Some Identification Issues in Nonparametric Linear Models with Endogenous Regressors

In applied work economists often seek to relate a given response variable y to some causal parameter mu* associated with it. This parameter usually represents a summarization based on some explanatory variables of the distribution of y, such as a regression function, and treating it as a conditional expectation is central to its identification and estimation. However, the interpretation of mu* as a conditional expectation breaks down if some or all of the explanatory variables are endogenous. This is not a problem when mu* is modelled as a parametric function of explanatory variables because it is well known how instrumental variables techniques can be used to identify and estimate mu*. In contrast, handling endogenous regressors in nonparametric models, where mu* is regarded as fully unknown, presents di±cult theoretical and practical challenges. In this paper we consider an endogenous nonparametric model based on a conditional moment restriction. We investigate identification related properties of this model when the unknown function mu* belongs to a linear space. We also investigate underidentification of mu* along with the identification of its linear functionals. Several examples are provided in order to develop intuition about identification and estimation for endogenous nonparametric regression and related models.

Tracking of plasma lipids and lipoproteins, within-person and year-to-year variability in plasma cholesterol levels among participants from age 8 to 18 in Project HeartBeat!

Coronary heart disease remains the leading cause of death in the United States and increased blood cholesterol level has been found to be a major risk factor with roots in childhood. Tracking of cholesterol, i.e., the tendency to maintain a particular cholesterol level relative to the rest of the population, and variability in blood lipid levels with increase in age have implications for cholesterol screening and assessment of lipid levels in children for possible prevention of further rise to prevent adulthood heart disease. In this study the pattern of change in plasma lipids, over time, and their tracking were investigated. Also, within-person variance and retest reliability defined as the square root of within-person variance for plasma total cholesterol, HDL-cholesterol, LDL-cholesterol, and triglycerides and their relation to age, sex and body mass index among participants from age 8 to 18 years were investigated. ^ In Project HeartBeat!, 678 healthy children aged 8, 11 and 14 years at baseline were enrolled and examined at 4-monthly intervals for up to 4 years. We examined the relationship between repeated observations by Pearson's correlations. Age- and sex-specific quintiles were calculated and the probability of participants to remain in the uppermost quintile of their respective distribution was evaluated with life table methods. Plasma total cholesterol, HDL-C and LDL-C at baseline were strongly and significantly correlated with measurements at subsequent visits across the sex and age groups. Plasma triglyceride at baseline was also significantly correlated with subsequent measurements but less strongly than was the case for other plasma lipids. The probability to remain in the upper quintile was also high (60 to 70%) for plasma total cholesterol, HDL-C and LDL-C. ^ We used a mixed longitudinal, or synthetic cohort design with continuous observations from age 8 to 18 years to estimate within person variance of plasma total cholesterol, HDL-C, LDL-C and triglycerides. A total of 5809 measurements were available for both cholesterol and triglycerides. A multilevel linear model was used. Within-person variance among repeated measures over up to four years of follow-up was estimated for total cholesterol, HDL-C, LDL-C and triglycerides separately. The relationship of within-person and inter-individual variance with age, sex, and body mass index was evaluated. Likelihood ratio tests were conducted by calculating the deviation of −2log (likelihood) within the basic model and alternative models. The square root of within-person variance provided the retest reliability (within person standard deviation) for plasma total cholesterol, HDL-C, LDL-C and triglycerides. We found 13.6 percent retest reliability for plasma cholesterol, 6.1 percent for HDL-cholesterol, 11.9 percent for LDL-cholesterol and 32.4 percent for triglycerides. Retest reliability of plasma lipids was significantly related with age and body mass index. It increased with increase in body mass index and age. These findings have implications for screening guidelines, as participants in the uppermost quintile tended to maintain their status in each of the age groups during a four-year follow-up. The magnitude of within-person variability of plasma lipids influences the ability to classify children into risk categories recommended by the National Cholesterol Education Program. ^

Identification and characterization of a molecular marker in epithelial ovarian cancer

Carcinomas that arise from the ovarian surface epithelium represent a great challenge in gynecologic oncology. Although the prognosis of ovarian cancer is influenced by many factors capable of predicting clinical outcome, including tumor stage, pathological grade, and amount of residual disease following primary surgery, the biological aspects of ovarian cancer are not completely understood, thus implying that there may be other predictive indicators that could be used independently or in conjunction with these factors to provide a clearer clinical picture. The identification of additional markers with biological relevance is desirable. To identify disease-associated peptides, a phage display random peptide library was used to screen immunoglobulins derived from a patient with ovarian cancer. One peptide was markedly enriched following three rounds of affinity selection. The presence of autoantibodies against the peptide was examined in a panel of ovarian cancer patients. Stage IV patients exhibited a high percentage of positive reactivity (59%). This was in contrast to stage III patients, who only displayed 7% positive reactivity. Antibodies against the peptide were affinity purified, and heat-shock protein 90 (Hsp90) was identified as the corresponding autoantigen. The expression profile of the identified antigen was determined. Hsp90 was expressed in all sections examined regardless of degree of anaplasia. This thesis shows that utilizing the humoral response to ovarian cancer can be used to identify a tumor antigen in ovarian cancer. The data show that certain antigens may be expressed in ovarian tumors independent of the disease stage or grade, whereas circulating antibodies against such epitopes are only found in a subset of patients. ^

Maternal ingestion of radon-222 in drinking water and the absorbed radiation dose to the embryo

Maternal ingestion of high concentrations of radon-222 (Rn-222) in drinking during pregnancy may pose a significant radiation hazard to the developing embryo. The effects of ionizing radiation to the embryo and fetus have been the subject of research, analyses, and the development of a number of radiation dosimetric models for a variety of radionuclides. Currently, essentially all of the biokinetic and dosimetric models that have been developed by national and international radiation protection agencies and organizations recommend calculating the dose to the mother's uterus as a surrogate for estimating the dose to the embryo. Heretofore, the traditional radiation dosimetry models have neither considered the embryo a distinct and rapidly developing entity, the fact that it is implanted in the endometrial layer of the uterus, nor the physiological interchanges that take place between maternal and embryonic cells following the implantation of the blastocyst in the endometrium. The purpose of this research was to propose a new approach and mathematical model for calculating the absorbed radiation dose to the embryo by utilizing a semiclassical treatment of alpha particle decay and subsequent scattering of energy deposition in uterine and embryonic tissue. The new approach and model were compared and contrasted with the currently recommended biokinetic and dosimetric models for estimating the radiation dose to the embryo. The results obtained in this research demonstrate that the estimated absorbed dose for an embryo implanted in the endometrial layer of the uterus during the fifth week of embryonic development is greater than the estimated absorbed dose for an embryo implanted in the uterine muscle on the last day of the eighth week of gestation. This research provides compelling evidence that the recommended methodologies and dosimetric models of the Nuclear Regulatory Commission and International Commission on Radiological Protection employed for calculating the radiation dose to the embryo from maternal intakes of radionuclides, including maternal ingestion of Rn-222 in drinking water would result in an underestimation of dose. ^

Bayesian generalized linear models for meta-analysis of diagnostic tests

With the recognition of the importance of evidence-based medicine, there is an emerging need for methods to systematically synthesize available data. Specifically, methods to provide accurate estimates of test characteristics for diagnostic tests are needed to help physicians make better clinical decisions. To provide more flexible approaches for meta-analysis of diagnostic tests, we developed three Bayesian generalized linear models. Two of these models, a bivariate normal and a binomial model, analyzed pairs of sensitivity and specificity values while incorporating the correlation between these two outcome variables. Noninformative independent uniform priors were used for the variance of sensitivity, specificity and correlation. We also applied an inverse Wishart prior to check the sensitivity of the results. The third model was a multinomial model where the test results were modeled as multinomial random variables. All three models can include specific imaging techniques as covariates in order to compare performance. Vague normal priors were assigned to the coefficients of the covariates. The computations were carried out using the 'Bayesian inference using Gibbs sampling' implementation of Markov chain Monte Carlo techniques. We investigated the properties of the three proposed models through extensive simulation studies. We also applied these models to a previously published meta-analysis dataset on cervical cancer as well as to an unpublished melanoma dataset. In general, our findings show that the point estimates of sensitivity and specificity were consistent among Bayesian and frequentist bivariate normal and binomial models. However, in the simulation studies, the estimates of the correlation coefficient from Bayesian bivariate models are not as good as those obtained from frequentist estimation regardless of which prior distribution was used for the covariance matrix. The Bayesian multinomial model consistently underestimated the sensitivity and specificity regardless of the sample size and correlation coefficient. In conclusion, the Bayesian bivariate binomial model provides the most flexible framework for future applications because of its following strengths: (1) it facilitates direct comparison between different tests; (2) it captures the variability in both sensitivity and specificity simultaneously as well as the intercorrelation between the two; and (3) it can be directly applied to sparse data without ad hoc correction. ^

Body mass index trajectories and predictors among 3rd to 12th graders using growth curve mixture modeling the Child and Adolescent Trial for Cardiovascular Health (CATCH) study

This dissertation examined body mass index (BMI) growth trajectories and the effects of gender, ethnicity, dietary intake, and physical activity (PA) on BMI growth trajectories among 3rd to 12th graders (9-18 years of age). Growth curve model analysis was performed using data from The Child and Adolescent Trial for Cardiovascular Health (CATCH) study. The study population included 2909 students who were followed up from grades 3-12. The main outcome was BMI at grades 3, 4, 5, 8, and 12. ^ The results revealed that BMI growth differed across two distinct developmental periods of childhood and adolescence. Rate of BMI growth was faster in middle childhood (9-11 years old or 3rd - 5th grades) than in adolescence (11-18 years old or 5th - 12th grades). Students with higher BMI at 3rd grade (baseline) had faster rates of BMI growth. Three groups of students with distinct BMI growth trajectories were identified: high, average, and low. ^ Black and Hispanic children were more likely to be in the groups with higher baseline BMI and faster rates of BMI growth over time. The effects of gender or ethnicity on BMI growth differed across the three groups. The effects of ethnicity on BMI growth were weakened as the children aged. The effects of gender on BMI growth were attenuated in the groups with a large proportion of black and Hispanic children, i.e., “high” or “average” BMI trajectory group. After controlling for gender, ethnicity, and age at baseline, in the “high BMI trajectory”, rate of yearly BMI growth in middle childhood increased 0.102 for every 500 Kcals increase (p=0.049). No significant effects of percentage of energy from total fat and saturated fat on BMI growth were found. Baseline BMI increased 0.041 for every 30 minutes increased in moderate-to-vigorous PA (MVPA) in the “low BMI trajectory”, while Baseline BMI decreased 0.345 for every 30 minutes increased in vigorous PA (VPA) in the “high BMI trajectory”. ^ Childhood overweight and obesity interventions should start at the earliest possible ages, prior to 3rd grade and continue through grade school. Interventions should focus on all children, but specifically black and Hispanic children, who are more likely to be highest at-risk. Promoting VPA earlier in childhood is important for preventing overweight and obesity among children and adolescents. Interventions should target total energy intake, rather than only percentage of energy from total fat or saturated fat. ^

Ethnic differences in tumor characteristics, treatment and survival in glioma patients

Purpose. A descriptive analysis of glioma patients by race was carried out in order to better elucidate potential differences between races in demographics, treatment, characteristics, prognosis and survival. ^ Patients and Methods. Among 1,967 patients ≥ 18 years diagnosed with glioma seen between July 2000 and September 2006 at The University of Texas M.D. Anderson Cancer Center (UTMDACC). Data were collated from the UTMDACC Patient History Database (PHDB) and the UTMDACC Tumor Registry Database (TRDB). Chi-square analysis, uni- /multivariate Cox proportional hazards modeling and survival analysis were used to analyze differences by race. ^ Results. Demographic, treatment and histologic differences exist between races. Though risk differences were seen between races, race was not found to be a significant predictor in multivariate regression analysis after accounting for age, surgery, chemotherapy, radiation, tumor type as stratified by WHO tumor grade. Age was the most consistent predictor in risk for death. Overall survival by race was significantly different (p=0.0049) only in low-grade gliomas after adjustment for age although survival differences were very slight. ^ Conclusion. Among this cohort of glioma patients, age was the strongest predictor for survival. It is likely that survival is more influenced by age, time to treatment, tumor grade and surgical expertise rather than racial differences. However, age at diagnosis, gender ratios, histology and history of cancer differed significantly between race and genetic differences to this effect cannot be excluded. ^

Involvement of lipocalin 2 in leukemia and breast cancer

Human lipocalin 2 is described as the neutrophil gelatinase-associated lipocalin (NGAL). The lipocalin 2 gene encodes a small, secreted glycoprotein that possesses a variety of functions, of which the best characterized function is organic iron binding activity. Elevated NGAL expression has been observed in many human cancers including breast, colorectal, pancreatic and ovarian cancers. I focused on the characterization of NGAL function in chronic myelogenous leukemia (CML) and breast cancer. Using the leukemic xenograft mouse model, we demonstrated that over-expression of NGAL in K562 cells, a leukemic cell line, led to a higher apoptotic rate and an atrophy phenotype in the spleen of inoculated mice compared to K562 cells alone. These results indicate that NGAL plays a primary role in suppressing hematopoiesis by inducing apoptosis within normal hematopoietic cells. In the breast cancer project, we analyzed two microarray data sets of breast cancer cell lines ( n = 54) and primary breast cancer samples (n = 318), and demonstrated that high NGAL expression is significantly correlated with several tumor characteristics, including negative estrogen receptor (ER) status, positive HER2 status, high tumor grade, and lymph node metastasis. Ectopic NGAL expression in non-aggressive (ZR75.1 and MCF7) cells led to aggressive tumor phenotypes in vitro and in vivo. Conversely, knockdown of NGAL expression in various breast cancer cell lines by shRNA lentiviral infection significantly decreased migration, invasion, and metastasis activities of tumor cells both in vitro and in vivo . It has been previously reported that transgenic mice with a mutation in the region of trans-membrane domain (V664E) of HER2 develop mammary tumors that progress to lung metastasis. However, we observed that genetic deletion of the 24p3 gene, a mouse homolog of NGAL, in HER2 transgenic mice by breeding with 24p3-null mice resulted in a significant delay of mammary tumor formation and reduction of lung metastasis. Strikingly, we also found that treatment with affinity purified 24p3 antibodies in the 4T1 breast cancer mice strongly reduced lung metastasis. Our studies provide evidence that NGAL plays a critical role in breast cancer development and progression, and thus NGAL has potential as a new therapeutic target in breast cancer.^

Proliferation index in ductal carcinoma in situ of the breast: Relation to estrogen, progesterone and HER2/neu receptors

Background. Ductal carcinoma in situ (DCIS) is the most prevalent precursor to invasive breast cancer (IBC), the second leading cause of death in women in the United States. The three most important prognostic markers for IBC are Estrogen receptor (ER), Progesterone receptor (PR) and HER2/neu. The four groups (IBC) defined as (1) ER and/or PR positive and HER2/neu negative, (2) ER and/or PR positive and HER2/neu positive (3) ER and/or PR negative and HER2/neu positive and (4) negative for all three of these receptors (Triple negative). However, they have not been well studied in DCIS. This is an exploratory study with a primary objective to examine the prevalence of ER, PR, and HER2/neu in DCIS, to explore if the defined groups of IBC occur in DCIS and to consider the biological relationship between these four groups and the proliferative activity of the tumor. A secondary goal of this study is to examine the relationship between grade and proliferative activity. Methods. Using immunohistochemistry, I have measured Ki-67, ER, PR and HER2/neu positivity for a series of cases of DCIS. Results. 20 ER and/or PR positive and HER2/neu negative (50%) with average PI of 0.05, 7 ER and/or PR positive and HER2/neu positive (17.5%) with average PI of 0.14, 10 ER and/or PR negative and HER2/neu positive (25%) with average PI of 0.18, and three triple negative (7.5%) with average PI of 0.18. ER and/or PR positive and HER2/neu positive group has the highest PI (p<0.001). Further, the ER and/or PR positive and HER2/neu positive group show a linear relationship between PI and average ER/PR positivity (R=0.6). PI increases with higher grades. Conclusion. PI appears to depend upon the average fraction of positive ER/PR tumor cells, possibly with a synergistic dependence when HER2/neu is positive. If ER/PR is negative, then both HER2/neu positive and the triple negative cases appear to cluster around an average PI that is higher than the average PI in HER2/neu negative ER/PR positive negative cases. In the triple negative tumors there must be another driver of proliferation.^

Is sexual violence associated with symptoms of depression and suicide attempts

Sexual assault, depression, and suicide are all very serious issues among youth today. This study sought to quantify the association between sexual violence, symptoms of depression, and suicide attempts through the use of 2007 Youth Risk Behavior Survey (YRBS) data. The YRBS is a nationally representative dataset of United States high school students, grades 9-12. It was hypothesized that sexual violence is significantly associated with symptoms of depression and suicide attempts. Through multivariate logistic regression, it was determined that students who had ever experienced forced sex were 3.10 (2.7-3.6) times as likely to be depressed, in the past 12 months, and 4.22 (3.5-5.1) times as likely to have attempted suicide. Female victims were 3.43 (2.9-4.0) times as likely to be depressed; male victims were 5.40 (3.7-7.9) times as likely to have attempted suicide. Sexual violence is significantly associated with both symptoms of depression and suicide attempt when stratified by gender, grade, and race/ethnicity. These results indicate that further study of the association between sexual violence and symptoms of depression and suicide attempts need to be conducted in order to establish temporality.^

Comparison of Bayesian and frequentist approaches in clinical outcomes between hospitals

Many public health agencies and researchers are interested in comparing hospital outcomes, for example, morbidity, mortality, and hospitalization across areas and hospitals. However, since there is variation of rates in clinical trials among hospitals because of several biases, we are interested in controlling for the bias and assessing real differences in clinical practices. In this study, we compared the variations between hospitals in rates of severe Intraventricular Haemorrhage (IVH) infant using Frequentist statistical approach vs. Bayesian hierarchical model through simulation study. The template data set for simulation study was included the number of severe IVH infants of 24 intensive care units in Australian and New Zealand Neonatal Network from 1995 to 1997 in severe IVH rate in preterm babies. We evaluated the rates of severe IVH for 24 hospitals with two hierarchical models in Bayesian approach comparing their performances with the shrunken rates in Frequentist method. Gamma-Poisson (BGP) and Beta-Binomial (BBB) were introduced into Bayesian model and the shrunken estimator of Gamma-Poisson (FGP) hierarchical model using maximum likelihood method were calculated as Frequentist approach. To simulate data, the total number of infants in each hospital was kept and we analyzed the simulated data for both Bayesian and Frequentist models with two true parameters for severe IVH rate. One was the observed rate and the other was the expected severe IVH rate by adjusting for five predictors variables for the template data. The bias in the rate of severe IVH infant estimated by both models showed that Bayesian models gave less variable estimates than Frequentist model. We also discussed and compared the results from three models to examine the variation in rate of severe IVH by 20th centile rates and avoidable number of severe IVH cases. ^

Assessment of the effect on statistical power of regression model misspecification by using techniques of mathematical statistics and simulation study

Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^

Information bias and lifetime mortality risks of radiation-induced cancer

Additive and multiplicative models of relative risk were used to measure the effect of cancer misclassification and DS86 random errors on lifetime risk projections in the Life Span Study (LSS) of Hiroshima and Nagasaki atomic bomb survivors. The true number of cancer deaths in each stratum of the cancer mortality cross-classification was estimated using sufficient statistics from the EM algorithm. Average survivor doses in the strata were corrected for DS86 random error ($\sigma$ = 0.45) by use of reduction factors. Poisson regression was used to model the corrected and uncorrected mortality rates with covariates for age at-time-of-bombing, age at-time-of-death and gender. Excess risks were in good agreement with risks in RERF Report 11 (Part 2) and the BEIR-V report. Bias due to DS86 random error typically ranged from $-$15% to $-$30% for both sexes, and all sites and models. The total bias, including diagnostic misclassification, of excess risk of nonleukemia for exposure to 1 Sv from age 18 to 65 under the non-constant relative projection model was $-$37.1% for males and $-$23.3% for females. Total excess risks of leukemia under the relative projection model were biased $-$27.1% for males and $-$43.4% for females. Thus, nonleukemia risks for 1 Sv from ages 18 to 85 (DRREF = 2) increased from 1.91%/Sv to 2.68%/Sv among males and from 3.23%/Sv to 4.02%/Sv among females. Leukemia excess risks increased from 0.87%/Sv to 1.10%/Sv among males and from 0.73%/Sv to 1.04%/Sv among females. Bias was dependent on the gender, site, correction method, exposure profile and projection model considered. Future studies that use LSS data for U.S. nuclear workers may be downwardly biased if lifetime risk projections are not adjusted for random and systematic errors. (Supported by U.S. NRC Grant NRC-04-091-02.) ^

Motivational interviewing and feedback for college drinkers: Handling missing values with complete case analysis and multiple imputation

Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis. ^ Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.^ Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results. ^ Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.^

Association between length of stay in the U.S. and diabetes risk factors among Chinese Americans in Houston, Texas in 2004-2005

Studies have suggested that acculturation is related to diabetes prevalence and risk factors among immigrant groups in the United States (U.S.), however scant data are available to investigate this relationship among Asian Americans and Asian American subgroups. The objective of this cross-sectional study was to examine the association between length of stay in the U.S. and type 2 diabetes prevalence and its risk factors among Chinese Americans in Houston, Texas. Data were obtained from the 2004-2005 Asian-American Health Needs Assessment in Houston, Texas (N=409 Chinese Americans) for secondary analysis in this study. Diabetes prevalence and risk factors (overweight/obesity and access to medical care) were based on self-report. Descriptive statistics summarized demographic characteristics, diabetes prevalence, and reasons for not seeing a doctor. Logistic regression, using an incremental modeling approach, was used to measure the association between length of stay and diabetes prevalence and related risk factors, while adjusting for the potential confounding factors of age, gender, education level, and income level. Although the prevalence of type 2 diabetes was highest among those living in the U.S. for more than 20 years, there was no significant association between length of stay in the U.S. and diabetes prevalence among these Chinese Americans after adjustment for confounding factors. No association was found between length of stay in the U.S. and overweight/obese status among this population either, after adjusting for confounding factors, too. On the other hand, a longer length of stay was significantly associated with increased health insurance coverage in both unadjusted and adjusted models. The findings of this study suggest that length of stay in the U.S. alone may not be an indicator for diabetes risk among Chinese Americans. Future research should consider alternative models to measure acculturation (e.g., models that reflect acculturation as a multi-dimensional, not uni-dimensional process), which may more accurately depict its effect on diabetes prevalence and related risk factors.^