Why Cohort Power Matters
A cohort study compares outcomes after exposure status is known. It may follow workers, patients, users, or communities. Power shows the chance of detecting a real difference between exposed and unexposed groups. Low power can waste time. Excessive enrollment can waste money. A balanced plan helps both science and budgets.
What This Tool Estimates
This calculator estimates power for two independent incidence proportions. Enter the baseline event rate, the expected exposed event rate, or a risk ratio. Then enter group sizes, alpha, tails, loss to follow-up, and target power. The tool returns adjusted sample sizes, expected events, risk difference, relative risk, odds ratio, and approximate power. It also estimates the sample size needed for your target power.
Planning Inputs Carefully
Good inputs come from pilot studies, audits, registries, or published work. The unexposed risk should match the planned follow-up period. The exposed risk should represent the smallest effect worth detecting. Loss to follow-up should be realistic, because missing outcomes reduce useful sample size. Allocation ratio matters when exposed subjects are harder to recruit.
Interpreting the Result
A power value near eighty percent is common for planning, but it is not a universal rule. Higher stakes may require ninety percent or more. Very small risk differences need larger cohorts. Rare outcomes also need larger groups. The result is an approximation, so confirm final protocols with a statistician when regulatory, clinical, or funding decisions depend on it.
Using Results in Reports
Use the exported files to record assumptions. Include baseline risk, expected effect, alpha, tail choice, loss rate, allocation, and target power. These details make the plan reproducible. They also help reviewers understand why the chosen sample size is reasonable. Revise assumptions whenever new evidence changes expected event rates.
Practical Design Notes
Before collecting data, check whether exposure groups will be measured the same way. Outcome definitions should be identical. Follow-up windows should be clear. Confounding is not solved by power alone. Power only addresses random error for the chosen contrast. You still need good design, clean measurement, and a careful analysis plan. Sensitivity checks are useful when event rates are uncertain. Try optimistic and conservative assumptions, then compare required enrollment. This protects decisions from fragile early guesses.