Leveraging “big data” in respiratory medicine – data science, causal inference, and precision medicine

Figures & data

Table 1. Scientific questions, required information, and methods of data science according to the ladder of causation

	Association and prediction	Intervention	Counterfactual causal inference
Examples of scientific question	What is the likelihood of developing childhood asthma for infants with a set of clinical predictors?	Will a biologic agent reduce the frequency of severe asthma exacerbation compared to placebo?	What would be the effect of a biologic agent if it had been given to an asthma patient with certain characteristics?
Required information	Inputs (risk factors/ predictors) Outputs	Eligibility criteria Exposures/treatments and outcomes	Eligibility criteria Exposures/treatments and outcomes Subject-matter knowledge and assumptions on the causal structure (e.g. confounders, mediators, colliders, IV)
Examples of analytics	Regression Bayesian network Supervised machine learning algorithms (e.g. random forests, neural network/deep learning)	Intention-to-treat analysis and per-protocol analysis in RCT Do-calculus Causal Bayesian network	Regression Propensity score matching G-formula IPW/MSM Doubly-robust estimation Targeted learning/TMLE Mendelian randomization/ IV-estimation

Abbreviations: IPW/MSM: inverse probability weighing for marginal structure model; IV: instrumental variable; RCT: randomized controlled trial; TMLE: targeted maximum likelihood estimation.

Figure 1. Causal structural learning to uncover underlying causal structures from data

As a simplified example, a causal structural learning approach (linear non-gaussian acyclic causal modeling [15]) is applied to part of the host transcriptomic and metatranscriptomic data in the upper airway of 222 infants with severe respiratory syncytial virus bronchiolitis. It identifies an underlying causal relationship between these host immune response factors (blue vertices) and bacterial species (orange vertices) in the niche. Size of the latter vertices represents the square root of relative abundance of species; direction of edges indicates the causal relationship between the vertices. This approach is distinctly different from co-occurrence networks which can reparent only correlations between variables and are agnostic about their underlying causal relationships. In this example, S. pneumoniae and M. catarrhalis not only have direct effects on the other species in the niche, but also have direct effects on the host immune response.

Figure 2. Data science that integrates big data and knowledge toward precision medicine

Development of precision medicine requires an integration of ‘big data’ from expanded data sources and capture with data science methodologies and analytics that encode subject-matter causal knowledge and counterfactual causal reasoning.

Shimizu S. Non-Gaussian methods for causal structure learning. Prev Sci. 2019;20(3):431–441.

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