Fitting parametric random effects models in very large data sets with application to VHA national data

<p>Abstract</p> <p>Background</p> <p>With the current focus on personalized medicine, patient/subject level inference is often of key interest in translational research. As a result, random effects models (REM) are becoming popular for patient level inference. However, for very large data sets that are characterized by large sample size, it can be difficult to fit REM using commonly available statistical software such as SAS since they require inordinate amounts of computer time and memory allocations beyond what are available preventing model convergence. For example, in a retrospective cohort study of over 800,000 Veterans with type 2 diabetes with longitudinal data over 5 years, fitting REM via generalized linear mixed modeling using currently available standard procedures in SAS (e.g. PROC GLIMMIX) was very difficult and same problems exist in Stata’s gllamm or R’s lme packages. Thus, this study proposes and assesses the performance of a meta regression approach and makes comparison with methods based on sampling of the full data.</p> <p>Data</p> <p>We use both simulated and real data from a national cohort of Veterans with type 2 diabetes (n=890,394) which was created by linking multiple patient and administrative files resulting in a cohort with longitudinal data collected over 5 years.</p> <p>Methods and results</p> <p>The outcome of interest was mean annual HbA1c measured over a 5 years period. Using this outcome, we compared parameter estimates from the proposed random effects meta regression (REMR) with estimates based on simple random sampling and VISN (Veterans Integrated Service Networks) based stratified sampling of the full data. Our results indicate that REMR provides parameter estimates that are less likely to be biased with tighter confidence intervals when the VISN level estimates are homogenous.</p> <p>Conclusion</p> <p>When the interest is to fit REM in repeated measures data with very large sample size, REMR can be used as a good alternative. It leads to reasonable inference for both Gaussian and non-Gaussian responses if parameter estimates are homogeneous across VISNs.</p

Measuring the Impact of the Affordable Care Act Medicaid Expansion on Access to Primary Care Using an Interrupted Time Series Approach

BACKGROUND: The Patient Protection and Affordable Care Act of 2010, commonly referred to as the Affordable Care Act (ACA), was created to increase access to primary care, improve quality of care, and decrease healthcare costs. A key provision in the law that mandated expansion of state Medicaid programme changed when states were given the option to voluntarily expand Medicaid. Our study sought to measure the impact of ACA Medicaid expansion on preventable hospitalization (PH) rates, a measure of access to primary care. METHODS: We performed an interrupted time series analysis of quarterly hospitalization rates across eight states from 2012 to 2015. Segmented regression analysis was utilized to determine the impact of policy reform on PH rates. RESULTS: The Affordable Care Act\u27s Medicaid expansion led to decreased rates of PH (improved access to care); however, the finding was not significant (coefficient estimate: -0.0059, CI -0.0225, 0.0107, p = 0.4856). Healthcare system characteristics, such as Medicaid spending per enrollee and Medicaid income eligibility, were associated with a significant decrease in rates of PH (improved access to care). However, the Medicaid-to-Medicare fee index (physician reimbursement) and states with a Democratic state legislature had a significant increase in rates of PH (poor access to care). CONCLUSION: Health policy reform and healthcare delivery characteristics impact access to care. Researchers should continue evaluating such policy changes across more states over longer periods of time. Researchers should translate these findings into cost analysis for state policy-makers to make better-informed decisions for their constituents. CONTRIBUTION TO KNOWLEDGE: Ambulatory care-sensitive conditions are a feasible method for evaluating policy and measuring access to primary care. Policy alone cannot improve access to care. Other factors (trust, communication, policy-makers\u27 motivations and objectives, etc.) must be addressed to improve access

MTPmle: A SAS Macro and Stata Programs for Marginalized Inference in Semi-Continuous Data

We develop a SAS macro and equivalent Stata programs that provide marginalized inference for semi-continuous data using a maximum likelihood approach. These software extensions are based on recently developed methods for marginalized two-part (MTP) models. Both the SAS and Stata extensions can fit simple MTP models for cross-sectional semi-continuous data. In addition, the SAS macro can fit random intercept models for longitudinal or clustered data, whereas the Stata programs can fit MTP models that account for subject level heteroscedasticity and for a complex survey design. Differences and similarities between the two software extensions are highlighted to provide a comparative picture of the available options for estimation, inclusion of random effects, convergence diagnosis, and graphical display. We provide detailed programming syntax, simulated and real data examples to facilitate the implementation of the MTP models for both SAS and Stata software users

Common brain structure findings across children with varied reading disability profiles

Dyslexia is a developmental disorder in reading that exhibits varied patterns of expression across children. Here we examined the degree to which different kinds of reading disabilities (defined as profiles or patterns of reading problems) contribute to brain morphology results in Jacobian determinant images that represent local brain shape and volume. A matched-pair brain morphometry approach was used to control for confounding from brain size and research site effects in this retrospective multi-site study of 134 children from eight different research sites. Parietal operculum, corona radiata, and internal capsule differences between cases and controls were consistently observed across children with evidence of classic dyslexia, specific comprehension deficit, and language learning disability. Thus, there can be common brain morphology findings across children with quite varied reading disability profiles that we hypothesize compound the developmental difficulties of children with unique reading disability profiles and reasons for their reading disability

Socio-demographic predictors of gender inequality among heterosexual couples expecting a child in south-central Uganda

Background: Gender inequality is a pervasive problem in sub-Saharan Africa, and has negative effects on health and development. Objective: Here, we sought to identify socioeconomic predictors of gender inequality (measured by low decision-making power and high acceptance of intimate partner violence) within heterosexual couples expecting a child in south-central Uganda. Method: We used data from a two-arm cluster randomized controlled HIV self-testing intervention trial conducted in three antenatal clinics in south-central Uganda among 1,618 enrolled women and 1,198 male partners. Analysis included Cochran Mantel-Haenzel, proportional odds models, logistic regression, and generalized linear mixed model framework to account for site-level clustering. Results: Overall, we found that 31.1% of men had high acceptance of IPV, and 15.9% of women had low decision-making power. We found religion, education, HIV status, age, and marital status to significantly predict gender equality. Specifically, we observed lower gender equality among Catholics, those with lower education, those who were married, HIV positive women, and older women. Conclusion: By better understanding the prevalence and predictors of gender inequality, this knowledge will allow us to better target interventions (increasing education, reducing HIV prevalence in women, targeting interventions different religions and married couples) to decrease inequalities and improve health care delivery to underserved populations in Uganda

Exploring Overlaps Between the Genomic and Environmental Determinants of LVH and Stroke: A Multicenter Study in West Africa

Background Whether left ventricular hypertrophy (LVH) is determined by similar genomic and environmental risk factors with stroke, or is simply an intermediate stroke marker, is unknown. Objectives We present a research plan and preliminary findings to explore the overlap in the genomic and environmental determinants of LVH and stroke among Africans participating in the SIREN (Stroke Investigative Research and Education Network) study. Methods SIREN is a transnational, multicenter study involving acute stroke patients and age-, ethnicity-, and sex-matched control subjects recruited from 9 sites in Ghana and Nigeria. Genomic and environmental risk factors and other relevant phenotypes for stroke and LVH are being collected and compared using standard techniques. Results This preliminary analysis included only 725 stroke patients (mean age 59.1 ± 13.2 years; 54.3% male). Fifty-five percent of the stroke subjects had LVH with greater proportion among women (51.6% vs. 48.4%; p \u3c 0.001). Those with LVH were younger (57.9 ± 12.8 vs. 60.6 ± 13.4; p = 0.006) and had higher mean systolic and diastolic blood pressure (167.1/99.5 mm Hg vs 151.7/90.6 mm Hg; p \u3c 0.001). Uncontrolled blood pressure at presentation was prevalent in subjects with LVH (76.2% vs. 57.7%; p \u3c 0.001). Significant independent predictors of LVH were age \u3c45 years (adjusted odds ratio [AOR]: 1.91; 95% confidence interval [CI]: 1.14 to 3.19), female sex (AOR: 2.01; 95% CI: 1.44 to 2.81), and diastolic blood pressure \u3e 90 mm Hg (AOR: 2.10; 95% CI: 1.39 to 3.19; p \u3c 0.001). Conclusions The prevalence of LVH was high among stroke patients especially the younger ones, suggesting a genetic component to LVH. Hypertension was a major modifiable risk factor for stroke as well as LVH. It is envisaged that the SIREN project will elucidate polygenic overlap (if present) between LVH and stroke among Africans, thereby defining the role of LVH as a putative intermediate cardiovascular phenotype and therapeutic target to inform interventions to reduce stroke risk in populations of African ancestry

Using quantile regression to investigate racial disparities in medication non-adherence

<p>Abstract</p> <p>Background</p> <p>Many studies have investigated racial/ethnic disparities in medication non-adherence in patients with type 2 diabetes using common measures such as medication possession ratio (MPR) or gaps between refills. All these measures including MPR are quasi-continuous and bounded and their distribution is usually skewed. Analysis of such measures using traditional regression methods that model mean changes in the dependent variable may fail to provide a full picture about differential patterns in non-adherence between groups.</p> <p>Methods</p> <p>A retrospective cohort of 11,272 veterans with type 2 diabetes was assembled from Veterans Administration datasets from April 1996 to May 2006. The main outcome measure was MPR with quantile cutoffs Q1-Q4 taking values of 0.4, 0.6, 0.8 and 0.9. Quantile-regression (QReg) was used to model the association between MPR and race/ethnicity after adjusting for covariates. Comparison was made with commonly used ordinary-least-squares (OLS) and generalized linear mixed models (GLMM).</p> <p>Results</p> <p>Quantile-regression showed that Non-Hispanic-Black (NHB) had statistically significantly lower MPR compared to Non-Hispanic-White (NHW) holding all other variables constant across all quantiles with estimates and p-values given as -3.4% (p = 0.11), -5.4% (p = 0.01), -3.1% (p = 0.001), and -2.00% (p = 0.001) for Q1 to Q4, respectively. Other racial/ethnic groups had lower adherence than NHW only in the lowest quantile (Q1) of about -6.3% (p = 0.003). In contrast, OLS and GLMM only showed differences in mean MPR between NHB and NHW while the mean MPR difference between other racial groups and NHW was not significant.</p> <p>Conclusion</p> <p>Quantile regression is recommended for analysis of data that are heterogeneous such that the tails and the central location of the conditional distributions vary differently with the covariates. QReg provides a comprehensive view of the relationships between independent and dependent variables (i.e. not just centrally but also in the tails of the conditional distribution of the dependent variable). Indeed, without performing QReg at different quantiles, an investigator would have no way of assessing whether a difference in these relationships might exist.</p