Discussion Overview
The discussion centers around detecting changes in the incidence rate of rare events over time, specifically focusing on identifying points at which the frequency of these events increases. Participants explore statistical tools and methodologies for analyzing such changes, considering various temporal profiles and the implications of random variation versus real trends.
Discussion Character
- Exploratory
- Technical explanation
- Debate/contested
- Mathematical reasoning
Main Points Raised
- One participant seeks a solid statistical tool to analyze the increase in event rates and to determine if observed changes are statistically significant or merely random.
- Another participant suggests using moving averages as a robust method adaptable to different temporal profiles, along with linear regression models for sustained trends.
- A reference to a paper on Autoregressive Integrated Moving Average (ARIMA) is provided as an example of a combined approach for analyzing trends in incidence rates.
- Concerns are raised about the adequacy of regression analysis in providing confidence intervals for trends, particularly given the limited number of events (about 200) in the dataset.
- A suggestion is made to consider control charts as another potential analytical tool for monitoring changes in incidence rates.
Areas of Agreement / Disagreement
Participants express varying opinions on the best statistical methods to use, with no consensus reached on a single approach. The discussion reflects differing views on the effectiveness of regression analysis and the importance of controlling for confounding factors.
Contextual Notes
Participants note limitations related to the small sample size of events and the potential impact of confounding factors on the analysis. There is also mention of the need to minimize beta error when assessing trends.