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Biomedical subjects

Enrique F Schisterman

Publications and source records attributed to Enrique F Schisterman.

At least 19 recordsLinked to original sources
PubMed2006

Receiver operating characteristic curve inference from a sample with a limit of detection.

The receiver operating characteristic curve is a commonly used tool for evaluating biomarker usefulness in clinical diagnosis of disease. Frequently, biomarkers being assessed have immeasurable or unreportable samples below some limit of detection. Ignoring observations below the limit of detection leads to negatively biased estimates of the area under the curve. Several correction methods are suggested in the areas of mean estimation and testing but nothing regarding the receiver operating characteristic curve or its summary measures. In this paper, the authors show that replacement values below the limit of detection, including those suggested, result in the same biased area under the curve when properly accounted for, but they also provide guidance on the usefulness of these values in limited situations. The authors demonstrate maximum likelihood techniques leading to asymptotically unbiased estimators of the area under the curve for both normally and gamma distributed biomarker levels. Confidence intervals are proposed, the coverage probability of which is scrutinized by simulation study. An example using polychlorinated biphenyl levels to classify women with and without endometriosis illustrates the potential benefits of these methods.

Biomarkers↗
PubMed2006

The birth weight "paradox" uncovered?

Low birth weight (LBW) infants have lower infant mortality in groups in which LBW is most frequent. For example, in 1991, US infants born to smokers had higher risks of both LBW and infant mortality than infants born to nonsmokers. However, among LBW infants, infant mortality was lower for infants born to smokers (relative rate = 0.79). There are competing theories regarding this so-called "paradox." One is that maternal smoking is beneficial for LBW infants. The authors use causal diagrams to show that, even in the absence of any beneficial effect of smoking, an inverse association due to stratification on birth weight can be found. This variable is affected by the exposure of interest and shares common causes with the outcome. That is, LBW infants born to smokers may have a lower risk of mortality than other LBW infants whose LBW is due to causes associated with high mortality (e.g., birth defects). Under realistic causal diagrams, adjustment for birth weight is unwarranted when the analytical goal is to estimate overall effects of prenatal variables on infant mortality. Even for estimating direct effects of prenatal variables, adjustment for birth weight may be invalid when there is an unmeasured common cause of LBW and mortality. An appropriate justification for conditioning on birth weight requires specifying 1) the causal question motivating this analytical approach and 2) the assumptions regarding the proposed underlying biologic mechanisms.

Causality↗
PubMed2006

Selecting controls is not selecting "normals": design and analysis issues for studying the etiology of polycystic ovary syndrome.

INTRODUCTION: Polycystic ovary syndrome (PCOS) is a highly prevalent disorder among postmenarcheal premenopausal women, accounting for much of anovulatory infertility, and it is associated with several comorbidities. The natural history of PCOS, as well as hypotheses regarding its etiology, facilitates employment of the case-control study design. However, a review of the literature suggested that inconsistency of research findings in relation to the etiology of PCOS might be, in part, due to the different choice of controls for case-control design and application. DESIGN: Investigators have often employed "healthy" control groups and neglected to consider the tenability of the "rare disease" assumption in the analysis when instituting the case-control strategy. This might result in the introduction of a positive bias or overestimation of odds ratios, producing an effect estimate that is more extreme than that in the underlying population. Using several quantitative, though hypothetical, literature-driven examples, this bias is described and demonstrated. In addition, recommendations are provided with regard to case-sampling strategy when the rare disease assumption is untenable, as it may frequently be in studies of PCOS. CONCLUSION(S): It is hoped that more consistent case-control methodology, in concert with recent consensus on case definition, will more effectively facilitate the elucidation of the causes and consequences of PCOS.

Bias↗
PubMed2006

Pooling biospecimens and limits of detection: effects on ROC curve analysis.

Frequently, epidemiological studies deal with two restrictions in the evaluation of biomarkers: cost and instrument sensitivity. Costs can hamper the evaluation of the effectiveness of new biomarkers. In addition, many assays are affected by a limit of detection (LOD), depending on the instrument sensitivity. Two common strategies used to cut costs include taking a random sample of the available samples and pooling biospecimens. We compare the two sampling strategies when an LOD effect exists. These strategies are compared by examining the efficiency of receiver operating characteristic (ROC) curve analysis, specifically the estimation of the area under the ROC curve (AUC) for normally distributed markers. We propose and examine a method to estimate AUC when dealing with data from pooled and unpooled samples where an LOD is in effect. In conclusion, pooling is the most efficient cost-cutting strategy when the LOD affects less than 50% of the data. However, when much more than 50% of the data are affected, utilization of the pooling design is not recommended.

Biomarkers↗
PubMed2006

ROC analysis for markers with mass at zero.

The receiver operating characteristic (ROC) curve and in particular the area under the curve (AUC) is commonly used to examine the discriminatory ability of diagnostic markers. Certain markers while basically continuous and non-negative have a positive probability mass (spike) at the value zero. We discuss a flexible modelling approach to such data and contrast it with the standard non-parametric approach. We show how the modelling approach can be extended to take account of the effect of explanatory variables. We motivate this problem and illustrate the modelling approach using data on the coronary calcium score, measured by electron beam tomography, which is a marker for atherosclerosis.

Adult↗
PubMed2006

The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve.

The use of biomarkers is of ever-increasing importance in clinical diagnosis of disease. In practice, a cutpoint is required for dichotomizing naturally continuous biomarker levels to distinguish persons at risk of disease from those who are not. Two methods commonly used for establishing the "optimal" cutpoint are the point on the receiver operating characteristic curve closest to (0,1) and the Youden index, J. Both have sound intuitive interpretations--the point closest to perfect differentiation and the point farthest from none, respectively--and are generalizable to weighted sensitivity and specificity. Under the same weighting of sensitivity and specificity, these two methods identify the same cutpoint as "optimal" in certain situations but different cutpoints in others. In this paper, the authors examine situations in which the two criteria agree or disagree and show that J is the only "optimal" cutpoint for given weighting with respect to overall misclassification rates. A data-driven example is used to clarify and demonstrate the magnitude of the differences. The authors also demonstrate a slight alteration in the (0,1) criterion that retains its intuitive meaning while resulting in consistent agreement with J. In conclusion, the authors urge that great care be taken when establishing a biomarker cutpoint for clinical use.

Biomarkers↗
PubMed2006

The limitations due to exposure detection limits for regression models.

Biomarker use in exposure assessment is increasingly common, and consideration of related issues is of growing importance. Exposure quantification may be compromised when measurement is subject to a lower threshold. Statistical modeling of such data requires a decision regarding the handling of such readings. Various authors have considered this problem. In the context of linear regression analysis, Richardson and Ciampi (Am J Epidemiol 2003;157:355-63) proposed replacement of data below a threshold by a constant equal to the expectation for such data to yield unbiased estimates. Use of such an imputation has some limitations; distributional assumptions are required, and bias reduction in estimation of regression parameters is asymptotic, thereby presenting concerns about small studies. In this paper, the authors propose distribution-free methods for managing values below detection limits and evaluate the biases that may result when exposure measurement is constrained by a lower threshold. The authors utilize an analytical approach and a simulation study to assess the effects of the proposed replacement method on estimates. These results may inform decisions regarding analytical plans for future studies and provide a possible explanation for some amount of the discordance seen in extant literature.

Bias↗
PubMed2006

Efficient design and analysis of biospecimens with measurements subject to detection limit.

Pooling biospecimens is a well accepted sampling strategy in biomedical research to reduce study cost of measuring biomarkers, and has been shown in the case of normally distributed data to yield more efficient estimation. In this paper we examine the efficiency of pooling, in the context of information matrix related to estimators of unknown parameters, when the biospecimens being pooled yield incomplete observations due to the instruments' limit of detection. Our investigation of three sampling strategies shows that, for a range of values of the detection limit, pooling is the most efficient sampling procedure. For certain other values of the detection limit, pooling can perform poorly.

Analytic Sample Preparation Methods↗
PubMed2006

Prediction of pelvic inflammatory disease among young, single, sexually active women.

OBJECTIVES: To assess prediction strategies for pelvic inflammatory disease (PID). STUDY DESIGN: One thousand one hundred seventy women were enrolled based on a high chlamydial risk score. Incident PID over a median of 3 years was diagnosed by either histologic endometritis or Centers for Disease Control and Prevention criteria. A multivariable prediction model for PID was assessed. RESULTS: Women enrolled using the risk score were young, single, sexually active, and often had prior sexually transmitted infections. Incident PID was common (8.6%). From 24 potential predictors, significant factors included age at first sex, gonococcal/chlamydial cervicitis, history of PID, family income, smoking, medroxyprogesterone acetate use, and sex with menses. The model correctly predicted 74% of incident PID; in validation models, correct prediction was only 69%. CONCLUSIONS: Our data validate a modified chlamydial risk factor scoring system for prediction of PID. Additional multivariable modeling contributed little to prediction. Women identified by a threshold value on the chlamydial risk score should undergo intensive education and screening.

Adolescent↗
PubMed2006

Multistage evaluation of measurement error in a reliability study.

We introduce sequential testing procedures for the planning and analysis of reliability studies to assess an exposure's measurement error. The designs allow repeated evaluation of reliability of the measurements and stop testing if early evidence shows the measurement error is within the level of tolerance. Methods are developed and critical values tabulated for a number of two-stage designs. The methods are exemplified using an example evaluating the reliability of biomarkers associated with oxidative stress.

Biomarkers↗
PubMed2006

Analysis of repeated pregnancy outcomes.

Women tend to repeat reproductive outcomes, with past history of an adverse outcome being associated with an approximate two-fold increase in subsequent risk. These observations support the need for statistical designs and analyses that address this clustering. Failure to do so may mask effects, result in inaccurate variance estimators, produce biased or inefficient estimates of exposure effects. We review and evaluate basic analytic approaches for analysing reproductive outcomes, including ignoring reproductive history, treating it as a covariate or avoiding the clustering problem by analysing only one pregnancy per woman, and contrast these to more modern approaches such as generalized estimating equations with robust standard errors and mixed models with various correlation structures. We illustrate the issues by analysing a sample from the Collaborative Perinatal Project dataset, demonstrating how the statistical model impacts summary statistics and inferences when assessing etiologic determinants of birth weight.

Adolescent↗
PubMed2005

On linear combinations of biomarkers to improve diagnostic accuracy.

We consider combining multiple biomarkers to improve diagnostic accuracy. Su and Liu derived the linear combinations that maximize the area under the receiver operating characteristic (ROC) curves. These linear combinations, however, may have unsatisfactory low sensitivity over a certain range of desired specificity. In this paper, we consider maximizing sensitivity over a range of specificity. We first present a simpler proof for Su and Liu's main theorem and further investigate some other optimal properties of their linear combinations. We then derive alternative linear combinations that have higher sensitivity over a range of high (or low) specificity. The methods are illustrated using data from a study evaluating biomarkers for coronary heart disease.

Adult↗
PubMed2005

The Youden Index and the optimal cut-point corrected for measurement error.

Random measurement error can attenuate a biomarker's ability to discriminate between diseased and non-diseased populations. A global measure of biomarker effectiveness is the Youden index, the maximum difference between sensitivity, the probability of correctly classifying diseased individuals, and 1-specificity, the probability of incorrectly classifying health individuals. We present an approach for estimating the Youden index and associated optimal cut-point for a normally distributed biomarker that corrects for normally distributed random measurement error. We also provide confidence intervals for these corrected estimates using the delta method and coverage probability through simulation over a variety of situations. Applying these techniques to the biomarker thiobarbituric acid reaction substance (TBARS), a measure of sub-products of lipid peroxidation that has been proposed as a discriminating measurement for cardiovascular disease, yields a 50% increase in diagnostic effectiveness at the optimal cut-point. This result may lead to biomarkers that were once naively considered ineffective becoming useful diagnostic devices.

Biomarkers↗
PubMed2005

Optimal cut-point and its corresponding Youden Index to discriminate individuals using pooled blood samples.

Costs can hamper the evaluation of the effectiveness of new biomarkers. Analysis of smaller numbers of pooled specimens has been shown to be a useful cost-cutting technique. The Youden index (J), a function of sensitivity (q) and specificity (p), is a commonly used measure of overall diagnostic effectiveness. More importantly, J is the maximum vertical distance or difference between the ROC curve and the diagonal or chance line; it occurs at the cut-point that optimizes the biomarker's differentiating ability when equal weight is given to sensitivity and specificity. Using the additive property of the gamma and normal distributions, we present a method to estimate the Youden index and the optimal cut-point, and extend its applications to pooled samples. We study the effect of pooling when only a fixed number of individuals are available for testing, and pooling is carried out to save on the number of assays. We measure loss of information by the change in root mean squared error of the estimates of the optimal cut-point and the Youden index, and we study the extent of this loss via a simulation study. In conclusion, pooling can result in a substantial cost reduction while preserving the effectiveness of estimators, especially when the pool size is not very large.

Algorithms↗
PubMed2005

Pregnancy outcomes in smokers who develop pre-eclampsia.

Maternal smoking reduces the risk of pre-eclampsia, but has been reported to increase the risk of adverse outcomes related to the disease. We used data from the trial of Calcium for Pre-eclampsia Prevention (CPEP) to explore whether clinical manifestations of pre-eclampsia were altered by maternal smoking. CPEP was a randomised study of 4589 nulliparous women conducted in five US medical centres. Smoking history was obtained at study enrolment and women were monitored for the development of hypertension, proteinuria, and other medical complications. Among pre-eclamptic women (n=274), the risk of severe disease was not elevated in smokers (adjusted odds ratio 0.87 [95% confidence interval (CI) 0.30, 2.51]). Compared with non-smokers, gestational age (days, +/-SE) at onset of pre-eclampsia was not reduced in smokers (264.8 +/- 1.5, and 268.2 +/- 5.5, respectively, P=0.48). The smoking-attributable deficit in birthweight was not increased in pre-eclamptic women compared with normotensive women (97 g [95% CI -49, 244] and 185 g [95% CI 141, 229] respectively). In conclusion, among women who developed pre-eclampsia, smoking during pregnancy was not associated with disease severity. We found no evidence that pre-eclampsia and smoking act synergistically to restrict fetal growth.

Cohort Studies↗
PubMed2005

Lipid adjustment in the analysis of environmental contaminants and human health risks.

The literature on exposure to lipophilic agents such as polychlorinated biphenyls (PCBs) is conflicting, posing challenges for the interpretation of potential human health risks. Laboratory variation in quantifying PCBs may account for some of the conflicting study results. For example, for quantification purposes, blood is often used as a proxy for adipose tissue, which makes it necessary to model serum lipids when assessing health risks of PCBs. Using a simulation study, we evaluated four statistical models (unadjusted, standardized, adjusted, and two-stage) for the analysis of PCB exposure, serum lipids, and health outcome risk (breast cancer). We applied eight candidate true causal scenarios, depicted by directed acyclic graphs, to illustrate the ramifications of misspecification of underlying assumptions when interpreting results. Statistical models that deviated from underlying causal assumptions generated biased results. Lipid standardization, or the division of serum concentrations by serum lipids, was observed to be highly prone to bias. We conclude that investigators must consider biology, biologic medium (e.g., nonfasting blood samples), laboratory measurement, and other underlying modeling assumptions when devising a statistical plan for assessing health outcomes in relation to environmental exposures.

Bias↗