Generalisability of prognostic factor research: further analysis of data from the IIPCOS2 study

Abstract

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. This yielded useful information since several medicines, including Belladonna, had LR >1 for cough, indicating that cough is not an indication for this medicine. The implications for improving homeopathic prescribing are discussed.

Keywords

Prognostic factors - Bayes' theorem - Likelihood Ratio - Conditional probability - Condition confined assessment - IIPCOS2 - Cough - Belladonna

Publication History

Received: 01 January 2017

Accepted: 19 June 2017

Article published online:
16 November 2021

• References

• 1 Güthlin C, Lange O, Walach H. Measuring the effects of acupuncture and homoeopathy in general practice: an uncontrolled prospective documentation approach. BMC Public Health 2004; 4: 6 DOI: 10.1186/1471-2458-4-6.
• 2 Croft P, Altman DG, Deeks JJ. et al. The science of clinical practice: disease diagnosis or patient prognosis? Evidence about “what is likely to happen” should shape clinical practice. BMC Med 2015; 13: 1-8 DOI: 10.1186/s12916-014-0265-4.
• 3 Prognostic factor. (n.d.) Farlex Medical Dictionary. (2009). Retrieved December 13 2016 from http://medical-dictionary.thefreedictionary.com/prognostic+factor
• 4 NCI Dictionary of cancer terms. Retrieved December 13 2016
• 5 Riley Richard D, Hayden Jill A, Steyerberg Ewout W. et al. Prognosis research strategy (PROGRESS) 2: prognostic factor research. PLoS Med 2013; 346 (02) e5595 DOI: 10.1371/journal.pmed.1001380.
• 6 Rutten ALB, Stolper CF, Lugten RFG, Barthels RWJM. Assessing likelihood ratio of clinical symptoms: handling vagueness. Homeopathy 2003; 92: 182-186
• 7 Rutten ALB. Bayesian homeopathy: talking normal again. Homeopathy 2007; 96: 120-124
• 8 Rutten ALB, Stolper CF, Lugten RFG, Barthels RWJM. ‘Cure’ as the gold standard for likelihood ratio assessment: theoretical considerations. Homeopathy 2004; 93: 78-83
• 9 Rutten L, van Wassenhoven M. Clinical verification of homeopathic symptoms: experience and statistics. In: Bonamin LV. (ed). Signals and Images. Springer, Dordrecht; 2008. DOI: 10.1007/978-1-4020-8535-2_11
• 10 Heger M, Riley DSHM. International integrative primary care outcomes study (IIPCOS-2): an international research project of homeopathy in primary care. Br Homeopath J 2000; 89 (Suppl. 01) 10-13
• 11 Haidvogl M, Riley DS, Heger M. et al. Homeopathic and conventional treatment for acute respiratory and ear complaints: a comparative study on outcome in the primary care setting. BMC Complement Altern Med 2007; 7: 7 DOI: 10.1186/1472-6882-7-7.