Multivariate Logistic Regression for Familial Aggregation of Two Disorders. II. Analysis of Studies of Eating and Mood Disorders

James I. Hudson1,,,4, Nan M. Laird3, Rebecca A. Betensky3, Paul E. Keck, Jr5 and Harrison G. Pope, Jr1,2

1 Biological Psychiatry Laboratory, McLean Hospital, Belmont, MA.
2 Department of Psychiatry, Harvard Medical School, Boston, MA.
3 Department of Biostatistics, Harvard School of Public Health, Boston, MA.
4 Department of Epidemiology, Harvard School of Public Health, Boston, MA.
5 Biological Psychiatry Program and Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Family studies have suggested that eating disorders and mood disorders may coaggregate within families. Previous studies, however, have been limited by use of univariate modeling techniques and failure to account for the correlation of observations within families. To provide a more efficient analysis and to illustrate multivariate logistic regression models for familial aggregation of two disorders, the authors analyzed pooled data from two previously published family studies (conducted in Massachusetts in 1984–1986 and 1986–1987) by using multivariate proband predictive and family predictive models. Both models demonstrated a significant familial aggregation of mood disorders and familial coaggregation of eating and mood disorders. The magnitude of the coaggregation between eating and mood disorders was similar to that of the aggregation of mood disorders. Similar results were obtained with alternative models, including a traditional univariate proband predictive model. In comparison with the univariate model, the multivariate models provided greater flexibility, improved precision, and wider generality for interpreting aggregation effects.

anorexia nervosa; bulimia; depressive disorder; eating disorders; family characteristics; logistic models; mood disorders

Abbreviations: ED alone-both, coaggregation of eating disorders alone and both disorders; ED alone-ED alone, aggregation of eating disorders alone within families; ED alone-MD alone, coaggregation of eating disorders alone and mood disorders alone within different family members; ED-ED, aggregation of eating disorders within families; ED-MD, coaggregation of eating and mood disorders within different family members; GEE, generalized estimating equations; MD alone-both, coaggregation of mood disorders alone and both disorders; MD alone-MD alone, aggregation of mood disorders alone within families; MD-MD, aggregation of mood disorders within families.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Family studies have raised the possibility that eating disorders and mood disorders cluster together, or coaggregate, within families. Specifically, relatives of patients with the eating disorders anorexia nervosa and bulimia nervosa exhibit a high prevalence of mood disorders (1GoGoGoGoGoGoGoGo–9Go). The interpretation of this finding is unclear, however. Patients with eating disorders have a high prevalence of mood disorders (10GoGoGo–13Go), and mood disorders, in turn, aggregate within families (14Go). Therefore, the high prevalence of mood disorders in relatives of patients with eating disorders might be attributable solely to the known familial aggregation of mood disorders. If so, a high prevalence of mood disorders would be expected in the relatives of persons with an eating disorder plus a mood disorder but not in the relatives of persons with an eating disorder alone. An alternative possibility is that, independently of the aggregation of mood disorders themselves, eating and mood disorders coaggregate in families. If so, a high prevalence of mood disorders would be expected even in the relatives of persons with an eating disorder alone.

Seven studies (3GoGoGoGoGoGo–9Go) have examined this issue by assessing the lifetime prevalence of a mood disorder (including major depressive disorder and bipolar disorder) in the first-degree relatives of probands with 1) an eating disorder (anorexia or bulimia nervosa) plus a mood disorder, 2) an eating disorder and no mood disorder, and 3) neither disorder (controls). Relative risk estimates for a mood disorder among relatives of probands with an eating disorder and no mood disorder, compared with relatives of control probands, range from 1.3 to 3.3, with a median of 2.8; all of the 95 percent confidence intervals include values of 1.3 to 3.1 (figure 1). The corresponding relative risks for relatives of probands with both disorders, compared with relatives of control probands, range from 2.7 to 7.0, with a median of 4.1; all of the confidence intervals include values of 2.1 to 5.1 (figure 2).



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FIGURE 1. Risk ratios (and 95% confidence intervals) for the risk of mood disorder in relatives of probands with eating disorder and no mood disorder versus relatives of probands with neither eating disorder nor mood disorder: findings from seven published studies. For our studies (5Go, 8Go) and three (4Go, 7Go, 9Go) that reported unadjusted data, we calculated the risk ratios and confidence intervals by using the number of relatives with and without a lifetime diagnosis of mood disorder in the two proband groups; for two studies (3Go, 6Go) reporting only adjusted lifetime prevalence by using an age-corrected calculation of morbid risk, we used the relative morbid risk as the risk ratio, and we calculated confidence intervals on the basis of the conservative assumption that 67% of the relatives had entered the period of risk for a mood disorder. For one study (3Go), we relied on the more complete reporting of the data in two other publications (19Go, 20Go).

 


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FIGURE 2. Risk ratios (and 95% confidence intervals) for the risk of a mood disorder in relatives of probands with eating disorder plus mood disorder versus relatives of probands with neither eating disorder nor mood disorder: findings from seven published studies. Refer to the figure 1 legend for an explanation of the method used to obtain risk ratios and confidence intervals.

 
The higher prevalence of mood disorders in relatives of probands with both disorders compared with probands with only an eating disorder indicates that familial aggregation of mood disorders contributes to the high prevalence of mood disorders among relatives of patients with eating disorders. In addition, while these individual studies did not uniformly show a significantly elevated prevalence of mood disorders among relatives of patients with eating disorders alone, the combined results support this possibility. Thus, the overall evidence appears to favor the hypothesis that familial coaggregation of eating and mood disorders also contributes to the elevated prevalence of mood disorders among relatives of patients with eating disorders. This hypothesis is also supported by preliminary findings from a family study of probands with and without mood disorder, in which we found a significant coaggregation of eating and mood disorders (15Go).

However, the analyses of these previous studies have several limitations. First, the crude risk ratio ignores the effect of relatives' covariates, including age, sex, and type of relative (such as sibling or parent). Admittedly, each study attempted to address this problem by either calculating an age-corrected morbidity risk (3GoGoGo–6Go, 8Go) or adjusting for covariates by using a logistic regression (7Go) or proportional hazards (9Go) model. Second, none of the studies accounted for correlation of outcomes within families. Third, to assess coaggregation, all analyses used information from relatives of probands with only an eating disorder and no mood disorder, ignoring information from relatives of probands with both disorders. Fourth, all used a univariate analysis to look at the association between only an eating disorder in a proband and mood disorder in a relative, ignoring the association between a mood disorder in a proband and an eating disorder in a relative. Fifth, all used information from proband-relative pairs only, ignoring information from other pairs of relatives.

These limitations can be overcome by using methods discussed in our companion paper (16Go), in which we present two multivariate models for familial aggregation of two disorders. The first and second limitations can be addressed by using a univariate logistic regression (or proportional hazards) model incorporating covariates, along with generalized estimating equations (GEE), to account for dependence of observations within families. The third can be dealt with by adopting a model that uses information from relatives for whom there is a proband with both disorders. Using multivariate models can overcome the fourth limitation. Finally, we can address the fifth by adopting models that use information from all pairs of family members.

In this paper, we analyze data from two family studies conducted at our center (5Go, 8Go), using multivariate proband predictive and family predictive models.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Study population
We used data from two studies conducted at McLean Hospital in Belmont, Massachusetts. One (5Go) assessed 50 clinically referred women with bulimia nervosa and 19 women who had had bulimia nervosa in the past and were recruited from newspaper advertisements; all women were studied in 1984–1986. The other (8Go) assessed 66 women entering pharmacologic treatment studies for bulimia nervosa; these women were studied in 1986–1987. For comparison, in 1984–1986 we recruited 24 women with major depressive disorder and no lifetime history of an eating disorder and 28 control women with no history of an eating disorder.

We obtained information about first-degree relatives of probands from the probands. This paper considers information on parents and siblings. We used data for all 69 bulimic probands in the first study plus one additional proband erroneously not reported originally; the second study included data from 65 of the 66 bulimic probands, because data on one subject were missing. Characteristics of the combined sample are shown in table 1, and information on the prevalence of disorders in relatives is presented in table 2.


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TABLE 1. Characteristics of the sample used to study familial aggregation of eating disorders and mood disorders, Massachusetts, 1984–1986 and 1986–1987

 

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TABLE 2. Lifetime prevalence of eating disorders and mood disorders among relatives in various proband groups, Massachusetts, 1984–1986 and 1986–1987

 
For simplification, we classified persons with either bulimia nervosa (or "bulimia," the previous term for this condition) or anorexia nervosa as having an "eating disorder" and persons with either major depressive disorder (or "major depression," the previous term for this condition) or bipolar disorder as having a "mood disorder." Of the 151 subjects with eating disorder, 123 had bulimia nervosa alone, 6 had anorexia nervosa, and 22 had bulimia nervosa and anorexia nervosa. Of the 249 subjects with mood disorder, 222 had major depressive disorder and 27 had bipolar disorder.

Analytical models
We applied the multivariate proband and family predictive models presented in the companion paper (16Go). For comparison, we conducted an analysis in which mutually exclusive response categories and a univariate proband predictive model were used.

Multivariate proband predictive model.
We consider, within each family, the bivariate vectors of responses (YAj, YBj)T) conditional on the proband's response. The first subscript denotes which disorder outcome is being considered (A for eating disorder, B for mood disorder), and subscript j indexes family members, with j = 1 for the proband and j > 1 for relatives. The two responses are not mutually exclusive; for example, YAj = 1 means that the jth family member has an eating disorder (regardless of the presence of a mood disorder).

We include terms for the covariates sex (using an indicator for male sex, denoted "MALE") and type of relative (parent or sibling; using an indicator for parent, denoted "PAR"). For each covariate, we add separate terms for each of the two outcomes.

The model for the responses of a relative (YAj, YBj)T, for j = 2, given the responses of the corresponding proband (YA1, YB1)T is




(1)
The {alpha}'s are intercepts for the two responses: eating disorder and mood disorder. The {theta}'s are coefficients for the covariate effects. The other parameters represent the main aggregation effects (for interpretations, refer to table 1 of the companion paper (16Go)).

We have written the model without interaction parameters that modify the main effects. The full model with three interactions is presented as model 7 in the companion paper (16Go). Throughout the present paper, "interactions" refer to these three interactions or their counterparts in the family predictive model.

Multivariate family predictive model.
To write this model, we use the notation for model 1, except that we allow j to go from 1 to n. In addition, we let SA,-j = {Sigma}YAk and SB,-j = YBk for k != j; that is, is the number of relatives of person j with an eating disorder, and SB,-j is the number with a mood disorder. We let Y-j denote the bivariate (eating disorder outcome and mood disorder outcome) vector of responses of the relatives of person j. We do not include as outcomes the disorders in the proband fixed by design (e.g., the eating disorder status of probands selected to have an eating disorder).

As with the proband predictive model, we include terms for sex and type of relative. We also include as a covariate interview status (using an indicator for interviewed, denoted "INTER") to adjust for the expected effect of obtaining more information from interviewed persons.

The model for the responses of family member j (YAj, YBj)T, given the responses of j's relatives (YA,-j, YB,-j)T for is j = 1, ..., n, is




(2)
As in model 1, {alpha}'s are intercepts for eating and mood disorders, and {theta}'s are intercepts for covariates. The other parameters measure main aggregation effects (for interpretations, refer to table 1 of the companion paper (16Go)). Again, we do not include interactions modifying main effects; the full model with interactions is represented by model 8 in the companion paper.

Univariate analysis.
For the univariate analysis, we use logistic regression equations to model separately the log odds of disorder for three mutually exclusive outcomes in relatives (eating disorder alone, mood disorder alone, and both disorders) as a function of the disorder status of the associated proband, adjusted for covariates:


(3)


(4)


(5)
Because mutually exclusive outcomes are used, we change the definition of the first subscript to A for eating disorder alone, B for mood disorder alone, and AB for both disorders.

The aggregation parameters have different designations because they are interpreted differently from those in model 1. For example, , , and assess the aggregation of eating disorders alone within families, mood disorders alone within families, and both eating and mood disorders within families, respectively. Furthermore, and assess the coaggregation of eating disorders alone and mood disorders alone within different family members: measures the increase in log odds of an eating disorder alone in a relative of a proband with a mood disorder alone compared with a relative of a proband with neither disorder, and measures the increase in log odds of a mood disorder alone in a relative of a proband with an eating disorder alone compared with a relative of a proband with neither disorder.

Model fitting.
We fitted models by using Stata 6.0 software (Stata Corporation, College Station, Texas). The Appendix shows how to prepare the data and fit the models by using Stata and SAS (SAS Institute, Inc., Cary, North Carolina) software; other software is also available (16Go).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Multivariate proband predictive model
Because no interactions, either alone or in combination, added significantly to the model, we did not add any of these parameters. This analysis yielded 1) a modest, but nonsignificant within-person association of mood and eating disorders; 2) a large aggregation of eating disorders within families (ED-ED), with a wide confidence interval that included 1.0–indicative of a poorly estimated effect; 3) a significant aggregation of mood disorders within families (MD-MD); and 4) a significant coaggregation of eating and mood disorders within different family members (ED-MD) (table 3).


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TABLE 3. Odds ratios and 95% confidence intervals for parameters of familial aggregation of eating disorders and mood disorders in multivariate proband predictive and family predictive models, Massachusetts, 1984–1986 and 1986–1987

 
The magnitude of the ED-MD coaggregation is almost identical to that of the MD-MD aggregation. Compared with the odds of a mood disorder in the relative of a proband without a mood disorder, the odds of a mood disorder are 1) 1.9 times greater for a relative of a proband with an eating disorder and 2) 2.0 times greater for a relative of a proband with a mood disorder. Conversely, in comparison to the odds of a mood disorder in a proband associated with a relative without a mood disorder, the odds of a mood disorder are 1) 1.9 times greater for a proband who has a relative with an eating disorder and 2) 2.0 times greater for a proband who has a relative with a mood disorder.

For comparison with the 95 percent confidence intervals derived from GEE, we also calculated the "naive" confidence intervals for the familial aggregation parameters under the assumption that observations within persons, and between persons within families, are independent. Although the naive confidence intervals were almost identical to those obtained by using GEE (table 3), this result is probably due to the small clusters in this data set and does not justify use of these confidence intervals.

Multivariate family predictive model
As with the proband predictive model, no interactions added significantly to the model. This model produced 1) a large and significant within-person association of mood and eating disorders; 2) a modest, but nonsignificant ED-ED aggregation; 3) a significant MD-MD aggregation; and 4) a significant ED-MD coaggregation (table 4). As with the proband predictive model, the magnitude of the ED-MD coaggregation was the same as the MD-MD aggregation. The interpretations of the odds ratios for the MD-MD aggregation and ED-MD coaggregation, respectively, are that the odds of a mood disorder in a family member increase by a factor of 1) 1.4 for each additional relative with a mood disorder and 2) 1.4 for each additional relative with an eating disorder.


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TABLE 4. Odds ratios and 95% confidence intervals for parameters of familial aggregation of eating disorders and mood disorders in multivariate proband predictive and family predictive models, using mutually exclusive definition of responses,* Massachusetts, 1984–1986 and 1986–1987

 
The naive 95 percent confidence intervals for the familial aggregation parameters were similar to the ones obtained by using GEE. However, the naive confidence interval for the ED-ED aggregation was slightly wider than the one obtained by using GEE (table 3).

Mutually exclusive response categories
We fitted models by using mutually exclusive response categories (expressions can be obtained by combining equations 3, 4, and 5 and setting , , and ). These models yielded a nonsignificant ED alone-ED alone aggregation with very wide confidence intervals as well as significant MD alone-MD alone aggregation and ED alone-MD alone coaggregation (table 4).

Univariate analysis
We attempted to fit models 3, 4, and 5. There were insufficient data to fit models 3 and 5, however. Thus, we derived the estimates of ED alone-MD alone coaggregation only from probands with mood disorder and relatives with eating disorder and the estimates of coaggregation of eating disorders alone and both disorders (ED alone-both) from probands with both disorders and relatives with mood disorder. The results of model 4 (table 5) revealed that the MD alone-MD alone aggregation, ED alone-MD alone coaggregation, and ED alone-both association were significant and were somewhat larger in magnitude than those of the proband predictive model with mutually exclusive outcomes.


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TABLE 5. Odds ratios and 95% confidence intervals for parameters of familial aggregation of eating disorders and mood disorders in univariate proband predictive model 4, Massachusetts, 1984–1986 and 1986–1987

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Multivariate proband and family predictive models
Comparison of results.
The proband and family predictive models yielded similar overall conclusions regarding the relative magnitude and the statistical significance of the familial aggregation parameters. By using these models, we found a significant aggregation of mood disorders and a significant coaggregation of eating and mood disorders within families. The estimated magnitude of the 1) aggregation of mood disorders and 2) coaggregation of eating and mood disorders was similar for both models. The models also found a large, but statistically not significant aggregation of eating disorders within families, consistent with the small amount of data available to estimate this effect.

The results differed in two important ways. First, estimates of the familial aggregation parameters in the family predictive model were smaller and had smaller standard errors than those in the proband predictive model. The difference in magnitude of effect is due to differences in the measure of association. In the proband predictive model, the odds ratios measure the ratio of the odds of disorder in a relative of a proband with versus without disorder. The proband can be viewed as a surrogate for disorder status of the family; that is, disorder status of the proband implicitly measures disorder status of the rest of the relatives. By contrast, the odds ratios from the family predictive model measure the odds of disorder in a family member with a given number of ill relatives versus a family member with one fewer ill relative. If aggregation of disorder exists within families, then the odds ratios reflect a reduced effect of that aggregation by conditioning on all but one relative.

Second, the within-person association was slightly larger (odds ratio = 2.77 vs. 2.03) and was considerably more significant (p value = 0.006 vs. 0.22) in the family predictive compared with the proband predictive model. This difference is due to the 1) greater prevalence of a mood disorder among probands with an eating disorder compared with relatives who have an eating disorder (68 vs. 38 percent, respectively) and 2) larger number of cases of eating disorders among the probands. The proband predictive model does not use disorders of probands as the response, whereas the family predictive model uses disorders of probands not fixed by design.

The higher prevalence of eating disorder plus mood disorder in the probands could be due to two factors. The first is the greater quantity of information obtained on the probands because relatives were not interviewed. The second is selection or ascertainment bias, in that either 1) women with an eating disorder who also had a mood disorder may have been more or less likely to enter the study than women with an eating disorder and no mood disorder or 2) women with a mood disorder may have been more or less likely to enter the study as controls than women with no mood disorder. Ascertainment bias of this type would influence the results of only the family predictive model, which included the mood disorder outcome of eating disorder and control probands. To eliminate effects of this potential bias, we can treat the disorder status of probands as completely fixed by design and not include any proband disorders as outcomes. The results of such an analysis (not presented) are similar to those of the family predictive model presented in this paper.

Influence of family size.
Because variation in family size can influence results of the family predictive model (refer to the companion paper (16Go)), we fitted the family predictive model to two data sets that excluded large families: one excluding the 7 families with more than eight members, the other excluding the 15 families with more than seven members. The results remained virtually unchanged. For the main aggregation effects, only the odds ratio for the within-person association in the set that excluded families with more than seven members changed by more than 10 percent (a 23 percent decrease), due largely to an influential family of eight.

Assumption of interchangeability.
The multivariate models assume that family members are interchangeable. While interchangeability is plausible, probands with a disorder might have a different form of illness than relatives do. In particular, the fact that probands had sought treatment, whereas relatives may not have, may have introduced bias (refer to the discussion of ascertainment bias in the Discussion section of the companion paper (16Go)). It is therefore reassuring that an analysis using the proband predictive model without assuming interchangeability of proband and relatives produces similar results, even though a residual bias due to treatment seeking cannot be excluded (refer to the companion paper). The estimates of the single-disorder aggregation parameters differed by less than 2 percent from those of model 1. The estimated odds ratios for the two ED-MD coaggregation parameters were very close to each other: 2.1 (95 percent confidence interval: 0.6, 6.9) for the association between an eating disorder in a relative and a mood disorder in a proband and 1.8 (95 percent confidence interval: 1.2, 2.9) for the association between a mood disorder in a relative and a mood disorder in a proband.

Alternative analyses
Mutually exclusive outcomes.
While yielding results similar for the most part to those with outcomes that were not mutually exclusive, estimates for the familial aggregation effects in the proband predictive model with mutually exclusive outcomes were larger and had wider confidence intervals than those in the analysis in which the outcomes were not mutually exclusive. As discussed in the companion paper (16Go), non-mutually-exclusive outcomes have two advantages: they 1) offer an interpretation of the aggregation effects that is not restricted to the disorders occurring alone and 2) can be used in conjunction with testing that permits dropping nonsignificant interaction terms to yield a potentially more parsimonious model.

Univariate proband predictive model.
The estimated magnitudes of the aggregation of mood disorders and the coaggregation of eating and mood disorders are larger than those from the multivariate proband predictive model, but the confidence intervals are wider. The univariate model makes fewer assumptions but at the cost of using less information and providing more-restricted interpretations of parameters.

Implications for the coaggregation hypothesis
In previously published univariate proband predictive analyses of the studies used in this paper, one (Go) found significant and the other (5Go) marginally significant (p < 0.10) evidence for familial coaggregation of eating and mood disorders. By using pooled data and better methods, the present analysis provides more convincing evidence for a significant coaggregation. We view the weight of the evidence, in combination with that from other studies ((2–9), figure 1), as favoring the hypothesis of coaggregation. However, this issue remains debated (17Go, 18Go).

Conclusions
Multivariate proband predictive and family predictive models revealed a significant familial aggregation of mood disorders and a significant familial coaggregation of eating and mood disorders. Furthermore, the magnitude of the coaggregation of eating and mood disorders was similar to that of the aggregation of mood disorders. In comparison with a traditional univariate model, the multivariate models provided 1) more flexibility and realism, 2) improved precision in estimates of aggregation effects, and 3) wider generality in the interpretation of aggregation effects.


    APPENDIX
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
Implementation of the Proband and Family Predictive Models in Stata and SAS Software
This Appendix shows how to prepare the data and fit models 1 and 2 with interactions (corresponding to models 7 and 8 in the companion paper (16Go)) by using Stata (Stata Corporation, College Station, Texas) and SAS (SAS Institute, Cary, North Carolina) software. First, organize the data in an ASCII file called "family.dat," for example, as described in Appendix table 1.


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APPENDIX TABLE 1. Form of the ASCII file of data for a single family for input into Stata or SAS software*

 
To enter the data, the Stata command is infile fam ind y a fix yother pra prb prother prbotha prbothb prbothab fa fb fbotha fbothb fbothab a za using family.dat

The SAS command is data family; infile ‘family.dat’; input fam ind y a fix yother pra prb prother prbotha prbothb prbothab fa fb fbotha fbothb fbothab a za; run;

To fit model 1 with interactions, the Stata command is xtgee y a yother pra prb prother prbotha prbothb prbothab z za if id~=1, fam(bin) corr(ind) id(fam) robust

The SAS command is data x1; set family; if id ne 1; proc genmod data=x1; class fam; model y=a yother pra prb prother prbotha prbothb prbothab z za /dist=bin; repeated subject=fam/ type=ind; run;

To fit model 2 with interactions, the Stata command is xtgee y a yother fa fb fother fbotha fbothb fbothab z za if fix==0, fam(bin) corr(ind) id(fam) robust

The SAS command is data x1; set family; if fix=0; proc genmod data=x1; class fam; model y=a yother fa fb fother fbotha fbothb fbothab z za /dist=bin; repeated subject=fam/ type=ind; run;

The correspondence between the coefficients and the parameters of the models is shown in Appendix table 2.


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APPENDIX TABLE 2. Correspondence between parameters and model coefficients*

 

    ACKNOWLEDGMENTS
 
Supported in part by National Institute of Mental Health grant T32 MH-017119.

The authors thank Drs. Garrett Fitzmaurice and Bernard Rosner for their comments on the manuscript.


    NOTES
 
Reprint requests to Dr. James I. Hudson, Biological Psychiatry Laboratory, McLean Hospital, 115 Mill Street, Belmont, MA 02478 (e-mail: jhudson{at}hsph.harvard.edu).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 

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  2. Hudson JI, Pope HG Jr, Jonas JM, et al. Family history study of anorexia nervosa and bulimia. Br J Psychiatry 1983;142:133–8. (Erratum published in Br J Psychiatry 1983;142:428–9).[Abstract]
  3. Gershon ES, Schreiber JL, Hamovit JR, et al. Clinical findings in patients with anorexia nervosa and affective illness in their relatives. Am J Psychiatry 1984;141:1419–22.[Abstract]
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  5. Hudson JI, Pope HG Jr, Jonas JM, et al. A controlled family history study of bulimia. Psychol Med 1987;17:883–90.[ISI][Medline]
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  11. Walsh BT, Roose SP, Glassman AH, et al. Bulimia and depression. Psychosom Med 1985;47:123–31.[Abstract]
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  13. Halmi KA, Eckert E, Marchi P, et al. Comorbidity of psychiatric diagnoses in anorexia nervosa. Arch Gen Psychiatry 1991;48:712–18.[Abstract]
  14. Tsuang MT, Faraone SV. The genetics of mood disorders. Baltimore, MD: Johns Hopkins University Press, 1990.
  15. Hudson JI, Mangweth B, Pope HG Jr, et al. Coaggregation of eating disorders and mood disorders in a family study of major depressive disorder. Presented at the International Conference on Eating Disorders, New York, New York, May 6, 2000.
  16. Hudson JI, Laird NM, Betensky RA. Multivariate logistic regression for familial aggregation of two disorders: I. Development of models and methods. Am J Epidemiol 2001:153:500–5.[Abstract/Free Full Text]
  17. Strober M, Katz JL. Do eating disorders and affective disorders share a common etiology? A dissenting opinion. Int J Eat Disord 1987;6:171–80.[ISI]
  18. Hudson JI, Pope HG Jr. Affective spectrum disorder: does antidepressant response identify a family of disorders with a common pathophysiology? Am J Psychiatry 1990;147:552–64.[Abstract]
  19. Gershon ES, Hamovit J, Guroff JJ, et al. A family study of schizoaffective, bipolar I, bipolar II, unipolar, and normal control probands. Arch Gen Psychiatry 1982;39:1157–67.[Abstract]
  20. Gershon ES, Hamovit JR, Schreiber JL, et al. Anorexia nervosa and major affective disorders associated in families: a preliminary report. In: Guze SB, Earls FJ, Barrett JE, eds. Childhood psychopathology and development. New York, NY: Raven Press, 1983:279–86.
Received for publication December 10, 1999. Accepted for publication June 5, 2000.