Improvement Of Indicators For Measuring The Effectiveness Of Clinical Interventions

The goal of clinical interventions is in three aspects:reducing the incidence of the disease,reducing the rinitial severity of the patients9 condition and speeding up the patients,recovery.How to assess the effectiveness of interventions is an important issue in clinical medicine.At present,the evaluation methods for the effectiveness of clinical interventions through medical patient's fixed-term follow-up data mainly include time by time significant analysis and the use of summary measures.But they all have their limitations:First,information leakage due to lag in visit time(the limitations of time by time significant aialysis).Second,containing a lot of noise unrelated to the intervention(the limitations of summary measures).Third,what most important is only focus on the role of interventions in recovery speed(The common limitations of both).This paper will improve on these limitations and build new summary measures.Compared to a large number of judgements which are relying on medical instruments or doctors,modern medicine pays more attention to the patients5 own clinical experience.Therefore,this paper introduces Patient Reported Outcomes(PRO),which measures the health status of patients'subjective feelings,and based on this index to build summary measures,evaluates the effectiveness of clinical interventions in improving the health of patients.PRO is often used as a basis to construct summary measures to measure the effectiveness of clinical interventions in improving a patient's health.Further,this paper proposes two new summary measures created by the improvement of the traditional methods:Maximum of Sequential Difference(MSD)and Modified Area Under the Curve(MAUC),which are based on PRO and address three limitations of traditional assessment methods.And they are used to isolate the intervention impact on incidence,initial severity,and recovery curve.More accurately measures the effectiveness of the intervention more accurately and clarify the way in which the intervention works.To observe the performance of new summary measures,the corresponding summary measures are adjusted under the condition of the follow-up of the PRO observations at fixed time intervals and estimating the time point PRO values of the patient's adverse event by using the local linear estimation method.In this paper,the new summary measures will be compared with the traditional methods,the simulation analysis and significance test will be carried out under three different scenarios.It is found that the performance of MSD and MAUC proposed in this paper is better than traditional indicators by simulation.Corresponding summary measures plus t-test or non-parametric test for the tail distribution have good power in identifying the different ways in which interventions work to be effective.Finally,the conclusions are further verified by empirical analysis,and the empirical conclusions are consistent with the simulation results.