Transcript Outcomes and Biometrics Initial VTE treatment

Sex and Gender Differences
in Clinical Research
Methodological Ramifications
Martin H. Prins
26-01-2007
Program
General Remarks
Basic Concepts
Miscellaneous Issues
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
Sex and Gender
No standards for reporting
Of ‘targeted’ publications
Of these issues in ‘general’ publications
- Systematic Reviews are challenging -
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
Sex / Gender
Influence on
therapeutic efficacy
diagnostic accuracy (predictive values)
etiologic impact
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
Basic Concept
Influence of male/female on efficacy
Effect = A + B*drug + C*drug*sex
Statistical Term
- Interaction
Epidemiological term - Effect modification
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
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
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)
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’
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.
Conclusion
Sex/gender
important to consider for
health education
medicine
challenging to incorporate in research
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)