Download Stats Glossary - Iowa Cancer Consortium

2011 Iowa Cancer Summit
Key Concepts: Statistics Glossary
Counts: The number of cases that meet a certain definition (e.g., number of people
diagnosed with colorectal cancer between 2003 and 2007). This is helpful for measuring the
absolute burden of disease, but it is difficult to use to make comparisons between groups of
different sizes.
Crude Rate: Computed by dividing the count of cases by the total number of people who are
“at risk” (e.g., the number of people in a certain county). These ratios are often scaled to
allow statements like “5 cases per 100,000 people”. Crude rates are comparable to each
other even they represent groups of different sizes.
Age-Adjusted Rate: This is an adjustment of the crude rate which removes the effects of age.
For all of the groups being compared, specific ages are given the same weight. Any observed
differences between groups cannot be attributed to age if you use age-adjusted rates.
Confidence Interval: An interval surrounding a rate that indicates the precision of the rate. A
small interval indicates a more precise estimate of the rate. The interval is associated with
the degree of confidence that the true rate is within that interval. The most common level of
confidence is 95%. The typical interpretation is “I am 95% certain that the true rate lies
within this interval.” The size of the confidence interval gets smaller (more precise) as the
size of the population increases.
Statistical Significance: If an observed difference is statistically significant, then we’ve
concluded that the difference is not due to random variation in the data. It is a “real”
difference. This is often conveyed by a statement like “p < .05”. There are a number of
statistical tests used to determine statistical significance, including t-tests, analysis of
variance, chi-square tests, and so on. Just because a difference is statistically significant does
not mean the difference is a truly meaningful one!
Relative Rate Ratio: The ratio of the probability of an event (e.g., a cancer diagnosis) for one
group (e.g., smokers) to the probability of the same event for another group (e.g., nonsmokers). This provides a measure of similarity for the two probabilities. To the extent that
the probabilities are equal across the two groups, the relative risk will be near 1.0.
Significance tests are performed to determine if a particular relative risk is different from
1.0.
Data Suppression: In order to protect the health information of people in the Registry and
other databases, tremendous care is taken in how data are reported. Specific counts below a
certain threshold are “suppressed” (i.e., not reported) to prevent people from identifying
specific cases. The Iowa Cancer Registry does not report specific counts of less than five cases
per group. This issue becomes more problematic with the small numbers that come with rare
conditions and short time-frames. It is therefore possible to get around suppression issues by
developing different groupings of the data or by using a longer time frame.