Generalizability of Homeopathic Prognostic Factor Research Outcome in COVID-19 Treatment: Comparison of Data.

BACKGROUND/OBJECTIVE: During the coronavirus disease 2019 (COVID-19) pandemic, several homeopathic prognostic factor research (PFR) projects have been undertaken. We found two projects with comparable outcomes to assess consistency and possible flaws. METHODS: Two comparisons were made. (1) Outcome of a PFR data collection from the Liga Medicorum Homoeopathica Internationalis (LMHI) by about 100 doctors with 541 cases was compared with a previous analysis of 161 cases in the same database. (2) The updated LMHI database was also compared with a data collection carried out in India by four doctors with a total of 1,445 cases. Differences that resulted in conflicting outcomes (indication in one, contraindication in the other) were examined for possible causes. RESULTS: There was only a single outcome in the updated LMHI database that conflicted with the previous dataset, and this could have been due to statistical variation. The Indian data contained many cases, from few doctors, while the LMHI database had few cases per doctor, but many doctors. The overlap between the projects (individual cases entered in both) was between zero and 22%. In 72 comparisons we found six (8.3%) conflicting outcomes. Possible causes were statistical error due to small numbers of cases and/or observers, confirmation bias, and keynote prescribing if this resulted in symptoms being inadequately checked. CONCLUSION: There was little conflict between the outcomes of the two versions of one project and between the two different PFR projects. Differences could mostly be explained by causes that can be managed. This consistency should primarily be interpreted as showing a strong overall consensus between homeopathic practitioners worldwide, but with variation of consensus between small groups of practitioners.

Impact of Bias in Data Collection of COVID-19 Cases.

BACKGROUND: Prognostic factor research (PFR), prevalence of symptoms and likelihood ratio (LR) play an important role in identifying prescribing indications of useful homeopathic remedies. It involves meticulous unbiased collection and analysis of data collected during clinical practice. This paper is an attempt to identify causes of bias and suggests ways to mitigate them for improving the accuracy in prescribing for better clinical outcomes and execution of randomized controlled studies. METHODS: A prospective, open label, observational study was performed from April 2020 to December 2020 at two COVID Health Centers. A custom-made Excel spreadsheet containing 71 fields covering a spectrum of COVID-19 symptoms was shared with doctors for regular reporting. Cases suitable for PFR were selected. LR was calculated for commonly occurring symptoms. Outlier values with LR ≥5 were identified and variance of LRs was calculated. RESULTS: Out of 1,889 treated cases of confirmed COVID-19, 1,445 cases were selected for pre-specified reasons. Nine medicines, Arsenicum album, Bryonia alba, Gelsemium sempervirens, Pulsatilla nigricans, Hepar sulphuricus, Magnesia muriaticum, Phosphorus, Nux vomica and Belladonna, were most frequently prescribed. Outlier values and large variance for Hepar sulphuricus and Magnesia muriaticum were noticed as indication of bias. Confirmation bias leading to lowering of symptom threshold, keynote prescribing, and deficiency in checking of all symptoms in each case were identified as the most important sources of bias. CONCLUSION: Careful identification of biases and remedial steps such as training of doctors, regular monitoring of data, checking of all pre-defined symptoms, and multicenter data collection are important steps to mitigate biases.