The sample size for a cohort study depends on some factors and there is not a one-size-fits-all answer. Because sample size in studies is very much influenced by the study's objective, design, dropout rate, alpha level and statistical consideration. However, tools like PASS and R Packages can calculate the required sample size based on specified factors.

To be more specific, the sample size depends on

1. The incidence of endpoints. There is a tradeoff between sample size and study duration here. The smaller the incidence, the longer it will take to produce a given number of endpoints.

2. The prevalence of the risk factors. As risk factors depart from a 50% prevalence, the required sample size goes up. This effect is barely noticeable with prevalences of 40% or 60% but by the time you get to prevalences of 10% or 90% it's really important.

3. The latency of the condition. If you are studying the effects of childhood exposure to air pollution on development of breast cancer, you will have o wait a long time for the data to come in. Indeed, long latency conditions are better studied in other ways.

The appropriate sample size for a cohort study depends on several factors and can vary widely based on the specific study design, research objectives, expected effect size, desired statistical power, and practical constraints. There's no one-size-fits-all answer, but here are the general guidelines and considerations:

1. Statistical power: Typically, researchers aim for 80-90% power to detect a clinically significant effect.

2. Effect size: Smaller expected effects require larger sample sizes.

3. Significance level: Usually set at 5% (α = 0.05).

4. Expected attrition rate: Cohort studies often face participant dropout over time, so initial sample sizes should account for this.

5. Subgroup analyses: If you plan to analyze specific subgroups, you'll need a larger overall sample.

6. Rarity of outcome: For rare outcomes, larger sample sizes are necessary.

7. Study duration: Longer studies may require larger initial samples to account for greater attrition.

8. Resources and feasibility: Practical constraints often influence sample size decisions.

As a rough guideline, cohort studies can range from a few hundred participants for more common outcomes or larger effect sizes, to several thousand or even tens of thousands for rarer outcomes or smaller effect sizes.

For a more precise determination, it's recommended to:

1. Consult with a biostatistician who can perform power calculations based on your specific study parameters.

2. Use statistical software or online calculators designed for cohort study sample size estimation.

3. Review similar studies in the literature to get an idea of typical sample sizes in your field.

Given my role in healthcare and AI, I typically consider how AI tools could assist in sample size calculations or in managing and analyzing large cohort datasets.

Alwielland Q. Bello Cohort studies are studies in which people are selected based on whether or not they are exposed to suspected risk factors, or based on having or not having a characteristic that is suspected to be related to the outcome under investigation (such as disease). Both groups of these people are followed up to obtain the final result, and the incidence rate of the disease in the two exposed groups is equal to the incidence rate in the non-exposed group. At Statistics Academy, we provide the sample size in the study engine using relative risk, relative accuracy and the expected probability of the outcome for researchers, professors and students.