stat_MCID {MRQoL}

Statistics of the Minimal Clinically Important Difference

Description

Calculate the number of patients, standard deviation and the confidence interval for each category of quality of life dimension.

Usage

stat_MCID(score_1, score_2, X)

Arguments

score_1

the post-test score at (T1) or (T2)

score_2

the Pre-test score if we calculate the minimal clinically important difference without Response shift effect, and it is the Then-Test score in the case of MCID with Response shift effect of each dimension

X

the Jaeschke's question with five categories

Details

This function help us to obtain the number of patients, standard deviation and the confidence interval in two columns (Lower Confidence interval and Upper confidence Interval) for each category of quality of life dimension, that it help us to interpret the result of the minimal clinically important difference.

Value

ID: Dimension: Global Health Status (GHS) dimension, MW: the category "much worse", LW: the category "little worse", NC: the category "no change", LB: the category "little better", MB: the category "much better".

N: column contain six values, he first value is the total number of patients for the quality of life dimension. The five others values are the number of patients for each category of quality of life dimension.

SD: column contain six values, he first value is the global SD for the quality of life dimension. The five others values are the SD for each category of quality of life dimension.

LCI: column contain five values, these values are the lower limits of the confidence interval of the minimal clinically important difference calculated for each category. UCI: column contain five values, these values are the upper limits of the confidence interval of the minimal clinically important difference calculated for each category.

Note

If we have more than five categories in the Jaeschke's question, we must recode this variable to obtain in the end five categories to implement this function.

Author(s)

Ahmad Ousmen

Examples

 
#Example to calculate the statistics of minimal clinically important difference:
data(dataghs)
stat_MCID(dataghs$GHS1,  dataghs$GHS0, dataghs$anchor1)