Abstract
Background
Homeopathy has always used algorithms, such as giving more weight to peculiar symptoms
and repertorisation of symptoms for differential diagnosis of medicines. However,
repertory entries are flawed and homeopathic data are liable to heuristic bias. Modernising
the homeopathic repertory with statistical tools, such as Bayes' theorem, should be
accompanied by handling (confirmation) bias.
Methods
After systematic collection of 731 ‘Best Chronic Homeopathic Cases’ (BCHC), we analysed
patterns in the frequency distribution of likelihood ratios (LRs). We did the same
with an existing Bayesian repertory based on historical materia medica data of more
uncertain quality. The frequency distributions are assessed with theoretical considerations,
mathematical tools such as (exponential) transformations and differentiation, and
expert knowledge.
Findings
The frequency distributions of LRs both showed the same two patterns: the middle part
of the frequency distribution showed a loglinear progression, but at both ends there
was an increasing slope of the curve. The confirmation bias in the middle part of
the LRs can be corrected mathematically with exponentiation (power calculations).
Clinical expertise and differentiation of the curve indicate LR = 7 as an eligible
maximum for the vast majority of symptoms. There was no clear difference between the
BCHC and the historical materia medica data in this respect.
Conclusion
It is possible to correct partly for confirmation bias in a repertorisation algorithm
by a combination of theoretical consideration, expert knowledge and mathematics. We
found a striking similarity between the BCHC and historical data regarding confirmation
bias.
Keywords
data collection - homeopathy - materia medica - confirmation bias
