| dc.description.abstract | In clinical trials, some researchers have proposed new methods for comparing drug efficacy. It is done by dividing patients into an experimental group that gets a new drug or course of treatment, and a control group that gets a placebo or no treatment. Then, the patients in the experimental group and the control group are paired according to their risk status, and the design is designed by sorting the severity of symptoms caused by the disease, taking cardiovascular disease as an example: priority is given to death, and then to stroke. If the patients who use the new drug do not die and the patients who use the placebo die, it is recorded as a win. On the contrary, it is recorded as a lose. Or if the two paired patients did not die, but the patient who received the new drug did not have a stroke and the patient who received the placebo had a stroke, it was also recorded as a win, and vice versa was also recorded as a lose. If neither of the two matched patients dies or suffers a stroke, it is a tie. After comparing multiple groups, the number of winning groups, the number of losing groups and the number of tie groups can be obtained. Therefore, the estimates of net benefit, win ratio, and win odds can be obtained, and then the therapeutic effects of new drugs or new courses of treatment can be further evaluated.
In this paper, we propose to use likelihood method to make inferences on the above three parameters under the matching design. We discuss the inference performance obtained by the likelihood method through simulation research and case analysis. And compare the confidence interval with the MOVER (method of variance estimates recovery) proposed by Zou and Donner (2008) and Donner and Zou (2012) used by Matsouaka (2022), and the confidence interval proposed by Pocock et al. (2011). | en_US |