Document Type : Editorial
Authors
1 PhD. in Biostatistics, Assistant professor of Biostatistic Department modelling in health center, Health School, Kerman University of Medical Sciences, Iran
2 M.Sc. student of Biostatistics, Health school, kerman University of Medical Sciences, Iran
3 Ph.D. in Nutrition, Assistant Professor of Nutrition Department , Health school, Kerman University of Medical Sciences, Iran
Abstract
In recent years using statistics in medical sciences is increasing and statistics is an essential part of each research At present for each study design and for each type of data there are appropriate statistical methods which yield to valid results and inferences Familiarity with such methods is essential especially for those involved in the field of medical research as they might confuse majority of readers who are not familiar with details of statistical analysis and data interpretation Other consequences include waste of money time and energy spent for each research project In recent years some papers have been published to enhance researchers knowledge of statistics and its application in research methodology but still there are many methodological mistakes in manuscripts In this article we aim to demonstrate appropriate methods for data analysis We will discuss importance of confidence intervals and their advantages over Pvalue We will finally highlight common errors in data analysis such as univariate methods ignoring power in detection of difference between groups and using correlation coefficient to assess the agreement between scores
Keywords
- Cehrei A, Haghdoost AA, Fereshtehezhad M, Bayat A. [Data analysis in medical research using SPSS software].1st ed. Tehran: Karimi; 2010. [In Persian]
- Nasirian M, Sadeghi M, Haghdoost AA. [Principals in data analysis and presentation of results]. Journal of Isfahan University of Medical Sciences 2009; 100: 646-59. [In Persian]
- Bahrampour A. [Inference in Biostatistics]. Kerman: Vadiat; 2002. [In Persian]
- Mohammad K, Malekafzali H, Nehaptian W. [Statistical methods and health indicators]. 12th ed. Tehran: Salman; 2004. [In Persian]
- Ayatollahi M.T. [Principals and Methods in Biostatistics]. Tehran: Sepehr;2000.[In Persian]
- Streiner DL. Maintaining standards: Differences between the standard deviation and standard error, and when to use each. Can J Psychiatry 1996; 41(8): 498-502.
- Altman DG, Bland JM. Standard deviations and standard errors. BMJ 2005; 331(7521): 903.
- Baena-Díez JM, Byram AO, Grau M, Gómez-Fernández C, Vidal-Solsona M, Ledesma-Ulloa G. Obesity is an independent risk factor for heart failure: Zona Franca cohort study. Clin Cardiol 2010; 33(12): 760-4.
- Gardner MJ, Altman DG. Confidence intervals rather than P values: Estimation rather than hypothesis testing. BMJ 1986; 292(6522): 746-50.
- Davies HTO, Crombie IK. What are confidence intervals and p-values? London: Hayward Medical Communications; 2009.
- Altman DG. Why we need confidence intervals. World Journal of Surgery 2005; 29(5): 554-6.
- Drezner Z, Turel O, Zerom D. A modified Kolmogorov-Smirnov test for normality. Munich Personal RePEc Archive; 2008.
- Chan YH. Biostatistics 102: Quantitative data–parametric & non-parametric tests. Singapore Medical Journal 2003; 44(6): 280-5.
- Sun GW, Shook TL, Kay GL. Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol 1996; 49(8): 907-16.
- Motulsky HJ. Prism 4 statistics guide: Statistical analyses for laboratory and clinical researchers. San Diego; GraphPad Software Inc: 2003.
- Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 307-10.
- Chan YH. Biostatistics 104: correlational analysis. Singapore Med J 2003; 44(12): 614-9.
)