Semiparametric inference for a 2-stage outcome-auxiliary-dependent sampling design with continuous outcome

Two-stage design has long been recognized to be a cost-effective way for conducting biomedical studies. In many trials, auxiliary covariate information may also be available, and it is of interest to exploit these auxiliary data to improve the efficiency of inferences. In this paper, we propose a 2-stage design with continuous outcome where the second-stage data is sampled with an "outcome-auxiliary-dependent sampling" (OADS) scheme. We propose an estimator which is the maximizer for an estimated likelihood function. We show that the proposed estimator is consistent and asymptotically normally distributed. The simulation study indicates that greater study efficiency gains can be achieved under the proposed 2-stage OADS design by utilizing the auxiliary covariate information when compared with other alternative sampling schemes. We illustrate the proposed method by analyzing a data set from an environmental epidemiologic study.     

Colpodean ciliate phylogeny and reference alignments for phylogenetic placements

The Colpodea form a major clade of ciliates that are often found in environmental DNA sequencing studies. They are united by similar somatic ciliature, but differentiated by complex oral structures. Although there are four well supported colpodean subclades, there is disagreement in molecular phylogenetic inferences about their branching order. Using available nuclear SSU-rRNA sequences, we evaluated if the bursariomorphids or the platyophryids are sister to the remaining colpodeans. We inferred the “platyophryids-early” topologies using different alignment and masking methods, but constrained analyses could not reject the “bursariomorphids-early” topology. Both bursariomorphids and platyophryids clades have a similar number of nucleotide positions shared with the outgroup, and both are interconnected with the outgroup in phylogenetic networks. Based on these discordant results, it is hard to determine which clade branched off first, although the “platyophryids-early topology” is also supported by mitochondrial SSU-rRNA data. We also offer different reference alignments that can be used to phylogenetically place short- and long-read data from environmental DNA sequencing studies, and we propose some tentative evolutionary and ecological interpretations of those placements.     

Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates

Stochastic compartmental models of the SEIR type are often used to make inferences on epidemic processes from partially observed data in which only removal times are available. For many epidemics, the assumption of constant removal rates is not plausible. We develop methods for models in which these rates are a time-dependent step function. A reversible jump MCMC algorithm is described that permits Bayesian inferences to be made on model parameters, particularly those associated with the step function. The method is applied to two datasets on outbreaks of smallpox and a respiratory disease. The analyses highlight the importance of allowing for time dependence by contrasting the predictive distributions for the removal times and comparing them with the observed data.      

What Can Be Done to Ensure the Usefulness of Epidemiologic Data for Risk Assessment Purposes?

Epidemiologic studies can play a central role in risk assessments. They are used in all risk assessment phases: hazard identification, dose-response, and exposure assessment. Epidemiologic studies have often been the first to show that a particular environmental exposure is a hazard to health. They have numerous advantages with respect to other sources of data which are used in risk assessments, the most important being that they do not require the assumption that they are generalizable to humans. For this reason, fewer and lower uncertainty factors may be appropriate in risk characterization based on epidemiologic studies. Unfortunately, epidemiologic studies have numerous problems, the most important being that the exposures are often not precisely measured. This article presents in detail the advantages of and problems with epidemiologic studies. It discusses two approaches to ensure their usefulness, biomarkers and an ordinance which requires baseline and subsequent surveillance of possible exposures and health effects from newly sited potentially polluting facilities. Biomarkers are biochemical measures of exposure, susceptibility factors, or preclinical pathological changes. Biomarkers are a way of dealing with the problems of poor measures, differential susceptibility and lack of early measures of disease occurrence that inherent in many environmental epidemiologic studies. The advantages of biomarkers is they can provide objective information on exposure days, months or even years later and evidence of pathology perhaps years earlier. The ordinance makes possible the use of a powerful epidemiologic study design, the prospective cohort study, where confounder(s) are best measured, and exposures, pathological changes, and health effects can be detected as soon as possible.     

Design of a binary biomarker study from the results of a pilot study

Biomarkers are increasingly used in clinical and epidemiologic studies. Prior to these studies, small pilot studies are often conducted to assess the reproducibility of the biomarker. This article discusses how the results of a pilot study can be used to design subsequent studies when the biomarker is a binary assessment. We consider situations in which the pilot study has two factors (e.g., laboratory and individual) that are either crossed or nested. We discuss how binary random-effects models can be used for estimating the sources of variation and how parameter estimates from these models can be used to appropriately design future studies. We also show that fitting a linear variance components model that ignores the binary nature of the data is a simple alternative method that results in nearly unbiased and moderately efficient estimators of important design parameters. We illustrate the methodology with data from a study assessing the reproducibility of p53 immunohistochemistry in bladder tumors.     

Polyploidy and genome evolution in plants

Genome doubling (polyploidy) has been and continues to be a pervasive force in plant evolution. Modern plant genomes harbor evidence of multiple rounds of past polyploidization events, often followed by massive silencing and elimination of duplicated genes. Recent studies have refined our inferences of the number and timing of polyploidy events and the impact of these events on genome structure. Many polyploids experience extensive and rapid genomic alterations, some arising with the onset of polyploidy. Survivorship of duplicated genes are differential across gene classes, with some duplicate genes more prone to retention than others. Recent theory is now supported by evidence showing that genes that are retained in duplicate typically diversify in function or undergo subfunctionalization. Polyploidy has extensive effects on gene expression, with gene silencing accompanying polyploid formation and continuing over evolutionary time.     

Relationships among non-native plants, diversity of plants and butterflies, and adequacy of spatial sampling

Non-native invasive species are altering ecosystems in undesirable ways, often leading to biotic homogenization and rapid reduction of evolutionary potential. However, lack of money and time hampers attempts to monitor the outcome of restoration efforts. Hence, it is useful to determine whether relatively limited sampling can provide valid inferences about biological responses to pattern-based and process-based variables that are affected by restoration actions. In the Mojave Desert, invasion of salt-cedar ( Tamarix ramosissima ) has altered vegetational communities and some measures of faunal diversity. We tested whether six vegetation-based predictor variables affected species richness of butterflies in the Muddy River drainage (Nevada, USA). We also explored whether similar conclusions about relationships between vegetation and butterflies could have been obtained by using data from a subset of the 85 locations included in the study. We found that the effect of non-native plants on species richness of butterflies was negligible. Availability of nectar had the greatest independent explanatory power on species richness of butterflies, followed by species richness of plants. In comparison with the full data set, subsamples including 10, 25 and 50% of sites yielded similar conclusions. Our results suggest that relatively limited data sets may allow us to draw reliable inferences for adaptive management in the context of ecological restoration and rehabilitation.  © 2005 The Linnean Society of London, Biological Journal of the Linnean Society , 2005, 85 , 157–166.     

Fundamental Identifiability Limits in Molecular Epidemiology

Viral phylogenies provide crucial information on the spread of infectious diseases, and many studies fit mathematical models to phylogenetic data to estimate epidemiological parameters such as the effective reproduction ratio (R_e) over time. Such phylodynamic inferences often complement or even substitute for conventional surveillance data, particularly when sampling is poor or delayed. It remains generally unknown, however, how robust phylodynamic epidemiological inferences are, especially when there is uncertainty regarding pathogen prevalence and sampling intensity. Here, we use recently developed mathematical techniques to fully characterize the information that can possibly be extracted from serially collected viral phylogenetic data, in the context of the commonly used birth-death-sampling model. We show that for any candidate epidemiological scenario, there exists a myriad of alternative, markedly different, and yet plausible “congruent” scenarios that cannot be distinguished using phylogenetic data alone, no matter how large the data set. In the absence of strong constraints or rate priors across the entire study period, neither maximum-likelihood fitting nor Bayesian inference can reliably reconstruct the true epidemiological dynamics from phylogenetic data alone; rather, estimators can only converge to the “congruence class” of the true dynamics. We propose concrete and feasible strategies for making more robust epidemiological inferences from viral phylogenetic data.     

Water-escape in weanling rats: A link between behaviour and biological fitness

Relationships between behaviour, measured in many standard laboratory settings, and biological fitness are often obscure. By making inferences from the genetic architecture revealed in psychogenetic studies, links between behaviour and biological fitness can be forged. To support these inferences, information is needed about the genetic architecture underlying behaviour which has unequivocal links to fitness. In this study, water-escape in rats is related a priori to fitness. Weanling rats from the Roman High Avoidance and Roman Low Avoidance strains and their reciprocal F₁ crosses are tested in a water-escape apparatus. A strong directional dominance found for rapid escape is in keeping with the assumptions made and confirms the test as a useful tool for psychogenetic studies of rats.     

Assessing familial aggregation of age at onset, by using estimating equations, with application to breast cancer.

In genetic research of chronic diseases, age-at-onset outcomes within families are often correlated. The nature of correlation of age-at-onset outcomes is indicative of common genetic and/or shared environmental risk factors among family members. Understanding patterns of such correlation may shed light on the disease etiology and, hence, is an important step to take prior to further searching for the responsible genes via segregation and linkage studies. Age-at-onset outcomes are different from those familiar quantitative or qualitative traits for which many statistical methods have been developed. In comparison with the quantitative traits, age-at-onset outcomes are often censored, i.e., instead of actual age-at-onset outcomes, only the current ages or ages at death are observed. They are also different from qualitative traits because of their continuity. Because of the complexity of correlated censored outcomes, few methods have yet been developed. A traditional approach is to impose a parametric joint distribution for the correlated age-at-onset outcomes, which has been criticized for requiring a stringent assumption about the entire distribution of age at onset. The purpose of this paper is to describe a method for assessing familial aggregation of correlated age-at-onset outcomes semiparametrically, by use of estimating equations. This method does not require any parametric assumption for modeling the age at onset. The estimates of parameters, including those quantifying the correlation within families, are consistent and have an asymptotic normal distribution that can be used to make inferences. To illustrate this new method, we analyzed two age-at-onset data sets that were obtained from studies conducted in the States of Washington and Hawaii, with the objective of quantifying the familial aggregations of age at onset of breast cancer.     

DNA methylation in white blood cells

Alterations in DNA methylation patterns, both at specific loci and overall in the genome, have been associated with many different health outcomes. In cancer and other diseases, most of these changes have been observed at the tissue level. Data on whether DNA methylation changes in white blood cells (WBC) can serve as a useful biomarker for different health outcomes are much more limited, but rapidly emerging. Epidemiologic studies have reported associations between global WBC methylation and several different cancers including cancers of the colon, bladder, stomach, breast and head and neck, as well as schizophrenia and myelodysplastic syndrome. Evidence for WBC methylation at specific loci and disease risk is more limited, but increasing. Differences in WBC DNA methylation by selected risk factors including demographic (age, gender, race), environmental exposures (benzene, persistent organic pollutants, lead, arsenic, and air pollution), and other risk factors (cigarette smoke, alcohol drinking, body size, physical activity and diet) have been observed in epidemiologic studies though the patterns are far from consistent. Challenges in inferences from the existing data are primarily due to the cross-sectional and small size of most studies to date as well as the differences in results across assay type and source of DNA. Large, prospective studies will be needed to understand whether changes in risk factors are associated with changes in DNA methylation patterns, and if changes in DNA methylation patterns are associated with changes in disease endpoints.     

Adjusting for selection bias in retrospective, case-control studies

Retrospective case–control studies are more susceptibleto selection bias than other epidemiologic studies as by designthey require that both cases and controls are representativeof the same population. However, as cases and control recruitmentprocesses are often different, it is not always obvious thatthe necessary exchangeability conditions hold. Selection biastypically arises when the selection criteria are associatedwith the risk factor under investigation. We develop a methodwhich produces bias-adjusted estimates for the odds ratio. Ourmethod hinges on 2 conditions. The first is that a variablethat separates the risk factor from the selection criteria canbe identified. This is termed the "bias breaking" variable.The second condition is that data can be found such that a bias-correctedestimate of the distribution of the bias breaking variable canbe obtained. We show by means of a set of examples that suchbias breaking variables are not uncommon in epidemiologic settings.We demonstrate using simulations that the estimates of the oddsratios produced by our method are consistently closer to thetrue odds ratio than standard odds ratio estimates using logisticregression. Further, by applying it to a case–controlstudy, we show that our method can help to determine whetherselection bias is present and thus confirm the validity of studyconclusions when no evidence of selection bias can be found.     

Shedding light on the chemical diversity of ectopic calcifications in kidney tissues: diagnostic and research aspects

In most industrialized countries, different epidemiologic studies show that chronic renal failure is dramatically increasing. Such major public health problem is a consequence of acquired systemic diseases such as type II diabetes, which is now the first cause for end stage renal failure. Furthermore, lithogenic diseases may also induce intratubular crystallization, which may finally result in end-stage renal failure (ESRF). Up to now, such rare diseases are often misdiagnosed. In this study, based on twenty four biopsies, we show that SR μFTIR (Synchrotron Radiation-μFourier transform infrared) spectroscopy constitutes a significant opportunity to characterize such pathological μcalcifications giving not only their chemical composition but also their spatial distribution in the tissues. This experimental approach offers new opportunities to the clinicians to describe at the cell level the physico-chemical processes leading to the formation of the pathological calcifications which lead to ESRF.     

A MALDI-TOF mass spectrometry-based approach for molecular profiling of leaves from pasture and feed forages species

This study compared the molecular data obtained by MALDI profiling of 20 tropical grasses. Biomolecules were extracted from leaves of each species/cultivars. Then, protein-enriched supernatants were analyzed by MALDI-TOF MS under linear mode at m/z 2000–20,000 to generate a main spectrum profile (MSP) for each sample stored as an in-house database. Several molecular components were detected in the samples, mostly below m/z 12,000 and particularly ranging from m/z 3000 to 6000 some of which species- and even cultivar-specific or at least distinguishable by signal intensities. Dendrogram analysis clearly showed three major clades which recapitulated not only phylogenetic relationships similar to those obtained from morphological and genomics data, but also other relevant inferences. The practical use of the method was validated and the high scores obtained proved it can be valuable not only to discriminate grasses, but future studies can apply the method in the evaluation of biotic and abiotic factors, diagnosis of diseases and pathogens, development of products towards weeds control, and evaluation of nutritional value of pastures and forages.     

An empirical Bayes' approach to joint analysis of multiple microarray gene expression studies

With the prevalence of gene expression studies and the relatively low reproducibility caused by insufficient sample sizes, it is natural to consider joint analysis that could combine data from different experiments effectively to achieve improved accuracy. We present in this article a model-based approach for better identification of differentially expressed genes by incorporating data from different studies. The model can accommodate in a seamless fashion a wide range of studies including those performed at different platforms by fitting each data with different set of parameters, and/or under different but overlapping biological conditions. Model-based inferences can be done in an empirical Bayes' fashion. Because of the information sharing among studies, the joint analysis dramatically improves inferences based on individual analysis. Simulation studies and real data examples are presented to demonstrate the effectiveness of the proposed approach under a variety of complications that often arise in practice.     

Inferring long-term nutrient changes in southeastern Ontario lakes: comparing paleolimnological and mass-balance models

Lake eutrophication continues to be a major concern in many lake regions, but long-term monitoring data are often lacking. Therefore, indirect proxy methods must be used to infer these missing data sets. Two methods were applied to infer pre-industrial and present-day lakewater total phosphorus concentrations (TP) in a suite of 50 hardwater lakes in southern Ontario (Canada). One method inferred TP from the diatom species composition in the tops (present-day inferences) and bottoms (pre-1850 inferences) of sediment cores. The other method applied the Lakeshore Capacity Model (LCM), which is a mass-balance model based on phosphorus export coefficients that relate lakes and their watershed characteristics to epilimnetic nutrient concentrations. Diatom-based estimates of preindustrial to present-day change show that 78% of the lakes increased in TP (29% significantly) and 8% decreased. According to model error, 63% of the lakes have not significantly changed. LCM estimates show that 56% of the lakes have increased in TP, and the remainder (44%) have not changed. The average inferred increase in TP was similar for both models, but a lake-by-lake comparison indicated marked differences in model output. In particular, a paired comparison of diatom-based and LCM-based inferences of preindustrial TP shows no correlation. It is suggested that lake managers be thorough when collecting data for either model, and model selection should be carefully considered. The LCM and diatom-based models perform better in regions that are geologically similar to where the respective models were calibrated. Advantages and disadvantages of each model are further discussed.     

Family-based association tests using genotype data with uncertainty

Family-based association studies have been widely used to identify association between diseases and genetic markers. It is known that genotyping uncertainty is inherent in both directly genotyped or sequenced DNA variations and imputed data in silico. The uncertainty can lead to genotyping errors and missingness and can negatively impact the power and Type I error rates of family-based association studies even if the uncertainty is independent of disease status. Compared with studies using unrelated subjects, there are very few methods that address the issue of genotyping uncertainty for family-based designs. The limited attempts have mostly been made to correct the bias caused by genotyping errors. Without properly addressing the issue, the conventional testing strategy, i.e. family-based association tests using called genotypes, can yield invalid statistical inferences. Here, we propose a new test to address the challenges in analyzing case-parents data by using calls with high accuracy and modeling genotype-specific call rates. Our simulations show that compared with the conventional strategy and an alternative test, our new test has an improved performance in the presence of substantial uncertainty and has a similar performance when the uncertainty level is low. We also demonstrate the advantages of our new method by applying it to imputed markers from a genome-wide case-parents association study.     

Dietary fish oils containing eicosapentaenoic acid and the prevention of atherosclerosis and thrombosis.

Recent epidemiologic studies have shown that rates of cardiovascular disease are lower in populations such as the Greenland Eskimos than in those that do not eat seafood, even though the levels of dietary fat intake are often similar. Dietary fish oils are rich in eicosapentaenoic acid (EPA), a polyunsaturated fatty acid of the omega-3 series. EPA has been shown to prolong bleeding time and to decrease platelet aggregation and blood viscosity. EPA inhibits the production of prostaglandins from endogenous arachidonic acid, which is associated with the formation of thromboxane A2 and may also dampen cyclo-oxygenase and lipoxygenase metabolites involved in mediating endothelial cell proliferation. Dietary fish oils are now available in the form of EPA-enriched capsules. Short-term trials in humans have shown that EPA significantly reduces the levels of plasma triglycerides and may increase the levels of high-density lipoproteins; however, no consistent effect on serum cholesterol levels has been shown. The results of evaluations of EPA''s use in patients with renal disorders, mild hypertension, inflammatory disorders or hyperlipidemia have been promising. On the basis of the epidemiologic and biologic evidence dietary fish oils warrant further study in extensive clinical trials.     

Use of NHANES Data to Link Chemical Exposures to Chronic Diseases: A Cautionary Tale

Background

The National Health and Nutrition Examination Survey (NHANES) is one example of cross-sectional datasets that have been used to draw causal inferences regarding environmental chemical exposures and adverse health outcomes. Our objectives were to analyze four NHANES datasets using consistent a priori selected methods to address the following questions: Is there a consistent association between urinary bisphenol A (BPA) measures and diabetes, coronary heart disease (CHD), and/or heart attack across surveys? Is NHANES an appropriate dataset for investigating associations between chemicals with short physiologic half-lives such as BPA and chronic diseases with multi-factorial etiologies? Data on urinary BPA and health outcomes from 2003–2004, 2005–2006, 2007–2008, and 2009–2010 were available.

Methodology and Findings

Regression models were adjusted for creatinine, age, gender, race/ethnicity, education, income, smoking, heavy drinking, BMI, waist circumference, calorie intake, family history of heart attack, hypertension, sedentary time, and total cholesterol. Urinary BPA was not significantly associated with adverse health outcomes for any of the NHANES surveys, with ORs (95% CIs) ranging from 0.996 (0.951–1.04) to 1.03 (0.978–1.09) for CHD, 0.987 (0.941–1.04) to 1.04 (0.996–1.09) for heart attack, and 0.957 (0.899–1.02) to 1.01 (0.980–1.05) for diabetes.

Conclusions

Using scientifically and clinically supportable exclusion criteria and outcome definitions, we consistently found no associations between urinary BPA and heart disease or diabetes. These results do not support associations and causal inferences reported in previous studies that used different criteria and definitions. We are not drawing conclusions regarding whether BPA is a risk factor for these diseases. We are stating the opposite–that using cross-sectional datasets like NHANES to draw such conclusions about short-lived environmental chemicals and chronic complex diseases is inappropriate. We need to expend resources on appropriately designed epidemiologic studies and toxicological explorations to understand whether these types of chemicals play a causal role in chronic diseases.     

Trees of unusual size: biased inference of early bursts from large molecular phylogenies

An early burst of speciation followed by a subsequent slowdown in the rate of diversification is commonly inferred from molecular phylogenies. This pattern is consistent with some verbal theory of ecological opportunity and adaptive radiations. One often-overlooked source of bias in these studies is that of sampling at the level of whole clades, as researchers tend to choose large, speciose clades to study. In this paper, we investigate the performance of common methods across the distribution of clade sizes that can be generated by a constant-rate birth-death process. Clades which are larger than expected for a given constant-rate branching process tend to show a pattern of an early burst even when both speciation and extinction rates are constant through time. All methods evaluated were susceptible to detecting this false signature when extinction was low. Under moderate extinction, both the [Formula: see text]-statistic and diversity-dependent models did not detect such a slowdown but only because the signature of a slowdown was masked by subsequent extinction. Some models which estimate time-varying speciation rates are able to detect early bursts under higher extinction rates, but are extremely prone to sampling bias. We suggest that examining clades in isolation may result in spurious inferences that rates of diversification have changed through time.