Regression Analysis of Multiple-Source Longitudinal Outcomes: A "Stirling County" Depression Study

Constantine Daskalakis1, Nan M. Laird2 and Jane M. Murphy3,4

1 Biostatistics Section, Division of Clinical Pharmacology, Thomas Jefferson University, Philadelphia, PA.
2 Department of Biostatistics, Harvard School of Public Health, Boston, MA.
3 Department of Psychiatry, Harvard Medical School and Massachusetts General Hospital, Boston, MA.
4 Department of Epidemiology, Harvard School of Public Health, Boston, MA.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Epidemiologic studies of psychiatric disorders have increasingly relied on multiple sources of information to improve the validity of diagnoses and repeated assessments over time to provide a longitudinal perspective. In this paper, the authors present a general multivariate logistic regression method for the simultaneous analysis of discrete outcomes that exhibit such features. This approach permits risk factor and agreement analyses within a unified framework and appropriately uses data from subjects who may be missing some outcomes. The authors use this approach to analyze data from a "Stirling County" study of depression. During a 3- to 4-year period in the early 1990s, 631 subjects were assessed in two separate interviews, on each occasion with two diagnostic schedules (the DePression and AnXiety schedule (DPAX) and the Diagnostic Interview Schedule (DIS)). The female:male ratio of depression was found to be different for the DPAX and the DIS (0.8 and 1.6, respectively). Education was inversely associated with depression, while the effects of time, the subject's age, and the interviewer's sex were essentially null. With respect to the outcomes' association, agreement between the DPAX and the DIS was low. In addition, stability of the DPAX over time was significantly higher than that of the DIS. No covariates were found to affect significantly the association between outcomes.

data interpretation; statistical; depression; epidemiologic methods; longitudinal studies; multivariate analysis

Abbreviations: CI, confidence interval; DIS, Diagnostic Interview Schedule; DPAX, DePression and AnXiety schedule; GEE, generalized estimating equations; OR, odds ratio


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The first large-scale epidemiologic studies of mental illness were initiated almost half a century ago, and many more have been carried out in the last two decades (1GoGoGoGoGo–6Go), with recent studies relying extensively on standardized lay-administered diagnostic instruments. Because an error-free "gold" standard is unavailable for most psychiatric diagnoses, the use of multiple sources for assessing the outcome of interest has become increasingly common. However, such information is often discrepant, and there is no clear consensus regarding the relative validity of each source or the optimal methods for analyzing the data. Repeated assessments over time and partially missing outcomes introduce additional levels of complexity.

With multiple-source data, there are two broad types of research aims: risk factor and agreement questions. Examples of risk factor questions are whether the level of the outcome of interest varies by source and/or by time and what is the effect of suspected risk factors. Although risk factor results may be similar across sources, this may or may not represent good source agreement; for example, sources may or may not be classifying the same persons as positive. Assessing the agreement between instruments and their stability over time, as well as the effect of factors that may be influencing these associations, is an example of association questions.

In the simple case of just two sources, the outcome data can be displayed in a 2 x 2 cross-classification table (table 1). Risk factor questions can be answered by analyzing the marginal distributions of the outcomes (i.e., a + b, c + d, a + c, and b + d; table 1). Traditionally, such risk factor analyses have been conducted after combining the outcomes into a single diagnosis (pooling) or performing separate analyses for each outcome—in both cases, using univariate analytical methods (i.e., methods for a single outcome).


View this table:
[in this window]
[in a new window]
 
TABLE 1. Cross-classification of data for a dichotomous outcome obtained from two different sources

 
Pooling has its roots in measurement error theory. Under certain conditions, averaging continuous outcomes yields a pooled outcome with higher reliability. For dichotomous or categorical outcomes, however, averaging is not meaningful, and a pooling algorithm has to be devised. Unfortunately, the validity of such an algorithm depends on the nature of measurement error, which is typically unknown. Furthermore, all pooling schemes discard potentially valuable information regarding differences across sources. Separate analyses for each outcome, on the other hand, are cumbersome and hard to interpret. They produce a different set of results for each source and/or timepoint, with no satisfactory way of assessing differences and possibly combining results if appropriate. Finally, traditional analyses are not well suited for dealing with missing data on one or more outcomes.

Although the risk factor questions can be answered via the analysis of the outcomes' marginal distributions, answers to association or agreement questions rely on the analysis of the outcomes' joint distribution (i.e., the cell counts a, b, c, and d; table 1). Agreement analyses are typically conducted separately from the risk factor analyses of the same data. In psychiatric epidemiology, agreement analyses often use the kappa statistic. Effects of covariates on it are investigated via stratification, but this approach has severe limitations because it quickly encounters sparse-data problems.

In this paper, we present a new multivariate regression approach for the simultaneous analysis of multiple categorical outcomes. This approach allows us to model risk factor and association effects within a single framework, and it appropriately uses all available data (even from subjects who may have some missing outcomes). We apply this method to analyze data from a study of depression, involving a group of subjects who were assessed twice in the 1990s, each time with two different instruments.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Study design
The data are part of an ongoing study of mental illness in a general population of adults, conducted in an area in Atlantic Canada that was given the fictitious name "Stirling County" to protect identity (7GoGoGoGoGo–12Go). The study has drawn repeated cross-sectional samples in 1952, 1970, and 1992 (as well as smaller samples from selected areas in 1962/1964) (7Go, 10Go). Each sample has been followed up as cohorts, and the survivors were reassessed at the time of each new cross-sectional sampling. Currently, the study as a whole involves 4,010 subjects, and participation rates have consistently been 80 percent or higher.

As the study has grown in size, the fieldwork has taken longer to complete. To have the majority of assessments closer in time, 631 subjects who were first interviewed in 1991 (the first year of the follow-up fieldwork) were sought for reinterview later on. Reinterviews were actually completed for 476 subjects (75 percent), on the average 3 years after the initial interview. A reinterview was not obtained for 155 subjects: 51 had died, 22 were not reassigned (too old or in nursing homes), 21 were unavailable or unable to complete the interview, and 61 refused. The subjects' assessments during the initial interview and their subsequent reinterview are the focus of this paper.

Depression assessments
The outcome of interest in this paper is depression. During both interviews, depression was assessed with two different diagnostic schedules, the DePression and AnXiety schedule (DPAX) and the Diagnostic Interview Schedule (DIS). The DPAX schedule was developed for the Stirling County Study and focuses on depression and anxiety, while the DIS was developed for the Epidemiologic Catchment Area Study and directly applies the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Third Edition (13Go, 14Go). In our study, both schedules were administered by lay interviewers during structured interviews in the subjects' homes.

Although the schedules are similar in that their depression components involve questions regarding essential features, associated symptoms, and duration or timing, they do have some important differences (11Go). The DIS covers all the criteria for the associated symptoms of depression as listed in the Diagnostic and Statistical Manual of Mental Disorders, Third Edition, while the DPAX covers only a portion of them. On the other hand, the DPAX takes into account impairment in everyday functioning as in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (15Go), while the DIS does not. The DPAX interview tends to identify both chronic and acute depression; thus, for compatibility, the DIS definition of depression in this paper includes both major depression and dysthymia. We report results on current or prevalent depression, that is, cases that meet criteria during the month preceding the interview.

Statistical methods and analyses
Building on previous work (16GoGo–18Go), we have developed a multivariate (i.e., multiple-outcomes) logistic regression approach to analyze all four observations per subject simultaneously (Daskalakis and Laird, Harvard University, unpublished manuscript, 2000), modeling the marginal distribution and the association of the outcomes within a single framework. To clarify this approach, consider that each subject contributes four separate observations, one for each assessment (schedule-by-timepoint combination). Observations of the same subject are distinguished from each other by a schedule and a time variable. Although time could have been used as a continuous variable (i.e., the exact time lag between interviews for each subject), we report results with time only as a dichotomous variable (i.e., first or second interview), because the time between interviews was similar for most subjects. Each observation also includes subject-specific covariates (study cohort, sex, age, and education), as well as the interviewer's sex (which could be different for the two interviews or timepoints of the same subject).

We modeled prevalent depression via logistic regression that included all of the above variables. We also considered interactions for schedule by time, schedule by risk factor, and time by risk factor, as well as covariate interactions that involved the subject's sex or age or the interviewer's sex. We illustrate the regression equation with a single covariate, subject's sex:

(1)
where {pi} is the probability of depression. The coefficients have the standard log odds ratio interpretation for ordinary logistic regression but, in our analyses, these are prevalence odds ratios. Note that the schedule-by-sex and time-by-sex interactions allow the effect of gender to be different for the two schedules and the two timepoints; conversely, these interactions allow the schedule and time effects to be different for men and women.

The logistic regression model can answer questions regarding the prevalence of depression for each schedule and timepoint and how it is affected by the risk factors. It is referred to as the marginal model because it analyzes the outcomes' marginal distribution (in a sense, reflecting separate but simultaneous regressions for each outcome). Fitting this model via ordinary logistic regression yields valid parameter estimates but incorrect estimated standard errors, because the observations that are contributed by each subject are correlated.

One solution to this problem is to use a robust variance estimator via the generalized estimating equations (GEE) approach (17Go). The GEE approach has the advantage that only a "working" correlation matrix between the outcomes needs to be specified rather than the full joint distribution of the outcomes. Furthermore, even if this association is misspecified, the results for the marginal model are still valid. However, the association between the outcomes is not easily modeled within the GEE framework. In this paper, we considered risk factor and association questions of similar importance. For this reason, we used a different approach, maximum likelihood estimation, which requires more work on the specification of the full multivariate likelihood but is more flexible in incorporating both risk factor and association modeling in the same framework (Daskalakis and Laird, Harvard University, unpublished manuscript, 2000).

Association between dichotomous outcomes can be measured with a number of different measures, including various types of odds ratios, the phi (correlation) coefficient, and the kappa statistic. In our analyses, we used odds ratios of association. To illustrate the difference between these odds ratios and those of the marginal model of equation 1, consider the 2 x 2 cross-classification table of the DPAX and the DIS at the first interview (table 1), where source A is DPAX1 and source B is DIS1. Note that the marginal proportions (c + d)/n and (b + d)/n are the depression prevalences for DPAX1 ({phi}DPAX1) and DIS1 ({phi}DIS1), respectively. Thus, the marginal odds ratio given by

is the marginal effect of schedule (DIS vs. DPAX) on prevalent depression at the first interview, that is, the odds ratio modeled as exp(ß1) in equation 1 (in the absence of schedule-by-covariate interactions). An odds ratio of unity implies that the DPAX and the DIS give the same prevalence at the first interview. On the other hand, the cross-product ratio

is the association odds ratio between the DPAX and the DIS at the first interview. This association odds ratio has a very different interpretation from the marginal odds ratio discussed above. In the two extreme cases of perfect agreement and perfect disagreement, the association odds ratio is infinity and zero, respectively; "chance agreement" (in the sense of a correlation or kappa equal to zero) corresponds to an association odds ratio equal to unity. The advantage of the odds ratio over kappa is that it is not constrained by the margins, is less sensitive to extreme marginal distributions, and can be modeled more easily as a function of covariates. For example, we can model the above odds ratio for the DPAX1 and the DIS1 as a function of the subject's sex:

(2)
The parameters in this association model can be interpreted as the effect of the covariate(s) on the association between outcomes.

With four outcomes, there are six possible pairwise associations. Two of them pertain to the association between the two schedules, when used at the same timepoint (i.e., DPAX1/DIS1 and DPAX2/DIS2). Two others pertain to the association between the two schedules when used at different times (i.e., DPAX1/DIS2 and DIS1/DPAX2). Conceptually, one expects the former to be somewhat stronger than the latter. The final two pairwise associations refer to the stability of a single schedule over time (i.e., DPAX1/DPAX2 and DIS1/DIS2). Following equation 2, each of these six odds ratios can be modeled separately as functions of covariates. With appropriate parameterization, we can then assess the similarity of the association odds ratios, as well as the similarity of a covariate's effect on each of those association odds ratios. Thus, this association model can answer questions regarding the association among the different outcomes and the effect of covariates on it.

The multivariate (i.e., multiple-outcomes) regression approach is well suited to handle missing data. In our analyses, we included the 476 subjects who contributed four assessments each (DPAX and DIS at both first and second interviews), as well as the 155 subjects who contributed only the two assessments at the first interview (for a total of 2,214 assessments). Both the complete-case analysis (using only the 476 subjects with complete data) and the GEE approach (using all available data) would be valid only under the "missing completely at random" assumption; that is, the probability that the second interview is missing may depend only on covariates and not on depression outcomes at either timepoint (19Go). However, assuming that the model is correctly specified and that relevant covariates are included, the likelihood-based approach is valid under the broader "missing at random" situation; that is, missingness may depend on covariates and depression outcomes at the first timepoint but again not on the values of the missing outcomes of the second timepoint (19Go).

We wrote a general SAS (PROC IML) (SAS Institute, Inc., Cary, North Carolina) macro that fits the multivariate logistic regression via maximum likelihood, incorporating a version of the expectation-maximization algorithm to handle subjects with missing outcomes. In our final model, we included all main effects regardless of their statistical significance to improve the validity of the model specification (a necessary requirement in the presence of missing data), the control of confounding, and the resulting fit of the estimated depression prevalences within various subgroups. Interaction terms were retained only if statistically significant (at {alpha} = 0.05). Model selection and testing (p values) were based on the likelihood ratio test, and Wald-type confidence intervals were constructed for the odds ratios.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Table 2 shows the descriptive results for the study sample (n = 631): study cohort; subject's sex, age, and education; and interviewer's sex. There were slightly more female than male subjects. Their mean age was 63 years, with a standard deviation of 12. About a quarter of the subjects had the equivalent of a high school education, and very few had a college degree. Table 2 also shows some information about the time lag between the first and second interviews, for the twice-assessed subjects (n = 476; mean = 34 months; standard deviation = 7 months).


View this table:
[in this window]
[in a new window]
 
TABLE 2. Descriptive results for the Stirling County short-interval depression study (n = 631), 1991–1995

 
We report results for current (1-month) depression as assessed by the DPAX and the DIS. Table 3 shows the empirical (observed) and model-based (fitted) prevalence of depression in our study. The observed values are simply the proportion of subjects in the sample that were classified as depressed, while the fitted values are based on the final regression model that we present below. For comparison purposes, table 3 also includes the empirical prevalence of depression from the Stirling County 1992 sample (subjects older than 40 years) (15Go, 16Go).


View this table:
[in this window]
[in a new window]
 
TABLE 3. Observed and fitted estimates of prevalence of current depression, in the Stirling County short-interval depression study, 1991–1995

 
It is important to note that, for the second timepoint, the model-based estimates of depression are substantially higher than the empirical (sample) estimates. This is because the empirical prevalences at the second timepoint do not reflect subjects who missed that interview. In our study, subjects scoring positive on depression on both the DPAX and the DIS at the first interview were significantly more likely to miss the second interview (62 percent of the former had no second interview as compared with 23 percent of the rest). It appears, then, that the assessments at the second timepoint are not missing completely at random; consequently, the empirical values are biased downward. Assuming that the model is correctly specified and that relevant covariates are included, our multivariate regression properly accounts for the missing data. The model-based estimates are higher than the empirical ones, reflecting the fact that a disproportionate number of subjects who were depressed at the first interview (and might be expected to also have an elevated prevalence of depression at the second interview) were actually not assessed for a second time.

Table 4 shows estimated odds ratios and 95 percent confidence intervals from the final marginal model for current depression. Strictly speaking, of course, the schedule-by-sex interaction term is not really an odds ratio because it represents a ratio of odds ratios. In analyzing current depression, we identified a significant interaction between schedule and the subject's sex (p = 0.021). This implies that the effect of schedule depends on sex and that the effect of sex depends on schedule. Specifically, among men, the DIS identified significantly fewer depressed subjects than did the DPAX (odds ratio (OR) = 0.55, 95 percent confidence interval (CI): 0.35, 0.87), but among women, there was no difference between the two schedules (OR = 1.10, 95 percent CI: 0.76, 1.58). Consequently, the sex ratios were different for the two instruments. With the DPAX, women had a slightly but nonsignificantly lower prevalence than did men (OR = 0.79, 95 percent CI: 0.44, 1.44). With the DIS, on the other hand, women were more likely to be depressed than were men (OR = 1.57, 95 percent CI: 0.83, 2.98), but the difference was again not statistically significant.


View this table:
[in this window]
[in a new window]
 
TABLE 4. Risk factor odds ratios and confidence intervals for current depression, in the Stirling County short-interval depression study, 1991–1995*

 
In view of the schedule-by-sex interaction, the estimated prevalences for men and women, by schedule and timepoint, were as follows. For men, the DPAX prevalence was 5.9 percent for the first interview and 6.9 percent for the second interview, while the corresponding DIS prevalences were 3.3 percent and 4.0 percent, respectively. For women, the corresponding values were as follows: DPAX, 4.5 and 5.3 percent; DIS, 4.9 and 5.8 percent.

We also found that subjects with a lower educational level were twice as likely to be depressed compared with subjects with a higher education (OR = 2.14, 95 percent CI: 1.06, 4.35). On the other hand, time and the subject's age at the first interview were not associated with depression. Following standard modeling recommendations (20Go), we also investigated age as a discrete variable, with no material change in the results. Finally, we found no effect of the study cohort or the interviewer's sex, and no interactions involving the latter were significant.

In addition to the risk factor results discussed above, our approach allowed us to investigate the association between the outcomes. Table 5 shows the results from the final association model. We found the association between the DPAX and the DIS at the first and second interviews (i.e., DPAX1/DIS1 and DPAX2/DIS2) to be similar. Furthermore, these same-time odds ratios of association between the two schedules were not significantly different from the cross-time odds ratios (i.e., DPAX1/DIS2 and DIS1/DPAX2), possibly because of the very short time elapsed between the two interviews. Therefore, we estimated a single odds ratio of association between the DPAX and the DIS that does not vary over time and also applies to measurements taken at different times (OR = 6.0, 95 percent CI: 3.8, 9.6). Regarding the association of the schedules over time, we found that the stability of the DPAX over time (i.e., DPAX1/DPAX2: OR = 14.2, 95 percent CI: 5.3, 38.4) was significantly higher than the stability of the DIS over time (i.e., DIS1/DIS2: OR = 2.9, 95 percent CI: 0.8, 10.0) (p value for their difference = 0.041). None of these association odds ratios was found to be significantly affected by any covariates.


View this table:
[in this window]
[in a new window]
 
TABLE 5. Association odds ratios and confidence intervals for current depression, in the Stirling County short-interval depression study, 1991–1995

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
In this paper, we presented a modern regression approach for analyzing outcomes obtained from multiple sources and timepoints. This approach has a number of advantages over traditional methods. First, it is a flexible regression approach that incorporates modeling of the risk factor effects and of the association between the outcomes in a single framework. Second, it allows us to formally test differences across sources and/or timepoints and to obtain more efficient, combined effect estimates if appropriate. Third, the maximum likelihood approach has substantial advantages with respect to missing data. Although no simple approach is valid if the probability of nonresponse depends on the value of the missing outcome itself ("nonignorable missingness"), the maximum likelihood method is valid if the missingness depends on observed outcomes or covariates ("ignorable missingness" or "missing at random"). Both the GEE approach and the complete-case analysis are valid if the missingness depends on covariates only ("missing completely at random").

The multivariate logistic regression approach can have broad application beyond psychiatric or behavioral-social epidemiology, with outcomes obtained from multiple sources (instruments, informants, methods) and/or timepoints. In this paper, we have shown how both can be handled by the method. Research is also currently under way to extend the methodology to "two-stage designs," which involve a brief screening instrument for all subjects at the first stage and a more elaborate diagnostic procedure for a small subset of them at the second stage.

The main disadvantage of our approach is that it is not implemented in standard software. However, we have written a general SAS (PROC IML) macro with accompanying documentation that is available upon request (c_daskalakis{at}lac.jci.tju.edu). Additional details on this approach and related multiple-informant research can be found on the Harvard Multiple Informant Web site (http://www.biostat.harvard.edu/multinform).

Our substantive results should be evaluated in the context of previous reports from both the Stirling County Study and other epidemiologic investigations of depression. For example, the inverse relation between education and the prevalence of depression in our analyses is consistent with previous reports regarding the effect of socioeconomic indices (21GoGo–23Go).

Gender differences regarding depression are a topic that has drawn much attention in psychiatric epidemiology (1Go, 5Go, 24GoGoGoGo–28Go). Recent studies have usually suggested a 2:1 female:male ratio. Where the DIS is concerned, our findings pointed in the same direction but not to the same magnitude. However, our estimated relative risk of 1.57 (women compared with men) was adjusted for other risk factors and is similar to the 1.37 reported by Blazer et al. (24Go) who also controlled for a number of similar variables. In contrast, our DPAX results indicated that men and women did not have significantly different prevalence. This fits with other Stirling County reports from 1952 onward, with the recent exception of the 1992 cross-sectional sample (10Go, 12Go). Although the overall current prevalence of depression remained stable at about 5 percent throughout the years, a redistribution by age and gender occurred in the 1992 sample due to an increased rate among women under 45 years of age (10Go). This change may account for a significant gender difference in the DPAX results for the 1992 sample, but it was absent in the results reported here, reflecting the fact that this sample did not include younger women.

As suggested above, we found that the schedule-by-gender interaction was significant, illustrated by the fact that prevalence by the DPAX and the DIS was similar for women, while the DPAX yielded an estimate for men that was almost double that of the DIS. A similar pattern was present among older adults of the Stirling County's most recent cross-sectional sample (DPAX and DIS prevalences of 6.3 and 6.4 percent among women and 5.3 and 2.7 percent among men) (10Go). Thus, the evidence of the schedule-by-gender interaction was consistent in both the cohort survivors and the cross-sectional sample when the focus was on the same age range, but the methods of the present analysis gave it explicit recognition.

Our analyses confirmed the low overall agreement between the DPAX and DIS, already reported for the 1992 cross-sectional sample (11Go). Examining disagreements between different data collection methods can be useful to the process of improving interview schedules. Although the earlier report found the DPAX-DIS disagreement to be higher among subjects with lower education (11Go), our present analyses did not confirm that result. This may be due to the older age distribution of our study compared with the 1992 cross-sectional sample. In fact, when analysis of the 1992 sample was restricted to older subjects, the association between education and the DPAX-DIS agreement was attenuated (unpublished results).

Our results indicate that the DPAX may be more stable over time than the DIS. This suggests that the DPAX may be more sensitive to stability of chronic disorders than the DIS, even though the DIS definition of depression used in our analyses combined the episodic and chronic forms. The DIS emphasizes the episodic nature of depression by asking respondents to remember specific periods of their lives when they experienced specified durations of depressed feelings (5Go). The DPAX, however, is oriented toward the current state of depression and its onset according to the respondent's own assessment (9Go). Evidence from longer follow-ups in the Stirling County Study has pointed to chronicity as a prominent feature of depression (8Go, 26Go), and our results suggest that the schedules may differ in their ability to capture this aspect of the disorder.


    ACKNOWLEDGMENTS
 
The Stirling County Study is funded by National Institute of Mental Health grant R01 MH39576. The work for this paper was conducted while Dr. Daskalakis was a Research Fellow at the Department of Biostatistics, Harvard School of Public Health, supported by National Institute of Mental Health grant R01 MH54693.

The authors thank Arthur Sobol for data preparation, Dr. Leighton as the instigator of the Stirling County Study, and both Drs. Leighton and Monson for longstanding consultations.


    NOTES
 
Reprint requests to Dr. Constantine Daskalakis, Biostatistics Section, Division of Clinical Pharmacology, Thomas Jefferson University, 125 South 9th Street #402, Philadelphia, PA 19107 (email: c_daskalakis{at}lac.jci.tju.edu).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Epidemiology of psychiatric disorders in Edmonton. Acta Psychiatr Scand Suppl 1988;338:1–80.[Medline]
  2. Essen-Möller E. Individual traits and morbidity in a Swedish rural population. Acta Psychiatr Neurol Scand 1956;100(suppl):1–80.
  3. Kessler RC, McGonagle KA, Zhao S, et al. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States—results from the National Comorbidity Survey. Arch Gen Psychiatry 1994;51:8–16.[Abstract]
  4. Leighton AH. My name is Legion: the Stirling County Study of psychiatric disorder and sociocultural environment. Vol 1. New York, NY: Basic Books, 1959.
  5. Robins LN, Regier DA, eds. Psychiatric disorders in America: the Epidemiologic Catchment Area Study. New York, NY: Free Press, 1991.
  6. Srole L, Langner TS, Michael ST, et al. Mental health in the metropolis: the Midtown Manhattan Study. New York, NY: McGraw-Hill, 1962.
  7. Hughes CC, Tremblay MA, Rapoport RN, et al. People of cove and woodlot: the Stirling County Study of psychiatric disorder and sociocultural environment. Vol 2. New York, NY: Basic Books, 1960.
  8. Leighton DC, Harding JS, Macklin DB, et al. The character of danger: the Stirling County Study of psychiatric disorder and sociocultural environment. Vol 3. New York, NY: Basic Books, 1963.
  9. Murphy JM, Monson RR, Laird NM, et al. Identifying depression and anxiety in a forty-year epidemiologic investigation: the Stirling County Study. Int J Methods Psychiatr Res 1998; 7:89–109.
  10. Murphy JM, Laird NM, Monson RR, et al. A 40-year perspective on the prevalence of depression: the Stirling County Study. Arch Gen Psychiatry 2000;57:209–15.[Abstract/Free Full Text]
  11. Murphy JM, Monson RR, Laird NM, et al. A comparison of diagnostic interviews for depression in the Stirling County Study: challenges for psychiatric epidemiology. Arch Gen Psychiatry 2000;57:230–6.[Abstract/Free Full Text]
  12. Murphy JM, Laird NM, Monson RR, et al. Incidence of depression in the Stirling County Study: historical and comparative perspectives. Psychol Med 2000;30:505–14.[ISI][Medline]
  13. Robins LN, Helzer J, Croughan J, et al. National Institute of Mental Health Diagnostic Interview Schedule: its history, characteristics, and validity. Arch Gen Psychiatry 1981;38:381–9.[Abstract]
  14. American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM III. 3rd ed. Washington, DC: American Psychiatric Association, 1980.
  15. American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-IV. 4th ed. Washington, DC: American Psychiatric Association, 1994.
  16. Fitzmaurice GM, Laird NM. A likelihood-based method for analysing longitudinal responses. Biometrika 1993;80:141–51.[ISI]
  17. Fitzmaurice GM, Laird NM, Zahner GEP, et al. Bivariate logistic regression analysis of childhood psychopathology ratings using multiple informants. Am J Epidemiol 1995;142:1194–203.[Abstract]
  18. Glonek GFV. A class of regression models for multivariate categorical responses. Biometrika 1996;83:15–28.[Abstract]
  19. Little RJA, Rubin DB. Statistical analysis with missing data. New York, NY: Wiley, 1987.
  20. Hosmer DW, Lemeshow S. Applied logistic regression. New York, NY: Wiley, 1989.
  21. Dohrenwend BP, Levav I, Shrout PW, et al. Socioeconomic status and psychiatric disorders: the causative-selection issue. Science 1992;255:946–52.[ISI][Medline]
  22. Holzer CE, Shea BM, Swanson JW, et al. The increased risk for specific psychiatric disorders among persons of low socioeconomic status. Am J Soc Psychiatry 1986;6:259–71.
  23. Murphy JM, Olivier DC, Monson RR, et al. Depression and anxiety in relation to social status. Arch Gen Psychiatry 1991;48:223–9.[Abstract]
  24. Blazer DG, Kessler RC, McGonagle KA, et al. The prevalence and distribution of major depression in a national comorbidity sample: the National Comorbidity Survey. Am J Psychiatry 1994;151:979–86.[Abstract]
  25. Jorm AF. Sex and age differences in depression: a quantitative synthesis of published research. Aust N Z J Psychiatry 1987; 21:46–53.[ISI][Medline]
  26. Murphy JM. What happens to depressed men? Harv Rev Psychiatry 1995;3:47–9.[ISI][Medline]
  27. Weissman MM, Bland RC, Canino GJ, et al. Cross-national epidemiology of major depression and bipolar disorder. JAMA 1996;276:293–9.[Abstract]
  28. Wilhelm K, Parker G. Sex differences in lifetime depression rates: fact or artefact? Psychol Med 1994;24:97–111.[ISI][Medline]
Received for publication October 16, 2000. Accepted for publication June 19, 2001.





This Article
Abstract
FREE Full Text (PDF)
Alert me when this article is cited
Alert me if a correction is posted
Services
Email this article to a friend
Similar articles in this journal
Similar articles in ISI Web of Science
Similar articles in PubMed
Alert me to new issues of the journal
Add to My Personal Archive
Download to citation manager
Search for citing articles in:
ISI Web of Science (3)
Disclaimer
Request Permissions
Google Scholar
Articles by Daskalakis, C.
Articles by Murphy, J. M.
PubMed
PubMed Citation
Articles by Daskalakis, C.
Articles by Murphy, J. M.