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Post Hoc Data Analysis (What is NOT in the Protocol)

Introduction to Clinical Research. Post Hoc Data Analysis (What is NOT in the Protocol). Forskningstræning Copenhagen University Hospital at Bispebjerg March 2012. Ingredients: Group comparability Confounders Placebo No significance – no effect?. Is the question valid?

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Post Hoc Data Analysis (What is NOT in the Protocol)

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  1. Introduction to Clinical Research Post Hoc Data Analysis(What is NOT in the Protocol) Forskningstræning Copenhagen University Hospital at Bispebjerg March 2012

  2. Ingredients: • Group comparability • Confounders • Placebo • No significance – no effect?

  3. Is the question valid? Can we do the comparison at all? Are we comparing apples with pears? Let look at a study that has done been over and over again: Which animal is more clever? We are comparing animals with animals...or are we deceiving ourselves?

  4. Another example: It is highly significant that smoking men get more penile cancer than smoking women, i.e. men are more susceptible to cancer from smoking. Yet one: It is highly significant that after training men are running faster than women after training, i.e. men are more trainable than women. ...and how about this one: It is highly significant that after school women are getting better grades than men, i.e. women are more intelligent than men.

  5. Conclusion: You have to make sure that the groups you are comparing are comparable. A randomised trial is the best way to statistically ensure that groups are comparable. With small sample sizes the risk increases that they are not and you have to demonstrate that they are. This is normally shown in a table for a reasonable and relevant set of parameters.

  6. Placebo: Is a placebo control/sham group relevant? Almost always! Some usual exemptions could be drug comparisons, e.g. pain reliever A versus pain reliever B. Couriously, very often the pharmaceutical industry is using placebo and not using relevant compeeting drugs. Why would that be so?

  7. Placebo: Placebo effects can be surprising and large.

  8. Confounders (or confusers?): People who take Vit E get less IHD (ischemic heart disease) than in a placebo group. However, careful analysis revealed that the placebo group had more smokers than the Vit E group. Smoking are surely a confounder in this analysis. It can be adjusted for using Mantel-Haenszel statistics after stratification of the datasets for smoking

  9. Confounders (or confusers?): People who take Vit E get less IHD (ischemic heart disease) than in a placebo group. However, careful analysis revealed that the placebo group had more smokers than the Vit E group. Smoking are surely a confounder in this analysis. It can be adjusted for using Mantel-Haenszel statistics after stratification of the datasets for smoking

  10. Confounders (or confusers?): Sometimes confounders are very hard to avoid. Example: Volunteers are given EPO for 8 weeks after a VO2max test. After 8 weeks there's a significant improvement in VO2max. A careful analysis showed, however, that they started training much harder because all of a sudden they could train hard (effects on the brain?). Hard training has a pronounced effect on VO2max. So did the VO2max go up because of EPO or hard training?

  11. Conclusion: Possible confounding factors ALWAYS have to be discussed, evaluated carefully and, if possible, adjusted fore.

  12. Significance and meaning of results: Double blinded, randomised, stratified, placebo controlled study: It's NOT significant! Is this a failure? Should it NOT be published? Didn't we design this correctly? Have we learned anything?

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