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1.  A randomised trial to evaluate the Effects of low dose Aspirin in Gestation and Reproduction (EAGeR): Design and baseline characteristics 
Paediatric and perinatal epidemiology  2013;27(6):10.1111/ppe.12088.
Background
Low dose aspirin (LDA) has been proposed to improve pregnancy outcomes in couples experiencing recurrent pregnancy loss. However, results from studies of LDA on pregnancy outcomes have been inconsistent, perhaps because most studies evaluated LDA-initiated post-conception. The purpose of the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial was to determine whether preconception-initiated LDA improves live-birth rates in women with 1–2 prior losses.
Methods
We performed a multicenter, block randomised, double-blind, placebo-controlled trial. Study participants were recruited using community-based advertisements and physician referral to four university medical centers in the US (2006–12). Eligible women were aged 18–40 years actively trying to conceive with 1–2 prior losses. Participants were randomised to receive daily LDA (81 mg/day) or a matching placebo, and all were provided with daily 400 mcg folic acid. Follow-up continued for ≤six menstrual cycles while attempting to conceive. For those that conceived, treatment was continued until 36 weeks gestation. The primary outcome was the cumulative live birth rate over the trial period.
Results
1228 women were randomised (615 LDA, 613 placebo). Participants had a mean age of 28.7, were mostly white (95%), well educated (86% >high school education), and employed (75%) with a household income >$100,000 annually (40%). Characteristics of those in the treatment and placebo arms were well-balanced.
Conclusions
We describe the study design, recruitment, data collection, and baseline characteristics of participants enrolled in EAGeR, which aimed to determine the effect of LDA on live birth and other pregnancy outcomes in these women.
doi:10.1111/ppe.12088
PMCID: PMC3821875  PMID: 24118062
Low-dose aspirin; conception; pregnancy; miscarriage; sub-fertility
2.  Estimation of the ROC Curve under Verification Bias 
Summary
The ROC (Receiver Operating Characteristic) curve is the most commonly used statistical tool for describing the discriminatory accuracy of a diagnostic test. Classical estimation of the ROC curve relies on data from a simple random sample from the target population. In practice, estimation is often complicated due to not all subjects undergoing a definitive assessment of disease status (verification). Estimation of the ROC curve based on data only from subjects with verified disease status may be badly biased. In this work we investigate the properties of the doubly robust (DR) method for estimating the ROC curve under verification bias originally developed by Rotnitzky et al. (2006) for estimating the area under the ROC curve. The DR method can be applied for continuous scaled tests and allows for a non ignorable process of selection to verification. We develop the estimator's asymptotic distribution and examine its finite sample properties via a simulation study. We exemplify the DR procedure for estimation of ROC curves with data collected on patients undergoing electron beam computer tomography, a diagnostic test for calcification of the arteries.
doi:10.1002/bimj.200800128
PMCID: PMC3475535  PMID: 19588455
Diagnostic test; Nonignorable; Semiparametric model; Sensitivity analysis; Sensitivity; Specificity
3.  Youden Index and the optimal threshold for markers with mass at zero‡ 
Statistics in medicine  2008;27(2):297-315.
SUMMARY
The Youden Index is often used as a summary measure of the receiver operating characteristic curve. It measures the effectiveness of a diagnostic marker and permits the selection of an optimal threshold value or cutoff point for the biomarker of interest. Some markers, while basically continuous and positive, have a spike or positive mass of probability at the value zero. We provide a flexible modeling approach for estimating the Youden Index and its associated cutoff point for such spiked data and compare it with the standard empirical approach. We show how this modeling approach can be adjusted to take covariate information into account. This approach is applied to data on the Coronary Calcium Score, a marker for atherosclerosis. Published in 2007 by John Wiley & Sons, Ltd.
doi:10.1002/sim.2993
PMCID: PMC2749250  PMID: 17624866
Box–Cox power transformations; Coronary Calcium Score; diagnostic markers; mixture model; ROC curve; sensitivity; specificity

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