Thus the distribution method can only be used to calculate an absolute MCID as there is no st and ard deviation of baseline score for a relative measure .
The anchor-based method relates the change in
To the Editor : The same patient outcome data from clinical trial results , when presented as absolute or relative changes , may appear different in magnitude .
Recommendations are to report both absolute and relative , or at least baseline , data from which to calculate absolute values [ 1 , 2 ] .
To inform clinical practice , outcome improvements , whether relative or absolute , must be statistically significant and clinical ly meaningful .
A minimal clinical ly important difference ( MCID ) should inform sample size calculations for clinical trials .
Clinically important differences in the intensity of chronic refractory breathlessness.
CONTEXT Clinical ly important differences in chronic refractory breathlessness are ill defined but important in clinical practice and trial design . OBJECTIVES To estimate the clinical relevance of differences in breathlessness intensity using distribution and patient anchor methods . METHODS This was a retrospective data analysis from 213 data sets from four clinical trials for refractory breathlessness .  Linear regression was used to explore the relationship between study effect size and change in breathlessness score ( 0 - 100 mm visual analogue scale ) and to estimate the change in score equivalent to small , moderate , and large effect sizes . Pooled individual blinded patient preference data from three r and omized controlled trials were analyzed . The difference between the mean change in Day 4 minus baseline scores between preferred and non-preferred arms was calculated . RESULTS There was a strong relationship between change in score and effect size ( P = 0.001 ; R(2 ) = 0.98 ) . Values for small , moderate , and large effects were -5.5 , -11.3 , and -18.2 mm . The participant preference change in score was -9 mm ( 95 % CI , -15.8 , -2.1 ) ( P = 0.008 ) . CONCLUSION This larger data set supports a clinical ly important difference of 10 mm . Studies should be powered to detect this difference
Thus the distribution method can only be used to calculate an absolute MCID as there is no st and ard deviation of baseline score for a relative measure .
The anchor-based method relates the change in
To the Editor : The same patient outcome data from clinical trial results , when presented as absolute or relative changes , may appear different in magnitude .
Recommendations are to report both absolute and relative , or at least baseline , data from which to calculate absolute values [ 1 , 2 ] .
To inform clinical practice , outcome improvements , whether relative or absolute , must be statistically significant and clinical ly meaningful .
A minimal clinical ly important difference ( MCID ) should inform sample size calculations for clinical trials .