David Faraggi

University of Haifa, H̱efa, Haifa District, Israel

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Publications (74)209.81 Total impact

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    ABSTRACT: We sought to assess the relationship between a short interpregnancy interval (IPI) following a pregnancy loss and subsequent live birth and pregnancy outcomes.
    American journal of obstetrics and gynecology. 09/2014;
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    ABSTRACT: Preconception-initiated low-dose aspirin might positively affect pregnancy outcomes, but this possibility has not been adequately assessed. Our aim was to investigate whether low-dose aspirin improved livebirth rates in women with one to two previous pregnancy losses. In this multicentre, block-randomised, double-blind, placebo-controlled trial, women aged 18-40 years who were attempting to become pregnant were recruited from four medical centres in the USA. Participants were stratified by eligibility criteria-the original stratum was restricted to women with one loss at less than 20 weeks' gestation during the previous year, whereas the expanded stratum included women with one to two previous losses, with no restrictions on gestational age or time of loss. Women were block-randomised by centre and eligibility stratum in a 1:1 ratio. Preconception-initiated daily low-dose aspirin (81 mg per day) plus folic acid was compared with placebo plus folic acid for up to six menstrual cycles; for women who conceived, study treatment continued until 36 weeks' gestation. Participants, trial staff, and investigators were masked to the assigned treatment. The primary outcome was livebirth rate, which was analysed by intention to treat. The trial is registered with ClinicalTrials.gov, number NCT00467363. Overall, 1228 women were recruited and randomly assigned between June 15, 2007, and July 15, 2011, 1078 of whom completed the trial and were included in the analysis (535 in the low-dose aspirin group and 543 in the placebo group). 309 (58%) women in the low-dose aspirin group had livebirths, compared with 286 (53%) in the placebo group (p=0·0984; absolute difference in livebirth rate 5·09% [95% CI -0·84 to 11·02]). Pregnancy loss occurred in 68 (13%) women in the low-dose aspirin group, compared with 65 (12%) women in the placebo group (p=0·7812). In the original stratum, 151 (62%) of 242 women in the low-dose aspirin group had livebirths, compared with 133 (53%) of 250 in the placebo group (p=0·0446; absolute difference in livebirth rate 9·20% [0·51 to 17·89]). In the expanded stratum, 158 (54%) of 293 women in the low-dose aspirin group and 153 (52%) of 293 in the placebo group had livebirths (p=0·7406; absolute difference in livebirth rate 1·71% [-6·37 to 9·79]). Major adverse events were similar between treatment groups. Low-dose aspirin was associated with increased vaginal bleeding, but this adverse event was not associated with pregnancy loss. Preconception-initiated low-dose aspirin was not significantly associated with livebirth or pregnancy loss in women with one to two previous losses. However, higher livebirth rates were seen in women with a single documented loss at less than 20 weeks' gestation during the previous year. Low-dose aspirin is not recommended for the prevention of pregnancy loss. Eunice Kennedy Shriver National Institute of Child Health and Human Development (US National Institutes of Health).
    The Lancet 04/2014; · 39.21 Impact Factor
  • American Journal of Obstetrics and Gynecology 01/2014; 210(1):S8. · 3.88 Impact Factor
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    ABSTRACT: 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 livebirth rates in women with one to two prior losses. We performed a multicentre, block randomised, double-blind, placebo-controlled trial. Study participants were recruited using community-based advertisements and physician referral to four university medical centres in the US (2006-12). Eligible women were aged 18-40 years actively trying to conceive, with one to two 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 ≤6 menstrual cycles while attempting to conceive. For those who conceived, treatment was continued until 36 weeks gestation. The primary outcome was the cumulative livebirth rate over the trial period. There were 1228 women randomised (615 LDA, 613 placebo). Participants had a mean age of 28.7, were mostly white (95%), well educated (86% more than high school education), and employed (75%) with a household income >$100 000 annually (40%). The characteristics of those in the treatment and placebo arms were well balanced. 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 livebirth and other pregnancy outcomes in these women.
    Paediatric and Perinatal Epidemiology 10/2013; · 2.16 Impact Factor
  • Ronen Fluss, Benjamin Reiser, David Faraggi
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    ABSTRACT: The ROC (receiver operating characteristic) curve is frequently used for describing effectiveness of a diagnostic marker or test. Classical estimation of the ROC curve uses independent identically distributed samples taken randomly from the healthy and diseased populations. Frequently not all subjects undergo a definitive gold standard assessment of disease status (verification). Estimation of the ROC curve based on data only from subjects with verified disease status may be badly biased (verification bias). In this work we investigate the properties of the doubly robust (DR) method for estimating the ROC curve adjusted for covariates (ROC regression) under verification bias. We develop the estimator's asymptotic distribution and examine its finite sample size properties via a simulation study. We apply this procedure to fingerstick postprandial blood glucose measurement data adjusting for age.
    Journal of Statistical Planning and Inference 01/2012; 142(1):1–11. · 0.71 Impact Factor
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    ABSTRACT: The efficacy of self-hypnosis (SH), masking (MA) and attentiveness to the patient's complaints (AT) in the alleviation of tinnitus was evaluated. Forty-five male patients close in age with chronic tinnitus related to acoustic trauma were assigned to three matched subgroups: SH, AT or MA. The therapeutic stimuli in the SH and MA sessions, recorded on audio cassettes, were given to the patients for use when needed. SH significantly reduced the tinnitus severity; AT partially relieved the tinnitus; MA did not have any significant effect.
    Audiology: official organ of the International Society of Audiology 07/2009; 32(3):205-12.
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    ABSTRACT: 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, Faraggi and Schisterman (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.
    Biometrical Journal 07/2009; 51(3):475-90. · 1.15 Impact Factor
  • European Journal of Epidemiology 01/2009; 24(8). · 5.12 Impact Factor
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    ABSTRACT: 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.
    Statistics in Medicine 02/2008; 27(2):297-315. · 2.04 Impact Factor
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    ABSTRACT: Previous studies have suggested that diabetes and metabolic syndrome are significant risk factors for coronary artery disease (CAD). However, in women, their relative importance remains controversial. To evaluate risk factors for CAD in women and their association with the severity and extent of coronary angiographic findings. We clinically evaluated 243 consecutive female patients with chest pain who underwent coronary angiography. The location and extent of coronary artery occlusions were assessed using the modified Gensini index. Compared with women with normal coronary arteries (n = 90), those with CAD (n = 153) reported less physical activity (p = 0.001), and had higher prevalences of diabetes (p = 0.046), hypertension (p = 0.002), and the metabolic syndrome (p = 0.001). They also had lower HDL cholesterol levels (p = 0.017), higher levels of triglycerides (p = 0.005), and higher fasting plasma glucose (FPG) (p < 0.001). Physical activity, FPG, serum triglycerides and HDL-cholesterol, but not the metabolic syndrome, were independent predictors of CAD. A score combining the extent and severity of angiographic findings was significantly higher in women with diabetes (p = 0.007), hypertension (p = 0.010) and FPG >or=100 mg/dl (p = 0.031), but showed no association with the metabolic syndrome. In a multivariate linear regression analysis, diabetes was an independent predictor of the extent and severity of angiographic score (p = 0.013). Diabetes, fasting plasma glucose and hypertension, but not the metabolic syndrome, were associated with severity of coronary angiographic findings in these women.
    QJM: monthly journal of the Association of Physicians 09/2007; 100(9):575-81. · 2.36 Impact Factor
  • David Faraggi, R. Simon, E. Yaskil, A. Kramar
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    ABSTRACT: Neural networks are considered by many to be very promising tools for classification and prediction. The flexibility of the neural network models often result in over-fit. Shrinking the parameters using a penalized likelihood is often used in order to overcome such over-fit. In this paper we extend the approach proposed by FARAGGI and SIMON (1995a) to modeling censored survival data using the input-output relationship associated with a single hidden layer feed-forward neural network. Instead of estimating the neural network parameters using the method of maximum likelihood, we place normal prior distributions on the parameters and make inferences based on derived posterior distributions of the parameters. This Bayesian formulation will result in shrinking the parameters of the neural network model and will reduce the over-fit compared with the maximum likelihood estimators. We illustrate our proposed method on a simulated and a real example.
    Biometrical Journal 01/2007; 39(5):519 - 532. · 1.15 Impact Factor
  • David Faraggi, Richard Simon
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    ABSTRACT: Neural networks have received much attention in recent years mostly by non-statisticians. The purpose of this paper is to incorporate neural networks in a non-linear regression model and obtain maximum likelihood estimates of the network parameters using a standard Newton-Raphson algorithm. We use maximum likelihood estimators instead of the usual back-propagation technique and compare the neural network predictions with predictions of quadratic regression models and with non-parametric nearest neighbor predictions. These comparisons are made using data generated from a variety of functions. Because of the number of parameters involved, neural network models can easily over-fit the data, hence validation of results is crucial.
    Biometrical Journal 01/2007; 37(6):713 - 725. · 1.15 Impact Factor
  • David Faraggi, Benjamin Reiser
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    ABSTRACT: Recently Faraggi and Reiser (1991) introduced a general dynamic treatment allocation scheme which incorporates the permuted block allocation procedure and the Begg Iglewicz procedure as special cases. A drawback of previous allocation methods is that they did not allow incorporation of prior knowledge of the relative importance of the prognostic factors. In this paper we extend our allocation model to incorporate the weighting of prognostic factors. A simulation study examines the effects of this procedure and an example of its implementation is given.
    Biometrical Journal 01/2007; 35(2):143 - 149. · 1.15 Impact Factor
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    ABSTRACT: In order to compare the discriminatory effectiveness of two diagnostic markers the equality of the areas under the respective Receiver Operating Characteristic Curves is commonly tested. A non-parametric test based on the Mann-Whitney statistic is generally used. Weiand et al. (1989) present a parametric test based on normal distributional assumptions. We extend this test using the Box-Cox power family of transformations to non-normal situations. These three test procedures are compared in terms of significance level and power by means of a large simulation study. Overall we find that transforming to normality is to be preferred. An example of two pancreatic cancer serum biomarkers is used to illustrate the methodology.
    Biometrical Journal 09/2006; 48(5):745-57. · 1.15 Impact Factor
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    ABSTRACT: 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.
    Statistics in Medicine 03/2006; 25(4):623-38. · 2.04 Impact Factor
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    ABSTRACT: The area under the receiver operating characteristic curve (AUC) is a popular summary measure of the efficacy of a medical diagnostic test to discriminate between healthy and diseased subjects. A frequently encountered problem in studies that evaluate a new diagnostic test is that not all patients undergo disease verfication because the verification test is expensive, invasive, or both. Furthermore, the decision to send patients to verification often depends on the new test and on other predictors of true disease status. In such cases, usual estimators of the AUC based on verified patients only are biased. In this article we develop estimators of the AUC of markers measured on any scale that adjust for selection to verification. These estimators adjust for measured patient covariates and diagnostic test results and also for an assumed degree of residual selection bias. They can then be used in a sensitivity analysis to examine how the AUC estimates change when different plausible degrees of residual association are assumed. As with other missing-data problems, due to the curse of dimensionality, a model for disease or a model for selection is needed to obtain well-behaved estimators of the AUC when the marker and/or the measured covariates are continuous. We describe a doubly robust estimator that has the attractive feature of being consistent and asymptotically normal if either the disease or the selection model (but not necessarily both) is correct.
    Journal of the American Statistical Association 02/2006; 101(September):1276-1288. · 1.83 Impact Factor
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    Ronen Fluss, David Faraggi, Benjamin Reiser
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    ABSTRACT: The Youden Index is a frequently used summary measure of the ROC (Receiver Operating Characteristic) curve. It both, measures the effectiveness of a diagnostic marker and enables the selection of an optimal threshold value (cutoff point) for the marker. In this paper we compare several estimation procedures for the Youden Index and its associated cutoff point. These are based on (1) normal assumptions; (2) transformations to normality; (3) the empirical distribution function; (4) kernel smoothing. These are compared in terms of bias and root mean square error in a large variety of scenarios by means of an extensive simulation study. We find that the empirical method which is the most commonly used has the overall worst performance. In the estimation of the Youden Index the kernel is generally the best unless the data can be well transformed to achieve normality whereas in estimation of the optimal threshold value results are more variable.
    Biometrical Journal 09/2005; 47(4):458-72. · 1.15 Impact Factor
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    ABSTRACT: To evaluate heart rate variability (HRV) of patients with traumatic brain injury (TBI). By a prospective study, the HRV was assessed in 20 patients with TBI during the sub-acute period post-injury (the first test was performed at a mean time post-insult of 38 days) and a matched control. The patients were examined twice, 1 month apart. The assessment included HRV (both in time and frequency domains), GCS, length of coma, brain CT, FIM and FAM. A significant difference was found between patients and controls concerning HRV total power, i.e. frequencies between 0.01-0.6 Hz (high frequency p = 0.003, low frequency p = 0.013, total power p = 0.034) and for standard deviation of RR interval p = 0.011. HRV changes were related more to the timing of the evaluation than to the severity of the brain damage. HRV differed of patients with TBI and in the control group. Tendency to HRV normalization changes was detected during the first 3 months after the injury, which suggests recovery of the autonomic nervous system.
    Brain Injury 09/2005; 19(8):605-11. · 1.51 Impact Factor
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    ABSTRACT: This study aimed to investigate the prevalence of a reduced glomerular filtration rate (GFR) with and without albuminuria and its ability to predict cardiac events in asymptomatic diabetic patients undergoing stress-rest thallium-201 myocardial perfusion single-photon emission computed tomography. Diabetic patients have a higher prevalence of asymptomatic coronary heart disease. Therefore, identifying predictors of cardiac events in asymptomatic diabetic patients is needed. In 269 asymptomatic patients, baseline evaluation included diabetes-related complications, including creatinine clearance (CrCl) and albuminuria. During follow-up (mean 2.3 +/- 1.0 years), all cardiac events were recorded. Seventy-seven patients (29%) had a reduced GFR defined by CrCl <60 ml/min/1.73 m(2). Compared with the 177 patients with CrCl >/=60 ml/min/1.73 m(2), the reduced GFR group was older (p < 0.0001), had a longer duration of diabetes (p = 0.002), and had a higher prevalence of albuminuria (p = 0.04). Nevertheless, 35% of the reduced GFR group had normoalbuminuria. Patients with reduced GFR had a significant two-fold increase in total cardiac events (unstable angina, nonfatal myocardial infarction, and cardiac procedures) (25% vs. 13%, p = 0.019), and multivariate analysis found that reduced GFR was an independent predictor of cardiac events (odds ratio [OR] 2.2, 95% confidence interval [CI] 1.1 to 4.46). Other independent predictors of cardiac events included stress-induced abnormal myocardial perfusion imaging (OR 3.1, 95% CI 1.3 to 7.5), an electrocardiographic ischemic response (OR 2.7, 95% CI 1.01 to 7.14), and peripheral artery disease (OR 2.1, 95% CI 1.05 to 4.23); however, albuminuria was not. A reduced GFR was common in our group of asymptomatic diabetic patients and was associated with a two-fold increase in cardiac events. Multivariate analysis found that reduced GFR independent of albuminuria was a significant predictor of cardiac events.
    Journal of the American College of Cardiology 01/2005; 44(11):2142-8. · 14.09 Impact Factor
  • Journal of Nuclear Cardiology 01/2005; 12(2). · 2.85 Impact Factor

Publication Stats

1k Citations
209.81 Total Impact Points

Institutions

  • 1990–2014
    • University of Haifa
      • Department of Statistics
      H̱efa, Haifa District, Israel
  • 2009
    • Sheba Medical Center
      Gan, Tel Aviv, Israel
    • Universidad Torcuato di Tella
      • Departamento de Economía
      Buenos Aires, Buenos Aires F.D., Argentina
  • 1994–2007
    • National Cancer Institute (USA)
      • Biometrics Research Branch
      Maryland, United States
  • 2001–2002
    • University at Buffalo, The State University of New York
      • Department of Social and Preventive Medicine
      Buffalo, NY, United States
  • 1992–2001
    • Technion - Israel Institute of Technology
      • Rambam Medical Center
      Haifa, Haifa District, Israel
  • 1998
    • Memorial Sloan-Kettering Cancer Center
      • Department of Pathology
      New York City, NY, United States
  • 1995
    • Rambam Medical Center
      • Department of Oncology
      Haifa, Haifa District, Israel
    • Biomedical Research Institute, Rockville
      Maryland, United States