LETTERS TO THE EDITOR
To the Editor: Coronary Prone Behavior: One Pattern or Several by C. D. Jenkins et al. is an intricate investigation, which has added a fascinating hypothesis to behavioral research. The separation of the three disease groups is not perfect, however, and Jenkins et al. state, "There is really much overlap, particularly among the healthy population . . . ." This overlap is critical in evaluating the clinical utility of the proposed behavior patterns, but only group means are presented without the sampling variation for each group, which would quantitate this overlap. The qualitative data imply that the disease groups were more easily separated from each other than from the control group. This is a common result for a discriminant function (DF) analysis of several groups, but detracts from the usefulness of these behavior patterns. The second study appears to be trying to validate the technique of DF analysis, rather than effectively testing the hypothesis generated in the first study. The usual testing procedure, as the authors' indicate, is to enter the test data (i.e., the JAS items) of a new sample into the DF and generate a score for each man. Based on these scores the men in each disease group can be compared. For example, using Jenkins's root dimension number one, more acute myocardial infarction (MI) men should have higher
Address correspondence to: Thomas Kosten, MD, Prescott House, Apt. lC, Greenwich, Conn. 06830.
scores than the angina pectoris, control and silent MI men. There is no data to indicate how well the DF actually classified the 2307 men who were not in the first study as belonging to the control, angina pectoris, silent MI, or acute MI groups. There is also no data describing the composition of the remaining 2307 men or the distribution of Type B behavior among the four groups. Including the 644 men who formed the original DF in the second study obviously biased the results in favor of the hypothesis and complicated the conclusions. Furthermore, the conclusions of Jenkins et al. add no support to their hypothesis and little support to the validity of using DF analysis in such empirical studies. Other multivariate approaches are also available. Cluster analysis, which purports to identify homogeneous groups and thereby minimizes psychological overlap between groups, has been applied to similar data by Hinkle (1). His analysis demonstrated nine groups or clusters—more than the four groups arbitrarily dictated by the disease grouping of Jenkins et al.—of which two clusters seemed related to Type A behavior. These two clusters included only twenty percent of this population. Since estimates of Type A behavior are generally higher (2), Hinkle's clusters probably had a higher specificity for Type A men than the JAS. In these groups coronary heart disease was not more prevalent than would be expected by chance, but silent MI, acute MI, and angina pectoris groups were not looked at separately. It is usually easier to separate small groups from each other than to separate them from a
Psychosomatic Medicine Vol. 40, No. 6 (October 1978) Copyright * 1978 by the American Psychosomatic Society, I Published by Elsevier North-Holland, Inc.
507
LETTERS TO EDITOR
large, widely dispersed control population. Therefore, in studying small groups, homogeneity becomes critical and an important hypothesis such as Jenkins et al. present deserves an analysis of the psychological homogeneity of their three disease groups. This tunction could be filled by cluster analysis.
THOMAS KOSTEN MD Department of Psychiatry YaJe-New Haven MedicaJ Center EDWARD K ^ ^ K , , M S D]
o/HumQn
Ecol
^
ComeJJ MedicflJ
May 8, 1978
REFERENCES 1. Hinkle LE: An estimate of the effects of "stress" on the incidence and prevalence of coronary heart disease in a large industrial population in the United States, in Thrombosis: Risk Factors and Diagnostic Approaches, New York, F. K. Schattauer Verlag, 1972, pp 15-65 2. Jenkins CD, Rosenman RH, Zyzanski SJ: Prediction of clinical coronary heart disease by test for coronary-prone behavior pattern. N Engl J Med 290:1271-1275, 1974
To the Editor: A Reply to Dr. Kosten and Mr. Kadyszewski Dr. Thomas Kosten and Mr. Edward Kadyszewski raise several important issues which indeed require further clarification and elaboration in order that the implications of our data analyses be placed in better perspective. In the first study reported in our paper, Coronary Prone Behavior: One Pattern or Several? (1), it is important to know the sampling variation of each clinical group on each of the three defined dimensions so that the degree of over-lap among groups can TABLE 1.
be estimated. The means and standard deviations are summarized in Table 1. It will be noted that the four means on each dimension vary in range from 20 points on the first root to 7.5 points on the third root. The standard deviations for each group on each root are quite consistent, ranging from 7.2 to 12.9. This indicates that, although the centroids of these four groups are quite distinct, there is still considerable overlap among members of these four clinical groups. It should be noted, however, that a similar degree of overlap would also exist for groups defined by various combinations
Means and Standard Deviations on 3 Discriminant Function Dimensions for Men who Later Developed Each of 3 Expressions of Coronary Disease or Remained Healthy a SMI
AP
Control
Root 1
X SD
5.2 9.6
- 2.7 8.2
-14.8 12.9
0.0 9.9
Root II
X SD
- 3.7 9.3
9.4 11.1
- 6.1 10.0
0.0 10.0
Root III
X SD
- 6.3 10.5
- 7.5 7.2
- 4.7 10.4
0.0 10.0
AMI
a
See also Fig. 1 of Ref. 1.
508
Psychosomatic Medicine Vol. 40, No. 6 (October 1978)
To the Editor: Coronary Prone Behavior: One Pattern or Several by C. D. Jenkins et al. is an intricate investigation, which has added a fascinating hypothesis to behavioral research. The separation of the three disease groups is not perfect, however, and Jenkins et al. state, "There is really much overlap, particularly among the healthy population . . . ." This overlap is critical in evaluating the clinical utility of the proposed behavior patterns, but only group means are presented without the sampling variation for each group, which would quantitate this overlap. The qualitative data imply that the disease groups were more easily separated from each other than from the control group. This is a common result for a discriminant function (DF) analysis of several groups, but detracts from the usefulness of these behavior patterns. The second study appears to be trying to validate the technique of DF analysis, rather than effectively testing the hypothesis generated in the first study. The usual testing procedure, as the authors' indicate, is to enter the test data (i.e., the JAS items) of a new sample into the DF and generate a score for each man. Based on these scores the men in each disease group can be compared. For example, using Jenkins's root dimension number one, more acute myocardial infarction (MI) men should have higher
Address correspondence to: Thomas Kosten, MD, Prescott House, Apt. lC, Greenwich, Conn. 06830.
scores than the angina pectoris, control and silent MI men. There is no data to indicate how well the DF actually classified the 2307 men who were not in the first study as belonging to the control, angina pectoris, silent MI, or acute MI groups. There is also no data describing the composition of the remaining 2307 men or the distribution of Type B behavior among the four groups. Including the 644 men who formed the original DF in the second study obviously biased the results in favor of the hypothesis and complicated the conclusions. Furthermore, the conclusions of Jenkins et al. add no support to their hypothesis and little support to the validity of using DF analysis in such empirical studies. Other multivariate approaches are also available. Cluster analysis, which purports to identify homogeneous groups and thereby minimizes psychological overlap between groups, has been applied to similar data by Hinkle (1). His analysis demonstrated nine groups or clusters—more than the four groups arbitrarily dictated by the disease grouping of Jenkins et al.—of which two clusters seemed related to Type A behavior. These two clusters included only twenty percent of this population. Since estimates of Type A behavior are generally higher (2), Hinkle's clusters probably had a higher specificity for Type A men than the JAS. In these groups coronary heart disease was not more prevalent than would be expected by chance, but silent MI, acute MI, and angina pectoris groups were not looked at separately. It is usually easier to separate small groups from each other than to separate them from a
Psychosomatic Medicine Vol. 40, No. 6 (October 1978) Copyright * 1978 by the American Psychosomatic Society, I Published by Elsevier North-Holland, Inc.
507
LETTERS TO EDITOR
large, widely dispersed control population. Therefore, in studying small groups, homogeneity becomes critical and an important hypothesis such as Jenkins et al. present deserves an analysis of the psychological homogeneity of their three disease groups. This tunction could be filled by cluster analysis.
THOMAS KOSTEN MD Department of Psychiatry YaJe-New Haven MedicaJ Center EDWARD K ^ ^ K , , M S D]
o/HumQn
Ecol
^
ComeJJ MedicflJ
May 8, 1978
REFERENCES 1. Hinkle LE: An estimate of the effects of "stress" on the incidence and prevalence of coronary heart disease in a large industrial population in the United States, in Thrombosis: Risk Factors and Diagnostic Approaches, New York, F. K. Schattauer Verlag, 1972, pp 15-65 2. Jenkins CD, Rosenman RH, Zyzanski SJ: Prediction of clinical coronary heart disease by test for coronary-prone behavior pattern. N Engl J Med 290:1271-1275, 1974
To the Editor: A Reply to Dr. Kosten and Mr. Kadyszewski Dr. Thomas Kosten and Mr. Edward Kadyszewski raise several important issues which indeed require further clarification and elaboration in order that the implications of our data analyses be placed in better perspective. In the first study reported in our paper, Coronary Prone Behavior: One Pattern or Several? (1), it is important to know the sampling variation of each clinical group on each of the three defined dimensions so that the degree of over-lap among groups can TABLE 1.
be estimated. The means and standard deviations are summarized in Table 1. It will be noted that the four means on each dimension vary in range from 20 points on the first root to 7.5 points on the third root. The standard deviations for each group on each root are quite consistent, ranging from 7.2 to 12.9. This indicates that, although the centroids of these four groups are quite distinct, there is still considerable overlap among members of these four clinical groups. It should be noted, however, that a similar degree of overlap would also exist for groups defined by various combinations
Means and Standard Deviations on 3 Discriminant Function Dimensions for Men who Later Developed Each of 3 Expressions of Coronary Disease or Remained Healthy a SMI
AP
Control
Root 1
X SD
5.2 9.6
- 2.7 8.2
-14.8 12.9
0.0 9.9
Root II
X SD
- 3.7 9.3
9.4 11.1
- 6.1 10.0
0.0 10.0
Root III
X SD
- 6.3 10.5
- 7.5 7.2
- 4.7 10.4
0.0 10.0
AMI
a
See also Fig. 1 of Ref. 1.
508
Psychosomatic Medicine Vol. 40, No. 6 (October 1978)
