Steady state trials: another valid substitution of counterfactual ideal to measure causal effects

OBJECTIVES: Many traditionally established medical interventions are not examined with randomized trials especially in emergency medicine. We researched what is the scientific basis of the measurement of the causal effect in these interventions and proposed another trial to measure causal effects. METHODS: We deduced steady state trials from the counterfactual model and used Bayesian approaches to estimate causal effects statistically. RESULTS: When the state of the observed person is fairly steady before an exposure, the ratio of the after-period to the before-period of the exposure is sufficiently small, and changes are obtained in relatively short time, it is possible to postulate that the state of the counterfactual person to be compared is almost equal to the state of the real person before the exposure. Bayesian approaches show that the causal effect of the exposure is estimated even in only one-person steady state trials, when large changes are observed. CONCLUSIONS: Steady state trials are valid methods to measure causal effects and can measure causal effects even in one-person trials. When we can measure the causal effect of interventions with steady state trials, these interventions should be regarded as scientific without use of randomized trials.