Enrique F. Schisterman

Overadjustment bias and unnecessary adjustment in epidemiologic studies

Overadjustment is defined inconsistently. This term is meant to describe control (eg, by regression adjustment, stratification, or restriction) for a variable that either increases net bias or decreases precision without affecting bias. We define overadjustment bias as control for an intermediate variable (or a descending proxy for an intermediate variable) on a causal path from exposure to outcome. We define unnecessary adjustment as control for a variable that does not affect bias of the causal relation between exposure and outcome but may affect its precision. We use causal diagrams and an empirical example (the effect of maternal smoking on neonatal mortality) to illustrate and clarify the definition of overadjustment bias, and to distinguish overadjustment bias from unnecessary adjustment. Using simulations, we quantify the amount of bias associated with overadjustment. Moreover, we show that this bias is based on a different causal structure from confounding or selection biases. Overadjustment bias is not a finite sample bias, while inefficiencies due to control for unnecessary variables are a function of sample size.

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 biomarkers 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.

Illustrating bias due to conditioning on a collider

That conditioning on a common effect of exposure and outcome may cause selection, or collider-stratification, bias is not intuitive. We provide two hypothetical examples to convey concepts underlying bias due to conditioning on a collider. In the first example, fever is a common effect of influenza and consumption of a tainted egg-salad sandwich. In the second example, case-status is a common effect of a genotype and an environmental factor. In both examples, conditioning on the common effect imparts an association between two otherwise independent variables; we call this selection bias.

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.

Use of the Social Security Administration Death Master File for ascertainment of mortality status

ObjectivesInternet sources that use the Social Security Administrations (SSA) Death Master File have demonstrated high sensitivity among males for detection of mortality status in comparisons to the National Death Index, but the sensitivity has not been investigated for other demographic groups.MethodsThe authors used the SSA Death Master File to determine the mortality status of 374 decedents from the ongoing Patient Outcomes Study at Cedars-Sinai Medical Center whose deaths were confirmed by physicians using hospital records.ResultsDecedents identified by the SSA Death Master File were significantly older than those not identified. Foreign-born decedents were significantly less likely to be identified as dead than American-born decedents. Gender and marital status were not significant factors for identification by the SSA Death Master File.ConclusionThe results of this study suggest that Internet sources may be used as an inexpensive and effective tool for determination of mortality status. However, among certain populations use of these databases alone may provide incomplete information.

Parental consumption of contaminated sport fish from Lake Ontario and predicted fecundability.

Wildlife studies suggest that consumption of contaminated fish from the Great Lakes may expose humans to polychlorinated biphenyls and persistent chlorinated pesticides. To assess whether time to pregnancy or fecundability is affected, we conducted a telephone survey in 1993 with female members of the New York State Angler Cohort Study who were considering pregnancy between 1991 and 1994 (N = 2,445). Among the 1,234 (50%) women who became pregnant, 895 (73%) had a known time to pregnancy. Upon enrollment into the cohort in 1991, both partners reported duration and frequency of Lake Ontario sport fish consumption. We estimated lifetime exposure to polychlorinated biphenyls from recent consumption and used a discrete-time analog of Cox proportional hazards analysis to estimate conditional fecundability ratios and 95% confidence intervals (CIs) for fish consumption among couples with complete exposure data who discontinued birth control to become pregnant (N = 575). Maternal consumption of fish for 3-6 years was associated with reduced fecundability (fecundability ratio = 0.75; 95% CI = 0.59-0.91), as was more than a monthly fish meal in 1991 (fecundability ratio = 0.73; 95% CI = 0.54-0.98). Our findings suggest that maternal but not paternal consumption of contaminated fish may reduce fecundability among couples attempting pregnancy.

Designing prospective cohort studies for assessing reproductive and developmental toxicity during sensitive windows of human reproduction and development--the LIFE Study.

The relationship between the environment and human fecundity and fertility remains virtually unstudied from a couple-based perspective in which longitudinal exposure data and biospecimens are captured across sensitive windows. In response, we completed the LIFE Study with methodology that intended to empirically evaluate a priori purported methodological challenges: implementation of population-based sampling frameworks suitable for recruiting couples planning pregnancy; obtaining environmental data across sensitive windows of reproduction and development; home-based biospecimen collection; and development of a data management system for hierarchical exposome data. We used two sampling frameworks (i.e., fish/wildlife licence registry and a direct marketing database) for 16 targeted counties with presumed environmental exposures to persistent organochlorine chemicals to recruit 501 couples planning pregnancies for prospective longitudinal follow-up while trying to conceive and throughout pregnancy. Enrolment rates varied from <1% of the targeted population (n = 424,423) to 42% of eligible couples who were successfully screened; 84% of the targeted population could not be reached, while 36% refused screening. Among enrolled couples, ∼ 85% completed daily journals while trying; 82% of pregnant women completed daily early pregnancy journals, and 80% completed monthly pregnancy journals. All couples provided baseline blood/urine samples; 94% of men provided one or more semen samples and 98% of women provided one or more saliva samples. Women successfully used urinary fertility monitors for identifying ovulation and home pregnancy test kits. Couples can be recruited for preconception cohorts and will comply with intensive data collection across sensitive windows. However, appropriately sized sampling frameworks are critical, given the small percentage of couples contacted found eligible and reportedly planning pregnancy at any point in time.

BioCycle study: design of the longitudinal study of the oxidative stress and hormone variation during the menstrual cycle.

Studies in both human and animal species have suggested that oxidative stress may be associated with health outcomes, including the risk of infertility in both males and females. Sex hormones have been shown to have antioxidant properties. The difficulty in studying the role of oxidative stress in females is partly due to fluctuation in these endogenous sex hormones across the menstrual cycle. The aim of this study was to determine the association of oxidative stress levels with endogenous reproductive hormone levels and antioxidants, including vitamin levels, across the menstrual cycle in a prospective cohort of premenopausal women. The goal was to enroll 250 healthy, regularly menstruating premenopausal women for two menstrual cycles. Participants visited the clinic up to 8 times per cycle, at which time blood and urine were collected. The visits occurred at key hormonally defined phases of the menstrual cycle, with the help of an algorithm based on cycle length and data from a fertility monitor. In addition, participants were administered standardised questionnaires, had various physical measures taken, and had other pertinent data collected. A total of 259 women were enrolled in this study, with 250 completing two cycles, despite a demanding study protocol which participants were required to follow. This report describes the study design, baseline characteristics and visit completion rate for the BioCycle study.

Timing Clinic Visits to Phases of the Menstrual Cycle by Using a Fertility Monitor: The BioCycle Study

Planning study visits during specific menstrual cycle phases is important if the exposure or outcome is influenced by hormonal variation. However, hormone profiles differ across cycles and across women. The value of using fertility monitors to time clinic visits was evaluated in the BioCycle Study (2005-2007). Women aged 18-44 years (mean, 27.4) with self-reported menstrual cycle lengths of 21-35 days were recruited in Buffalo, New York, for 2 cycles (n = 250). Participants were provided with home fertility monitors that measured urinary estrone-3-glucuronide and luteinizing hormone (LH). The women were instructed to visit the clinic for a blood draw when the monitor indicated an LH surge. The monitor recorded a surge during 76% of the first cycles and 78% of the second cycles. Scheduling visits by using set cycle days or algorithms based on cycle length, such as a midcycle window or a window determined by assuming a fixed luteal phase length, would be simpler. However, even with perfect attendance in a 3-day window, these methods would have performed poorly, capturing the monitor-detected LH surge only 37%-57% of the time. Fertility monitors appear to be useful in timing clinic visits in a compliant population with flexible schedules.

The relationship between male BMI and waist circumference on semen quality: data from the LIFE study

Michael L. Eisenberg1,*, Sungduk Kim2, Zhen Chen2, Rajeshwari Sundaram2, Enrique F. Schisterman2, and Germaine M. Buck Louis2 Departments of Urology, Obstetrics and Gynecology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305-5118, USA Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, 6100 Executive Blvd., Room 7B03, Rockville, MD 20852, USA