Reconsidering evidence-based management of endometriosis
evidence, statistical inference, Bayesian, endometriosis, evidence-based medicine
Published online: Sep 30 2022
Background: Without an adequate animal model permitting experiments the pathophysiology of endometriosis remains unclear and without a non-invasive diagnosis, information is limited to symptomatic women. Lesions are macroscopically and biochemically variable. Hormonal medical therapy cannot be blinded since recognised by the patient and the evidence of extensive surgery is limited because of the combination of low numbers of interventions of variable difficulty with variable surgical skills. Experience is spread among specialists in imaging, medical therapy, infertility, pain and surgery. In addition, the limitations of traditional statistics and p-values to interpret results and the complementarity of Bayesian inference should be realised.
Objectives: To review and discuss evidence in endometriosis management
Materials and Methods: A PubMed search for blinded randomised controlled trials in endometriosis.
Results: Good-quality evidence is limited in endometriosis.
Conclusions: Clinical experience remains undervalued especially for surgery.
What is new? Evidence-based medicine should integrate traditional statistical analysis and the limitations of P-values, with the complementary Bayesian inference which is predictive and sequential and more like clinical medicine. Since clinical experience is important for grading evidence, specific experience in the different disciplines of endometriosis should be used to judge trial designs and results. Finally, clinical medicine can be considered as a series of experiments controlled by the outcome. Therefore, the clinical opinion of many has more value than an opinion.