alexa Count Data Analysis in Randomised Clinical Trials | OMICS International | Abstract
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Review Article

Count Data Analysis in Randomised Clinical Trials

Jakobsen JC1,2*, Tamborrino M3, Winkel P1, Haase N4, Perner A4, Wetterslev J1 and Gluud C1

1Copenhagen Trial Unit, Centre for Clinical Intervention Research, Department 7812, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark

2Emergency Department, Holbaek Hospital, Holbaek, Denmark

3Institute for Stochastics, Johannes Kepler University Linz, Austria

4Department of Intensive Care, Rigshospitalet, University of Copenhagen, Denmark

*Corresponding Author:
Jakobsen JC
Department 7812, Rigshospitalet, Copenhagen University Hospital
Blegdamsvej 9, DK-2100 Copenhagen, Denmark
Tel: +45 2618 6242
E-mail: [email protected]

Received date: April 24, 2015; Accepted date: May 29, 2015; Published date: June 05, 2015

Citation: Jakobsen JC, Tamborrino M, Winkel P, Haase N, Perner A, et al. (2015) Count Data Analysis in Randomised Clinical Trials. J Biomet Biostat 6: 227. doi: 10.4172/2155-6180.1000227

Copyright: © 2015 Jakobsen JC, 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 are credited.

Abstract

Choosing the best model for analysis of count data in randomised clinical trials is complicated. In this paper, we review count data analysis with different parametric and non-parametric methods used in randomised clinical trials, and we define procedures for choosing between the two methods and their subtypes. We focus on analysis of simple count data and do not consider methods for analyzing longitudinal count data or Bayesian statistical analysis. We recommend that: (1) a detailed statistical analysis plan is published prior to access to trial data; (2) if there is lack of evidence for a parametric model, both non-parametric tests (either the van Elteren test or the Tadap2 test, based on an aligned rank test with equal stratum weights) and bootstrapping should be used as default methods; and (3) if more than two intervention groups are compared, then the Kruskal–Wallis test may be considered. If our recommendations are followed, the risk of biased results ensuing from analysis of count data in randomised clinical trials is expected to decrease.

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