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Chapter: Basic Concept of Biotechnology - Computer Applications and Biostatistics

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Common problems faced by researcher in any trial and how to address them

Whenever any researcher thinks of any experimental or clinical trial, number of queries arises before him/her.

Common problems faced by researcher in any trial and how to address them

Whenever any researcher thinks of any experimental or clinical trial, number of queries arises before him/her. To explain some common difficulties, we will take one example and try to solve it. Suppose, we want to perform a clinical trial on effect of supplementation of vitamin C on blood glucose level in patients of type II diabetes mellitus on metformin. Two groups of patients will be involved. One group will receive vitamin C and other placebo.


a) How much should be the sample size?

In such trial, first problem is to find out the sample size. As discussed earlier, sample size can be calculated if we have S.D, minimum expected difference, alpha level, and power of study. S.D. can be taken from the previous study. If the previous study report is not reliable, you can do pilot study on few patients and from that you will get S.D. Minimum expected difference can be decided by investigator, so that the difference would be clinically important. In this case, Vitamin C being an antioxidant, we will take difference between the two groups in blood sugar level to be 15. Minimum level of significance may be taken as 0.05 or with reliable ground we can increase it, and lastly, power of study is taken as 80% or you may increase power of study up to 95%, but in both the situations, sample size will be increased accordingly. After putting all the values in computer software program, we will get sample size for each group.


b) Which test should I apply?

After calculating sample size, next question is to apply suitable statistical test. We can apply parametric or non-parametric test. If data are normally distributed, we should use parametric test otherwise apply non-parametric test. In this trial, we are measuring blood sugar level in both groups after 0, 6, 12 weeks, and if data are normally distributed, then we can apply repeated measure ANOVA in both the groups followed by Turkey's post-hoc test if we want to compare all pairs of column with each other and Dunnet's post-hoc for comparing 0 with 6 or 12 weeks observations only. If we want to see whether supplementation of vitamin C has any effect on blood glucoselevel as compared to placebo, then we will have to consider change from baseline i.e. from 0 to 12 weeks in both groups and apply unpaired‘t’ with two-tailed test as directions of result is non-specific. If we are comparing effects only after 12 weeks, then paired‘t’ test can be applied for intra-group comparison and unpaired ‘t’ test for inter-group comparison. If we want to find out any difference between basic demographic data regarding gender ratio in each group, we will have to apply Chi-square test. 


c) Is there any correlation between the variable?

To see whether there is any correlation between age and blood sugar level or gender and blood sugar level, we will apply Spearman or Pearson correlation coefficient test, depending on Gaussian or non-Gaussian distribution of data. If you answer all these questions before start of the trial, it becomes painless to conduct research efficiently.

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