺,得到荠菜中维生素C的最佳提取工艺条件。结果表明,影响荠菜中维生素C提取的显著因素为超声时间、超声温度、料液比;最佳提取工艺条件:1%草酸;料液比1∶10(g/mL);超声时间12 min;超声温度32℃;超声功率100 W,在此条件下提取荠菜中维生素C的量为(54.88±0.22)mg/100 g。响应面法优化提取方程拟合度高,可用于预测荠菜中维生素C的提取。

Diet and Rat Strain as Factors in Nervous System Function and Influence of Confounders

The necessity for understanding normal human cognitive processes and behavior, and themechanisrns which result in dysfunction in these processes are dependant on utilization of a suitable animal model. In order to develop pharmaceutical agents to alleviate mental disturbances and enable the individual to cope within the norms of society, it is incumbent upon investigators to choose a species in which pharmacokinetic principles are established and resemble those of hurnans. The choice of rats in cognition research studies has specific advantages in that these anirnals possess similar pharrnacodynamic parameters to hurnans. Further advantages include availability, low cost, ease of breeding, maintenance and an extensive literature database which enable comparisons to present findings. However, there are substantial differences in the perforrnance of various rat strains in tasks of learning, memory, attention, and responses to stress or drugs. In addition to rat strain, quantity of thed also exerts profound consequences on animal behavior. The aim of this review is to demonstrate that there are differences in the central nervous systern responsivencess of rat strains to chemicals and these could be related to factors such as source of supplier, type and quantity of feed, or season of the year. It is also evident that the genotype differs amongst strains and this may be responsible for the observed differences in CNS sensitivity to chemicals. Strain differences must be identified and taken into consideration in interpretation of assessrnent of neurobehavioural functions. It is also incumbent upon the investigators to utilize healthy (diet-controlled) animal models.

Effects of Dietary Intake of Arsenosugars and Other Organic Arsenic Species on Studies of Arsenic Methylation Efficiency in Humans

Extensive research has used dimethylarsinic acid(DMA)in urine as a marker of arsenic methylation.The premise is that humans methylate inorganic arsenicals to monomethylarsonic acid(MMA)and DMA and excrete these arsenic species into the urine.However,DMA in urine not only comes from the methylation of inorganic arsenic but also could be a result of metabolism of other arsenic species,such as arsenosugars and arsenolipids.Most environmental health and epidemiological studies of arsenic methylation might have overlooked confounding factors that contribute to DMA in urine.Here we critically evaluate reported studies that used methylation indexes,concentration ratios of methylated arsenicals,or the percentage of DMA in urine as markers of arsenic methylation efficiency.Dietary intake of arsenosugars potentially confounds the calculation and interpretation of the arsenic methylation efficiencies.Many studies have not considered incidental dietary intake of arsenosugars,arsenolipids,and other organic arsenic species.Future studies should consider the dietary intake of diverse arsenic species and their potential effect on the urinary concentrations of DMA.

Cross-Entropy Loss for Recommending Efficient Fold-Over Technique

Due to the limited resources and budgets in many real-life projects, it is unaffordable to use full factorial experimental designs and thus fractional factorial(FF) designs are used instead. The aliasing of factorial effects is the price we pay for using FF designs and thus some significant effects cannot be estimated. Therefore, some additional observations(runs) are needed to break the linages among the factorial effects. Folding over the initial FF designs is one of the significant approaches for selecting the additional runs. This paper gives an in-depth look at fold-over techniques via the following four significant contributions. The first contribution is on discussing the adjusted switching levels foldover technique to overcome the limitation of the classical one. The second contribution is on presenting a comparison study among the widely used fold-over techniques to help experimenters to recommend a suitable fold-over technique for their experiments by answering the following two fundamental questions:Do these techniques dramatically lessen the confounding of the initial designs, and do the resulting combined designs(combining initial design with its fold-over) via these techniques have considerable difference from the optimality point of view considering the markedly different searching domains in each technique? The optimality criteria are the aberration, confounding, Hamming distance and uniformity. Many of these criteria are given in sequences(patterns) form, which are inconvenient and costly to represent and compare, especially when the designs have many factors. The third innovation is on developing a new criterion(dictionary cross-entropy loss) to simplify the existing criteria fromsequence to scalar. The new criterion leads to a more straightforward and easy comparison study. The final contribution is on establishing a general framework for the connections between initial designs and combined designs based on any fold-over technique.

Mendelian randomization and pleiotropy analysis

Background:Mendelian randomization(MR)analysis has become popular in inferring and estimating the causality of an exposure on an outcome due to the success of genome wide association studies.Many statistical approaches have been developed and each of these methods require specific assumptions.Results:In this article,we review the pros and cons of these methods.We use an example of high-density lipoprotein cholesterol on coronary artery disease to illuminate the challenges in Mendelian randomization investigation.Conclusion:The current available MR approaches allow us to study causality among risk factors and outcomes.However,novel approaches are desirable for overcoming multiple source confounding of risk factors and an outcome in MR analysis.

Designing Optimal Large Four-Level Experiments:A New TechniqueWithout Recourse to Optimization Softwares

Experimental design is an effective statistical tool that is extensively applied in modern industry,engineering,and science.It is proved that experimental design is a powerful and efficient means to screen the relationships between input factors and their responses,and to distinguish significant and unimportant factor effects.In many practical situations,experimenters are faced with large experiments having four-level factors.Even though there are several techniques provided to design such experiments,the challenge faced by the experimenters is still daunting.The practice has demonstrated that the existing techniques are highly time-consuming optimization procedures,satisfactory outcomes are not guaranteed,and non-mathematicians face a significant challenge in dealing with them.A new technique that can overcome these defects of the existing techniques is presented in this paper.The results demonstrated that the proposed technique outperformed the current techniques in terms of construction simplicity,computational efficiency and achieving satisfactory results capability.For non-mathematician experimenters,the new technique is much easier and simpler than the current techniques,as it allows them to design optimal large experiments without the recourse to optimization softwares.The optimality is discussed from four basic perspectives:maximizing the dissimilarity among experimental runs,maximizing the number of independent factors,minimizing the confounding among factors,and filling the experimental domain uniformly with as few gaps as possible.

A practical guide to promote informatics-driven efficient biotopographic material development

Micro/nano topographic structures have shown great utility in many biomedical areas including cell therapies,tissue engineering,and implantable devices.Computer-assisted informatics methods hold great promise for the design of topographic structures with targeted properties for a specific medical application.To benefit from these methods,researchers and engineers require a highly reusable“one structural parameter-one set of cell responses”database.However,existing confounding factors in topographic cell culture devices seriously impede the acquisition of this kind of data.Through carefully dissecting the confounding factors and their possible reasons for emergence,we developed corresponding guideline requirements for topographic cell culture device development to remove or control the influence of such factors.Based on these requirements,we then suggested potential strategies to meet them.In this work,we also experimentally demonstrated a topographic cell culture device with controlled confounding factors based on these guideline requirements and corresponding strategies.A“guideline for the development of topographic cell culture devices”was summarized to instruct researchers to develop topographic cell culture devices with the confounding factors removed or well controlled.This guideline aims to promote the establishment of a highly reusable“one structural parameter-one set of cell responses”database that could facilitate the application of informatics methods,such as artificial intelligence,in the rational design of future biotopographic structures with high efficacy.

Recommendation on unbiased estimation of population attributable fraction calculated in"prevalence and risk factors of active pulmonary tuberculosis among elderly people in China:a population based cross-sectional study"

Population attributable fraction(PAF)refers to the proportion of all cases with a particular outcome in a population that could be prevented by eliminating a specific exposure.The authors of a recent paper evaluated the prevalence and estimated the PAFs for risk factors of TB among elderly people in China[Inf Dis Poverty.2019;8:7].Confounding is inevitable in observational studies and Levin’s formula is of limited use in practice for unbiasedly estimating PAF.In a complex survey design,an unbiased estimation of the PAF can be calculated using a sample-weighted version of the Miettinen formula or a sample weighed parametric g-formula.With respect to causal interpretation of PAF in public health setting,computation of PAF is logical and practical when the exposure is amenable to intervention.

Overview of methodologies and statistical strategies in observational studies and meta-analyses on the risk of hepatocellular carcinoma in patients with chronic hepatitis B on entecavir or tenofovir therapy

Entecavir(ETV)and tenofovir disoproxil fumarate(TDF)are first-line antiviral therapies for patients with chronic hepatitis B(CHB)and reduce the risk of disease progression and liver-related complications,as well as improve survival by effectively suppressing viral replication.Nevertheless,since the first publication in 2019 on a lower risk of hepatocellular carcinoma(HCC)in Korean patients receiving TDF than those receiving ETV,the topic has remained a hot and unsettled debate.Multiple studies and meta-analyses have yielded conflicting results.As HCC takes time to develop,studies are mainly observational to benefit from a larger sample size and longer follow-up that provides a higher statistical power to compare the two treatments.However,TDF was available to CHB patients a few years later than ETV in most countries,thus leading to a difference in follow-up duration.Moreover,despite studying the same topic,the difference in data sources and available parameters,inclusion and exclusion criteria,and use of statistical methods complicated the interpretation and comparison of the findings and contributed to between-study heterogeneity in meta-analyses.This review describes some caveats in interpreting and comparing the results from these observational studies and meta-analyses.Future studies should explore better designed observational studies with high-quality data sources,and aggregation of patient data in metaanalysis to tackle between-study heterogeneity.

Methods for Population-Based eQTL Analysis in Human Genetics

Gene expression is a critical process in biological system that is influenced and modulated by many factors including genetic variation. Expression Quantitative Trait Loci（e QTL） analysis provides a powerful way to understand how genetic variants affect gene expression. For genome wide e QTL analysis, the number of genetic variants and that of genes are large and thus the search space is tremendous. Therefore, e QTL analysis brings about computational and statistical challenges. In this paper, we provide a comprehensive review of recent advances in methods for e QTL analysis in population-based studies. We first present traditional pairwise association methods, which are widely used in human genetics. To account for expression heterogeneity, we investigate the methods for correcting confounding factors. Next, we discuss newly developed statistical learning methods including Lasso-based models. In the conclusion, we provide an overview of future method development in analyzing e QTL associations. Although we focus on human genetics in this review, the methods are applicable to many other organisms.