A short introduction to sample size estimation for Research methodology workshop at Dr. BVP RMC, Pravara Institute of Medical Sciences(DU), Loni by Dr. Mandar Baviskar

1. ESTIMATION Dr. Mandar Baviskar M.D.
Associate Professor, Community Medicine
Dr. Balasaheb Vikhe Patil RMC, PIMS(DU), Loni
BY the end of this Session you will Know
• Why we estimate sample size
• What information you need prior to sample size estimation
• What methods are available to estimate sample size

2.  Complete enumeration of study population is
known as CENSUS
 It is often not feasible to cover the entire study
population, therefore we take a sample.
 A sample is a subset of the study population
which ideally is representative of the population.
 If the sample is representative of the population
then our findings can be generalized to the
population.

3.  In research we want to be able to say that whatever results we have
got are REAL & NOT by chance.
 There is bound to be some difference between the ACTUAL value of
variable in Population (parameter) and what we find in the STUDY
(statistic).
 We want to have a Level of Confidence that we have enough readings
to correctly identify REAL difference in study population.
 In medical studies we usually want to be at least 95% CONFIDENT
and allow for 5% error.
 Therefore, Z for 95% CL is Zα=1.96.
 Parameter: Denotes value for
population (μ)
 Statistic: Denotes same value
for sample (m)

4.  Power (1-β): Is the probability that study will detect predetermined effect size (any deviation
from null hypothesis) should such a deviation exist. It should be at least 80%
Alpha error: Saying there is significant relationship when there is
none.
It is taken as 5% i.e. Alpha=0.05
Beta error: Saying there is no significant relationship when it is there.
It should be 20% or less i.e. Beta=0.2

5.  Depending on study design and outcome variables this may require putting
 Prevalence of condition
 Proportion of exposed
 Expected difference between means & proportion of groups under study
 What is the Target power
 How much Confidence you want
 How much error you can allow
 Target variance
 Help of statistician must be taken for correctly estimating sample size
BEFORE beginning the study.
 The expected values of exposure can be found from Previous study,
Existing records, Pilot study, Assumption (maximum)

6.  Mathematical formulae are used to estimate the minimum number of
patients/respondents needed to make inferences about a population.
 Software like
 Epi Info,
 G* power,
 SAS, STATA, R studio
 GLIMMPSE (repeated measures)
 Simulation

7.  Examples
 We want to assess Stress levels in Students of RMC, Loni. (Similar study shows past
prevalence was 30%)
 We want to estimate Hb levels in Sickle Cell Anemia patients coming to PRH, Loni (Pilot
study mean 11, SD=2)
 We want to find effectiveness of NRT inclusive behavior therapy regimen compared to
behaviour therapy alone in Tobacco Cessation (Other studies show NRT 20% cure rate &
non NRT 10%)
 We want to find difference in dose required to produce motor block using two anesthetic
drugs. (mean A=110, SD=10; mean B=100, SD=11)
 We want to conduct a web based survey about use of masks in Maharashtra

8.  Just taking the desired NUMBER
of samples is NOT ENOUGH
 Taking them using correct
SAMPLING TECHNIQUE is also
equally important.
 These methods are
1. Probability
2. Non Probability
• Simple Random
• Stratified Random
• Systematic
Random
• Cluster
• Multi Stage
Probability
• Convenience
• Purposive
• Quota
• Snowball
Non-
Probability

9.  A sample size should be large enough to sufficiently describe the phenomenon of
interest, and address the research question at hand.
 But at the same time, a large sample size risks having repetitive data.
 The goal of qualitative research should thus be the attainment of saturation.
 Saturation occurs when adding more participants to the study does not result in
obtaining additional perspectives or information.

10. Help will Always be given to those who ASK for it.
-Dulbus Ambeldore