Michelle Shardell

A genome-wide association study of aging

Human longevity and healthy aging show moderate heritability (20%-50%). We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death. No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p < 5 × 10(-8)). We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p < 10(-5)). These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimers disease. In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings. These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity.

Inflammatory cytokine levels and depressive symptoms in older women in the year after hip fracture: findings from the Baltimore Hip Studies.

To determine whether interleukin (IL)‐6 or soluble tumor necrosis factor alpha receptor 1 (sTNF‐αR1) is associated with depressive symptoms in the year after hip fracture.

Association of serotonin-1A and 2A receptor promoter polymorphisms with depressive symptoms and functional recovery in elderly persons after hip fracture

BACKGROUNDnDepression is common after hip fracture and is associated with poorer functional recovery. Polymorphisms of the serotonin 1a (5HTR1A) and 2a receptors (5HTR2A) are associated with depression; therefore, we examined their association with depressive symptoms and functional recovery after hip fracture.nnnMETHODSn145 elderly women were followed for 12 months after hip fracture. Depressive symptoms were measured with the 15-item Geriatric Depression Scale (GDS). Functional status was measured by Lower Extremity Physical and Instrumental Activity of Daily Living scales (LPADLs and IADLs). Time-adjusted general linear regression models compared mean GDS between those with and without risk alleles for 5HTR1A and 5HTR2A.nnnRESULTSnWomen with 1-2 copies of the 5HTR1A (-1019) G allele had higher GDS scores (Adjusted Mean Difference=0.59; 95% CI, 0.12-1.06), and poorer IADL scores (Adjusted Mean Difference=0.24; 95%CI -0.002 to 0.49), compared to those without this allele, controlling for potential confounders and 5HTR2A. Depressive symptoms partly accounted for poorer IADL recovery. Women with 1-2 copies of the 5HTR2A (-1438) C allele did not have significantly higher GDS scores (Adjusted Mean Difference=0.34; 95%CI, -0.20 to 0.87) and had better IADL scores (Adjusted Mean Difference=-0.40; 95%CI -0.74 to 0.06) than those with A/A genotype.nnnLIMITATIONSnThe findings are limited by small sample size and the use of a screening scale to measure depression.nnnCONCLUSIONSnThe 5HTR1A (-1019) G allele is associated with increased depressive symptoms after hip fracture, which in turn accounts for poorer functional recovery. These results suggest a role for serotonergic genetic variation in elderly persons resilience and recovery from medical events.

Doubly robust estimation and causal inference in longitudinal studies with dropout and truncation by death

Motivated by aging research, we propose an estimator of the effect of a time-varying exposure on an outcome in longitudinal studies with dropout and truncation by death. We use an inverse-probability weighted (IPW) estimator to derive a doubly robust augmented inverse-probability weighted (AIPW) estimator. IPW estimation involves weights for the exposure mechanism, dropout, and mortality; AIPW estimation additionally involves estimating data-generating models via regression. We demonstrate that the estimators identify a causal contrast that is a function of principal strata effects under a set of assumptions. Simulations show that AIPW estimation is unbiased when weights or outcome regressions are correct, and that AIPW estimation is more efficient than IPW estimation when all models are correct. We apply the method to a study of vitamin D and gait speed among older adults.

Assessment of the 48‐Hour Rule for Identifying Community‐Associated Methicillin‐Resistant Staphylococcus aureus Infection Complicated by Bacteremia

Staphylococcus aureus Infection Complicated by Bacteremia • Author(s): Kristen Kreisel, PhD; Mary‐Claire Roghmann, MD; Michelle Shardell, PhD; O. Colin Stine, PhD; Eli Perencevich, MD; Alan Lesse, MD; Fred Gordin, MD; Michael Climo, MD; J. Kristie Johnson, PhD Source: Infection Control and Hospital Epidemiology, Vol. 31, No. 6 (June 2010), pp. 657-659 Published by: The University of Chicago Press on behalf of The Society for Healthcare Epidemiology of America Stable URL: http://www.jstor.org/stable/10.1086/653068 . Accessed: 16/05/2014 02:42

Inference for cumulative incidence functions with informatively coarsened discrete event-time data

We consider the problem of comparing cumulative incidence functions of non-mortality events in the presence of informative coarsening and the competing risk of death. We extend frequentist-based hypothesis tests previously developed for non-informative coarsening and propose a novel Bayesian method based on comparing a posterior parameter transformation with its expected distribution under the null hypothesis of equal cumulative incidence functions. Both methods use estimates derived by extending previously published estimation procedures to accommodate censoring by death. The data structure and analysis goal are exemplified by the AIDS Link to the Intravenous Experience (ALIVE) study, where researchers are interested in comparing incidence of human immunodeficiency virus seroconversion by risk behavior categories. Coarsening in the forms of interval and right censoring and censoring by death in ALIVE is thought to be informative; thus, we perform a sensitivity analysis by incorporating elicited expert information about the relationship between seroconversion and censoring into the model.

Statistical analysis with missing exposure data measured by proxy respondents: a misclassification problem within a missing-data problem.

In studies of older adults, researchers often recruit proxy respondents, such as relatives or caregivers, when study participants cannot provide self-reports (e.g., because of illness). Proxies are usually only sought to report on behalf of participants with missing self-reports; thus, either a participant self-report or proxy report, but not both, is available for each participant. Furthermore, the missing-data mechanism for participant self-reports is not identifiable and may be nonignorable. When exposures are binary and participant self-reports are conceptualized as the gold standard, substituting error-prone proxy reports for missing participant self-reports may produce biased estimates of outcome means. Researchers can handle this data structure by treating the problem as one of misclassification within the stratum of participants with missing self-reports. Most methods for addressing exposure misclassification require validation data, replicate data, or an assumption of nondifferential misclassification; other methods may result in an exposure misclassification model that is incompatible with the analysis model. We propose a model that makes none of the aforementioned requirements and still preserves model compatibility. Two user-specified tuning parameters encode the exposure misclassification model. Two proposed approaches estimate outcome means standardized for (potentially) high-dimensional covariates using multiple imputation followed by propensity score methods. The first method is parametric and uses maximum likelihood to estimate the exposure misclassification model (i.e., the imputation model) and the propensity score model (i.e., the analysis model); the second method is nonparametric and uses boosted classification and regression trees to estimate both models. We apply both methods to a study of elderly hip fracture patients.

Calculating sample size for studies with expected all-or-none nonadherence and selection bias.

SUMMARYnWe develop sample size formulas for studies aiming to test mean differences between a treatment and control group when all-or-none nonadherence (noncompliance) and selection bias are expected. Recent work by Fay, Halloran, and Follmann (2007, Biometrics 63, 465-474) addressed the increased variances within groups defined by treatment assignment when nonadherence occurs, compared to the scenario of full adherence, under the assumption of no selection bias. In this article, we extend the authors approach to allow selection bias in the form of systematic differences in means and variances among latent adherence subgroups. We illustrate the approach by performing sample size calculations to plan clinical trials with and without pilot adherence data. Sample size formulas and tests for normally distributed outcomes are also developed in a Web Appendix that account for uncertainty of estimates from external or internal pilot data.