Which answer would you prefer: 1. “This medicine works better than a placebo”, or:
2. “I estimate the chance that this medicine will work in your case to be 60%”?
These two answers reflect a two and a half century lasting dispute between two statistical
methods, ‘classical’ (frequentist) and Bayesian. The first is regarded to be more
scientific, the latter played a major part in solving many of history's most important
problems, like deciphering coded messages in WO II and predicting disasters. Nowadays
many computer programs incorporate Bayes’ theorem to handle experiential knowledge.
Because RCT evidence does not allows other conclusions, the patient can only expect
a yes-or-no statement about efficacy of conventional medicines. But this answer might
still be false in his particular case. Other factors, like correct diagnosis, genetic
susceptibility and comorbidity, also determine if the medicine works. The diagnostic
process preceding the prescription is Bayesian and renders the probability of a specific
diagnosis.
Bayesian philosophy is about learning from past experience, e.g. about characteristics
of patients responding well to specific medicines. Like a medical diagnosis, the choice
of a homeopathic medicine is a Bayesian process. Different personal characteristics
add up, stepwise increasing the chance that a specific homeopathic medicine will work.
Hitherto homeopathic doctors have been using Bayesian statistics implicitly: characteristic
symptoms pointing towards a specific medicine occur more frequently in patients responding
well to that medicine than in patients responding to other medicines. It is a small
step to make this rule explicit in various types of research and data collection.
All we need to know is the prevalence of a symptom in the population responding well
to a specific symptom and in the remainder of the population. The ratio between these
two is called the Likelihood Ratio (LR).
The research we need is accepted in conventional diagnosis research. Like all kinds
of research we will have to deal with possible bias; like our reference standard:
what is a good result? Symptoms should be defined more accurately, etcetera. These
problems have been neglected in the past. We must realise that this research is meant
to improve homeopathy, not to prove it. However, improved homeopathy will render better
proof.
Several methods for Bayesian assessment of symptoms are demonstrated. The most valid
and time-consuming method is prospective research of a small set of symptoms, but
even with this method we can achieve a tremendous scientific improvement of homeopathy
within a limited amount of time. Within ten years we could know LRs of characteristic
symptoms for our most frequently prescribed homeopathic medicines. Applying the formula
that goes with Bayesian theory we will be able to tell the patient: “Based on the
symptoms you gave me I expect the chance that medicine A works for you to be x%”.
Keywords:
Clinical research - research methodology
