.I. psychint. Rev., Vol. IS, pp. 67-75. PergamonPress Ltd. 1979..Printedin Great Britain.
A COMPARISON OF ANALYSIS OF COVARIANCE AND THE 8 TECHNIQUE AS APPLIED TO ILLUSTRATIVE PSYCHOPHARMACOLOGICAL DATA?* DONALD C. Ross
and DONALD F. KLEIN
New York State Psychiatric Institute (Received
14 Much
1977; revised 8 August 1977)
Abstract-A method of data analysis, 8, aimed at detecting differences in response patterns to drug and placebo is described. Instead of positing a linear relationship between pre-treatment and post-treatment scores and then testing for differences between slopes or between adjusted post-treatment means, as is done in analysis of covariancc (ANOCOVA) the pre-treatment by post-treatment data matrix is partitioned into drug-typical and placebo-typical regions. One may stipulate beforehand the shape of these regions and a significance test of whether the partition discriminates between the groups is provided. Alternately, one may search a number of partitions, and a significance test over the set of searched partitions is provided. The method is illustrated with real psychopharmacological data and the results are compared with ANOCOVA. It appears that the null hypothesis of no differences between treatments is rejected in approximately the same set of items with some indication of superior power for 8.
INTRODUCTION THE CONVENTIONAL method
of data analysis that has been used in studies of drug itiduced symptom change has been to randomly assign subjects to drug and placebo groups, to rate subjects before and after treatment with respect to relevant symptoms, and then to apply analysis of covariance (ANOCOVA) to the data. ANOCOVA is generally recommended rather than simple analysis of variance of the post-treatment scores because of the reduction of the error variance and the possibility of correcting for random differences in pre-treatment symptom levels that can be achieved with ANOCOV&. Unfortunately, ANOCOVA rests upon a~number of assumptions about the nature of the data that could lead one to question the propriety of using it with psychopharmacological data. KLEIN, Ross and FELWAN~ have reviewed these issues in some detail and have suggested, as a method that would require fewer assumptions, a searching procedure that would select the set of pre-treatment to post-treatment rating shifts that maximally discriminate between the drug and placebo groups. Three-way weighted Kappa,W, was suggested as the index of discrimination to be maximized. Ross4 has presented a second index, 6, which has somewhat more desirable properties for this use. Kappa and significance tests based upon it have two shortcomings. First, they take 3-way (pie x post x treatment) independence as
A COMPARISON OF ANALYSIS OF COVARIANCE AND THE 8 TECHNIQUE AS APPLIED TO ILLUSTRATIVE PSYCHOPHARMACOLOGICAL DATA?* DONALD C. Ross
and DONALD F. KLEIN
New York State Psychiatric Institute (Received
14 Much
1977; revised 8 August 1977)
Abstract-A method of data analysis, 8, aimed at detecting differences in response patterns to drug and placebo is described. Instead of positing a linear relationship between pre-treatment and post-treatment scores and then testing for differences between slopes or between adjusted post-treatment means, as is done in analysis of covariancc (ANOCOVA) the pre-treatment by post-treatment data matrix is partitioned into drug-typical and placebo-typical regions. One may stipulate beforehand the shape of these regions and a significance test of whether the partition discriminates between the groups is provided. Alternately, one may search a number of partitions, and a significance test over the set of searched partitions is provided. The method is illustrated with real psychopharmacological data and the results are compared with ANOCOVA. It appears that the null hypothesis of no differences between treatments is rejected in approximately the same set of items with some indication of superior power for 8.
INTRODUCTION THE CONVENTIONAL method
of data analysis that has been used in studies of drug itiduced symptom change has been to randomly assign subjects to drug and placebo groups, to rate subjects before and after treatment with respect to relevant symptoms, and then to apply analysis of covariance (ANOCOVA) to the data. ANOCOVA is generally recommended rather than simple analysis of variance of the post-treatment scores because of the reduction of the error variance and the possibility of correcting for random differences in pre-treatment symptom levels that can be achieved with ANOCOV&. Unfortunately, ANOCOVA rests upon a~number of assumptions about the nature of the data that could lead one to question the propriety of using it with psychopharmacological data. KLEIN, Ross and FELWAN~ have reviewed these issues in some detail and have suggested, as a method that would require fewer assumptions, a searching procedure that would select the set of pre-treatment to post-treatment rating shifts that maximally discriminate between the drug and placebo groups. Three-way weighted Kappa,W, was suggested as the index of discrimination to be maximized. Ross4 has presented a second index, 6, which has somewhat more desirable properties for this use. Kappa and significance tests based upon it have two shortcomings. First, they take 3-way (pie x post x treatment) independence as
