David Zucker | The Hebrew University of Jerusalem (original) (raw)

Papers by David Zucker

International Journal of Environmental Research and Public Health

Young children are particularly vulnerable to harms from tobacco smoke exposure (TSE). This study... more Young children are particularly vulnerable to harms from tobacco smoke exposure (TSE). This study aimed to compare TSE: (1) between children who live in smoking families and those who do not; and (2) among children who live in smoking households with varying smoking locations. The data came from two studies that were conducted concurrently in Israel (2016–2018). Study 1: a randomized controlled trial of smoking families (n = 159); Study 2: a cohort study of TSE among children in non-smoking families (n = 20). Hair samples were collected from one child in each household. Baseline hair nicotine data were analyzed for 141 children in Study 1 and 17 children in Study 2. Using a logistic regression analysis (exposed vs. not exposed as per laboratory determination) and a linear regression (log hair nicotine), we compared TSE between: (1) children in Study 1 vs. Study 2; (2) children in families with different smoking locations in Study 1: balcony; garden, yard, or other place outside of t...

Cancers, Apr 2, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Statistics in Medicine, 1993

Sequential monitoring has been a topic of major interest in clinical trials methodology over the ... more Sequential monitoring has been a topic of major interest in clinical trials methodology over the past two decades. This paper presents a unified conceptual framework for sequential monitoring that covers a wide variety of monitoring procedures in a wide variety of clinical trial settings. The central elements of this framework consist of a suitable concept of statistical information and a scheme for using this concept as a basis for summarizing the accumulating results of a trial in a standardized form, through a stochastic process that can be shown to approximate classical Brownian motion. The ideas are developed in a simple step‐by‐step fashion and illustrated by several practical examples.

Encyclopedia of Biostatistics, 2005

Health Education Research, 2009

This paper describes the effect of a preschool hygiene intervention program on psychosocial measu... more This paper describes the effect of a preschool hygiene intervention program on psychosocial measures of educators regarding handwashing and communicable pediatric disease. A clusterrandomized trial, with randomization at the level of the preschool, was run in 40 Jerusalem preschool classrooms. Eighty preschool educators participated. The program used a multipronged approach which included elements aimed at staff, children, parents, school nurses and the classroom environment. Frontal lectures by medical, epidemiological and educational experts, along with printed materials and experiential learning, were provided to staff. Responses from a validated survey instrument were used to build four scales for each respondent regarding beliefs, attitudes, self-efficacy and knowledge. The scales were built on a Likert-type 1-7 scale (1 5 minimum, 7 5 maximum). The effect of the intervention was tested using mixed model analysis of variance. Response was received from 92.5% of educators. Educators believed that handwashing could affect health (mean 5 5.5, SD 5 1.1), had high levels of self-efficacy (mean 5 6.1, SD 5 0.9) and had positive attitudes toward handwashing (mean 5 5.7, SD 5 1.2). Knowledge was affected by the intervention (intervention: mean 5 6.2, SD 5 0.7; control: mean 5 5.8, SD 5 0.8). The combination of positive attitudes toward handwashing among educators and the program's effectiveness in imparting knowledge helped to create a sustained social norm of handwashing among many children in disparate locations.

American Heart Journal, 1987

It is now more than 20 years since it was first suggested that mitral valve prolapse (MVP) was as... more It is now more than 20 years since it was first suggested that mitral valve prolapse (MVP) was associated with an increased risk of infective endocarditis (IE).’ Support for the existence of this association was initially provided by case reports of IE in patients with preexisting MVP,2 series of IE patients in which persons with MVP appeared to be overrepresented,3.6 and small follow-up studies of patients with MVP documenting a relatively high incidence of IIL6eQ More recently, several casecontrol studies have been conducted in an effort to determine more accurately the magnitude of the association between MVP and IE.10-‘2 These studies have provided estimates of the relative risk of IE in persons with MVP in comparison with those without MVP. However, it is also possible to make some estimate of the absolute risk of IE (i.e., the actual probability of IE developing over a specific time intervaI) using the results of these studies together with data about the incidence of IE and prevalence of MVP in the general population. In an effort to produce the best estimates of the relative and absolute risks of IE in patients with MVP, we examine the published results of the relevant studies together with new data from a study conducted at the New York Hospital-Cornell Medical Center. We consider the risks in the total population of persons with MVP and the risks in certain subgroups identified by various characteristics postulated to be associated with a higher than average risk of IE. We consider the implications of our estimates of the risks of IE for decisions about strategies for antibiotic prophylaxis in persons with MVP.

arXiv (Cornell University), Aug 24, 2018

The standard Cox model in survival analysis assumes that the covariate effect is constant across ... more The standard Cox model in survival analysis assumes that the covariate effect is constant across the entire covariate domain. However, in many applications, there is interest in considering the possibility that the covariate of main interest is subject to a threshold effect: a change in the slope at a certain point within the covariate domain. Often, the value of this threshold is unknown and need to be estimated. In addition, often, the covariate of interest is not measured exactly, but rather is subject to some degree of measurement error. In this paper, we discuss estimation of the model parameters under an independent additive error model where the covariate of interesting is measured with error and the potential threshold value in this covariate is unknown. As in earlier work which discussed the case of konwn threshold, we study the performance of several bias correction methods: two versions of regression calibration (RC1 and RC2), two versions of the fitting a model for the induced relative risk (RR1 and RR2), maximum pseudo-partial likelihood estimator (MPPLE) and simulation-extrapolation (SIMEX). These correction methods are compared with the naive estimator. We develop the relevant theory, present a simulation study comparing the several correction methods, and illustrate the use of the bias correction methods in data from the Nurses Health Study (NHS) concerning the relationship between chronic air pollution exposure to particulate matter of diameter 10 µm or less (PM10). The simulation results suggest that the best overall choice of bias correction method is either the RR2 method or the MPPLE method.

International Journal of Environmental Research and Public Health, 2020

Children who live with smokers are at risk of poor health, and of becoming smokers themselves. Mi... more Children who live with smokers are at risk of poor health, and of becoming smokers themselves. Misperceptions of the nature of tobacco smoke exposure have been demonstrated among parents, resulting in continued smoking in their children’s environment. This study aimed to change parents’ perceptions of exposure by providing information on second- and third-hand exposure and personalised information on children’s exposure [NIH registry (NCT02867241)]. One hundred and fifty-nine families with a child < 8 years and at least one smoking parent were randomized into intervention (69), control (70), and enhanced control (20) groups. Reported exposure, parental smoking details, and a child hair sample were obtained at the start of the study and 6–8 months later. Parental perceptions of exposure (PPE) were assessed via a questionnaire. The intervention consisted of motivational interviews, feedback of home air quality and child’s hair nicotine level, and information brochures. PPE were sig...

Biometrical Journal, 2020

The Cox regression model is a popular model for analyzing the relationship between a covariate ve... more The Cox regression model is a popular model for analyzing the relationship between a covariate vector and a survival endpoint. The standard Cox model assumes a constant covariate effect across the entire covariate domain. However, in many epidemiological and other applications, the covariate of main interest is subject to a threshold effect: a change in the slope at a certain point within the covariate domain. Often, the covariate of interest is subject to some degree of measurement error. In this paper, we study measurement error correction in the case where the threshold is known. Several bias correction methods are examined: two versions of regression calibration (RC1 and RC2, the latter of which is new), two methods based on the induced relative risk under a rare event assumption (RR1 and RR2, the latter of which is new), a maximum pseudo-partial likelihood estimator (MPPLE), and simulation-extrapolation (SIMEX). We develop the theory, present simulations comparing the methods, and illustrate their use on data concerning the relationship between chronic air pollution exposure to particulate matter PM10 and fatal myocardial infarction (Nurses Health Study (NHS)), and on data concerning the effect of a subject's long-term underlying systolic blood pressure level on the risk of cardiovascular disease death (Framingham Heart Study (FHS)). The simulations indicate that the best methods are RR2 and MPPLE.

BMC Public Health, 2018

Background: It is estimated that around 40% of children worldwide are exposed to tobacco smoke, l... more Background: It is estimated that around 40% of children worldwide are exposed to tobacco smoke, largely by their parents. Discrepancies between biochemical measures of exposure and parental report imply that parents may be under-reporting children's exposure. Previous research has shown that there may be a fundamental misunderstanding among smoking parents as to what exactly exposure is and in what circumstances it occurs. Methods: We aimed to develop and validate a measure to assess parental perceptions of exposure (PPE) regarding child tobacco smoke exposure (TSE). A model was developed based on a qualitative study of smoking parents and a questionnaire constructed using pictures and vignettes to assess parental rating of children's exposure in hypothetical situations. The questionnaire was completed online by 220 Israeli parents recruited via social media. Exploratory factor analysis was performed, and reliability and internal consistency were assessed using test-retest reliability and Cronbach's alpha coefficient. Results: Factor analysis produced 6 factors for PPE which explained a cumulative total variance of 76.3%. Factors were termed: 1) secondhand exposure; 2) third-hand exposure; 3) perceived knowledge/certainty; 4) sensory perceptions; 5) time perceptions; and 6) distance perceptions. All sub-scales showed good internal consistency and variance. Test-retest reliability was high (r = 0.856, p = .001). Total PPE score and subscales were highly correlated with risk perceptions r = 0.766. Smokers scored significantly lower on PPE than non-smokers, defining fewer situations as involving greater exposure (p < 0.001). Logistic regression showed PPE was able to discriminate smoking status. Conclusions: Results provide supporting evidence for the PPE as a reliable and valid construct, which can be feasibly measured. Smokers perceived exposure less frequently than non-smokers. This new measure can shed light on parental smoking behaviour and may help us to increase parental awareness of exposure in order to potentially reduce children's exposure to tobacco smoke.

Pediatrics, 2018

Tobacco smoke exposure (TSE) harms children, who are often "captive smokers" in their o... more Tobacco smoke exposure (TSE) harms children, who are often "captive smokers" in their own homes. Project Zero Exposure is a parent-oriented, theory-based intervention designed to reduce child TSE. This paper reports on findings from the pilot study, which was conducted in Israel from 2013 to 2014. The intervention consisted of motivational interviews, child biomarker and home air quality feedback, a Web site, a video, and self-help materials. The primary outcome was child TSE as measured by hair nicotine. Secondary outcome measures were air nicotine and particulate matter, parental reports of TSE, parental smoking behavior, and TSE child protection. A single-group pre- and posttest design was used. Twenty-six of the 29 recruited families completed the study. The intervention was feasible to implement and acceptable to participants. Among the 17 children with reliable hair samples at baseline and follow-up, log hair nicotine dropped significantly after the intervention (= ....

Statistics in medicine, Jan 24, 2016

A common paradigm in dealing with heterogeneity across tumors in cancer analysis is to cluster th... more A common paradigm in dealing with heterogeneity across tumors in cancer analysis is to cluster the tumors into subtypes using marker data on the tumor, and then to analyze each of the clusters separately. A more specific target is to investigate the association between risk factors and specific subtypes and to use the results for personalized preventive treatment. This task is usually carried out in two steps-clustering and risk factor assessment. However, two sources of measurement error arise in these problems. The first is the measurement error in the biomarker values. The second is the misclassification error when assigning observations to clusters. We consider the case with a specified set of relevant markers and propose a unified single-likelihood approach for normally distributed biomarkers. As an alternative, we consider a two-step procedure with the tumor type misclassification error taken into account in the second-step risk factor analysis. We describe our method for bina...

International journal of environmental research and public health, Jan 30, 2015

Tobacco smoke air pollution (TSAP) measurement may persuade parents to adopt smoke-free homes and... more Tobacco smoke air pollution (TSAP) measurement may persuade parents to adopt smoke-free homes and thereby reduce harm to children from tobacco smoke in the home. In a pilot study involving 29 smoking families, a Sidepak was used to continuously monitor home PM2.5 during an 8-h period, Sidepak and/or Dylos monitors provided real-time feedback, and passive nicotine monitors were used to measure home air nicotine for one week. Feedback was provided to participants in the context of motivational interviews. Home PM2.5 levels recorded by continuous monitoring were not well-accepted by participants because of the noise level. Also, graphs from continuous monitoring showed unexplained peaks, often associated with sources unrelated to indoor smoking, such as cooking, construction, or outdoor sources. This hampered delivery of a persuasive message about the relationship between home smoking and TSAP. By contrast, immediate real-time PM2.5 feedback (with Sidepak or Dylos monitor) was feasible...

Wiley Encyclopedia of Clinical Trials, 2007

Randomization-allocation of study treatments to subjects in a random fashion-is a fundamental pil... more Randomization-allocation of study treatments to subjects in a random fashion-is a fundamental pillar of the modern controlled clinical trial. Randomization bolsters the internal validity of a trial in three major respects:

Statistics in Medicine, 2006

For survival data regression, the Cox proportional hazards model is the most popular model, but i... more For survival data regression, the Cox proportional hazards model is the most popular model, but in certain situations the Cox model is inappropriate. Various authors have proposed the proportional odds model as an alternative. Yang and Prentice recently presented a number of easily implemented estimators for the proportional odds model. Here we show how to extend the methods of Yang and Prentice to a family of survival models that includes the proportional hazards model and proportional odds model as special cases. The model is defined in terms of a Box–Cox transformation of the survival function, indexed by a transformation parameter ρ. This model has been discussed by other authors, and is related to the Harrington–Fleming Gρ family of tests and to frailty models. We discuss inference for the case where ρ is known and the case where ρ must be estimated. We present a simulation study of a pseudo‐likelihood estimator and a martingale residual estimator. We find that the methods perf...

Pediatrics, 2014

BACKGROUND AND OBJECTIVE:Worldwide, roughly 40% of children are exposed to the damaging and somet... more BACKGROUND AND OBJECTIVE:Worldwide, roughly 40% of children are exposed to the damaging and sometimes deadly effects of tobacco smoke. Interventions aimed at reducing child tobacco smoke exposure (TSE) have shown mixed results. The objective of this study was to perform a systematic review and meta-analysis to quantify effects of interventions aimed at decreasing child TSE.METHODS:Data sources included Medline, PubMed, Web of Science, PsycNet, and Embase. Controlled trials that included parents of young children were selected. Two reviewers extracted TSE data, as assessed by parentally-reported exposure or protection (PREP) and biomarkers. Risk ratios and differences were calculated by using the DerSimonian and Laird random-effects model. Exploratory subgroup analyses were performed.RESULTS:Thirty studies were included. Improvements were observed from baseline to follow-up for parentally-reported and biomarker data in most intervention and control groups. Interventions demonstrated ...

Lifetime Data Analysis, 2013

Journal of Statistical Theory and Practice, 2013

The Cox regression model is a popular model for analyzing the effect of a covariate on a survival... more The Cox regression model is a popular model for analyzing the effect of a covariate on a survival endpoint. The standard Cox model assumes that the covariate effect is constant across the entire covariate domain. However, in many epidemiological and other applications, there is interest in considering the possibility that the covariate of main interest is subject to a threshold effect: a change in the slope at a certain point within the covariate domain. In this paper, we discuss testing for a threshold effect in the case where the potential threshold value is unknown. We consider a maximum efficiency robust test (MERT) of linear combination form and supremum type tests. We present the relevant theory, present a simulation study comparing the power of various test statistics, and illustrate the use of the tests on data from the Nurses Health Study (NHS) concerning the relationship between chronic exposure to particulate matter of diameter 10 µm or less (PM 10) and fatal myocardial infarction. We also discuss power calculation for studies aimed at investigating the presence of a threshold effect, and present an illustrative power calculation. The simulation results suggest that the best overall choice of test statistic is a three-point supremum type test statistic. The power calculation methodology will be useful in study planning. Matlab software for performing the tests and power calculation is available by download from the first author's website.

Journal of Statistical Planning and Inference, 2008

In this work we present a simple estimation procedure for a general frailty model for analysis of... more In this work we present a simple estimation procedure for a general frailty model for analysis of prospective correlated failure times. Earlier work showed this method to perform well in a simulation study. Here we provide rigorous large-sample theory for the proposed estimators of both the regression coefficient vector and the dependence parameter, including consistent variance estimators.