1. Sex and Gender Differences in Clinical Research Methodological Ramifications Martin H. Prins 26-01-2007

2. Program General Remarks Basic Concepts Miscellaneous Issues

3. Standardization Recently CONSORT – STARD initiatives Improved reporting in literature Articles are already long enough Challenge to put ‘required’ info in the maximum number of words

4. Sex and Gender No standards for reporting Of ‘targeted’ publications Of these issues in ‘general’ publications - Systematic Reviews are challenging -

5. Sex and/or Gender In any analysis - for the start just a single binary variable - statistics show associations not ‘causes’ Decision on sex/gender bears on clinical / epidemiological reasoning and could be explored by introducing additional variables

6. Sex / Gender Influence on therapeutic efficacy diagnostic accuracy (predictive values) etiologic impact

7. Sex / Gender Absence of a measurable effect does not exclude effects Observed ‘therapeutic equivalence’ Due to balance of less ‘true’ therapeutic efficacy better compliance with prescription

8. Basic Concept Influence of male/female on efficacy Effect = A + B*drug + C*drug*sex Statistical Term - Interaction Epidemiological term - Effect modification

9. Model Dependent Absolute Effect = A + B*drug + C*drug*sex Relative Effect = lnA + lnB^drug + lnC^drug*sex If difference in baseline risk for sex then either model will find a positive interaction for sex - Effect measure modification

10. Prognosis vs Effect modification Abs Effect = A + B*drug + C*drug*sex Abs Effect = A1 + A2*sex + B*drug + C*drug*sex - More difficult to separate (2 rather than 1 term) - RCT best vehiculum – but comparison M/F not randomized

11. Design Issues Sample Size To show that a drug works: ‘XXX’ To show that a drug works different in males/females: 2 x ‘XXX’ (or more)

12. Design Issues Current paradigm to demonstrate causal relationship is ‘RCT’ Not possible to use for a causal relationship of ‘X’ with male/female Strength of conclusions on sex-influence is generally ‘limited’

13. Confounding If (known or unknown) ‘variables’ that are causally related an outcome are unequally distributed over males/females (biologically/socially) there is always the potential for confounding. RCT – does not solve the problem.

14. Conclusion Sex/gender important to consider for health education medicine challenging to incorporate in research

15. Solutions Awareness Unlikely to be achieved in single studies, thus ability for systematic revieew /meta-analysis – Standards for reporting – Consort/Stard Web references to detailed tables Regulatory documents (Therapeutics only)