{{ message }}

Cox Model

Let \(b\) be the hazard ratio of two survival curves. Cox's proportional hazard model allows you to test hypotheses about the value of \(b\).

Power Calculation Parameters

To input multiple values, seperate them by a comma.

{{ this.errors }}

Power Calculation Explanation

Solve For
The unknown you are interested in solving for.
N
The sample size used to test the hypothesis.
Alpha
The \(\alpha\) (Type I error rate) level of the hypothesis test.
Power
The power (1 - Type II error rate) of the hypothesis test.
Hazard Ratio
The hazard ratio being tested, \(b\).
Treatment Proportion
The proportion of subjects that recieve the treatment.
Proportion Visible
The proportion of event that are observed (1 minus the proportion that are censored).
Margin
The margin is a value the effect size needs to exceed to be meaningful. For hypotheses of equivalence, the margin must be greater than 0, or the calculation will not be solvable. For one sided tests, a margin is less than 0 implies a non-inferiority hypothesis. Otherwise, a superiority hypothesis is implied. Read More
Hypothesis
There are three types of hypotheses that can be tested: two-sided, one-sided and equivalence. Tests of equivalence must include a margin if the unknown and null means are equal. Read More

Calculation Results

No calculation has been generated yet.

Sign Up to Save Power and Sample Size Calculators!
N Alpha Power Hazard Ratio Treatment Proportion Proportion Visible Margin
{{ val }}

Power Graph

There are no results to graph yet.

References

  • Chow, S., Shao, J., & Wang, H. (2003), Sample size calculations in clinical research, New York: Marcel Dekker.