**Stats #71: Control charts for continuous monitoring
of the number needed to harm.**

Content:While most of the efforts in signal detection use newly developed data mining algorithms that are both complex and computer intensive, there is still room in your research arsenal for simpler approaches that have withstood the test of time, like the statistical process control chart. By applying a straightforward data transformation, you can use the control chart to monitor the Number Needed to Harm (NNH), an easily interpreted measure of absolute risk.

Teaching strategies:Didactic lectures and small group exercises.

Objectives:In this class you will learn how to

- Identify those situations where simple control charts are preferable, but also recognize their risks and limitations.
- Adapt different decision rules and alternate control chart formats to increase your sensitivity for small but consistent shifts in risk.
- Establish rational targets for the NNH that balance the benefits of a new drug against its risks.

**Outline of this talk.**

Introduction

- Information about my book.
- Where can you find this handout?
- Why don't I use PowerPoint?
- A plea for open-mindedness

Review

- What is a control chart?
- What is a special cause of variation?
- What is a common cause of variation?
- Statistical koan: The Busy Tailor.
- Advantages of control charts
- Disadvantages of control charts
- Advantages of data mining models
- Two cautionary tales about data mining

A new and simple approach for monitoring safety data

- Date gaps rather than rates
- Adjustments for patient load and the number needed to harm calculations
- What is a reasonable value for NNH?
- Monitoring targets with a CUSUM chart
- Bayesian prior distributions and their application to safety data

Examples

- Monitoring adverse events during peritoneal dialysis
- Tracking central line infections over time
- Tracking adverse events during kidney biopsy.

Conclusion

*The following story illustrates the problems that can occur when you fail
to recognize the difference between common cause and special cause variation.*