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

Outline of this talk.

Introduction

Review

A new and simple approach for monitoring safety data

Examples

Conclusion


[12a/extras/book.htm]
[12a/extras/brief.htm]
[weblog2006/OpenMindedness.asp]
[12a/www.childrensmercy.org/definitions/ControlChart.htm]
[12a/www.childrensmercy.org/definitions/SpecialCause.htm]
[12a/www.childrensmercy.org/definitions/CommonCause.htm]

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

[koans/BusyTailor.asp]
[http://www.childrensmercy.org/stats/weblog2007/ProsControlCharts.asp]
[weblog2005/DataMining.asp]
[http://www.childrensmercy.org/stats/weblog2007/DateGapIntroduction.asp]
[http://www.childrensmercy.org/stats/weblog2007/PeritonealDialysis.asp]
[http://www.childrensmercy.org/stats/weblog2007/CentralLineInfections.asp]
[http://www.childrensmercy.org/stats/weblog2007/KidneyBiopsy.asp]