Review articles
 

By Dr. Brijesh Sathian , Dr. Jayadevan Sreedharan , Dr. Ankush Mittal
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

Dr. Ankush Mittal
Department of Biochemistry, Manipal College of Medical Sciences, - India

EPIDEMIOLOGY

Sample Size Calculation, Epidemiological Studies, Nepal

Sathian B, Sreedharan J, Mittal A. Importance of Sample Size Calculation in the Original Medical Research Articles from Developing Countries. WebmedCentral EPIDEMIOLOGY 2012;3(5):WMC003371
doi: 10.9754/journal.wmc.2012.003371

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.
No
Submitted on: 13 May 2012 06:33:16 AM GMT
Published on: 14 May 2012 12:25:30 PM GMT

Abstract


Sample size calculation is very important for medical research. Because medical data is with uncertainty and most of the studies deals with a small sample and infer about a big population.  But most of the researchers from developing countries like Nepal are not aware about this and who aware are not able to use this scientific area. Medical Journals should keep a criterion for publication of manuscripts to the authors that it will not be published, if the sample size calculation is not done.  Then only the actual objective of the study will be proved. 

Introduction


In Medical research, it is important to determine a sample size sufficient enough to ensure reliable conclusions. If the study is well designed with a desired sample size then the standard error will be less and the power and precision will be good. All statistical procedures become valid in this context. Every researcher must strive for the proper sample size and the protocol should contain its details.

Inferential statistics has two parts: estimation of population parameter and testing of hypothesis. According to the type of medical research, any one of them can be adopted. The estimation method is used in prevalence/descriptive studies and the testing of hypothesis is used for cohort/case control/clinical trials.

Using estimation method, the best estimates for population characteristics such as prevalence, incidence, mean, standard deviation, etc. can be found out.

By testing the hypothesis, correctness of whatever values or any relationship or association between variables derived from estimation can be verified.

These are the two requirements for the analysis of data in medical research. Before the testing of the hypothesis, one must confirm the type of normality of the data so that the type of the test (parametric or non parametric) can be decided. Violation of this rule will result in wrong conclusion. Once the correct test is selected, the next important step is to determine the sample size. If proper attention is not given to the determination of the sample size, a real difference will become statistically insignificant. Thus, the study has to be repeated on a larger sample so that the real difference can be statistically proved. A randomly decided sample will invite non sampling errors in the study. An under sized sample will not give correct information and that will turn into a waste of time and resources. An over sized sample will end up with loss of resources with respect to money, man power and time. As a result, both errors will entail even unethical outcomes.

Therefore, sample size determination is an important issue in medical research but availability of literature in this topic is scanty. On a recent survey a few of them were located[1-24].

Practical Examples


1. Sample size calculation for The Significance of Hepatobiliary Enzymes for Differentiating Liver and Bone Diseases: A Case Control Study from Manipal Teaching Hospital of Pokhara Valley with 95% confidence interval and significance level ? = 5%. We conducted a pilot study of 100 cases each of all the diseases included in this study. In extra hepatic cholestasis, ?= SD of the ALP = 285, allowable error = 35, and required sample size was 255. In Paget's disease, ?= SD of the ALP = 220, allowable error = 25, and required sample size was 298. In Osteomalacia, ?= SD of the ALP = 200, allowable error = 24, and required sample size was 267. ?= SD of the ALP in Osteomalacia cases. In Viral hepatitis, ?=500, allowable error = 57, and required sample size was 296. ?= SD of the ALT in cases of viral hepatitis.

2. Sample size calculation for Depression and its Cure: A Drug Utilization Study from a Tertiary Care Centre of Western Nepal.  For 95% confidence interval and, significance level ? = 5%, P = 90%, Q = 10%, allowable error = 11%, required sample size was 35. P = percentage of antidepressants drugs used for the treatment of depression. In the pilot study done prior to the original study with 10 patients were admitted in the psychiatry ward with depression.

3. Sample size calculation for Attitude of Basic Science Medical Students towards Post Graduation in Medicine and Surgery: A Questionnaire based Cross-sectional Study from Western Region of Nepal. For 99% confidence interval and, significance level ? = 1%, P = 70%, Q = 30%, allowable error = 10%, required sample size was 218.  P = percentage of students selected their PG as Medicine and surgery.

4. Sample size calculation for Suicidal ideation among students of a medical college in Western Nepal: A cross-sectional study.  Minimum sample size calculated was 185, after considering the findings from the pilot study which showed that 10% had recent suicidal ideation and keeping the confidence level at 95% and fixing the allowable error at 10%.

5. Sample size calculation for Estimation and Comparison of Serum Levels of Sodium, Potassium, Calcium and Phosphorus in Different Stages of Chronic Kidney Disease. In a pilot study of 9 patients with stage I CKD, we found Mean potassium was 4 and ? = 0.1 = standard deviation. For, 95% confidence interval, Z = 1.96, 5% significance level, E = 0.04 = Allowable error. Therefore required sample size with n = {Z2 * ?2}/E2 was 24.

There are several other good studies which will be helpful for the researchers to understand the methodology of sample size calculation in Medical research[25-36]. 

Suggestions


1. If the effect of a clinical treatment is not marked when compared to a placebo, or power of the study is low, or a lower significance level (lower ‘p’ value) is expected, the sample size should be increased.
2. If the measurements are highly varying, use the average of repeated measurements.
3. Determine the scientifically acceptable power and level of significance.
4. Estimate the event rate form similar population.
5. In research protocols, statistically determined sample size, power of the study, significance level, event rate, duration of the study, and compliance should be mentioned.
6. The sample size should be increased to adequate level for each sub-group when dealing with multiple sub-groups in a population.
7. Always aim for a cost effective sample size.
8. In small negative trials, meta analysis can be tried.
9. When a study requires very large sample size net working with other researchers engaged in similar projects and Multi-centre trials will be beneficial.
10. A study which needs a large sample size to prove any significant difference in two treatments must ensure the required sample size. Otherwise such studies may not provide much information by any method and are better terminated so that the money and time are at least saved.

Conclusion


Carefully and well planned Medical research will result in relevant and socially useful results. Planning has several parts, such as well defined relevant research hypothesis, objectives, subjects must be selected from appropriate population, and instruments should be reliable, carefully undergone through best possible procedures and other guidelines. Sample size determination is very important and always difficult process to handle. It requires the collaboration of a specialist who has a good scientific knowledge in the art and practice of medical statistics. 

Reference


1. Mace AE. Sample-Size Determination. New York: Reinhold Publishing Corporation, 1964.
2. Cochran WG. Sampling techniques. New York, NY: John Wiley & Sons, 1977.
3. Altman DG. Statistics and ethics in medical research: III How large a sample?. Br Med J 1980;281(6251): 1336-8.
4. Gore SM. Statistics in question. Assessing clinical trials--trial size. Br Med J (Clin Res Ed) 1981;282(6277):1687-9.
5. Boen JR, Zahn DA. The Human Side of Statistical Consulting. Belmont, CA: Lifetime Learning Publications, 1982.
6. Freiman JA, Chalmers TC, Smith H Jr, Kuebler RR. The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial. Survey of 71 "negative" trials. N Engl J Med 1978;299(13): 690-4.
7. Kraemer HC, Thiemann S. How many subjects? Statistical power analysis in research. Newbury Park, CA: Sage Publications, 1987.
8. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd Edition. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. 1988.
9. Desu MM, Raghavarao D. Sample Size Methodology. Boston, MA: Academic Press, Inc. 1990.
10. Lipsey MW. Design Sensitivity: Statistical Power and Experimental Research. Newbury Park, CA: Sage Publications, 1990.
11. Shuster JJ. CRC Handbook of Sample Size Guidelines for Clinical Trials. Boca Raton, FL: CRC Press, 1990.
12. Odeh RE, Fox M. Sample Size Choice: Charts for Experiments with Linear Models. 2nd ed. New York: Marcel Dekker, 1991.
13. Muller KE, Benignus VA. Increasing scientific power with statistical power. Neurotoxicol Teratol 1992;14(3):211-9.
14. Murray DM, Rooney BL, Hannan PJ, Peterson AV, Ary DV, Biglan A et al. Intraclass correlation among common measures of adolescent smoking: estimates, correlates, and applications in smoking prevention studies. Am J Epidemiol. 1994;140(11): 1038-50.
15. Murray DM, Short B. Intraclass correlation among measures related to alcohol use by young adults: estimates, correlates and applications in intervention studies. J Stud Alcohol. 1995;56(6):681-94.
16. Murray DM, Short B. Intraclass correlation among measures related to alcohol use by school aged adolescents: estimates, correlates and applications in intervention studies. J Drug Educ 1996;26(3):207-30.
17. Friedman L, Furberg C, DeMets D. Fundamentals of Clinical Trials. 3rd ed. New York, NY: Springer-Verlag,1998.
18. Kerry SM, Bland JM. Analysis of a trial randomised in clusters. BMJ 1998;316(7124):54.
19. Thornley B, Adams C. Content and quality of 2000 controlled trials in schizophrenia over 50 years. BMJ 1998;317(7167):1181–4.
20. Donner A, Klar N. Design and analysis of cluster randomization trials in health research. London, UK: Arnold Publishing Co, 2000.
21. Hoenig JM, Heisey DM. The Abuse of Power: The Pervasive Fallacy of Power Calculations for Data Analysis. Am Stat 2001;55(1):19–24.
22. Lui KJ, Cumberland WG. Sample size determination for equivalence test using rate ratio of sensitivity and specificity in paired sample data. Control Clin Trials 2001;22(4):373-89.
23. Gebski V, Marschner I, Keech AC. Specifying objectives and outcomes for clinical trials. Med J Aust 2002; 176(10): 491-2.
24.  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.
25. 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.
26. 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.
27. 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.
28. 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.
29. 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.
30. 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.
31. 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.
32. 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.
33. 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.
34. 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.
35. 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.
36. 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


NA

Competing Interests


No competing interests

Disclaimer


This article has been downloaded from WebmedCentral. With our unique author driven post publication peer review, contents posted on this web portal do not undergo any prepublication peer or editorial review. It is completely the responsibility of the authors to ensure not only scientific and ethical standards of the manuscript but also its grammatical accuracy. Authors must ensure that they obtain all the necessary permissions before submitting any information that requires obtaining a consent or approval from a third party. Authors should also ensure not to submit any information which they do not have the copyright of or of which they have transferred the copyrights to a third party.
Contents on WebmedCentral are purely for biomedical researchers and scientists. They are not meant to cater to the needs of an individual patient. The web portal or any content(s) therein is neither designed to support, nor replace, the relationship that exists between a patient/site visitor and his/her physician. Your use of the WebmedCentral site and its contents is entirely at your own risk. We do not take any responsibility for any harm that you may suffer or inflict on a third person by following the contents of this website.

Comments
0 comments posted so far

Please use this functionality to flag objectionable, inappropriate, inaccurate, and offensive content to WebmedCentral Team and the authors.

 

Author Comments
0 comments posted so far

 

What is article Popularity?

Article popularity is calculated by considering the scores: age of the article
Popularity = (P - 1) / (T + 2)^1.5
Where
P : points is the sum of individual scores, which includes article Views, Downloads, Reviews, Comments and their weightage

Scores   Weightage
Views Points X 1
Download Points X 2
Comment Points X 5
Review Points X 10
Points= sum(Views Points + Download Points + Comment Points + Review Points)
T : time since submission in hours.
P is subtracted by 1 to negate submitter's vote.
Age factor is (time since submission in hours plus two) to the power of 1.5.factor.

How Article Quality Works?

For each article Authors/Readers, Reviewers and WMC Editors can review/rate the articles. These ratings are used to determine Feedback Scores.

In most cases, article receive ratings in the range of 0 to 10. We calculate average of all the ratings and consider it as article quality.

Quality=Average(Authors/Readers Ratings + Reviewers Ratings + WMC Editor Ratings)