Sample Size Calculation :The Basic

Important considerations for estimating sample size in clinical trials:

1. Study Design

       There are many statistical designs have been used to achieve objectives. The most common design used are parallel group design and crossover design. For calculating sample size, the study design should be explicitly defined in the objective of the trial. Each design will have different approach and formula for estimating sample size

2. One Sided or Two sided Test

     This is another important parameter needed for sample size estimation, which explains the objective of study. The objective can be equality, non-inferiority, superiority or equivalence. Equality and equivalence trials are two-sided trials where as non-inferiority and superiority trials are one-sided trials. Superiority or non-inferiority trials can be conducted only if there is prior information available about the test drug on a specific end point.

3. Primary end point of the study

  The sample size calculation depends on primary end point of study. The description of primary study end point should cover whether it is discrete or continuous or time-to-event. Sample size is estimated differently for each of these end points. Sample size will be adjusted if primary end point involves multiple comparisons.

4. Expected response of the treatment

The information about expected response is usually obtained from previous trials done on the test drug. If this information is not available, it could be obtained from previous published literature.

5. Clinically important meaningful decisions

     This is one of most critical and one of most challenging parameters. The challenge here is to define a difference between test and reference which can be considered clinically meaningful. The selection of the difference might take account of the severity of the illness being treated (a treatment effect of that reduce mortality by one percent might be clinically important while a treatment effect that reduces transient asthma by 20% may be of little interest). It might take account of the existence of alternate treatments It might also take account of the treatments cost and side effects

6. Level of Significance

  Usually 5% . %.Type I error is inversely proportional to sample size

7. Power of the test

      As per ICH E9guideline). power should not be less that 80%.Type II error is directly proportional to sample size

8. Withdrawals, missing data and Lost to Follow UP

     Any sample size calculation is based on the total number of subjects who are needed in the final study. In practice, eligible subjects will not always be willing to take part and it will be necessary to approach more subjects than are needed in the first instance. Subjects may fail or refuse to give valid responses to particular questions, physical measurements may suffer from technical problems, and in studies involving follow up (e.g. trials or cohort studies) there will always be some degree of attrition. It may therefore be necessary to calculate the number of subjects that need to be approached in order to achieve the final desired sample size. More formally, suppose a total of N subjects are required in the final study but a proportion (q) are expected to refuse to participate or to drop out before the study ends. In this case the following total number of subjects would have to be approached at the outset to ensure that the final sample size is achieved

General Rules for calculating Sample Size in clinical trials

The rules are as follows:

1. Level of significance : It is most commonly taken as 5%. The sample size is inversely proportional to level of significance i.e sample size increases as level of significance decreases.
2. Power :For calculating sample size, power of test should be more than or equal to 80%. Sample size increases as power increases. Higher the power, lower the chance of missing a real effect of treatments.
3. Clinically meaningful difference : To detect a smaller difference, one needs a sample of large size and vice a versa.
4. Sample size required to demonstrate equivalence is highest and to demonstrate equality is lowest.
   The sample size estimation is challenging for complex designs such as non-inferiority or, time to event end points. Also, the sample size estimation needs adjustment in accommodating
• unplanned interim analysis
• planned interim analysis and
• adjustment for covariates.