COVID and Causal Inference -- CogX 6/2020
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This document discusses challenges in estimating causal effects of treatments and interventions for COVID-19. It notes that randomized controlled trials are ideal but difficult for COVID-19, and observational studies and simulations have their own challenges, such as identifying appropriate control groups and accounting for uncertainty. It emphasizes the need for well-defined causal questions, interventions, and outcomes when studying complex exposures like pandemics. Special difficulties in COVID-19 research are also outlined.
COVID and Causal Inference -- CogX 6/2020
- 1. @EpiEllie COVID-19 and causal inference Eleanor Murray, ScD Department of Epidemiology CogX June 8, 2020
- 2. What do we want to know? What will the world look like in the future? How would things have changed if the world had been slightly different? Treat now Treat later @EpiEllie
- 3. How do we estimate causal effects? Miguel Hernàn’s two-step causal algorithm: 1. Ask good questions 2. Answer them with appropriate methods ? ! @EpiEllie
- 4. What do we want to know? If we can’t have a time machine, we’d like to have a randomized trial. Treat now Treat later @EpiEllie
- 5. Target trial framework for estimating causal effects If we can’t have a randomized trial, we’d like to emulate what would have happened if we could have done one. We can do this with: Imperfect trials Observational data Simulation modeling Treat now Treat later @EpiEllie
- 6. What assumptions do we need? No unmeasured confounding: all common causes of the treatment and outcome are known and measured in the data No open colliders: all common effects of the treatment and outcome are known and not conditioned on in the data or analysis @EpiEllie
- 7. What assumptions do we need? Positivity: there is a non-zero probability of all levels of treatment for all types of individuals in our population @EpiEllie
- 8. What assumptions do we need? Consistency: our treatment levels are clearly specified, aka: Well-defined interventions Well-defined causal questions @EpiEllie
- 9. Why are well-defined causal questions important for complex exposures? When there are multiple possible ‘interventions’ and we don’t specify one, our answer is a weighted average of all ‘interventions’ but we don’t know the weights Murray, 2016. Agent-based models for causal inference. Harvard University. @EpiEllie
- 10. Why are well-defined causal questions important for complex exposures? Worse, if the ‘intervention’ is ill-defined, the confounding is probably also ill-defined! Murray, 2016. Agent-based models for causal inference. Harvard @EpiEllie
- 11. All communicable disease by nature involve complex exposures We cannot define the causal effect of an intervention on a communicable disease without accounting for who is infected and how people come into contact This is the problem of interference
- 12. Special challenges for COVID19 RCTs Identifying an appropriate control group Can we ethically use placebo? What is ‘standard of care’? Identifying an appropriate outcome Many early studies assessed ‘symptom improvement’ as a surrogate outcome, but failed to properly account for ICU admission or death Many later studies looked at death but only in hospital. Individuals who were discharged were ‘lost to follow-up’ @EpiEllie
- 13. Special challenges for COVID19 RCTs Understanding to whom the results apply Can a trial run at a hospital in NYC tell us how to treat patients in Singapore? Kinshasa? London? Can a trial run among patients were never on ventilators tell us how to treat new patients who may need ventilators? @EpiEllie
- 14. Special challenges for COVID19 observational studies All the same problems as with RCTs apply, plus: Identifying an appropriate control group Who should we compare to when experimental treatments are preferentially given to the sickest patients? We know we have confounding. How do we control for it? @EpiEllie
- 15. Special challenges for COVID19 simulation studies All the same problems as with RCTs & observational studies apply, plus: Identifying parameter inputs We need external validity for not only our effect estimates but also our model inputs Understanding uncertainty This is a big unanswered question in simulation modelling that needs more statistical attention @EpiEllie
- 16. Uncertainty in simulation models for causal effects We cannot generate confidence intervals for our simulation models because we do not have a way to capture the full variance. Modelers recognize three sources of uncertainty: Stochastic Parametric Structural Solution: Increase sample size or number of runs Solution: Probabilistic or Bayesian sensitivity analyses on key parameters. Which are key? What distributions should we use? What impact does it have to assume other parameters don’t have uncertainty? Solution: ?? Probably will involve synthesizing across different model structures but how do we decide which ones & how do we quantify uncertainty? @EpiEllie
- 17. Summary COVID19 urgently needs good causal effect estimates for identifying effective prevention and treatment strategies. Many existing studies are committing basic statistical and epidemiologic errors. There are also important gaps in our statistical methods for quantifying uncertainty in simulation model estimates. @EpiEllie