Premenstrual changes: Patterns and correlates of daily ratings

127

JAD 00362

Premenstrual

Changes:

Jean Endicott ’

Depcrrtmenr

of Reseorc~h Assessment of Prychrcrtrr.

of Daily Ratings

‘, John Nee ‘, Jacob Cohen ’ and Uriel Halbreich and Troinrng,

.’ Quantrtutrw .’ Department

Patterns and Correlates

Store

P.yhologv,

New

York State Ps,,c,hratrlc Nen’ York

Unwer.v!,~ of New

Unrrwsr~~,

York at Buffdo.

(Received (Accepted

23 August. 22 January

Instrtute,

Nm

York.

7’ M/ IORth Street. 7__

New

3 York.

NY

100.1.

N Y 10003. cmd

462 Grrder Street.

Buffalo.

NY

14215 (U.S.A.)

19X5) 1986)

Summary

Daily ratings of 20 measures of mood, behavior, and physical condition made by 64 women for one menstrual cycle were analysed to determine patterns of covariance between the pre- and postmenstrual periods. Five discriminantly different dimensions of premenstrual change were identified. They were found to be differentially related to a lifetime diagnosis of affective disorder. These results, and others, support the recommendation that research should be focused upon diversified premenstrual changes rather than a single premenstrual syndrome.

Key words:

Dui& ratings - Mood - Premenstrual

Introduction

Most clinicians and investigators. interested in changes in mood. behavior and physical condition along the menstrual cycle are aware of the need to obtain some form of daily evaluation throughout the cycle in order to assess the pattern and severity of such changes. It has become apparent that the sole reliance upon retrospective descriptions of a woman’s ‘typical’ premenstrual or menstrual cycle changes may result in misclassification by the clinician or in the selection of some patients or subjects for treatment or study whose daily ratings would not agree with their retrospective reports. The documentation of (and reasons suggested to account for) discrepancies between retrospective reports and daily ratings have been described 0165-0327/X6/$03.50

3’ 19X6 Elsevier Suence

Publishers

chunges

elsewhere (Endicott and Halbreich 1982: Halbreich and Endicott 1985b). The use of daily ratings made by the subject herself is now widespread in both treatment and research settings. Many different formats are used to collect such ratings (including 100 mm lines, dichotomous items, 3-7 point scales), and the number of signs and symptoms rated also varies from a single rating of unpleasant feelings to multiple ratings of over 100 individual items (Abplanalp et al. 1980: Rubinow and Roy-Byrne 1984). When daily ratings are collected for one or more cycles, it is difficult to summarize and interpret the data regardless of the number of variables evaluated. There has been a tendency to classify the cycles clinically (‘by eyeball’) as showing

B.V. (Biomedxal

Diwaion)

128

either: (I) premenstrual changes on particular items, (2) a relatively flat pattern, or (3) changes with no particular linkage to phases of the menstrual cycle. The reliable recognition and classification of patterns of change is difficult. particularly when there are more than a few items. Although a few investigators summarize the data by determining its degree of fit to a sine curve (Sampson and Jenner 1977: Backstrom and Mattison 1983; Rubinow et al. 1984). most clinicians and investigators describe their findings in terms of changes in the rating levels of individual items (Backstrom and Mattison 1973; Abplanalp et al. 1980). Clinical description as well as the search for correlates of premenstrual changes would be enhanced if the items could be grouped in a meaningful way to reflect different underlying dimensions in the sets of individual measures. In this study, we illustrate the use of several different procedures to help detect and summarize specific patterns of change between the pre- and post-menses periods using data collected on a 20-item. 6-point daily rating form. Neither the subjects studied nor the particular set of 20 items are thought to be typical of the ‘average’ woman or the most representative types of changes. However. they do serve as examples of useful ways of approaching menstrual cycle data and are very similar to the type of subjects and type of premenstrual changes frequently studied. Methods

Women were recruited initially by notices posted around two medical centers and in local newspapers seeking subjects for studies of mood. behavior, and physical condition during the menstrual cycle. They had to volunteer to be interviewed. to complete questionnaires, and to make daily ratings. The degree of self-selection is unknown but the nature of the data collection required cooperative subjects. They appear to be fairly similar to volunteer groups described in menstrual cycle studies elsewhere. They also underwent a fairly standard screening procedure commonly used in menstrual cycle studies. Respondents were given the Premenstrual Assessment Form (Halbreich et al. 1982). menstrual

history form. and a medical screening form. They were screened out if they had a current physical illness. were taking medication. were on birth control pills, had markedly irregular menstrual cycles. or were under 18 or over 45. Two groups of women were asked to complete at least one cycle of daily ratings, (1) if their Premenstrual Assessment Form scores indicated that they had moderate-to-severe dysphoric premenstrual changes, or (2) if they reported that they had minimal or no dysphoric premenstrual changes. These ratings were collected as part of a screening procedure used to help select contrasting groups of women. For this reason, the women who reported slight or mild changes only were not studied further since it has been shown previously that the daily ratings of this middle group often do not confirm their reported magnitude of premenstrual change (Endicott and Halbreich 1982). Furthermore. most current studies of correlates of premenstrual changes attempt to select women with and without clear-cut changes. Subjects were also evaluated using the Schedule for Affective Disorders and Schizophrenia (SADS) (Endicott and Spitzer 1978) and lifetime and current diagnoses were made using the Research Diagnostic Criteria (RDC) (Spitzer et al. 1978). The Family History Research Diagnostic Criteria were used to collect information regarding first-degree relatives (Andreasen et al. 1977). Daily ratings were available for 100 subjects. These ratings were reviewed by 2 staff members and 36 were rejected for all further analysis because of (1) physical illness or incomplete data (n = 7) or (2) presence of an erratic pattern (n = 29) of ratings which indicated frequent and at least moderate problems during the entire cycle (n = 26) or changes continuing into the postmenstrual period (n = 3). As a result of screening, the final set of daily ratings studied is for women who were having regular menstrual cycles and who had no current evidence of physical or mental disorder, from either interview or daily ratings. There was no attempt to represent ‘women’ in general. The group is more representative of subjects who pass a typical screening process for biological studies focused upon the menstrual cycle. As such, some factors likely to contribute ‘noise’ are eliminated. resulting in a more easily

129

replicated sample than that of a broader-based one. The various analyses performed used 61-64 cases depending upon their demands for a specific number of days of ratings both pre- and postmenses.

The Daily Rating Form used in this study (Fig. 1) was developed as part of an ongoing program of studies of premenstrual changes (Endicott and Halbreich 1982). Prior experience suggested that the willingness of women to complete daily ratings is partially determined by the number of ratings to be done so we limited our form to 20 items out of potentially hundreds that could be studied. The 20 items rated are shown in Table 1. Most of the items were selected because there is fairly consistent evidence that they describe changes that are often more severe during the premenstrual than during the postmenstrual phase. e.g.. fatigue. breast pain. depressed mood, and irritability. Two items were included to help determine severity of social impairment associated with reported changes (i.e., impaired work and

social withdrawal). Others were selected because of a particular interest of ours (e.g., alcohol and drug use). The subjects made ratings on the items each evening using a scale of 1 for ‘none of the feature’ to 6 for ‘extremely severe levels of the feature’. They also indicated the days of menses and noted if they had a physical illness or if there had been life events that may have been related to the occurrence (or severity) of any feature. Data Analysis and Results Given the selection of subjects, sets of daily ratings. and specific behaviors studied. the question was not whether changes could be detected but rather on ways of summarizing a complex set of data. Analyses of the data focused upon detection of patterns of change and their interrelationships. Furthermore. evidence of their differential correlates was sought. Detec,tion of patterns of chunge Several different data analytic techniques were used to determine if there was a single underlying dimension of ‘premenstrual syndrome’ or if sets of daily ratings of specific clinical features covaried differentially between the pre- and postmenstrual periods. To enhance the degree of difference, if such were exhibited. and to increase the sensitivity of the measures, the focus was upon the 5 days prior to onset of menses (premenstrual period) versus the 5 days after the end of menses (postmenstrual period). These 2 time periods were chosen because the 5 days prior to menses will usually include the most severe changes, although the premenstrual changes for a particular symptom or woman may last more or less than 5 days. The few days post-menses are used as the baseline period because this is the period during the cycle when most women show a stable pattern of ratings that is usually lower than that of other phases of the cycle. The late follicular period was not used for baseline because some women have dysphoric changes in mood for a few days around ovulation. Similarly, the period during menses was not used because most women continue to manifest some of the ‘premenstrual’ changes during the early days of menses as well.

130

TABLE

I

LOADINGS THE

OF THE

WITHIN-SUBJK‘T

CORRF.L>ATION

Irritable.

angq.

Depressed. Anw~~s,

OF DAILY

F.4CTOR

RATINGS

OVER

ANALYSIS TIME

OF

hlF.ASURES

OF

PRF.MENSTRL’AL

C‘fiANGE

impatient

sad. 10~. blue. loneI)

Jitter).

nervous

Stay home. a\wd

social nCtivit\

0.99

0.31

0.43

0.89

_

0.56

0.50

Less sexual interet

0.88

0.49

0.40

Back.Joint

0.86

_ _

\ 0.3x I 0.5X

0.33

0.61

0.86

0.40

0.41

0.60

0.59 i

0.43

0.91

0.3x

0.4oj

0.71

0.90

0.61

0.x1

0.45

0.33

Abdominal

pain

Breast pain or muscle pun

Feel bloated.

Marc Lea

have edema

sleep. naps. stay in hed work.

unpaired

Low energ!.

Drink

coffee.

Appetite Drink More Active.

(Job. home)

tired. weah

tu.

alcohol.

0.43

0.47 0.45

0.13I

use drugs

(1.36

0.8X

0.4x

0.77 0.70 0.69

0.55

0.56

0.61

0.60

I

0.77

0.67j

0.37

0.45

\

~

! _ 1

sexual interest restless, can’t hit still enJoymrnt.

rffrcienq

Headaches ” Rattngs

of the 20 item\

63 uomen

for 5 days prr-

and the resulting

Between-subject

i

o.xx

cold drinks

up. cat more. crave food\

Increased

USING

(n = 63) ,’

and post-mensa

20 x 25 matrix

correlutiom

\vas factored

0.44

were correlated and rotated

und cluster unu!\..se.s.

Change scores were calculated for each of the 64 women for the 20 items by contrasting the mean score of the highest 3 consecutive days during the 5 premenstrual days with the mean of the scores of the 5 postmenstrual days. Given the variability of the timing and peak of premenstrual changes. the mean of the 3 days of most severe manifestations was chosen to assure a greater contrast with the postmenstrual period than would be the case if the mean of the 5 premenstrual days were used. To assure high reliability of the baseline measure, the mean of the 5 postmenstrual days was chosen to avoid the undue influence of a single bad day. Conventional (between-subject) factor analyses of these 20 change scores were run using the SPSS-X programs (SPSS 19X3) and the factors

for rach woman.

0.42

The correlations

were a\eragcd

over the

hq obllrnin.

derived from the 3- and 5-factor solution were rotated using oblimin and varimax procedures. An empirical taxonomy of the 64 women was accomplished by a K-means cluster analysis of the calculated pre-/post-change scores (Hartigan 1975). The solution for 3 clusters appeared to be the most clinically and psychometrically compelling, with sample sizes of 7, 32, and 25 women. Within-subject correhtion m~~~~~.sm~For each of 63 subjects. correlations between each pair of items over 10 days (5 pre-. 5 post-) were calculated. Thus, each subject had a 20 x 20 matrix (n = 10 days) which described the association among the items for her over the 10 days. The resulting 63 correlation matrices were then averaged via Fisher’s ;-transformation. resulting in a single correlation matrix that reflected only

131

within-subject association. This within-subject procedure was used to avoid the influence on the conventional factor analysis of differential tendencies among subjects tb rate extremely. This matrix was then factored with oblimin and varimax rotation of the 3- and 5-factor solutions as had been the case with the between-subject matrix.

All of the data analytic procedures produced evidence for more than a single overall pattern of covarying change among the 20 items with some variability in content of the subsets of items, depending upon the analytic method used. There is strong support in all of the analyses for the association of the subset of items which describe physical changes (abdominal discomfort; breast pain; back. muscle and joint pains, and a feeling of being bloated). The results for the mood and behavioral items were somewhat more variable. Although the solutions were quite similar, the oblimin rotation of the 5-factor solution for the within-subject factor analysis was selected to serve as the basis for a summary scoring system because it seemed the most clinically meaningful (see Table 1). Furthermore, the grouping of items was also fairly consistent with taxonomy yielded by the K-means cluster analysis which supported the division of the 64 women into 3 groups on the basis of their patterns of premenstrual changes. The results of these analyses were used to develop a summary scoring system of 5 scales. Four of the summary scales were named without difficulty (Dysphoric Mood, Physical Discomfort. Low Energy. and Consumption). The fifth factor was less readily interpreted as a functional entity. It was finally named for some of its constituent items (More Alcohol. Sex, Active). The 5 summary scores may be calculated in 2 ways: (1) if the focus is on premenstrual changes, i.e., the contrast between the 5 days pre- and the 5 days post-menses. a summation of the composite difference scores can be averaged and used; (2) if the focus is on the daily pattern for one of the summary scores over the entire cycle (or for any particular period), then the average of the ratings for component items during the days of that period can be used. Example: A woman’s 5 premenstrual scores on

the 3 items making up the Low Energy summary scale were ‘more sleep’ 4-5-5-6-4, ‘less work’ 2-3-4-4-4. and ‘fatigue’ 4-5-5-6-5. Her postmenstrual scores were all 1’s. To calculate a summary scale score for Low Energy one would first calculate the means of the 3 highest premenstrual days (5.3. 4, 5.3). then subtract the means of the 5 postmenstrual days (1, 1, 1) to obtain the 3 difference scores (4.3, 3. 4.3). The average of the 3 difference scores would be 3.87 for the summary change score. On the other hand, the daily Low Energy summary score for the 5 premenstrual days would be an average of the scores of the 3 composite items (3.3. 4.3, 4.7, 5.3, 4.3). (Computer programs are available from the authors to calculate these scores from the specific Daily Rating Form used.) Relutionship among the summary scale scores The intercorrelations of the 5 summary scale score measures of premenstrual change, i.e.. the difference in severity between the pre- versus postmenstrual periods, are shown in Table 2. The matrix shows relatively high intercorrelations among 4 of the scores implying a general factor of ‘premenstrual miseries’ which does not include the ambiguous fifth summary score of More Alcohol, Sex, Active. However. the individual scattergrams among the 4 scores that are highly related to each other indicate that the relationships among them are heteroscedastic (e.g., of unequal variability of ~3 for different values of x). The scatter plots are fan-shaped, suggesting that most respondents who rate one of the sets of clinical features as absent or at low levels during the premenstrual (as compared with the postmenstrual) period will also rate the other sets as absent or at low levels. On the other hand, a score indicative of a high degree of premenstrual change on any set of clinical features is associated with varying amounts on the other sets. This fan-shaped type of relationship between the sets of clinical features is illustrated in Fig. 2. Relutionship of summury scale scores for pre-/ postmenstrual changes to derived clusters The cluster procedure described earlier groups the subjects into 3 clusters of 7. 32, and 25 women who appeared to differ in their severity and pattern of pre- versus postmenstrual clinical features.

T.4BLE

2

INTERCORRELATION CHANGES (n = 6.1)

AMONG

THE

5 SUMMARY

SCALE

0.68 0.69 0.61 0.26

Physical Discomfort Low Energ> <‘onaumption More .4lcohol. Sew. Acttve

SCORE.

MEASURES

0.56 0.61 0.05

Fig. 3 shows how the taxonomy of the 3 clusters of women (derived from the 20 items) relates to the 5 summary scale scores. The distribution of each cluster for each of the 5 summary scales is represented in the figure as follows: The top and bottom of the boxed section mark the 75th and 25th percentiles. The thin line extends one standard deviation from the mean and the mean is denoted by the horizontal line in the box. The Dysphoric Mood summary scale score items most prominently determined the taxonomy with clear separation of those groups each from the other. The items of the summary scale scores of Physical Discomfort, Low Energy. and Consumption operated similarly. but to a lesser degree.

Extreme 5. Change

OF

PRb.-

0.57 0.1 I

vs. POSTMENSTRUAL

0.21

There was essentially no contribution of the item5 in the More Alcohol, Sex, Active score to the differentiation of the 3 clusters of subjects. although the women in cluster 1 (the more severe group) tended to score higher on these items as well. The scores for the 7 women who showed the greatest amount of pre-/postmenstrual change (cluster 1) are shown for the entire menstrual cycle using daily summary scale scores for Dysphoric Mood (Fig. 4). This figure indicates that for these women the pre- versus post-menses differences are apparent. In addition. it indicates that there are differences in individual patterns of change along the cycle for the 7 women. The patterns of change for 3 of the other summary scores arc similar (Physical Discomfort, Low Energy. and Consumption) while that for the summary score of More Alcohol, Sex. Active was less clearly related to

. .

. .

l

.

.

.

I-

-I

I-

-I

0

T

1

I

I

2

3

NO

Change

Dysphoric

4

c

Extreme Change

Mood

Fig. 2. RelationshIp between daily rating wmmary scale scores of pre- vs. post-menses Physical Discomfort and Dyphoric Mood (n = 63. r = - 0.68).

Dysphoric Mood

Physical Discomfort

LOW Energy

Consumplion

-31

More Alcohol, Sex, Active

Fig. 3. Relationship between ? clusters and summary scale axes based upon pre- v. post-menses daily ratings (n = 64).

Extreme

6

Moderately Worse Pre-meox

Mildly Worse Pre-meose

-IS

-I2

-9

-6

-3

0 3 Menses

6

9

12

T/me Relative to Menses Fig. 4. Summary scale scores of Dysphoric Mood items rated daily along the menstrual cycle by the 7 women in cluster 1.

5

No Chongl Worse Post-mensr

!5

Dysphoric Mood

Physical Discomfort

Low Energy

Consumptmn

More Alcohol, Sex, Aclwe

Fig. 5. Relationship between lifetime diagnosis of affective disorder and summary scale scores based upon pre- vs. postmenses daily ratings (n = 60).

phases of the menstrual cycle. This figure also illustrates and supports the choice of the 5 days pre-menses and the 5 days post-menses as the periods showing the greatest contrast. Relutionship of summ~r)~ scale scores to lifetime diugnosis Retrospective reports of premenstrual depression have been found to be related to a lifetime diagnosis of depressive disorder (Endicott et al. 1985; Halbreich and Endicott 1985a). Fig. 5 illustrates the relationship between the 5 summary scale scores and a lifetime diagnosis of a past RDC Major Depressive (n = 31) or Minor Depressive (n = 11) Disorder in contrast to an RDC diagnosis of Never Mentally Ill (n = 18). The group who had Major Depressive Disorder in the past had the highest scores on 4 of the 5 summary scales. A test of the significance was performed for the association between diagnosis and the summary scale scores presupposing the logical order of Major, Minor, and Never Mentally Ill (the linear component of the between-groups variation). The Physical Discomfort scale score was significantly associated with diagnosis ( F = 4.90, P < 0.05) and the Dysphoric Mood scale score almost so (F = 3.70, P < 0.06). Although the subjects who had had a Major Depressive Disorder in the past scored highest on 16 of the 20 specific items, on only one (Stay Home, Avoid Social Activities) was the difference statistically significant at the 0.05 level.

Relutionship of clusters to lifetime diugnosis The relationship between the clusters generated by the numerical taxonomy and lifetime diagnosis of affective disorder was also examined. A Xl-test of the association between membership in one of the 3 clusters and a lifetime diagnosis of Major or Minor Depressive Disorder (versus one of Never Mentally 111) was not significant (x2 = 4.4. df = 0.4). However, when the severity of premenstrual change for the 3 clusters and the severity of diagnosis were logically coded 1, 2, 3. a trend indicative of a positive association was shown (r = 0.24, P < 0.06). Relationship of summuty scale scores to other measures Other measures available on the women were family history of mental disorders collected with the FH-RDC (n = 64) and measures of gonadal hormones along the menstrual cycle for a subset of the women who were followed for another month with daily ratings and blood drawn on Monday, Wednesday and Friday (n = 17) (Halbreich et al. 1985). There were no significant relationships found between the relatively gross measures of positive or negative family history of specific mental disorders and the summary scale scores or the individual items. The details of the study of changes in gonadal

134

hormones are given elsewhere. However. the findings illustrate the potential value of the use of summary scores based upon daily ratings of pre-/postmenstrual change in biological studies (Halbreich et al. 19X5). The summary scale scores as well as changes in 11 of the 20 specific daily ratings were found to be significantly correlated with some of the biological measures and at a higher level than a summary clinical judgment of the degree of premenstrual dysphoric change. Discussion

That some women demonstrate change5 in mood and behavior along the menstrual cycle Lvith pre- versus postmenstrual increases has been well documented. However. the issue of a single underlying dimension of change (e.g.. (I premenstrual syndrome) versus 2 or more dimensions in patterns of change has not been addressed through the use of prospective daily ratings although retrospective reports give evidence of such diversity. In this study a group of women was selected following careful screening that would allow one to address this issue. These women are not representative of women in general since those with chronic problems were screened out as were those with medical illness and of course those who failed to volunteer to complete daily ratings. However, these selection factors should not directly affect the issue of one versus more than one pattern of covariance of change along the menstrual cycle. Changes in mood, behavior, and physical condition were found to covary over the time of the menstrual cycle by differential groupings of specific measures. Evidence was found for 5 discriminantly different dimensions of premenstrual change. Although their generally positive correlations suggest a weak general factor of severity of premenstrual change, this is further mitigated by the fan-shaped scatter diagram noted previously. The patterns of relationship and evidence of differential correlates of the 5 summary measures support the recommendation made previously that the term ‘premenstrual changes’ be used rather than ‘premenstrual syndrome’ (Halbreich et al. 1982. 1983). These results suggest that efforts to find consistent differential relationships with biological measures, treatment response. and lifetime

diagnosis are apt to be enhanced by focusing on specific kinds of changes and obscured b\ measures that are too general. The results presented here should also be considered illustrative of a method of deriving summary and therefore more reliable measures of change between the pre- and postmenstrual periods based upon daily ratings of multiple items as well as measures that can he used across the entire cycle or for other specific periods. The changes in the 64 women studied uere not necessarily representative of women in general. However. they can serve to illustrate the major points of the study even if they differ from some of the other samples of women studied by other investigators, particularly from those who seek treatment for premenstrual problems (Endicott and Halbreich 1982: Harrison et al. 19X4). We recognize the possibility that the patterns of covarying change in clinical features may be somewhat different among other groups of women selected by different methods. We will be able to investigate this when we have daily ratings on the same set of items from sufficient numbers ol women who are seeking treatment. Such a data set will help us determine the degree to which the dimensions noted here are more generalizable. Furthermore. somewhat different sets of daily ratings may result in somewhat different groupings of types of changes. A noted in previous sections, the factor analysis of the within-subject correlations over time produced the factor structure which seemed to have the greatest correspondence to the authors’ clinical observations. This procedure. while less easily performed than the more standard analyses. is also likely to result in more stable measures because they are based upon the correlations among the 20 items over 10 days of each of the women’s ratings and are not influenced by individual differences in response style regarding severity levels. Previous studies of a positive association between a lifetime diagnosis of Major Depressive Disorder and dysphoric premenstrual change, all based upon retrospective descriptions of premenstrual change, are summarized elsewhere (Endicott et al. 1985; Halbreich and Endicott 1985a. b). The women who participated in this study were being screened for diagnostic studies and

135

excluding the women with reports of milder premenstrual change probably resulted in a higher rate of affective disorder than most samples. However. the findings reported here, among a relatively homogeneous group of women volunteers who were not seeking treatment for premenstrual problems. suggest that daily measures of pre- and postmenstrual change in physical discomfort and dysphoric mood are also related to a lifetime diagnosis of Major Depressive Disorder. These findings, supportive of a differential relationship between some types of premenstrual change and a lifetime diagnosis of one type of mental disorder in a specific volunteer sample should encourage efforts to find even more specific relationships in other samples of women. For example, are the clinical features seen premenstrually similar to those displayed during an episode of Major Depressive Disorder by the same woman? Are other types of premenstrual change related to other types of mental disorder? How do the patterns of covariance of premenstrual change found in this study compare with patterns of change in women with chronic depressive disorder? Furthermore. efforts to find biological correlates for specific changes in mood, behavior, and physical condition along the menstrual cycle are more likely to be successful when daily ratings and differential measures of change are used. This may well be the case for other studies of clinical features in which daily diaries are used without particular focus on the menstrual cycle (e.g., panic attacks. compulsive behavior, bulimia). Although the masses of data produced by daily ratings are difficult to summarize, the data reported here suggest that efforts to use them should be encouraged. Acknowledgements

Supported in part by New York State Department of Mental Hygiene, Albert Einstein College of Medicine, NIMH grants MH36186 and MH30906, and the Ritter Foundation. Sybil Schacht, M.S.W.. Juliet Lesser, M.S., Katherine Bacon, M.S.. and Susan Goldstein, M.D. performed the screening and diagnostic evaluations of the subjects.

References

Abplanalp. J.M., Hnskett.

R.F. and Rose. R.M.. The premenstrual syndrome, Psychiatr. Clan. North Am.. 3 (1980) 321-347. Andrrasen. N.. Endicott, J.. Spitzer, R.L. and Winokur. G.. The family history method using diagnostic criteria: reliahillty and validity, Arch. Gen. Psychiatry. 34 (1977) 1229-1235. Backstrom. T. and Mattison. B.. Correlation of symptoms of premenstrual tension to estrogen and progesterone concentrations in blood plasma. Neuropsychohiology. 1 (1973) 80&X6. Endicott. J. and Halhreich. U., Retrospective reports of premenstrual depressive changes: factors affecting confirmation by daily ratings. Psychopharmacol. Bull.. 1X (1982) 109-112. Endicott. J. and Spitzer. R.L., A diagnostic rnterview: the schedule for Affective Disorders and Schizophrenia. Arch. Gen. Psychiatry, 35 (197X) X37-844. Endicott. J.. Halhreich. U.. Schacht. S. and Nee, J.. Affective disorder and premenstrual depression. In: H.J. Osofaky and S.J. Blumenthal (Eds.), Premenstrual Syndrome: Current Findings and Future Directions. APA Press. Washington. DC. 1985, pp. 3-11. Halbreich. U. and Endicott. J.. Relationship of premenstrual changes to depressive disorders. Acta Psychiatr. Stand.. 71 (19X5a) 331-33X. Halhreich, U. and Endicott. J.. Methodological issues in atudies of premenstrual changes, Psychoneuroendocrinology. 10 (19XSh) 15-32. Halhreich. U., Endicott. J.. Schacht. S. and Nee, J.. The diversity of premenstrual changes as reflected in the Premenstrual Assessment Form. Acta Psychiatr. Stand., 65 (1982) 46-56. Halhreich, U.. Endicott, J. and Nee. J.. Premenstrual depressive changes: value of differentiation. Arch. Gen. Psychiatry. 40 (19X3) 535-542. Halbreich. U.. Endicott. J., Goldstein. S. and Nee, J.. Premenstrual changes and changes in gonadal hormones, (19X5) submitted. Harrison. W., Endlcott. J.. Rabkin. J. and Nee. J., Treatment of premenstrual depressive changes: clinical and methodological implications. Psychopharmacol. Bull., 20 (1984) 118~120. Hartigan, .l., Cluster Algorithms. John Wiley and Sons. New York. 1975. Rubinow, D.R. and Roy-Byrne, P.. Premenstrual syndromes: overview from a methodological perspective. Am. J. Psychiatry, 141 (1984) 163-171. Ruhinow. D.R.. Roy-Byrne, P.. Hohan, C.. Gold. P.W. and Post. R.M.. Prospective assessment of menstrual related mood disorders, Am. J. Psychiatry, 141 (1984) 6X4-686. Sampwn, G.A. and Jenner. F.A.. Studies of daily recordrngs from the Moos Menstrual Distress Questionnaire, Br. J. Psychiatry, 130 (1977) 265-271. Spitzer, R.L.. Endicott. J. and Robins. R., Research diagnostic criteria: rationale and reliability. Arch Gen. Psychiatry, 35 (197X) 773-7X2. SPSS, SPSS-X User’s Guide. McGraw-Hill. New York, 19X3.