In this study, 232 inpatients with esophageal cancer diagnosed by pathological examination and diagnosed by thoracic surgery in a provincial cancer hospital were selected as the research objects. The inclusion and exclusion criteria are as follows:
Inclusion criteria: (1)Patients diagnosed with esophageal cancer by X-ray and fiberoptic esophagoscopy or gastroscopy biopsy; (2) Inpatients receiving treatments for esophageal cancer; (3) Understanding of the questionnaire due to the fact that the patient himself have a primary school education or above; (4)Patients who voluntarily participated in the test on this quality of life scale.
Exclusion criteria: (1) Patients who are illiterate or lack the ability to read and write;(2) Patients who are unable to express their true feelings clearly due to vague consciousness during hospitalization;(3) Patients who are unable to participate the test due to suffering from other serious diseases.
The QLICP-ES (V2.0) scale and EORTC QLQ-C30 were given to 232 patients with esophageal cancer before and after treatments, and was filled in once before and after treatments.
The anchor-based method is to clarify the meaning of the scale score change by examining the relationship between the scale and another independent measurement tool score or other indicators. There are cross-sectional anchors and longitudinal anchors. In this study, longitudinal anchors were selected to compare the curative effect before and after treatments. First, the Raw Score (RS) of domains were computed according to the number of questions and the answers of patients in each domain. Then, linear transformation is carried out with range method to convert the original score into a standardized score within 0-100.The score calculation method of each domain is as follows:
Second, the Q29 item in the EORTC QLQ-C30 scale, "how would you evaluate your overall health in the past week", was selected as an anchor after considering the correlation coefficient between Q29 and QLICP-ES (V2.0) . Then, patients with a difference of one grade (standard A) and at least one grade (standard B) in Q29 before and after treatments were selected, and the score differences in various domains before and after treatments were calculated respectively, and the mean value of the difference was denoted as MCID.
The distribution method is to determine the MCID from a statistical point of view by using the distribution(variation) of the sample data of the evaluation tool. The commonly used indexes to calculate variation include Effect-Size (ES), Standard Error of the Measurement (SEM), and Reliable Change Index (RCI).
Effect-Size (ES) is obtained by dividing the difference in mean scores from baseline 0 to post-intervention 1 by the standard-deviation of the baseline score (SDbaseline) , the calculation formula and the corresponding MCID are as follows:
ES is often used to compare two or more groups to measure the size of the difference between groups. In health-related quality of life assessments, ES is currently the most recognized parameter in determining the importance of group or individual changes. Cohen  empirically defined an effect size of 0.2 as small,0.5 as moderate, and 0.8 as large.
Standard Error of the Measurement (SEM), defined as the baseline SD multiplied by the square root of one minus sample test-retest reliability coefficient, were also calculated for comparison purpose . The reliability is usually estimated using a test-retest reliability estimate, but some authors also use an internal consistency estimate, for example Cronbach’s alpha . The calculation formula and the corresponding MCID are as follows:
SEM is assumed to be fairly sample-independent , which is its best advantage: a growing standard deviation is balanced by a higher reliability. Some authors like Wyrwich et al. consider one SEM as an approximation of the MCID [23,24]. X can be assigned to 1(small effect), 1.96 (medium effect), 2.77(large effect).
Reliable Change Index (RCI) is the change value of the questionnaire score divided by the square root of SEM. If RCI is greater than 1.96, then the change value has a 95% chance of being a meaningful change . The calculation formula and the corresponding MCID are as follows: