What's this about?
Stata's power provides power and sample-size (PSS) analysis. Six new
methods are supported: three for contingency tables and three for survival
The new methods work just like the existing ones. You can compute power,
sample size, and effect size. Enter any two and get the third. You can
specify single values or, to compare multiple scenarios, ranges of values of
study parameters. You can obtain results either in tabular form or as a graph.
Also, do not forget the three ANOVA methods and facilities to easily add your
own new methods to power that we added in 13.1.
Stata's power provides three new methods for contingency-tables
power cmh performs power and sample-size analysis for a
Cochran–Mantel–Haenszel test of association in stratified 2x2 tables. It
computes sample size, power, or effect size (common odds ratio) given
other study parameters. It provides computations for designs with
unbalanced stratum sizes as well as unbalanced group sizes within each
power mcc performs power and sample-size analysis for a test of
association between a risk factor and a disease in 1:M matched
case–control studies. It computes sample size, power, or effect size
(odds ratio) given other study parameters.
power trend performs power and sample-size analysis for a
Cochran–Armitage test of a linear trend in a probability of response in
Jx2 tables. The rows of the table correspond to ordinal exposure levels.
The command computes sample size or power given other study parameters.
It provides computations for unbalanced designs and for unequally spaced
exposure levels (doses). With equally spaced exposure levels, a
continuity correction is available.
Here we demonstrate PSS for matched case–control studies. Consider cancer
among smokers and nonsmokers. How many case–control pairs do we need to
achieve 80% power of detecting a 1.7 odds ratio with a 5% significance test if
we used a two-sided association test? From previous studies, the probability
of exposure (smoking) for controls is known to be roughly 0.22. We type
. power mcc .22, oratio(1.7)
Performing iteration ...
Estimated sample size for a matched case-control study
Asymptotic z test, 1:1 matched design
Ho: OR = 1 versus Ha: OR != 1
alpha = 0.0500
power = 0.8000
delta = 1.7000
p0 = 0.2200
oratio = 1.7000
corr = 0.0000
M = 1
Estimated sample size:
N cases = 285
and learn that we need 285 cases (and 285 controls).
1:M matching is often used to reduce the required number of cases because
cases are often more difficult to obtain than controls. It is thus useful to
evaluate designs with different values of M.
We plot power curves for designs with 1:1, 1:2, 1:3, and 1:4 matching by typing
. power mcc 0.22, oratio(1.7) n(200(10)300) m(1 2 3 4) graph
The graph reveals that as the number of matched controls increases, the
power increases. The graph also suggests that the increase in power is
substantial between M=1 and M=2 and becomes less beneficial after
In Stata 13, stpower provided power and sample-size analysis for
survival studies. stpower's methods have now been integrated into
power, meaning you now have the ability to obtain power and
other curves automatically.
The three survival PSS methods are
power cox estimates required sample size, power, and effect size for
survival analysis using Cox PH models with possibly multiple covariates.
It provides options to account for possible correlation between the
covariate of interest and other predictors and for withdrawal of subjects
from the study.
power exponential estimates required sample size and power for survival
analysis comparing two exponential survivor functions using the
exponential test (in particular, the Wald test of the difference between
hazards or, optionally, of the difference between log hazards). It
accommodates unequal allocation between the two groups, flexible accrual
of subjects into the study (uniform and truncated exponential), and
group-specific losses to follow-up.
power logrank estimates required sample size, power, and effect size for
survival analysis comparing survivor functions in two groups using the
log-rank test. It provides options to account for unequal allocation of
subjects between the two groups, possible withdrawal of subjects from the
study (loss to follow-up), and uniform accrual of subjects into the study.
Suppose we wish to compare the one-year survival rates of patients
treated with a drug or placebo. We plan to use a one-sided log-rank test with
5% significance level. To explore the required sample sizes, we will look at
the different powers and different increases in survival of the treated group:
. power logrank 0.5 (0.6 0.65 0.7), p(0.75(0.05)0.95) onesided graph
As expected, as power increases, sample size increases (but gently), and
detecting smaller differences requires a substantially larger sample.
Tell me more
Learn much more about these new methods in
Stata Power and Sample-Size Reference Manual.
You will find many more worked examples, extended discussion, methods and
formulas, references, and more. More than 150 pages are dedicated to the new
methods for contingency tables and survival analysis.
Read the overview from the Stata News.