An Approximate Test for Homogeneity of Correlated Correlation Coefficients

This paper develops and evaluates an approximate procedure for testing homogeneity of an arbitrary subset of correlation coefficients among variables measured on the same set of individuals. The sample may have some missing data. The simple test statistic is a multiple of the variance of Fisher r-to-z transformed correlation coefficients relevant to the null hypothesis being tested and is referred to a chi-square distribution. The use of this test is illustrated through several examples. Given the approximate nature of the test statistics, the procedure was evaluated using a simulation study. The accuracy in terms of the nominal and the actual significance levels of this test for several null hypotheses of interest were evaluated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43560/1/11135_2004_Article_394854.pd

A Multiple‐Imputation Analysis of a Case‐Control Study of the Risk of Primary Cardiac Arrest Among Pharmacologicallytreated Hypertensives

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146847/1/rssc02669.pd

Improving on analyses of self-reported data in a large-scale health survey by using information from an examination-based survey

Common data sources for assessing the health of a population of interest include large-scale surveys based on interviews that often pose questions requiring a self-report, such as, ‘Has a doctor or other health professional ever told you that you have 〈 health condition of interest〉 ?’ or ‘What is your 〈 height/weight〉 ?’ Answers to such questions might not always reflect the true prevalences of health conditions (for example, if a respondent misreports height/weight or does not have access to a doctor or other health professional). Such ‘measurement error’ in health data could affect inferences about measures of health and health disparities. Drawing on two surveys conducted by the National Center for Health Statistics, this paper describes an imputation-based strategy for using clinical information from an examination-based health survey to improve on analyses of self-reported data in a larger interview-based health survey. Models predicting clinical values from self-reported values and covariates are fitted to data from the National Health and Nutrition Examination Survey (NHANES), which asks self-report questions during an interview component and also obtains clinical measurements during a physical examination component. The fitted models are used to multiply impute clinical values for the National Health Interview Survey (NHIS), a larger survey that obtains data solely via interviews. Illustrations involving hypertension, diabetes, and obesity suggest that estimates of health measures based on the multiply imputed clinical values are different from those based on the NHIS self-reported data alone and have smaller estimated standard errors than those based solely on the NHANES clinical data. The paper discusses the relationship of the methods used in the study to two-phase/two-stage/validation sampling and estimation, along with limitations, practical considerations, and areas for future research. Published in 2009 by John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65032/1/3809_ftp.pd

Bayesian Variable Selection with Joint Modeling of Categorical and Survival Outcomes: An Application to Individualizing Chemotherapy Treatment in Advanced Colorectal Cancer

Colorectal cancer is the second leading cause of cancer related deaths in the United States, with more than 130,000 new cases of colorectal cancer diagnosed each year. Clinical studies have shown that genetic alterations lead to different responses to the same treatment, despite the morphologic similarities of tumors. A molecular test prior to treatment could help in determining an optimal treatment for a patient with regard to both toxicity and efficacy. This article introduces a statistical method appropriate for predicting and comparing multiple endpoints given different treatment options and molecular profiles of an individual. A latent variable-based multivariate regression model with structured variance covariance matrix is considered here. The latent variables account for the correlated nature of multiple endpoints and accommodate the fact that some clinical endpoints are categorical variables and others are censored variables. The mixture normal hierarchical structure admits a natural variable selection rule. Inference was conducted using the posterior distribution sampling Markov chain Monte Carlo method. We analyzed the finite-sample properties of the proposed method using simulation studies. The application to the advanced colorectal cancer study revealed associations between multiple endpoints and particular biomarkers, demonstrating the potential of individualizing treatment based on genetic profiles.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66395/1/j.1541-0420.2008.01181.x.pd

The shape of health to come: prospective study of the determinants of 30-year health trajectories in the Alameda County Study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57268/1/Kaplan GA et al The shape of health to come prospective study of the determinants of 30 year health trajectories in the Alameda County Study.pd

A Bayesian model for longitudinal count data with non-ignorable dropout

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73907/1/j.1467-9876.2008.00628.x.pd

Combining data from primary and ancillary surveys to assess the association between neighborhood-level characteristics and health outcomes: the Multi-Ethnic Study of Artherosclerosis

There is increasing interest in understanding the role of neighborhood-level factors on the health of individuals. Many large-scale epidemiological studies that accurately measure health status of individuals and individual risk factors exist. Sometimes these studies are linked to area-level databases (e.g. census) to assess the association between crude area-level characteristics and health. However, information from such databases may not measure the neighborhood-level constructs of interest. More recently, large-scale epidemiological studies have begun collecting data to measure specific features of neighborhoods using ancillary surveys. The ancillary surveys are composed of a separate, typically larger, set of individuals. The challenge is then to combine information from these two surveys to assess the role of neighborhood-level factors. We propose a method for combining information from the two data sources using a likelihood-based framework. We compare it with currently used ad hoc approaches via a simulation study. The simulation study shows that the proposed approach yields estimates with better sampling properties (less bias and better coverage probabilities) compared with the other approaches. However, there are cases where some ad hoc approaches may provide adequate estimates. We also compare the methods by applying them to the Multi-Ethnic Study of Atherosclerosis and its Neighborhood Ancillary Survey. Copyright © 2008 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/61232/1/3384_ftp.pd

Modeling cortisol rhythms in a population-based study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/51503/1/Ranjit N, Modeling Cortisol Rhythms in a Population-based Study, 2005.pd

Childhood socioeconomic position, gender, adult body mass index, and incidence of type 2 diabetes mellitus over 34 years in the Alameda County Study

http://deepblue.lib.umich.edu/bitstream/2027.42/61273/4/ajph.2007.123653.pd

Mujahid et al. Respond to "Beyond the Metrics for Measuring Neighborhood Effects"

In her commentary, Dr. Lynne Messer (1) recognizes the important contributions of our paper (2) to the discussion of methodological issues related to measurement of neighborhood or area-level properties. Dr. Messer reviews the many challenges involved in observational studies of neighborhood health effects, which we and other investigators have noted (3–8). A major challenge is developing theoretical models of the processes through which neighborhoods (or areas) may affect health. Messer argues that our paper "promises more, from a theoretical perspective, than it delivers" (1, p. 869). Our paper is merely a methodological illustration, with no grandiose theoretical aims. However, we do base the measures we explore on a theoretical model of the processes through which residential context may affect cardiovascular disease risk (1, 9). In her discussion of this model, Messer confuses inconsistent empirical support for aspects of the model with the absence of theory itself. Theorizing on the spatial scale at which different area processes operate is obviously important, but unfortunately there is very little information on which to base this theory. Additional qualitative research on the ways in which individuals interact with spaces may help us develop better theoretical models that may then be empirically tested. However, even if we were able to offer some crude hypotheses regarding spatial scales relevant to different processes, there are features of areas that could plausibly operate at multiple levels. Ultimately, we must rely on empirical research to uncover such relations rather than make a priori assertions under the guise of theory. For this, improving the validity of area-level measures and sensitivity analyses like the ones we present is crucial. Dr. Messer also alludes to the well-established challenges in estimating causal effects from observational data. Nonexchangeability (or its simpler and less fashionable synonym, "residual confounding") is always a concern. Messer implies that because of this, observational work in neighborhood health-effects research is meaningless. Firm believers in nonexchangeability will accept no defense of observational studies because it is impossible to categorically rule out residual confounding, except in the case of the ideal counterfactual experiment. However, claims of residual confounding also need to be subjected to empirical inquiry: What specific confounders have been omitted, and how strong are their effects expected to be? Careful observational work can empirically examine the sensitivity of results to different degrees of residual confounding and degrees of extrapolation. In this, neighborhood effects research is no different than the rest of epidemiology. Given the many limitations and logistical challenges of randomized trials (particularly for the study of neighborhood effects), reliance on observational and quasi-experimental data is likely to continue. Hence, anything we can do to improve the rigor of observational work is crucial. Our objective in the current paper was (merely) to contribute to emerging work on the measurement of area-level constructs, not to fully develop a theory on neighborhood causal effects or to resolve the issue of relevant spatial scale. Our objective was not even to estimate associations between neighborhood characteristics and health outcomes. Instead, we wanted to further develop and evaluate our ability to measure area-level constructs. Epidemiologists are very sophisticated at measuring individual-level characteristics but not as sophisticated at measuring features of ecologic settings. This seriously hampers their ability to examine contextual effects. Our analyses illustrate one approach to quantifying the measurement properties of area-based measures. This approach can be adapted to different constructs and different spatial scales, depending on the research problem and underlying theory. We firmly believe that improving the quality of measurement of area-level constructs is a prerequisite for more rigorous observational work. In fact, several of the inferential problems that arise when area socioeconomic status characteristics are used as proxies for features of areas may be reduced when specific features of areas are examined instead of aggregate socioeconomic status measures (which are, by definition, correlated with individual socioeconomic status, thus magnifying the extrapolation and exchangeability problems). We hope that the illustration we provide in our paper (2) will encourage other investigators to develop and test theoretically relevant area measures and to contrast different approaches to their measurement. Understanding if and how contexts (including neighborhoods) affect health is challenging and complex, but it is also enormously important from the point of view of public health and policy. In order to answer questions regarding these effects, we need to move beyond blanket (and sometimes facile) critiques, roll up our sleeves, and see if we can improve on the work that has been done to date. This means dealing with a messy, correlated, and confounded reality and doing the best we can to glean truth from our observations. As epidemiologists, this is our job, and also our responsibility to the public.http://deepblue.lib.umich.edu/bitstream/2027.42/58002/1/Mujahid et al Respond to Beyond the Metrics for Measuring Neighborhood Effects.pd