Introduction to the Design and Analysis of Trials can be found .ppt

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1、Introduction to the Design and Analysis of Trials can be found on: http:/www-users.york.ac.uk/djt6/,Before and After Studies: A Reminder,Background,Many researchers (?) use before and after studies they are, of course, nearly completely useless. Why? This is because of: Regression to the mean Tempor

2、al changes,Which Researchers (?) use before and after?,Clinicians, teachers assessing individuals. Action researchers. Audit.,Temporal Change,Things change, people get better, policy changes all of which may make a difference. A before and after study CANNOT possibly cope with these temporal events.

3、,Regression to the Mean,Is a group phenomenon applies when we measure a group of people and re-measure them. Those with values below or above the mean will tend to regress back towards the mean on re-measurement.,Before and after treatment for neck pain,Improvement highly significant p 0.0001,Plot o

4、f difference scores,A symptom of regression to the mean is if you plot change scores (baseline follow up) against baseline scores. A correlation indicates RTM. Thus, those with the lowest baseline improve the most and those with the highest improve the least.,Scatterplot showing RTM,Correlation of C

5、hange Score with baseline values = 0.33 p 0.0001,Some benefit of vaccination is due to regression to mean,Meningitis,After vaccination new cases of meningitis fell from about 240 to 35 an 85% decrease. HOWEVER, of the 205 cases that were prevented the majority 120 were due to regression to the mean

6、effects ONLY 41% were probably due to the efficacy of the vaccine.,Education intervention,Wheldall selected 40 pupils whose reading was at least 2 years behind their peers. Half were exposed to an intervention.,Wheldall Educational Review 2000;52:29.,Before and after reading programme,Difference hig

7、hly statistically significant p 0.001,Before and after reading programme,Differences between groups NOT statistically significant,RTM misunderstanding,“the mean gain scores translated to impressive effect sizes of 0.6.” “It could be argued that it is asking too much of any program to demonstrate enh

8、anced efficacy on top of such high existing efficacy” “control group gains were largely attributable to pre-existing literacy programme” Perhaps, BUT much of the gain will be due to RTM.,Evaluation of School intervention,A secondary school routinely offered children who scored badly on a reading tes

9、t an ICT intervention. This was shown to improve childrens literacy.,ICT and Reading,Did it work?,Impossible to tell. Regression to the mean and temporal effects does not allow us to find this out. Fortunately, we are doing a RCT of ICT and reading.,RTM and Policy Decisions,Government policy targets

10、 10 worst areas for street crime. 1 year later 17% fall in crime some or all due to RTM. 40% increase in gun crime results in a months amnesty for fire arms will probably work through RTM.,Annual Increase in offences with firearms,Amnesty,Exam marking,In MSc double blind marking. Two markers disagre

11、e at the extremes of the distribution. We might fool ourselves that one marker is hard and the other a softie but really it is RTM.,RTM and exam scripts,Policy Changes,Regression to the mean is an excellent method of proving something works; Failing schools or hospitals can have an expensive managem

12、ent change and there is a good chance that regression to the mean will do the job.,Proving Effective Treatments,RTM is an excellent phenomenon to prove to doubting clinicians the value of a new treatment. Choose an outcome measure with a high variance (e.g., single BP measure, FEV). Identify patient

13、s with extreme values (preferably only measured once), treat and re-measure. The group mean ought to decline (not all patients will improve but most will).,Dealing with RTM,Sequential measurements taking an average (e.g., 3 BP measurements averaged out) will reduce the problem. The only way to relia

14、bly deal with the problem is through randomised trials. Which is why before and after data are generally regarded as almost USELESS.,Ceiling and Floor Effects,As well as RTM before and after studies are blighted by ceiling and floor problems. Often measurement instruments have a floor (e.g., 0) or a

15、 ceiling (e.g., 100%), which means if someones value is close to either of these extremes they cannot change much except towards the mean.,League Tables,Classic problem of RTM with ceiling and floor effects. For example, schools that get close to 100% 5 GCSEs cannot do any better, whereas schools wi

16、th very low levels can only go upwards. This phenomenon is skillfully exploited by politicians to show an effect. Similarly with hospital league tables. Same problem applies to quality of life measures. EuroQol for example, has ceiling problems.,Summary,Before and after studies are the weakest evaluative method of proving something does or does not work. To control for temporal changes and regression to the mean controlled trials are required.,Conclusion,You can prove virtually any crackpot theory using RTM. NEED a control group.,