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Corrigendum to “Generalisability of prognostic factor research: further analysis of data from the IIPCOS2 study” [Homeopathy 106 (3) (2017) 155–159]
17 December 2017 (online)
Unfortunately there was an editing error in the abstract of this paper. The corrected abstract appears below.
Prognostic factor research is important as it helps in refining diagnosis, taking clinical and therapeutic decisions, enhances the design and analysis of intervention trials and helps to identify targets for new interventions that aim to modify the course of a disease. Prognostic factor research in homeopathy can be done by applying Bayes' theorem. This paper considers Bayes' theorem; Likelihood Ratio, conditional probability and research in subpopulations of a condition with examples. We analysed the likelihood ratios for 11 homeopathic medicines for the symptom ‘cough’ and other upper respiratory tract symptoms, based on data from the IIPCOS2 study. It appears that the combination of cough with other respiratory tract infections yielded biased LR values related to cough for medicines that were preferred for other indications.
We apologise to the authors for any inconvenience caused.