The CAMP model enables a more flexible and accurate description of the SNP effect size by introducing a slope term. The linear change model enables three example questions to be addressed: (1) what is the interval at which there is a significant effect on the trait (significant interval)? (2) At which age is there strongest evidence for an effect (strongest evidence for effect)? (3) Is the slope (1) significantly different from zero (1)? Even though a more complex model can result in generally wider confidence intervals, it can still result in a more accurate representation of the effect size, often accompanied by higher statistical power. By estimating the effect-size change, it is also possible to accurately determine trends, which constant effect assumptions cannot capture. The lower two panels represent corresponding survival function estimates showing the probability that an individual has not experienced an event. We observe that under linear effect assumption, the survival functions can cross, which is not possible with the constant effect assumption.
