Charnigo, Richard

Owned by UKY CCS RCD
Jun 09, 2024

Project – Improved group-based trajectory analysis applied to healthcare quality grouping


Computing method: PROC TRAJ is a published SAS package which has been applied in criminology and epidemiology, among other fields, to study groupings with similar temporal trends. This package is available for download freely from Dr. Jones’s website https://www.andrew.cmu.edu/user/bjones/.  Both Windows and Unix versions are available.

This SAS package depends on numerical procedures which can at times fail to converge to estimates or provide standard errors. These procedures seem particularly vulnerable when datasets contain gross outliers, but an investigation is required to better understand when the procedures are likely to fail.

The present study entails running Monte Carlo simulations for investigation of the convergence issues of PROC TRAJ. There are several thousand possible combinations of settings under which convergence will be explored. At least 100 simulations will be run for each setting, necessitating hundreds of thousands of simulations in total.  The SAS package also has a large computational burden with increased sample sizes and numbers of outcomes. Each simulation may require half an hour on an ordinary desktop computer.  Our hope is that the high-performance cluster will greatly speed up the completion of the simulations.

Students:

Graduate student: Gaixin Du, Ph.D. Candidate, Epidemiology and Biostatistics, Added 04/26/2021

Undergraduate: Jiacheng Xu, Statistics, Added 08/04/2021

Graduate student: Shaowli Kabir, MCC cluster, Added 10/06/2021
Supervising faculty: Richard Charnigo, Professor of Statistics and Biostatistics

Software:

SAS

Project - Developments In Nonparametric Regression Methods with Application to Raman Spectroscopy Analysis


Computing method: Compound estimation and generalized C_p criterion (published methods, readily programmable; programs also available upon request from originators), simultaneous confidence bands for mean responses and their derivatives (under development at UK), modified versions of stacking and boosting for inverse problems with nonparametric regression components (under development at UK)

Students:

Graduate student: Jing Guo, Ph.D. Student, Epidemiology and Biostatistics
Supervising Faculty: Richard Charnigo, Professor of Statistics and Biostatistics

Software:

R

Collaboration:

Srinivasan, Cidambi --- University of Kentucky
Dasari, Ramachandra --- Massachusetts Institute of Technology
Fitzmaurice, Maryann --- Case Western Reserve University
Haka, Abigail --- Cornell University

Publication:

  1. Brewster J, Sexton T, Dhaliwal G, Charnigo R, Morales G, Parrott K, Darrat Y, Gurley J, Smyth S, Elayi CS. Acute Effects of Implantable Cardioverter-Defibrillator Shocks on Biomarkers of Myocardial Injury, Apoptosis, Heart Failure, and Systemic Inflammation. Pacing and Clinical Electrophysiology: PACE. 2017. 40(4): 344-352.
  2. Sinner GJ, Gupta VA, Seratnahaei A, Charnigo RJ, Darrat YH, Elayi SC, Leung SW, Sorrell VL. Atrioventricular Dyssynchrony from Empiric Device Settings is Common in Cardiac Resynchronization Therapy and Adversely Impacts Left Ventricular Morphology and Function. Echocardiography (Mount Kisco, N.Y.). 2017. 34(4): 496-503.

RELEVANT PUBLICATIONS CARDIOVASCULAR RESEARCH GILL HEART & VASCULAR INSTITUTE 2017 STATE OF THE HEART CARDIOVASCULAR RESEARCH

  1. Omar HR, Charnigo R, Guglin M. Prognostic Significance of Discharge Hyponatremia in Heart Failure Patients with Normal Admission Sodium (from the ESCAPE Trial). The American Journal of Cardiology. 2017. In Press.
  2. Kido K, George B, Charnigo RJ, Macaulay TE, Brouse SD, Guglin M. Chronological Changes and Correlates of Loop Diuretic Dose in Left Ventricular Assist Device Patients. ASAIO Journal (American Society for Artificial Internal Organs: 1992) 2017. In Press.
  3. Charnigo R, Guglin M. Obesity Paradox in Heart Failure: Statistical Artifact, or Impetus to Rethink Clinical Practice? Heart Failure. 2017. 22(1): 13-23.
  4. Omar HR, Charnigo R, Guglin M. Ratio of Systolic Blood Pressure to Right Atrial Pressure, a Novel Marker to Predict Morbidity and Mortality in Acute Systolic Heart Failure. The American Journal of Cardiology. 2017. 119(7): 1061-1068.
  5. Wallace EL, Tasan E., Cook BS, Charnigo R, Abdel-Latif AK, Ziada KM. Long-Term Outcomes and Causes of Death in Patients With Renovascular Disease Undergoing Renal Artery Stenting. Angiology. 2016. 67(7): 657-663.
  6. Kim MH, de Beer MC, Wroblewski JM, Charnigo RJ, Ji A, Webb NR, de Beer FC, Westhuyzen DR. Impact of Individual Acute Phase Serum Amyloid A Isoforms on HDL Metabolism in Mice. Journal of Lipid Research. 2016. 57(6):969-979.
  7. Kotter J, Lolay G, Charnigo R, Leung S, McKibbin C, Sousa M, Jimenez L, Gurley J, Biase LD, Natale A, Smyth S, Darrat Y, Morales G, Elayi CS. Predictors, Morbidity, and Costs Associated with Pneumothorax during Electronic Cardiac Device Implantation. Pacing and Clinical Electrophysiology: PACE. 2016. 39(9):985-991.
  8. Sexton TR, Wallace EL, Chen A, Charnigo RJ, Reda HK, Ziada KM, Gurley JC, Smyth SS. Thromboinflammatory Response and Predictors of Outcomes in Patients undergoing Transcatheter Aortic Valve Replacement. Journal of Thrombosis and Thrombolysis. 2016. 41(3): 384-393.
  9. Guglin M, Rajagopalan N, Anaya P, Charnigo R. Sildenafil in Heart Failure with Reactive Pulmonary Hypertension (Sildenafil HF) Clinical Trial (rationale and design). Pulmonary Circulation. 2016. 6(2): 161-167.
  10. Metawee M, Charnigo R, Morales G, Darrat Y, Sorrell V, Di Biase L, Natale A, Delisle B, Elayi CS and the Magic investigators. Digoxin and Short Term Mortality after Acute STEMI: Results from the MAGIC Trial. International Journal of Cardiology. 2016. 13(218) 176-180.
  11. Guglin M, Rajagopalan N, Anaya P, Charnigo R. Sildenafil in Heart Failure with Reactive Pulmonary Hypertension (Sildenafil HF) Clinical Trial (rationale and design). Pulmonary Circulation. 2016. 6(2): 161-167.


Contributed talk to Joint Statistical Meetings, 2013

Abstract: Derivatives of Raman Spectra for Breast Cancer Diagnosis

Raman spectra have been successfully employed in the classification of normal, benign, and malignant breast tissue, based on coefficient estimates from a linear combination model involving basis spectra for chemical constituents of breast tissue. Motivated by recent work showing that nanoparticles can be classified using not only Mueller scattering profiles but also their derivatives, we investigate whether derivatives of Raman spectra can be effectively employed in the classification of breast tissue. We consider two approaches. The first entails calculating the cross-validated distance between the estimated derivative of a Raman spectrum for a breast tissue to be diagnosed and a reference curve defined as the average estimated derivative for breast tissues known to have a particular diagnosis. The second is based on constructing simultaneous confidence bands about the estimated derivative of a Raman spectrum for a breast tissue to be diagnosed and examining the aforementioned reference curves in relation to the confidence bands. Numerical results from real ex vivo data are presented, and potential implications for the non-invasive diagnosis of breast cancer are discussed.

Grants:

Charnigo, Richard 5P50DA005312-23 CDART - Center for Drug Abuse Research Translation (Stats Core) National Institute on Drug Abuse 9/30/1992 - 6/30/2017 (SCOPE)

Center for Computational Sciences