Journal of Veterinary Medicine and Health
Open Access

Our Group organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations
700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
  • Review Article   
  • J Vet Med Health,

Treatment Effects and Risk Factors Evaluation in Longitudinal Studies: A Statistical Help for Data Analysis

Patrizia Boracchi1*, Roberta Ferrari2, Debora Groppetti2 and Damiano Stefanello2
1Department of Clinical Sciences and Community Health, G. A. Maccacaro Laboratory of Medical Statistics Epidemiology and Biometry, University of Milan, Milan, Italy
2Department of Veterinary Medicine, University of Milan, Milan, Italy
*Corresponding Author : Patrizia Boracchi, Department of Clinical Sciences and Community Health, G. A. Maccacaro Laboratory of Medical Statistics Epidemiology and Biometry, University of Milan, Via A, Vanzetti, 520133 Milan, Italy, Tel: +39 02 2390 3204, Fax: +39 02 50320866, Email: patrizia.boracchi@unimi.it

Received Date: Dec 15, 2017 / Accepted Date: Jan 12, 2018 / Published Date: Jan 19, 2018

Abstract

This paper was inspired by the experience of the Authors research group composed by oncologist veterinarians and a biostatistician to evaluate treatments and prognostic factors with the aim to help veterinarians involved in longitudinal studies into evaluating and writing prognostic results.

Longitudinal studies are commonly analysed by techniques for survival data, taking into account for the time elapsed from the beginning of observation and the occurrence of an event related to treatment effect or disease course. The presence of incomplete follow-up information for some subjects requires specific descriptive and inferential statistical methods. Two literature datasets were analysed to show statistical models implementation techniques and to discuss statistical issues: I) A multicentre clinical trial on remission maintenance of children with acute Lymphoblastic leukaemia and II) A randomized clinical trial on advanced inoperable lung cancer. Data sets concerned studies on “humans”, nevertheless the peculiar data structure allowed to discuss some aspects which are common to survival analysis studies, regardless on subject’s characteristics. Log-rank test was used to compare survival curves for treatments and the relationship between Log-Rank test and univariate Cox model results was explained. As the evaluation of prognostic impact cannot be based only on p-values, the strength of the association between treatments and prognosis was estimated to take into account for the clinical relevance of results. On the second data set, beside of treatment, other clinical variables were available and a multivariate Cox model was applied. Model implementation was discussed concerning the coding of categorical variables and the relationship between continuous variables and model response. Suggested rules for the maximum number of covariates to be included in order to obtain reliable results were cited. Finally, the predictive ability of the model was discussed based on a measure of the area under ROC curve specific for survival data.

Keywords: Survival analysis; Prognosis; Interpretation of model results

Citation: Boracchi P, Ferrari R, Groppetti D, Stefanello D (2018) Treatment Effects and Risk Factors Evaluation in Longitudinal Studies: A Statistical Help for Data Analysis. J Vet Med Health 2:103.

Copyright: © 2018 Boracchi P, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Top