Review articles

By Dr. Brijesh Sathian , Dr. Jayadevan Sreedharan
Corresponding Author Dr. Brijesh Sathian
Community Medicine, Manipal College of Medical Sciences, Department of Community Medicine, Manipal College of Medical Sciences - Nepal 155
Submitting Author Dr. Brijesh Sathian
Other Authors Dr. Jayadevan Sreedharan
Research Division, Gulf Medical University, - United Arab Emirates


Testing of Hypothesis, Sample size, Power, Confidence interval, Medical research.

Sathian B, Sreedharan J. Importance of Biostatistics to Improve the Quality of Medical Journals. WebmedCentral BIOSTATISTICS 2012;3(5):WMC003332
doi: 10.9754/journal.wmc.2012.003332

This is an open-access article distributed under the terms of the Creative Commons Attribution License(CC-BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submitted on: 04 May 2012 05:22:12 PM GMT
Published on: 05 May 2012 12:12:52 PM GMT


Most of the Medical journals are facing the methodological rigor problem. A p value of <0.05 means that this result would have arisen by chance on less than five occasion in 100. The confidence interval around a result in a clinical trial indicates the limits within which the “real” difference between the treatments is likely to lie, and hence the strength of the inference that can be drawn from the result. A statistically significant result may not be clinically significant. Many researchers are not giving due importance to optimum size calculation, confidence intervals and testing of hypothesis while undertaking their research. This negligence results in wrong conclusions and thus reducing the quality of their research. Absence of evidence is not evidence of absence. Medical researcher should follow the tenets of biostatistics and the suggestions of a qualified biostatistician even from the stage of conceptualization to the finality of publication of the work. 


The major applications of biostatistics started in the middle of the 17th century in the analysis of vital statistics. After the early developments in vital statistics, the field of genetics was the next area that benefited most from the new statistical ideas emerging in the works of Charles Darwin (1809-1882), Francis Galton (1822-1910), Karl Pearson (1857-1936), and Ronald A. Fisher (1890-1962). Now, the fields of application and areas of concern of biostatistics include, among others, bioassay, demography, epidemiology, clinical trials, surveys of human populations, community diagnosis, bio-mathematical modelling, etc. Findings of good research deserve to be presented well, and a good presentation is as much a part of the research as the painstaking collection and analysis of the data. Critical reviewers of the biomedical literature have consistently found that more than half of the published articles (including scientific articles, published even in the best journals) that used statistical methods contained unacceptable errors1-8. The term “statistics” here in this context, has a wider meaning and includes the methodology of research, study design, or epidemiological methodology etc9-14. A recent study on the published literature of biomedical journals has shown that these errors mainly concern the sample size, statistical power, agreement between aim and conclusion, distribution of data, as well as description of location and variability of data1. A brief glance through almost any recently published medical journal will show that statistical methods are playing an increasingly visible role in modern medical research. At the very least, most research papers quote at least one ‘p-value’ to communicate. At the same time, a growing number of papers are now presenting the results of relatively sophisticated, statistical analyses of complex sets of medical data8. There are several good quality researches reported in medical journals from developing counties without utilizing the full findings of the study. Result part became poor because of the lack of knowledge in appropriate test for the analysis of data and the coding of data.  If the researcher is not aware about the proper research design in descriptive studies, case control studies, cohort studies and clinical trials better to terminate the study rather than reporting clinical trials in the methodology part and the study will be a hospital based observational study. Medical Statistics helps the researcher to arrive at a scientific judgement about a hypothesis. It has been argued that decision making is an integral part of a physician’s work. Frequently, decision making is probability based.

Varying quantity is known as variable. Variables are of two types. They are categorical and numerical.

Categorical Variables: Individuals are classified into one of several categories. For example: Blood group which is A,B,AB or O.

Binary variables: If there are only two categories, then the variable is known as binary (or dichotomous). Binary variables are very common. For example: Yes/no responses, female/male, low/normal birth weight. Individuals are classified into the two groups for comparison according to a binary variable. For example: diseased/disease-free, treated/placebo.

Ordinal variables: If there are more than two categories and the categories have an obvious order, then the variable is ordinal. For example: social class (1,2,3a,3b,4,5), pain (non/mild/moderate/ severe).

Nominal variables: Categoric variables which are neither binary nor ordinal are known as nominal. For example: ethnic group (caucasian/asian/afro-carribean), marital status (married/ single/ divorced/separated/widowed).

Numeric Variables: A number describes each individuals' value. For example: number of transfusions, haemaglobin level.

Discrete variables: If the numeric variable can take only a distinct number of values, usually complete integers (0,1,2,3,...) then it is known as discrete. For example: age in years, parity, number of visits to clinic, number of transfusions.

Continuous variables: In theory, continuous variables can take any value within a certain range. In practice, the possible values the variable takes may be restricted by the accuracy of the recording device. For example: 'exact' age (usually meaning age to the nearest day or month), blood pressure, head circumference, haemaglobin level.

According to the variable researcher should select the appropriate statistical test15. If data is following normal distribution then select parametric tests. Whenever data is not following normal distribution should use non parametric tests. Ex: In a drug utilization study of anti depressants with independent variables age, gender, monthly income, employment of the patient and dependent variable Essential drug list of Nepal, generic and trade logistic regression is the appropriate. Another case in significance of hepatobiliary enzymes for differentiating liver and bone diseases with independent variable age, gender and dependent variable is the levels of AST, ALT, ALP, γ-gt were assessed in cases of viral hepatits, extra hepatic cholestasis, pagets disease osteomalacia and controls ANOVA is the appropriate test16-32.


Presenting the preliminary report of the study in reputed conferences will allow the researchers to improve the quality by the comments from the experts and seniors. It is sincerely recommended and encouraged by the editors and author that the contributing researchers follow a diligent and systematic pattern in conducting and presenting their studies. This will not only lead to improved quality of research but will also enhance and augment the quality of journals and thus, contribute meaningfully to the progress of research and improvement of medical care.


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15. Sathian B. Methodological Rigors in Medical Journals from Developing Countries: An Appraisal of the Scenario in Asia. Nepal Journal of Epidemiology 2011; 1(5): 141-43.
16. Sathian B, Sreedharan J, Mittal A, Chandrasekharan N, Baboo NS, Abhilash ES, Case Control Studies in Medical Research. Nepal Journal of Epidemiology 2011;1(3): 77-8.
17. Sathian B. Reporting dichotomous data using Logistic Regression in Medical Research: The scenario in developing countries. Nepal Journal of Epidemiology 2011;1(4):111-113.
18.  Sathian B, Sreedharan J, Baboo NS, Sharan K, Abhilash E S, Rajesh E. Relevance of Sample Size Determination in Medical Research. Nepal Journal of Epidemiology 2010; 1(1): 4-10.
19. Roy B, Banerjee I, Sathian B, Mondal M, Saha CG. Blood Group Distribution and Its Relationship with Bleeding Time and Clotting Time: A Medical School Based Observational Study among Nepali, Indian and Srilankan Students. Nepal Journal of Epidemiology 2011;1(4):135-40.
20. Sreeramareddy CT, Ramakrishnareddy N, Harsha KumarHN, Sathian B, Arokiasamy JT. Prevalence, distribution andpredictors of tobacco smoking and chewing in Nepal: a secondary data analysis of Nepal Demographic and HealthSurvey-2006. Substance Abuse Treatment, Prevention, and Policy 2011;6:33.
21. Roy B, Banerjee  I, Sathian B, Mondal M, Kumar SS, Saha CG. Attitude of Basic Science Medical Students towards  Post Graduation in Medicine and Surgery: A Questionnaire based Cross-sectional Study from Western Region of Nepal. Nepal Journal of Epidemiology 2010; 1(4):126-34.
22. Banerjee I, Roy B, Sathian B, Banerjee I, Kumar SS, Saha A. Medications for Anxiety: A Drug utilization study in Psychiatry Inpatients from a Tertiary Care Centre of Western Nepal. Nepal Journal of Epidemiology 2010; 1(4):119-25.
23. Mittal A, Sathian B, Kumar A, Chandrasekharan N, Farooqui MS, Singh S, Yadav KS. Hyperuricemia as an Additional Risk Factor for Coronary Artery Disease: A Hospital Based Case Control Study in Western Region of Nepal. Nepal Journal of Epidemiology 2011;1(3):81-5.
24. Basha AS, Mathew E, Sreedharan J, Muttappallymyalil J, Sharbatti AS, Shaikh BR. Pattern of Blood Pressure Distribution among University Students in Ajman, United Arab Emirates. Nepal Journal of Epidemiology 2011;1(3):86-9.
25. Banerjee I, Jauhari AC, Bista D, Johorey AC, Roy B, Sathian B. Medical Students View about the Integrated MBBS Course: A Questionnaire Based Cross-sectional Survey from a Medical College of Kathmandu Valley. Nepal Journal of Epidemiology 2011;1(3): 95-100.
26. Mittal A, Sathian B, Poudel B, Farooqui MS, Chandrasekharan N, Yadav KS. The Significance of Hepatobiliary Enzymes for Differentiating Liver and Bone Diseases: A Case Control Study from Manipal Teaching Hospital of Pokhara Valley. Nepal Journal of Epidemiology 2011;1(5): 153-9.
27. Poudel B, Mittal A, Yadav BK, Sharma P, Jha B, Raut KB. Estimation and Comparison of Serum Levels of Sodium, Potassium, Calcium and Phosphorus in Different Stages of Chronic Kidney Disease. Nepal Journal of Epidemiology 2011;1 (5): 160-7.
28. Mittal A, Sathian B, Chandrasekharan N, Lekhi A, Rahib R,  Dwedi S. Hepatic Steatosis and Diabetes Mellitus: Risk Factors, Pathophysiology and with its Clinical Implications: A Hospital Based Case Control Study in Western Region of Nepal. Nepal Journal of Epidemiology 2011;1(2):51-56.
29. Banerjee I, Roy B, Banerjee I, Sathian B, Mondol M, Saha A. Depression and its Cure : A Drug Utilization Study from a Tertiary Care Centre of Western Nepal. Nepal Journal of Epidemiology 2011;1 (5):144-52.
30. Mittal A, Sathian B, Kumar A, Chandrasekharan N, Sunka A. Diabetes mellitus as a Potential Risk Factor for Renal Disease among Nepalese: A Hospital Based Case Control Study. Nepal Journal of Epidemiology 2010; 1(1): 22-5.
31. Mittal A ,  Sathian B, Chandrasekharan N , Lekhi A, Farooqui M S, Pandey N. Diagnostic Accuracy of Serological Markers in Viral Hepatitis and Non Alcoholic Fatty Liver Disease. A Comparative Study in Tertiary Care Hospital of Western Nepal. Nepal Journal of Epidemiology 2011;1(2): 60-3.
32. Mittal A, Sathian B, Kumar A, Chandrasekharan N, Dwedi S. The Clinical Implications of Thyroid Hormones and its Association with Lipid Profile: A Comparative Study from Western Nepal. Nepal Journal of Epidemiology 2010; 1(1): 11-6.

Source(s) of Funding

Not Applicable

Competing Interests

The authors have no competing interest arising from the study.


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