Marginal Regression Modeling under Irregular, Biased Sampling

In longitudinal studies observations are often obtained at continuous subject-specific times. Frequently the availability of outcome data may be related to the outcome measure or other covariates that are related to the outcome measure. Under such biased sampling designs unadjusted regression analysis yield biased estimates. Building on the work of Lin & Ying (2001) that integrates counting processes techniques with longitudinal data settings we propose a class of estimators that can handle biased sampling. We call those estimators ``inverse--intensity--rate--ratio--weighted\u27\u27 (IIRR) estimators. Of major focus is a mean--response model where we examine the marginal effect of the covariate X at time t on the mean of the response Y at that time. The proposed class of closed-form estimators are root n-consistent and asymptotically normal and do not require estimating any infinite--dimensional parameters. The estimators and estimators of their variance are relatively simple and computationally feasible. Simulation studies demonstrate that asymptotic approximations are accurate for moderate sample sizes. %Also, when comparing squared error, the proposed estimators are largely favored when compared to the Lin and Ying estimates or the GEE estimates. We illustrate our approach using data from a health service research study with extreme noncompliance to the scheduled visits that can not be explained by the intervention assignment alone

Longitudinal Data Analysis for Generalized Linear Models under Irregular, Biased Sampling: Situations with Follow-up Dependent on Outcome or Auxiliary Outcome-related Variables

In longitudinal studies, observations are often obtained at subject-specific observation times. Those times can be continuous times, not at a set of prespecified times. Frequently the observation times may be related to the outcome measure or other auxiliary variables that are related to the outcome measure but undesirable to condition upon in the regression model for outcome. Regression analysis unadjusted for such sampling designs yield biased estimates. Based on estimating equations, we propose a class of estimators in generalized linear regression models that can handle biased sampling under continuous observation times. We call those estimators ``inverse--intensity rate--ratio--weighted\u27\u27 (IIRR) estimators. The proposed estimators are simple and easily computed as they are readily available in many statistical software packages. We integrate counting processes techniques with longitudinal data settings. We leave stochastic structure of the outcome completely unspecified. Covariates modeling the association in the regression model for outcome as well as covariates predicting the observation times can contain lagged outcome or lagged covariates. The estimators are root n consistent and asymptotically normal. We avoid estimation of the infinite--dimensional baseline sampling intensity. The finite sample performance of the proposed estimation procedure is investigated in a simulation study. Finally, we illustrate our approach with a data set from a health services research study that was subject to high noncompliance to predefined visit times

Semiparametric Loglinear Regression for Longitudinal Measurements Subject to Irregular, Biased Follow-up

We propose a method for analysis of loglinear regression models for longitudinal data that are subject to continuous and irregular follow-up. Frequently, if the follow-up is irregular, the availability of outcome data may be related to the outcome measure or other covariates that are related to the outcome measure. Under such biased sampling designs unadjusted regression analysis yield biased estimates. We examine the marginal association of the covariates X at time t and the logarithm of the mean of response Y at time t. We focus on semiparametric regression with unspecified baseline function of time. To predict the follow-up times we use a marginal rate model with arbitrary baseline intensity and possibly time-varying covariates Z. We avoid the estimation of infinitely dimensional baseline intensity of follow-up as well as the intercept function in the outcome model. Our estimation procedure is based on estimating equations. The proposed class of estimators are root n consistent and asymptotically normal. We present simulation studies that assess the performance of the estimator under finite samples. We illustrate our approach using data from a health services research study

Panel Count Data Regression with Informative Observation Times

When patients are monitored for potentially recurrent events such as infections or tumor metastases, it is common for clinicians to ask patients to come back sooner for follow-up based on the results of the most recent exam. This means that subjects’ observation times will be irregular and related to subject-specific factors. Previously proposed methods for handling such panel count data assume that the dependence between the events process and the observation time process is time-invariant. This article considers situations where the observation times are predicted by time-varying factors, such as the outcome observed at the last visit or cumulative exposure. Using a joint modeling approach, we propose a class of inverse-intensity-rate-ratio weighted estimators that are root n consistent and asymptotically normal. The proposed estimators use estimating equations and are fairly simple and easy to compute. We demonstrate the performance of the method using simulated data and illustrate the approach using a cancer study dataset

Cystatin C based estimation of glomerular filtration rate and association with atherosclerosis imaging markers in people living with HIV

Introduction: Reduced estimated glomerular filtration rate (eGFR) is associated with increased risk of cardiovascular disease among people living with HIV (PLWH). It is unclear whether eGFR equations incorporating Cystatin C (CysC) measurements are more predictive of preclinical CVD than those using only creatinine (Cr). Objectives: The study aimed to determine which of the three Chronic Kidney Disease Epidemiology (CKD-EPI) eGFR equations is most associated with carotid intima media thickness (CIMT) and coronary artery calcium (CAC) score. Methods: This cross-sectional analysis of pooled data from three large cohorts compared the associations between the three CKD-EPI eGFR equations (Cr, CysC, and Cr-CysC) with CIMT and CAC score using multivariable regression analysis. eGFR and CIMT were analyzed as continuous variables. CAC scores were analyzed as a binary variable (detectable calcification versus nondetectable) and as a log10 Agatston score in those with detectable CAC. Results: 1487 participants were included, and of these 910 (562 HIV+, 348 HIV-) had CIMT measurements and 366 (296 HIV+, 70 HIV-) had CAC measurements available. In HIV- participants, GFR estimated by any CKD-EPI equation did not significantly correlate with CIMT or CAC scores. When PLWH were analyzed separately including HIV-specific factors, only GFR estimated using Cr-Cys C correlated with CIMT [β= -0.90, 95% CI (-1.67,-0.13) μm; p=0.023]. Similarly, eGFR correlated with Agatston scores only when using cystatin C-based eGFR [β= -8.63, 95% CI (-16.49,-0.77) HU; p=0.034]. Associations between other eGFR formulas and CAC did not reach statistical significance. Conclusion: In PLWH, preclinical atherosclerosis may be more closely correlated with eGFR using formulae that incorporate CysC measurements than Cr alone

A Systematic Mapping Approach of 16q12.2/FTO and BMI in More Than 20,000 African Americans Narrows in on the Underlying Functional Variation: Results from the Population Architecture using Genomics and Epidemiology (PAGE) Study

Genetic variants in intron 1 of the fat mass- and obesity-associated (FTO) gene have been consistently associated with body mass index (BMI) in Europeans. However, follow-up studies in African Americans (AA) have shown no support for some of the most consistently BMI-associated FTO index single nucleotide polymorphisms (SNPs). This is most likely explained by different race-specific linkage disequilibrium (LD) patterns and lower correlation overall in AA, which provides the opportunity to fine-map this region and narrow in on the functional variant. To comprehensively explore the 16q12.2/FTO locus and to search for second independent signals in the broader region, we fine-mapped a 646-kb region, encompassing the large FTO gene and the flanking gene RPGRIP1L by investigating a total of 3,756 variants (1,529 genotyped and 2,227 imputed variants) in 20,488 AAs across five studies. We observed associations between BMI and variants in the known FTO intron 1 locus: the SNP with the most significant p-value, rs56137030 (8.3×10-6) had not been highlighted in previous studies. While rs56137030was correlated at r2>0.5 with 103 SNPs in Europeans (including the GWAS index SNPs), this number was reduced to 28 SNPs in AA. Among rs56137030 and the 28 correlated SNPs, six were located within candidate intronic regulatory elements, including rs1421085, for which we predicted allele-specific binding affinity for the transcription factor CUX1, which has recently been implicated in the regulation of FTO. We did not find strong evidence for a second independent signal in the broader region. In summary, this large fine-mapping study in AA has substantially reduced the number of common alleles that are likely to be functional candidates of the known FTO locus. Importantly our study demonstrated that comprehensive fine-mapping in AA provides a powerful approach to narrow in on the functional candidate(s) underlying the initial GWAS findings in European populations

Lack of associations of ten candidate coronary heart disease risk genetic variants and subclinical atherosclerosis in four U.S. populations: The Population Architecture using Genomics and Epidemiology (PAGE) study

A number of genetic variants have been discovered by recent genome-wide association studies for their associations with clinical coronary heart disease (CHD). However, it is unclear whether these variants are also associated with the development of CHD as measured by subclinical atherosclerosis phenotypes, ankle brachial index (ABI), carotid artery intima-media thickness (cIMT) and carotid plaque

Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci

Genome-wide association studies have identified over 150 loci associated with lipid traits, however, no large-scale studies exist for Hispanics and other minority populations. Additionally, the genetic architecture of lipid-influencing loci remains largely unknown. We performed one of the most racially/ethnically diverse fine-mapping genetic studies of HDL-C, LDL-C, and triglycerides to-date using SNPs on the MetaboChip array on 54,119 individuals: 21,304 African Americans, 19,829 Hispanic Americans, 12,456 Asians, and 530 American Indians. The majority of signals found in these groups generalize to European Americans. While we uncovered signals unique to racial/ethnic populations, we also observed systematically consistent lipid associations across these groups. In African Americans, we identified three novel signals associated with HDL-C (LPL, APOA5, LCAT) and two associated with LDL-C (ABCG8, DHODH). In addition, using this population, we refined the location for 16 out of the 58 known MetaboChip lipid loci. These results can guide tailored screening efforts, reveal population-specific responses to lipid-lowering medications, and aid in the development of new targeted drug therapies

A Systematic Mapping Approach of 16q12.2/FTO and BMI in More Than 20,000 African Americans Narrows in on the Underlying Functional Variation: Results from the Population Architecture using Genomics and Epidemiology (PAGE) Study

Genetic variants in intron 1 of the fat mass– and obesity-associated (FTO) gene have been consistently associated with body mass index (BMI) in Europeans. However, follow-up studies in African Americans (AA) have shown no support for some of the most consistently BMI–associated FTO index single nucleotide polymorphisms (SNPs). This is most likely explained by different race-specific linkage disequilibrium (LD) patterns and lower correlation overall in AA, which provides the opportunity to fine-map this region and narrow in on the functional variant. To comprehensively explore the 16q12.2/FTO locus and to search for second independent signals in the broader region, we fine-mapped a 646–kb region, encompassing the large FTO gene and the flanking gene RPGRIP1L by investigating a total of 3,756 variants (1,529 genotyped and 2,227 imputed variants) in 20,488 AAs across five studies. We observed associations between BMI and variants in the known FTO intron 1 locus: the SNP with the most significant p-value, rs56137030 (8.3×10−6) had not been highlighted in previous studies. While rs56137030was correlated at r2>0.5 with 103 SNPs in Europeans (including the GWAS index SNPs), this number was reduced to 28 SNPs in AA. Among rs56137030 and the 28 correlated SNPs, six were located within candidate intronic regulatory elements, including rs1421085, for which we predicted allele-specific binding affinity for the transcription factor CUX1, which has recently been implicated in the regulation of FTO. We did not find strong evidence for a second independent signal in the broader region. In summary, this large fine-mapping study in AA has substantially reduced the number of common alleles that are likely to be functional candidates of the known FTO locus. Importantly our study demonstrated that comprehensive fine-mapping in AA provides a powerful approach to narrow in on the functional candidate(s) underlying the initial GWAS findings in European populations

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms