In order to obtain a consistent characterization of population trends in factors related to the prevention, early detection, or treatment of cancer, the joinpoint statistical methodology was used in this report (http://srab.cancer.gov/joinpoint/). This methodology characterizes a trend using joined linear segments on a logarithmic scale; the point where two segments meet is called a "joinpoint." The methodology has previously proven useful in characterizing trends in cancer incidence and mortality rates (e.g., in the Annual Report to the Nation on the Status of Cancer, 1975–2004, Featuring Cancer in American Indians and Alaska Natives).
The joinpoint software (Joinpoint Version 3.4.2) uses statistical criteria to determine:
In addition, a 95-percent confidence interval around the APC was used to determine if the APC for each segment differed significantly from zero. Whenever possible, weighted regression lines (utilizing standard errors) were calculated using the joinpoint software. Using a log response variable, the weight (motivated by the delta method) equals the square of the response variable divided by the square of the standard error. If the standard errors were unavailable, an unweighted regression was used.
Using the results of these analyses, we characterize trends in this report with respect to both their public health importance and statistical significance. If a trend was:
While these categorizations are somewhat arbitrary, they do provide a consistent method to characterize the trends across disparate measures. However, statistical significance in addition to the absolute value of change for incidence and mortality trends were used to ensure consistency with all major publications on national cancer trends.
To avoid statistical anomalies, segments had to contain at least three observed data points, and no segment could begin or end closer than three data points from the beginning or end of the data series. Because we constrained the joinpoint models to those in which no segment could begin or end closer than three data points from the beginning or end of the data series, if there were four data points or fewer, only one segment could be fit; from five to seven data points, up to two segments could be fit; and from eight to 10 data points, up to three segments could be fit. To avoid some of these limitations, for two to six data points we connected the data points to determine the APC for each time period, and then employed a two-sample test using the standard errors derived from the survey to determine the statistical significance of the change across periods For 7-13 data points we allowed a maximum of 1 joinpoint, 14-20 data points, we allowed a maximum of 2 joinpoints, for 21-27 data points, we allowed a maximum of 3 joinpoints, and for 28 or more data points, we allowed a maximum of 4 joinpoints.
A new addition to the methodology in the 2009/2010 update of the CTPR was the Average Annual Percent Change (AAPC), a measure which uses the underlying joinpoint model to compute a summary measure of the trend over a fixed pre-specified interval The AAPC is useful for comparing the most recent trend across different groups (e.g., racial groups or gender) when the final joinpoint segments are not directly comparable because they are of different lengths. Regardless of where the joinpoints occur for the different series, the AAPC can be computed over the same fixed interval for all the series (e.g., 2002–2006 to characterize the most recent trend). The AAPC is computed as a weighted average of the APC's from the joinpoint model, with the weights equal to the length of the APC intervals included. For more information on the AAPC, see http://srab.cancer.gov/joinpoint/aapc.html. When there are seven or fewer data points, the AAPC was computed based on the connected data points, rather than an underlying joinpoint model. The derivation of the AAPC and its standard error based on a series of connected points is presented in a technical report (http://srab.cancer.gov/reports/tech2009.02.pdf).
Age adjustment (to a standard population) for measures was done using the direct method of standardization. Whenever possible, age adjustment for measures was done using the age adjustment groups specified for Healthy People age-adjusted measures (http://wonder.cdc.gov/data2010/aagroups.htm). The year 2000 standard population for specific age groups is available in Klein and Shoenborn (2001). For cancer incidence, 19 age groups were used with the 2000 standard population as specified in http://seer.cancer.gov/stdpopulations.