Quantifying the speed of symptomatic improvement with electroconvulsive therapy: comparison of alternative statistical methods.
Although electroconvulsive therapy (ECT) is believed to have a rapid onset of antidepressant activity, there has been limited investigation in this area. This study contrasted alternative statistical methods for testing treatment group differences in the rapidity of clinical response to ECT. Patients with major depression were randomly assigned to receive right unilateral or bilateral ECT and low or high electrical dosage relative to seizure threshold. The 24-item Hamilton Rating Scale for Depression (HRSD) was administered by blinded clinical raters twice weekly (non-treatment days). We evaluated four alternative statistical strategies. Two methods considered time to improvement as a dependent variable: (a) time (treatment number) to reach various cutoffs for percentage decrease in HRSD from baseline; and (b) survival analysis using the same cutoffs for percentage decreases as endpoints. Two methods considered time to improvement as an independent variable: (c) the slope of linear regression of HRSD scores against treatment number; and (d) a random regression model using the HRSD scores as repeated measures. The statistical methods differed in whether or not omnibus group differences were observed, the criterion level of improvement associated with group differences, and the results of pairwise comparisons establishing specific therapeutic advantages. Survival analysis generally displayed the greatest sensitivity in detecting treatment group differences.
Nobler
M S
MS
Department of Biological Psychiatry, New York State Psychiatric Institute, NY 10032, USA.
Sackeim
H A
HA
Moeller
J R
JR
Prudic
J
J
Petkova
E
E
Waternaux
C
C
D000704:Analysis of Variance; D003866:Depressive Disorder / Q000523:psychology* / Q000628:therapy*; D004565:Electroconvulsive Therapy; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011569:Psychiatric Status Rating Scales; D012044:Regression Analysis; D016019:Survival Analysis