Full Paper View Go Back

Statistical Model Differences in Hepatic or Renal Impairment Trials – All Impaired Categories

C. Bhupathi1 , V.V. Haragopal2

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.1 , pp.41-45, Feb-2019


CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i1.4145


Online published on Feb 28, 2019


Copyright © C. Bhupathi, V.V. Haragopal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library


XML View     PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: C. Bhupathi, V.V. Haragopal, “Statistical Model Differences in Hepatic or Renal Impairment Trials – All Impaired Categories,” International Journal of Scientific Research in Mathematical and Statistical Sciences, Vol.6, Issue.1, pp.41-45, 2019.

MLA Style Citation: C. Bhupathi, V.V. Haragopal "Statistical Model Differences in Hepatic or Renal Impairment Trials – All Impaired Categories." International Journal of Scientific Research in Mathematical and Statistical Sciences 6.1 (2019): 41-45.

APA Style Citation: C. Bhupathi, V.V. Haragopal, (2019). Statistical Model Differences in Hepatic or Renal Impairment Trials – All Impaired Categories. International Journal of Scientific Research in Mathematical and Statistical Sciences, 6(1), 41-45.

BibTex Style Citation:
@article{Bhupathi_2019,
author = {C. Bhupathi, V.V. Haragopal},
title = {Statistical Model Differences in Hepatic or Renal Impairment Trials – All Impaired Categories},
journal = {International Journal of Scientific Research in Mathematical and Statistical Sciences},
issue_date = {2 2019},
volume = {6},
Issue = {1},
month = {2},
year = {2019},
issn = {2347-2693},
pages = {41-45},
url = {https://www.isroset.org/journal/IJSRMSS/full_paper_view.php?paper_id=1138},
doi = {https://doi.org/10.26438/ijcse/v6i1.4145}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i1.4145}
UR - https://www.isroset.org/journal/IJSRMSS/full_paper_view.php?paper_id=1138
TI - Statistical Model Differences in Hepatic or Renal Impairment Trials – All Impaired Categories
T2 - International Journal of Scientific Research in Mathematical and Statistical Sciences
AU - C. Bhupathi, V.V. Haragopal
PY - 2019
DA - 2019/02/28
PB - IJCSE, Indore, INDIA
SP - 41-45
IS - 1
VL - 6
SN - 2347-2693
ER -

355 Views    337 Downloads    115 Downloads
  
  

Abstract :
The statistical analysis for organ impairment studies are performed using the mixed model techniques. The analysis is carried out by considering the impairment categories as fixed effects and/or the matched pair as random effect. By using simulated data for mild, moderate and severe impaired categories and the corresponding matched healthy volunteer groups, we have attempted to compare the results of these two models in multiple situations. We have used a pool of healthy volunteers group to execute the model without random effects. Since the organ impairment trials are conducted on a very small number of subjects, the aim of this research paper is to avoid the use of complicated models on such small data.

Key-Words / Index Term :
Mixed models, Parallel designs, Clinical trials, Hepatic or Renal impairment

References :
[1] FDA Guidance for industry, “Pharmacokinetics in Patients with Impaired Hepatic Function: Study Design, Data Analysis, and Impact on Dosing and Labeling”, Food & Drug Administration, 2003.
[2] FDA Guidance for industry, “Pharmacokinetics in Patients with Impaired Renal Function: Study Design, Data Analysis, and Impact on Dosing and Labeling”, Food & Drug Administration, 2010.
[3] D. McNeish, K. Kelly, “Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations”, Psychological Methods, 2018.
[4] R. C. Littell, G. A. Milliken, W. W. Stroup, D. Wolfinger R. “SAS for mixed models (2nd ed.)” SAS Institute Inc., Cary, NC, 2006.
[5] R. Urso, P. Blardi, G. Giorgi, “A short introduction to pharmacokinetics”, Eur. Rev. Med. Pharmacol. Sci, 6, pp.33, 2002.

Authorization Required

 

You do not have rights to view the full text article.
Please contact administration for subscription to Journal or individual article.
Mail us at  support@isroset.org or view contact page for more details.

Go to Navigation