Conflation of prediction and causality in the TB literature

Abstract

BACKGROUND: Observational data can answer both predictive and etiologic research questions; however, the model-building approach and interpretation of results differ based on the research goal (i.e., prediction versus causal inference). Conflation occurs when aspects of the methodology and/or interpretation that are unique to prediction or etiology are combined or confused, potentially leading to biased results and erroneous conclusions. METHODS: We conducted a rapid review using MEDLINE (2018–2023) of a subset of the observational TB literature: cohort studies among people with drug-resistant TB that considered HIV status an exposure of interest and reported on TB treatment outcomes. For each article, we assessed the research question, statistical approach, presentation of results, and discussion and interpretation of results. RESULTS: Among the 40 articles included, 32 (80%) had evidence of conflation. The most common specific types of conflation were recommending or proposing interventions to modify exposures in a predictive study and having a causal interpretation of predictors, with both types frequently co-occurring. CONCLUSION: Conflation between prediction and etiology was common, highlighting the importance of increasing awareness about it and its potential consequences. We propose simple steps on how TB and lung health researchers can avoid conflation, beginning with clearly defining the research question.

Keywords

• tuberculosis
• drug-resistant
• epidemiologic methods
• data interpretation
• risk factors