Online Workshop: Cohort Study; Sample size with analysis (Clean video will be uploaded later)
Stata, R, RStudio, Statistics এর উপর নতুন নতুন ভিডিও দেখতে আমাদের চ্যানেলটি সাবস্ক্রাইব করতে ভুলবেন না
Cohort Study: Setting, Sample Size Calculation, and Data Analysis in Stata (Bangla)
Cohort Study Setting:
A cohort study is a type of observational study where researchers follow a group of individuals (the cohort) over time to study the relationship between exposure to a risk factor and the development of a specific outcome (such as a disease). Cohort studies can be prospective (data collected forward in time) or retrospective (using historical data).
In a cohort study:
Exposed Group: Participants who are exposed to the risk factor.
Non-exposed Group: Participants who are not exposed.
Outcome: The development of a disease or condition is monitored.
For example, a cohort study could be conducted to determine whether smoking increases the risk of developing lung cancer over 10 years.
Sample Size Calculation in Cohort Studies:
Sample size calculation is crucial to ensure that the study has enough power to detect a meaningful difference between the exposed and non-exposed groups. The following factors should be considered:
Outcome Incidence: The rate at which the outcome occurs in the population.
Relative Risk (RR): Expected difference in the outcome between the exposed and non-exposed groups.
Alpha (α) Level: Typically 0.05, which represents a 5% chance of a Type I error (false positive).
Power: Usually set at 80% or 90%, representing the ability to detect a true effect.
Lost to Follow-Up: An estimate of the proportion of participants who may drop out of the study.
Example:
Consider a cohort study aiming to assess the association between physical inactivity and cardiovascular disease (CVD). The researcher assumes:
10% incidence of CVD in the non-exposed (active) group.
A relative risk (RR) of 1.5 for the exposed (inactive) group.
Alpha = 0.05, Power = 80%, and a 10% lost to follow-up rate.
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