Andrzej T. Galecki

Bio: Andrzej T. Galecki is an academic researcher from University of Michigan. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 38, co-authored 78 publications receiving 6008 citations. Previous affiliations of Andrzej T. Galecki include United States Department of Veterans Affairs & Michigan State University.

Linear Mixed Models: A Practical Guide Using Statistical Software

Abstract: INTRODUCTION What Are Linear Mixed Models (LMMs)? A Brief History of Linear Mixed Models LINEAR MIXED MODELS: AN OVERVIEW Introduction Specification of LMMs The Marginal Linear Model Estimation in LMMs Computational Issues Tools for Model Selection Model-Building Strategies Checking Model Assumptions (Diagnostics) Other Aspects of LMMs Power Analysis for Linear Mixed Models Chapter Summary TWO-LEVEL MODELS FOR CLUSTERED DATA: THE RAT PUP EXAMPLE Introduction The Rat Pup Study Overview of the Rat Pup Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model Estimating the Intraclass Correlation Coefficients (ICCs) Calculating Predicted Values Diagnostics for the Final Model Software Notes and Recommendations THREE-LEVEL MODELS FOR CLUSTERED DATA THE CLASSROOM EXAMPLE Introduction The Classroom Study Overview of the Classroom Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model Estimating the Intraclass Correlation Coefficients (ICCs) Calculating Predicted Values Diagnostics for the Final Model Software Notes Recommendations MODELS FOR REPEATED-MEASURES DATA: THE RAT BRAIN EXAMPLE Introduction The Rat Brain Study Overview of the Rat Brain Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model The Implied Marginal Variance-Covariance Matrix for the Final Model Diagnostics for the Final Model Software Notes Other Analytic Approaches Recommendations RANDOM COEFFICIENT MODELS FOR LONGITUDINAL DATA: THE AUTISM EXAMPLE Introduction The Autism Study Overview of the Autism Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model Calculating Predicted Values Diagnostics for the Final Model Software Note: Computational Problems with the D Matrix An Alternative Approach: Fitting the Marginal Model with an Unstructured Covariance Matrix MODELS FOR CLUSTERED LONGITUDINAL DATA: THE DENTAL VENEER EXAMPLE Introduction The Dental Veneer Study Overview of the Dental Veneer Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model The Implied Marginal Variance-Covariance Matrix for the Final Model Diagnostics for the Final Model Software Notes and Recommendations Other Analytic Approaches MODELS FOR DATA WITH CROSSED RANDOM FACTORS: THE SAT SCORE EXAMPLE Introduction The SAT Score Study Overview of the SAT Score Data Analysis Analysis Steps in the Software Procedures Results of Hypothesis Tests Comparing Results across the Software Procedures Interpreting Parameter Estimates in the Final Model The Implied Marginal Variance-Covariance Matrix for the Final Model Recommended Diagnostics for the Final Model Software Notes and Additional Recommendations APPENDIX A: STATISTICAL SOFTWARE RESOURCES APPENDIX B: CALCULATION OF THE MARGINAL VARIANCE-COVARIANCE MATRIX APPENDIX C: ACRONYMS/ABBREVIATIONS BIBLIOGRAPHY INDEX

Trajectory of Cognitive Decline after Incident Stroke

Abstract: Importance Cognitive decline is a major cause of disability in stroke survivors. The magnitude of survivors’ cognitive changes after stroke is uncertain. Objective To measure changes in cognitive function among survivors of incident stroke, controlling for their prestroke cognitive trajectories. Design, Setting, and Participants Prospective study of 23 572 participants 45 years or older without baseline cognitive impairment from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort, residing in the continental United States, enrolled 2003-2007 and followed up through March 31, 2013. Over a median follow-up of 6.1 years (interquartile range, 5.0-7.1 years), 515 participants survived expert-adjudicated incident stroke and 23 057 remained stroke free. Exposure Time-dependent incident stroke. Main Outcomes and Measures The primary outcome was change in global cognition (Six-Item Screener [SIS], range, 0-6). Secondary outcomes were change in new learning (Consortium to Establish a Registry for Alzheimer Disease Word-List Learning; range, 0-30), verbal memory (Word-List Delayed Recall; range, 0-10), and executive function (Animal Fluency Test; range, ≥0), and cognitive impairment (SIS score Results Stroke was associated with acute decline in global cognition (0.10 points [95% CI, 0.04 to 0.17]), new learning (1.80 points [95% CI, 0.73 to 2.86]), and verbal memory (0.60 points [95% CI, 0.13 to 1.07]). Participants with stroke, compared with those without stroke, demonstrated faster declines in global cognition (0.06 points per year faster [95% CI, 0.03 to 0.08]) and executive function (0.63 points per year faster [95% CI, 0.12 to 1.15]), but not in new learning and verbal memory, compared with prestroke slopes. Among survivors, the difference in risk of cognitive impairment acutely after stroke, compared with immediately before stroke, was not statistically significant (odds ratio, 1.32 [95% CI, 0.95 to 1.83]; P = .10); however, there was a significantly faster poststroke rate of incident cognitive impairment compared with the prestroke rate (odds ratio, 1.23 per year [95% CI, 1.10 to 1.38]; P Conclusions and Relevance Incident stroke was associated with an acute decline in cognitive function and also accelerated and persistent cognitive decline over 6 years.

The GLP-1 Derivative NN2211 Restores β-Cell Sensitivity to Glucose in Type 2 Diabetic Patients After a Single Dose

Abstract: Glucagon-like peptide 1 (GLP-1) stimulates insulin secretion in a glucose-dependent manner, but its short half-life limits its therapeutic potential. We tested NN2211, a long-acting GLP-1 derivative, in 10 subjects with type 2 diabetes (means +/- SD: age 63 +/- 8 years, BMI 30.1 +/- 4.2 kg/m(2), HbA(1c) 6.5 +/- 0.8%) in a randomized, double-blind, placebo-controlled, crossover study. A single injection (7.5 micro g/kg) of NN2211 or placebo was administered 9 h before the study. beta-cell sensitivity was assessed by a graded glucose infusion protocol, with glucose levels matched over the 5-12 mmol/l range. Insulin secretion rates (ISRs) were estimated by deconvolution of C-peptide levels. Findings were compared with those in 10 nondiabetic volunteers during the same glucose infusion protocol. In type 2 diabetic subjects, NN2211, in comparison with placebo, increased insulin and C-peptide levels, the ISR area under the curve (AUC) (1,130 +/- 150 vs. 668 +/- 106 pmol/kg; P < 0.001), and the slope of ISR versus plasma glucose (1.26 +/- 0.36 vs. 0.54 +/- 0.18 pmol x l[min(-1) x mmol(-1) x kg(-1)]; P < 0.014), with values similar to those of nondiabetic control subjects (ISR AUC 1,206 +/- 99; slope of ISR versus plasma glucose, 1.44 +/- 0.18). The long-acting GLP-1 derivative, NN2211, restored beta-cell responsiveness to physiological hyperglycemia in type 2 diabetic subjects.

General class of covariance structures for two or more repeated factors in longitudinal data analysis

Abstract: The main difficulty in parametric analysis of longitudinal data lies in specifying covariance structure. Several covariance structures, which usually reflect one series of measurements collected over time, have been presented in the literature. However there is a lack of literature on covariance structures designed for repeated measures specified by more than one repeated factor. In this paper a new, general method of modelling covariance structure based on the Kronecker product of underlying factor specific covariance profiles is presented. The method has an attractive interpretation in terms of independent factor specific contribution to overall within subject covariance structure and can be easily adapted to standard software.

High-normal serum uric acid increases risk of early progressive renal function loss in type 1 diabetes: Results of a 6-year follow-up

Abstract: Objective: We previously described cross-sectional association between serum uric acid (UA) and reduced glomerular filtration rate (GFR) in non-proteinuric patients with type 1 diabetes. Here we prospectively investigated whether baseline UA impacts the risk of early Declining Renal Function (early DRF) in these patients. Research Design and Methods: Patients with elevated urinary albumin excretion (n=355) were followed for 4-6 years for changes in urinary albumin excretion and GFR. The changes were estimated by multiple determinations of albumin to creatinine ratios (ACR) and serum cystatin C (GFRcystatin). Results: At baseline the medians (25 th , 75 th percentiles) for UA, ACR, and GFRcystatin values were 4.6 mg/dl (3.8,5.4), 26.2 mg/g (15.1, 56.0) and 129 ml/min/1.73 m 2 (111,145), respectively. During the 6 year follow-up, significant association (p Conclusions: We found a clear dose-response relation between serum UA and risk of early DRF in patients with Type 1 diabetes. Clinical trials are warranted to determine whether UA lowering drugs can halt renal function decline before it becomes clinically significant.

Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association

Abstract: March 5, 2019 e1 WRITING GROUP MEMBERS Emelia J. Benjamin, MD, ScM, FAHA, Chair Paul Muntner, PhD, MHS, FAHA, Vice Chair Alvaro Alonso, MD, PhD, FAHA Marcio S. Bittencourt, MD, PhD, MPH Clifton W. Callaway, MD, FAHA April P. Carson, PhD, MSPH, FAHA Alanna M. Chamberlain, PhD Alexander R. Chang, MD, MS Susan Cheng, MD, MMSc, MPH, FAHA Sandeep R. Das, MD, MPH, MBA, FAHA Francesca N. Delling, MD, MPH Luc Djousse, MD, ScD, MPH Mitchell S.V. Elkind, MD, MS, FAHA Jane F. Ferguson, PhD, FAHA Myriam Fornage, PhD, FAHA Lori Chaffin Jordan, MD, PhD, FAHA Sadiya S. Khan, MD, MSc Brett M. Kissela, MD, MS Kristen L. Knutson, PhD Tak W. Kwan, MD, FAHA Daniel T. Lackland, DrPH, FAHA Tené T. Lewis, PhD Judith H. Lichtman, PhD, MPH, FAHA Chris T. Longenecker, MD Matthew Shane Loop, PhD Pamela L. Lutsey, PhD, MPH, FAHA Seth S. Martin, MD, MHS, FAHA Kunihiro Matsushita, MD, PhD, FAHA Andrew E. Moran, MD, MPH, FAHA Michael E. Mussolino, PhD, FAHA Martin O’Flaherty, MD, MSc, PhD Ambarish Pandey, MD, MSCS Amanda M. Perak, MD, MS Wayne D. Rosamond, PhD, MS, FAHA Gregory A. Roth, MD, MPH, FAHA Uchechukwu K.A. Sampson, MD, MBA, MPH, FAHA Gary M. Satou, MD, FAHA Emily B. Schroeder, MD, PhD, FAHA Svati H. Shah, MD, MHS, FAHA Nicole L. Spartano, PhD Andrew Stokes, PhD David L. Tirschwell, MD, MS, MSc, FAHA Connie W. Tsao, MD, MPH, Vice Chair Elect Mintu P. Turakhia, MD, MAS, FAHA Lisa B. VanWagner, MD, MSc, FAST John T. Wilkins, MD, MS, FAHA Sally S. Wong, PhD, RD, CDN, FAHA Salim S. Virani, MD, PhD, FAHA, Chair Elect On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association

Abstract: Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovas...

Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association.

Abstract: Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascul...