Table 1 Summary of included 13 papers (See Supplementary File 1 for the completed extraction table)
From: A scoping review of causal methods enabling predictions under hypothetical interventions
Title | Intervention Scenario | Clinical topic area | Types of outcomes | Stated assumptions | Reported limitations | Code availability |
|---|---|---|---|---|---|---|
Candido dos Reis, F. J. et al. (2017) An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation, Breast Cancer Research, 19(1), 58 | Single intervention, Discrete choice, Average effect | Breast cancer | Survival | Generalisability of effect from clinical trial. | Prediction of non-breast cancer deaths was excellent in the model development data set but could under-predict or over-predict in the validation data sets. | Stata code are available from the author on request. |
Brunner, F. J. et al. (2019) Application of non-HDL cholesterol for population-based cardio-vascular risk stratification: results from the Multinational Cardiovascular Risk Consortium., The Lancet 394.10215: 2173-2183. | Single intervention, Discrete choice, Average effect | CVD | Binary | The therapeutic benefit of lipid-lowering intervention investigated in the study is based on a hypothetical model that assumes a stable reduction of non-HDL cholesterol. | (1) Data limitation in the derivation cohort. (2) Strong clinical assumption that treatment effects are sustained over a much longer term than has been studied in clinical trials. | Reported using R but codes not available |
Silva, R. (2016), Observational-Interventional Priors for Dose-Response Learning. In Advances in Neural Information Processing Systems 29. | Single intervention, Treatment dose (continuous), Conditional effect | Infant Health and Development Program (IHDP) | Continuous | A(1) and additionally: It is possible to collect interventional data such that treatments are controlled | (1) Computation complexity. (2) Have not discussed at all the important issue of sample selection bias. (3) Generalisability issue. | Code available from OLS |
Van Amsterdam, W. A. C. et al. (2019). Eliminating biasing signals in lung cancer images for prognosis predictions with deep learning. npj Digital Medicine, 2(1), 1-6. | Single intervention, Discrete choice (0/1), Average effect | Lung caner | Survival | A(1), A(2) and additionally: An image is hypothesized to contain important information for the clinical prediction task. The collider can be measured from the image. | (1) Provide an example of how deep learning and structural causal models can be combined. Methods combining machine learning with causal inference need to be further developed. | Code available from OLS |
Alaa, A. M., & Van Der Schaar, M. (2017). Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes. In Advances in Neural Information Processing Systems 30. | Single intervention, Discrete choice (0/1), Conditional effect | IHDP & Heart transplantation for cardiovascular patients | Continuous/Survival times | A(2) | (1) No experiments regarding outcome prediction accuracy. (2) The computational burden is dominated by the O(n3) (matrix inversion on line 13 in Alg.1. | Code available from authors' website. |
Arjas, E. (2014) Time to Consider Time, and Time to Predict? Statistics in Biosciences. Springer New York LLC, 6(2), pp. 189-203 | Single intervention, Discrete choice, Conditional effect | Acute middle ear infections | Survival | A(2) and local independence | In studies involving real data the computational challenge can become formidable and even exceed what is feasible in practice. | NA |
Pajouheshnia R. et al. (2020) Accounting for time-dependent treatment use when developing a prognostic model from observational data: A review of methods. Stat Neerl. 74(1). | Multiple intervention; Discrete choice (0/1); Average effect | Chronic obstructive pulmonary disease (COPD) | Survival | A(1) and A(2) | A very strong indication for treatment will result in structural non-positivity leading to biased estimates of treatment-naïve risk. | NA |
Sperrin, M. et al. (2018) Using marginal structural models to adjust for treatment drop-in when developing clinical prediction models, Statistics in Medicine. John Wiley & Sons, Ltd, 37(28), pp. 4142-4154. | Multiple intervention; Discrete choice (0/1); Conditional effect | CVD | Binary | A(1) and A(2) | (1) Have not modelled statistical interaction between treatment and prognostic factors; (2) Did not explicitly model statin discontinuation; (3) Only consider single treatment. | Code available from OLS |
Lim, B. et al. (2018). Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks. In Conference on Neural Information Processing Systems 32. | Multiple intervention; No restriction on treatment choices; Average effect | Cancer growth and treatment responses | No restriction | A(2) | NA | Code available from OLS |
Bica, I. et al. (2020). Estimating Counterfactual Treatment Outcomes over Time through Adversarially Balanced Representations. ICLR 2020 | Multiple intervention; Discrete treatment choices; Average effect | Treatment response in a tumour growth model | No restriction | A(2) | Additional theoretical understanding is needed for performing model selection in the causal inference setting with time-dependent treatments and confounders. | Code available from authors' website. |
Xu, Y. et al. (2016) A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves. Edited by F.Doshi-Velez et al. PMLR , pp. 282-300. | Multiple intervention; Discrete treatment choices; Conditional treatment effect | (1) kidney function deterioration in ICU; (2) the effects of diuretics on fluid balance. | Continuous | A(2) | NA | NA |
Soleimani, H. et al. (2017). Treatment-response models for counterfactual reasoning with continuous-time, continuous-valued interventions. In Uncertainty in Artificial Intelligence. Proceedings of the 33rd Conference, UAI 2017. | Continuous-time intervention; Continuous-valued treatments; Conditional treatment effect | Modelling physiologic signals with EHRs for treatment effects on renal function | No restriction | A(2), A(3) | While this approach relies on regularisation to decompose the observed data into shared and signal-specific components, new methods are needed for constraining the model in order to guarantee posterior consistency of the sub-components of this model. | NA |
Schulam, P., & Saria, S. (2017). Reliable Decision Support using Counterfactual Models. In Advances in Neural Information Processing Systems 30. | Continuous-time intervention; Continuous-valued treatments; Conditional treatment effect | Applicable to data from EHR but not restrict to such medical settings | Continuous-time; no restriction on data type | A(2), A(3), A(4) | (1) the validity of the CGP is conditioned upon a set of assumptions that are, in general, not testable. The reliability of approaches therefore critically depends on the plausibility of those assumptions in light of domain knowledge. | Code available from authors' website. |