Power analysis to detect time trends on population-based cancer registries data: When size really matters.

ABSTRACT

Detecting statistically significant trends in incidence with cancer registries data not only depends on the size of their covered population but also on the levels of incidence rates, duration of diagnostic period and type of temporal variation. We simulated sample sizes of newly diagnosed cases based on a variety of plausible levels of cancer rates and scenarios of changing trends over a period of about 30 years. Each simulated set of cases was then analysed with joinpoint regression models. The power was derived as the relative frequency of the simulation runs where the p-value of the coefficient was less than 0.05 under the alternative model. In case of a decreasing trend with no change of direction (join), an Annual Percentage Change (APC) of 1% for an average rate of 10 per 100,000 is detectable in populations of half a million inhabitants or more with a nominal power of 80%. In a model with one joinpoint followed by an increasing trend, the minimum detectable APC increases, and an APC of about 2%, can be detected only with populations of at least 2 million. For analyses requiring a larger sample size than the actual covered population, alternative organisational strategies should be considered, such as an extension of population coverage or data pooling and merging from registries with comparable data. (i.e. when heterogeneity across merging registries is low or acceptable for the specific study question).