The Cochran-Armitage trend test can be used to assess time trends in prevalence of outcomes across repeated cross-sectional surveys.
Sample size Calculation in Stata
Statistics > Power and Sample Size > Outcome > Binary > Linear Trend in proportions in Jx2 table OR power trend Examples follow
Sample size for probability of 0.80, 0.85, 0.9, 0.95 across four surveys
power trend 0.80 0.85 0.90 0.95, power(0.8) Code language: Stata (stata)What If there are just two Cross-sectional surveys ?
This situation is akin to comparing two proportions. Once cannot compare trend with just two comparison groups, minimum three are required. The sample size calculations of teh Cochran-Armitage test and two sample proportions comparison using chi-sqaured tests are going to yield same results
power trend 0.15 0.1
note: exposure levels are assumed to be equally spaced
Performing iteration ...
Estimated sample size for a trend test
Cochran-Armitage trend test
Ho: b = 0 versus Ha: b != 0; logit(p) = a + b*x
Study parameters:
alpha = 0.0500
power = 0.8000
N_g = 2
p1 = 0.1500
p2 = 0.1000
Estimated sample sizes:
N = 1,372
N per group = 686
power twoproportions 0.15 0.1, test(chi2)
Performing iteration ...
Estimated sample sizes for a two-sample proportions test
Pearson's chi-squared test
Ho: p2 = p1 versus Ha: p2 != p1
Study parameters:
alpha = 0.0500
power = 0.8000
delta = -0.0500 (difference)
p1 = 0.1500
p2 = 0.1000
Estimated sample sizes:
N = 1,372
N per group = 686Code language: R (r)About the Cochran-Armitag test
From the STATA HELP manual: The Cochran–Armitage trend test (Cochran 1954 and Armitage 1955) is commonly used to test for trend in J X 2 tables. It is based on the linear logit model:
logit(pj) = a + bxj
where pj is the hypothesized probability of a success in group j, and a and b are unknown coefficients.
- Armitage, P. 1955. Tests for linear trends in proportions and frequencies. Biometrics 11: 375–386.
- Cochran, W. G. 1954. Some methods for strengthening the common chi-squared tests. Biometrics 10: 417–451.