- Email: [email protected]
Selection bias in studies of major depression using clinical subjects
Journal of Clinical Epidemiology 53 (2000) 351–357
Selection bias in studies of major depression using clinical subjects Scott B. Patten* Departments of Community Health Sciences and Psychiatry, University of Calgary Faculty of Medicine, Calgary, Alberta, Canada
Abstract Selection bias may systematically distort estimates deriving from psychiatric studies using clinical subjects. Such bias may impact on cross-sectional studies using samples of convenience and also on clinical case-control studies. The objective of this report is to describe examples of such bias, and to identify probably mechanisms underlying it. A series of cases was recruited from among inpatients at a general hospital in Calgary, Canada. This case-series consisted of consenting subjects with current episodes of major depression according to a structured diagnostic interview. Comparison subjects consisted of non-depressed (according to the structured interview) individuals admitted to the same units and a sample of community subjects scoring negatively on a major depression predictor. Bayesian calculations using ancillary census and national survey data were used to estimate the selection probabilities underlying bias apparent in several of the odds ratio estimates. Neither a cross-sectional analysis incorporating all of the clinical subjects, the use of a community comparison group nor a case-control analysis using a subset of the clinical subjects resulted in valid estimation. This study confirms that the probability of selection of clinical subjects can be conditionally dependent on diagnosis and other variables in ways that create a substantial vulnerability to selection bias. © 2000 Elsevier Science Inc. All rights reserved. Keywords: Cross-sectional studies; Case-control studies; Depressive disorders; Mental disorders; Epidemiology; Bias (epidemiology)
1. Introduction Comprehensive coverage of the wide range of potential biopsychosocial risk factors for depressive disorders has not yet been achieved by the large scale community surveys that have been prominent in the psychiatric epidemiological literature in the past 15 years. The use of clinical subjects may facilitate research in this area by providing economical access to large numbers of subjects with depressive disorders. In fact, studies comparing depressed and non-depressed subjects in clinical settings are frequently published [1–3]. Also, comparisons of clinical subjects to normal controls are sometimes made [4,5]. Often, these studies are cross-sectional and their samples may be regarded as samples of convenience. However, case-control studies of major depression using clinical subjects have also been conducted [6–8]. While cross-sectional studies using samples of convenience and case-control studies can be vulnerable to selection bias, estimates of association (such as odds ratios) deriving from such studies are not necessarily biased. When
the odds of exposure to a variable is measured in subjects with a disease or outcome and in subjects without a disease or outcome, selection bias will distort the odds ratio only when the risk factor is related to the probability of selection in a way that differs between the comparison groups [9]. In a case-control study, the ratio of selection probabilities for exposed and non-exposed cases (selection odds for the cases) and the ratio of selection probabilities for exposed and nonexposed controls (selection odds for the controls), must be equal for unbiased estimation of the population odds ratio. The objective of this study was to utilize data collected from clinical subjects in a general hospital psychiatric unit, along with ancillary data about the underlying population, in order to determine whether selection bias would distort estimates of association between major depression and certain variables known to be associated with major depression. Another objective was to illustrate possible mechanisms underlying such bias. 2. Methods
* Corresponding author. Population Health Investigator, The Alberta Heritage Foundation for Medical Research. Assistant Professor, Departments of Community Health Sciences and Psychiatry, Faculty of Medicine, The University of Calgary. 3330 Hospital Drive N.W., Calgary, Alberta, Canada T2N-4N1. Tel: (403) 220-8752; fax: (403) 270-7307. E-mail address: [email protected]; Website: http://www.ucalgary.ca/ ⵑpatten
2.1. Subject selection 2.1.1. Clinical cases Between October 1996 and March 1998 a preliminary consent form was made available to clinical staff for distribution to psychiatric inpatients at the Calgary General Hos-
0895-4356/00/$ – see front matter © 2000 Elsevier Science Inc. All rights reserved. PII: S0895-4356(99)00 2 1 5 - 2
352
S.B. Patten / Journal of Clinical Epidemiology 53 (2000) 351–357
pital. Patients signing a preliminary consent form were approached by a research assistant who provided a description of the study, and a detailed consent form, in order to obtain informed consent. After consent was obtained, each clinical subject was administered the demographic and mood disorders section from the automated Composite International Diagnostic Interview (CIDI-A) [10]. Cases consisted of subjects meeting DSM-IV criteria for Major Depressive Disorder. 2.1.2. Non-depressed clinical subjects These subjects were selected using the same procedure as the clinical cases, except that these subjects did not have a depressive disorder according to the CIDI-A. Although only the mood disorders section of the CIDI-A was administered, additional diagnoses were recorded from clinical records at the time of admission. The most common diagnoses among the clinical subjects without major depressive disorders were psychotic conditions (24.7%), bipolar disorders (23.6%), substance use disorders (21.3%) and other non-psychotic mental disorders (16.9%). There were 12 (13.5%) subjects who were admitted for a diagnostic evaluation. Clinical diagnoses recorded from the hospital chart were used to evaluate the impact of selecting a subset (consisting of individuals with schizophrenia, other non-affective psychoses, or manic, or mixed bipolar episodes) of nondepressed clinical subjects as controls, as could be carried out in a case-control study (see below). This allowed an exploration of the impact of this restriction on the occurrence of bias. 2.1.3. Non-depressed community subjects Community controls were selected by random digit dialling (RDD) using a modification of the Mitofsky-Waksberg procedure [11]. Initially, three-digit prefixes were randomly selected from among those in service within the Calgary telephone system. Next, four-digit suffixes were randomly generated and assigned to each randomly selected prefix. Each resulting seven-digit telephone number was called and, if it was determined to be a residential number, 10 additional numbers consisting of the same first five digits, but with a different randomly generated final two digits were called. From each household, one resident over the age of 17 was selected using the next birthday method. Data were collected by telephone interview between January 1997 and March 1998. These RDD data were incorporated into the analysis in an unweighted form. To confirm the absence of a depressive disorder, the community subjects were interviewed using a short form of the CIDI [12].
Form in order to facilitate comparisons with census data. This was essential to the procedure employed for estimating selection probabilities. In fact, the variables specified were chosen specifically because census data were available, facilitating an evaluation of the occurrence of, and apparent mechanisms underlying, selection bias (see below). 2.3. Other data sources The analysis required estimates of the frequency of the various exposures in the underlying population (see below). Frequencies applicable to the local population deriving from the 1996 Canadian Census [13,14] were used in this study. This were obtained by data request from Statistics Canada. Also, estimates of the prevalence of major depression in various exposure groups within the underlying population were needed. These estimates were obtained from the first wave of the Canadian National Population Health Survey (NPHS), which was conducted in 1994. Statistics Canada guidelines for analysis of this data set [15] were followed in making the estimates, which are summarised in Table 1. The NPHS used a diagnostic predictor for major depression that was developed, based on the CIDI, by Kessler and Mroczek for use in the American National Health Interview Survey [12]. The prevalence of major depression observed in the NPHS, and the associations of major depression with gender, age, and martial status were all consistent with estimates from elsewhere in the literature [16]. Because age was classified in five-year intervals in the NPHS data set, all relevant analyses were restricted to subjects over the age of 19.
Table 1 Major depression 12-month period prevalence in the Canadian population over the age of 19 by age, sex, marital status, employment status, income adequacy, and educationa Variable
Exposure group
Estimated prevalence (%)
Age
⬍30 30–39 40⫹ Female Male Separated/divorced/widowed Never married Marriedb Not employed Employedc Low income Greater than low income threshold Less than high school graduation High school graduate College or university (at least some)
6.8 6.2 4.4 7.1 3.4 8.7 7.3 4.1 6.4 4.6 8.1 5.2 5.2 4.8 5.5
Gender Marital status
Employment status Income
2.2. Data collection
Education level
Each subject was interviewed using a questionnaire to classify certain variables of interest to the study: age, gender, marital status, employment status, family income, and educational status. The questions focussing on these variables were taken verbatim from the 1996 Canadian Census
a Data from the 1994 Canadian National Population Health Survey (NPHS). b Including subjects with a common-law partner. c Subjects who reported that their main activity was working for pay or profit.
S.B. Patten / Journal of Clinical Epidemiology 53 (2000) 351–357
It was sometimes necessary to approximate categories across the different data sources. In the analysis concerned with employment status, subjects reporting no hours of paid employment were classified as not employed using the census data (and the data collected from the study subjects, where the census questions were used). In the NPHS data, subjects who reported that they were “currently working” were classified as employed, and all other responding subjects were classified as not employed. In the analysis of income data, subjects were categorized in a low income category if they had ⬍$10,000 in household income for single person households and ⭐$20,000 in household income for households consisting of ⭓2 persons. These cut-points were somewhat arbitrary, but were necessary for congruous integration of data from the various data sources employed.
3. Analysis The occurrence of selection bias, and its magnitude, depend upon the probabilities associated with the selection of subjects in relation to their disease and exposure status [9]. In the current study, selection probabilities were conceptualized from a population perspective: the population was conceptually divided into four compartments corresponding to the four exposure-disease contingencies in an exposuredisease 2 ⫻ 2 table. The probability of a subject in a specific compartment being selected into the study was the selection probability. Generally, it is not possible to estimate selection probabilities since a direct calculation would require access to disease and exposure information for the entire population. In this analysis, Bayesian calculations were used to estimate the selection fractions within each disease and exposure category: p(selection|disease status) = p(disease status|selection) p(selection) -------------------------------------------------------------------------------------------p(disease status) The conditional probability in the numerator on the right side of the equation could be evaluated within each disease and exposure group using the study data. The overall probability of selection (the second term in the numerator) was the proportion of subjects in the population recruited into the study and the denominator of the right side of the equation was the overall prevalence of major depression in the exposure group in question. Estimates of the latter proportions were taken from the NPHS. Selection probabilities for the RDD subjects were estimated as follows. Using the reported prevalence from the NPHS, the number of nondepressed females and males in the Calgary population was estimated by multiplying the total population in these groups by the compliment of the prevalence, resulting in an estimated non-depressed population of 279,173 males and 281,811 females over the age of 19 in the city of Calgary. The number of subjects recruited was divided by this value.
353
Selection probabilities calculated as described above, and using data from all available subjects, would assist in the description of bias as this might appear in a cross-sectional study using a sample of convenience consisting of a clinical or community comparison group. In the case-control context, efforts to avoid selection bias centre upon the selection of a control group. Where subjects from a clinical setting, such as a hospital comprise the case group, it is often reasonable to assume that patients admitted to the same hospital as the cases are members of the same (secondary) study base [17]. However, this approach can easily violate other principles underlying control selection in case-control studies. Specifically, if there is a relationship between the exposure(s) and the diagnoses used to determine inclusion of controls, bias may result [17]. Therefore, a subset of the non-depressed subjects comprising the sample of convenience described above was defined in order to evaluate whether this subgroup could be used to generate valid estimates in a case-control analysis. This control group consisted of those subjects who were diagnosed with schizophrenia or manic episodes, both highly heritable conditions [18]. As there may be an overlap in the demographic and risk factor profiles associated with unipolar and bipolar mood disorders, it may have been preferable not to include manic subjects in this group. However, the size of the sample did not permit the use of smaller subsets of subjects in the analysis. 4. Results Because of the sensitive nature of the inpatient psychiatric population, no data collection was possible without informed consent. As a result, the proportion of subjects who received a consent form and the proportion of these who provided consent in the clinical setting was unknown. However, previous experience at the same hospital with the same consent procedure was determined to achieve informed consent from approximately 25% of new admissions [19,20]. Of 440 residential households contacted in the RDD, 256 indicated acceptance so that the household response rate was 58.2%. Two hundred twenty (220) eligible subjects were successfully contacted (85.9%), and 177 (80.5%) of these participated in the data collection. The age, gender, marital status, employment status, family income, and educational status of the study subjects are summarized in Table 2, the same variables in the Calgary population according to the 1996 census are summarized in Table 3. Note that in all estimates, the 1996 Census and the NPHS were restricted to the ⬎19-year-old age group (except for census data pertaining to education and employment status where, for reasons of data availability, the tables show figures applicable to members of the population over the age of 15). In the analysis concerned with gender, using all of the hospitalized subjects in the analysis as a sample of convenience, the observed odds ratio for female gender was 2.17
354
S.B. Patten / Journal of Clinical Epidemiology 53 (2000) 351–357
Table 2 Socio-demographic features of the study subjects
Exposure level Age group
Gender Marital status
Employment status Incomec Education level
Hospitalized, no major depression, n ⫽ 101 (%)
Hospitalized, no major depression, psychotic & bipolar, n ⫽ 43 (%)
52 (29.5) 55 (31.3) 69 (39.2) 82 (46.6) 94 (53.4) 56 (31.8)
33 (32.7) 30 (29.7) 38 (37.6) 29 (28.7) 72 (71.3) 41 (40.6)
14 (36.2) 13 (30.2) 16 (37.2) 16 (37.2) 27 (62.8) 20 (46.5)
29 (20.3) 43 (30.1) 71 (49.7) 82 (57.3) 61 (42.7) 32 (22.4)
72 (40.9) 48 (27.3) 113 (64.2) 63 (35.8) 25 (19.0) 107 (81.1) 40 (22.7)
47 (46.5) 13 (12.9) 76 (75.2) 25 (24.8) 17 (23.9) 54 (76.1) 36 (35.6)
20 (46.5) 3 (7.0) 32 (74.4) 11 (25.6) 11 (34.4) 21 (65.6) 14 (32.6)
34 (23.8) 77 (53.8) 42 (29.4) 101 (70.6) 2 (1.9) 101 (98.1) 13 (9.1)
62 (35.2) 74 (42.0)
26 (25.7) 39 (38.6)
12 (27.9) 17 (39.5)
32 (22.4) 98 (68.5)
Hospitalized, major depression, n ⫽ 176 (%) ⬍30 30–39 40⫹ Female Male Separated/divorced/ widowed Never married Marrieda Not employedb Employed Below threshold Above threshold Less than high school graduate High school graduate Some ⬎high school
Community subjects, no major depression, n ⫽ 143 (%)
a
Including common-law. Includes those not in the labour force. c Missing data due to refusal for 44 cases, 30 hospital controls and 40 community controls. b
with a 95% confidence interval of 1.25–3.80. This point estimate is consistent with what has previously been reported from community surveys (see discussion, below) including the NPHS, suggesting that there was not a major impact of
Table 3 Demographic features of the Calgary population [13,14]a Exposure level Age
Gender Marital status
Employment status Income
Education level
a
⬍30 30–39 40⫹ Female Male Never married Marriedb Separated/widowed/ divorced Employed Not employedc Low income family members ⬎Low income threshold Less than high school graduation High school graduate Some trade, college, or university
Number
Percent
124,675 163,345 304,220 300,510 291,730 125,290 382,575 84,385
21.1% 27.6% 51.4% 50.7% 49.3% 21.2% 64.6% 14.2%
441,575 197,460 66,330d
69.1% 30.9% 11.2%
525,900d 172,600
88.8% 27.0%
72,965 393,480
11.4% 61.6%
Age, gender, and marital status have been restricted to the members of the population over the age of 19 (n ⫽ 592,240). For reasons of data availability, employment and education status refer to members of the population over the age of 15. b Including common-law. c Includes labour force non-participants. d An estimate calculated by multiplying the proportion of families in the two income groups by the total population over the age of 19.
selection bias on the odds ratio calculated from this crosssectional analysis. However, when the community subjects were used in the calculation, the odds ratio was 0.65 (95% CI: 0.41–1.04) which is inconsistent with estimates deriving from previous studies, suggesting a considerable impact of bias. The estimated probability of disease (major depression) given selection for females was 82/111 (see Table 2). The unconditional probability of selection for females was 111/300,510 (see Tables 2 and 3) and the unconditional probability of major depression from the NPHS data for females was .071 (see Table 1). This led to an estimated selection fraction of 3.84 ⫻ 10⫺3. For males, analogous calculations led to a selection fraction of 9.48 ⫻ 10⫺3. Hence, the selection odds [9] were ⵑ0.41 for the cases. For nondepressed subjects, selection fractions for females and males were 1.04 ⫻ 10⫺4 and 2.55 ⫻ 10⫺4, respectively, leading to an identical selection odds of 0.41. In the RDD selection of non-depressed community subjects, 82 females and 61 males were selected, this resulting in estimated selection fractions of 2.94 ⫻ 10⫺4 and 2.16 ⫻ 10⫺4 respectively, and a selection odds of 1.36. Using these calculations, the apparent absence of bias in the estimated odd’s ratio for female gender when clinical cases and controls were used, and the bias apparent in the estimate incorporating community control is conceptualized as follows: The probability of selection of clinical subjects depended on gender (males were more likely to be selected than females in the clinical population employed here, and using the selection procedures described), but this tendency did not differ between depressed and non-depressed subjects in the sample of convenience so that there was no se-
S.B. Patten / Journal of Clinical Epidemiology 53 (2000) 351–357
355
Table 4 Estimated Selection Odds for the Clinical and Community Subjects
Selection odds (cases)
Selection odds (clinical w/o MDE)
Selection odds (psychotic/ bipolar)
Selection odds (community subjects w/o MDE)
Odds ratio (calculated with...)
Exposure level Age group Gender Marital status
Employment Incomec Education level
⬍30 30–39 Female Separated/divorced/ widowed Never marrieda Not employedb Below threshold ⬍High school graduation High school graduation
Clinical subjects w/o MDE
psychotic & bipolar inpatients w/o MDE
Community subjects w/o MDE
0.87 1.01 2.17 0.37
0.86 0.98 1.47 0.18
1.85 1.32 0.65 2.81
1.19 1.05 0.41 2.49
2.17 1.50 0.41 15.02
2.19 1.54 0.60 31.75
1.02 1.15 1.36 1.98
0.41 0.59 0.74 0.59 1.26
0.23 0.62 0.45 0.66 1.19
3.40 4.31 11.80 4.07 2.57
2.57 2.88 1.19 1.30 5.18
11.42 6.93 2.57 2.10 3.57
21.06 6.63 4.28 1.87 3.78
1.39 0.95 0.16 0.30 1.75
a
Including common-law. Includes those not in the labour force. c Missing data due to refusal for 44 cases, 30 hospital controls, and 40 community controls. b
lection bias. In the RDD selection of non-depressed community dwelling subjects, females rather than males had a higher probability of selection, such that the odds of female gender was systematically greater in the community controls that in the cases and the odd’s ratio for female gender was biased as a result. Calculations pertinent to other variables are summarized in Table 4. The apparent impact of bias (and the effect of method of control selection on the occurrence of bias) was uneven for different variables, with dramatic distortions in various directions being evident. The association of younger age with major depression was underestimated in the sample of convenience, but not when the depressed clinical subjects were compared to community subjects. The association of divorced, widowed, or separated marital status with major depression was underestimated when depressed clinical subjects were compared to non-depressed clinical subjects and overestimated when comparisons to community subjects were used. Being married appeared to alter the chance of selection by various extents in the various groups compared. A consistent pattern was observed for employment status, income and education level in the sample of convenience. These variables appeared to increase the probability of selection of clinical subjects (generally to a greater degree in non-depressed than depressed subjects) and to diminish the probability of selection of non-depressed community subjects. As a result, there was a tendency to overestimate associations when comparisons were made to community subjects and to underestimate these when comparisons to non-depressed clinical subjects were made. Restriction of the comparison group to subjects with schizophrenia or manic or mixed bipolar episodes did not substantially change the pattern of distortion, see Table 4.
5. Discussion These findings confirm that strong distortions in estimates of association can occur in psychiatric epidemiological studies using clinical subjects. General principles of control selection in case-control studies have been articulated by Wacholder et al. [21]. The concept of a study base was central to the formulation of these principles. A study base is the set of persons, or person-time, in which diseased subjects become cases. A study base may be primary or secondary [22]. In this study, the cases were individuals admitted to a particular hospital with major depression, hence the study base was a secondary one. A valid set of controls would consist of individuals without major depression but representative of the same base of experience. In a study of this type, the control group should consist of individuals who would have become study cases had they developed the outcome of interest [22], major depression. In conducting a hospital based case-control study, selection of controls from the same clinical services as the cases is sometimes undertaken. When controls are selected from a hospital, validity depends on the assumption that the distribution of exposure in this group is the same as that of the study base, or differs by measurable factors [21]. This assumption seems reasonable in circumstances where a hospital has identical catchment populations for case and control conditions (individuals admitted for one set of conditions would have been admitted for the other) or where exposure is independent of admission [21]. Since neither condition was met by the procedures employed here, nor by many other case-control investigations in the psychiatric literature, considerable vulnerability to bias is evident. The impact of exposure status on the probability of selection in a hospital-based case-control study could occur as a result of a direct impact on the probability of admission or
356
S.B. Patten / Journal of Clinical Epidemiology 53 (2000) 351–357
by an effect of exposure on the probability of development of one of the mental disorders for which controls were admitted. The latter possibility was evaluated in this study by defining a group of controls with conditions (schizophrenia, other non-affective psychotic conditions, and manic or mixed bipolar episodes) that are generally not thought to be etiologically related to the specific exposures evaluated. Distortion due to selection bias remained evident, however. The most probable explanation is that although the exposures evaluated in this project did not cause the conditions characterizing the control group, the exposures were nevertheless related to these conditions either by an impact on the probability of admission, or as a result of the control subjects’ mental disorders on the variables themselves. However, since an overlap of risk factors may exist for depressive disorders and bipolar disorders, it is possible that the inclusion of bipolar disorders in the control group caused a negative bias for some of the associations examined. For example, since bipolar disorders have a younger age of onset than unipolar depression [23], a negative bias may have been introduced into the estimate of the odds ratio representing the ⬍30-year-old category. Certain other factors must be considered in the interpretation of these results. First, the NPHS did not use the full CIDI to identify cases of major depression. Rather, a screener was used (the same screener [24] used to exclude cases of major depression from the RDD control sample in this study). Since this brief instrument may misclassify some subjects, the NPHS estimates, which were used as a standard to detect bias in this study, may have been distorted by misclassification bias. However, the set of associations identified in the NPHS major depression data [16] is very consistent with the rest of the epidemiological literature concerned with major depression. Second, the NPHS data refer to the Canadian national population (more specifically, household residents), whereas the census data used in this analysis pertained only to the city of Calgary. However, there is no evidence that the epidemiology of major depression in Calgary differs from that of the national population. The analysis presented here was possible because information about associations between several demographic variables and major depression were available from a large national survey, and additional necessary information was available from the 1996 Canadian census. This state of affairs is not likely to apply in real world situations where investigators wish to evaluate undetermined associations between potential risk factors and major depression. However, the observation that severe bias can result from selective pressures occurring with the use of clinical subjects indicates that the methodological spectre of selection bias cannot be lightly dismissed in this context. While the theoretical possibility of making valid estimates of population parameters using clinically recruited subjects exists, it remains to be confirmed that any practical procedure for subject selection can provide confidence about the validity of the resulting estimates.
Acknowledgments Author is Population Health Investigator with The Alberta Heritage Foundation for Medical Research; and Assistant Professor in the Departments of Community Health Sciences and Psychiatry of The University of Calgary Faculty of Medicine. This study was supported by a grant from the Alberta Heritage Foundation for Medical Research. References [1] Barry KL, Fleming MF, Manwell LB, Copeland LA, Appel S. Prevalence of and factors associated with current and lifetime depression in older adult primary care patients. Fam Med 1998;30:366–71. [2] Langs G, Quehenberger F, Fabisch K, Klug G, Fabisch H, Zapotocsky HG. Prevalence, patterns and role of personality disorders in panic disorder patients with and without comorbid (lifetime) major depression. Acta Psychiatr Scand 1998;98:116–23. [3] Soskolne V, Bonne O, Denour AK, Shalev AY. Depressive symptoms in hospitalized patients: a cross-sectional survey. Int J Psychiatry Med 1996;26:271–85. [4] Roy A. Is introversion a risk factor for suicidal behaviour in depression? Psychol Med 1998;28:1457–61. [5] Hodges S, Marks M. Cognitive characteristics of seasonal affective disorder: a preliminary investigation. J Affect Disord 1998;50:59–64. [6] Roy A. Five risk factors for depression. Br J Psychiatry 1987;150: 536–541. [7] Roy A. A case-control study of social risk factors for depression in American patients. Can J Psychiatry 1997;42:307–9. [8] Shapira B, Zislin J, Gelfin Y, Osher Y, Gorfine M, Souery D, Mendlewicz J, Lerer B. Social adjustment and self-esteem in remitted patients with unipolar and bipolar affective disorder: a case-control analysis. Compr Psychiatry 1999;40:24–30. [9] Kleinbaum DG, Morgenstern H, Kupper LL. Selection bias in epidemiological studies. Am J Epidemiol 1981;113:452–63. [10] Wittchen HU. Reliability and validity studies of the WHO-Composite International Diagnostic Interview (CIDI): A critical review. J Psychiatr Res 1994;28(1):57–84. [11] Potthoff RF. Telephone sampling in epidemiological research: to reap the benefits, avoid the pitfalls. Am J Epidemiol 1994;139:967–78. [12] Mroczek, DK, and Kessler, RC. Final CIDI screening scales with timing estimates. Ann Arbor, Michigan: The University of Michigan Survey Research Center, 1994. [13] Diskette Product 93F0022XDB96000. Age, sex, marital status and common-law status families: number, type and structure, structural type of dwelling and household size. Ottawa: Statistics Canada, 1998. [14] Profiles of the component municipalities for the CMA of Calgary. Ottawa, Canada: Statistics Canada, 1998. [15] Statistics Canada Health Statistics Division. National Population Health Survey, 1994–95, Public Use Microdata Files. Ottawa, Canada: Minister of Industry, 1995. [16] Beaudet MP. Depression. Health Reports 1996;7(4):11–24. [17] Wacholder S, Silverman DT, McLaughlin JK, Mandel JS. Selection of controls in case-control studies. II. Types of controls. Am J Epidemiol 1992;135:1029–41. [18] Cardno AG, Marshall EJ, Coid B, Macdonald AM, Ribchester TR, Davies NJ, Venturi P, Jones LA, Lewis SW, Sham PC, et al. Heritability estimates for psychotic disorders. The Maudsley twin psychosis series. Arch Gen Psychiatry 1999;56:162–8. [19] Patten SB, Williams JVA, Haynes L, McCruden J, Arboleda-Florez J. The incidence of delirium in psychiatric inpatient units. Can J Psychiatry 1997;42:858–63. [20] Patten SB. Integrating administrative and research data in pharmacoepidemiological research. Can J Clin Pharmacol 1998;5:92–7.
S.B. Patten / Journal of Clinical Epidemiology 53 (2000) 351–357 [21] Wacholder S, McLaughlin JK, Silverman DT, Mandel JS. Selection of controls in case-control studies. I. Principles. Am J Epidemiol 1992;135:1019–28. [22] Miettinen OS. Design of the study base. [Chapter 3] In: Design of the study base. Chapter 3, Theoretical epidemiology: principles of occurrence research in medicine. New York, NY, USA: John Wiley & Sons, Inc., 1985, pp. 46–68.
357
[23] Blazer DG, Kaplan HI, Sadock BJ, Editors. In: Mood disorders, epidemiology. Comprehensive Textbook of Psychiatry/VI. 6th ed. Baltimore, MD, USA: Williams and Wilkins, 1995, pp. 1079–88. [24] Kessler, R, and Mroczek, D. An update on the development of mental health screening scales for the U.S. National Health Interview Scales. Ann Arbor, MI, USA: The University of Michigan Survey Research Center, 1994.
Selection bias in studies of major depression using clinical subjects Scott B. Patten* Departments of Community Health Sciences and Psychiatry, University of Calgary Faculty of Medicine, Calgary, Alberta, Canada
Abstract Selection bias may systematically distort estimates deriving from psychiatric studies using clinical subjects. Such bias may impact on cross-sectional studies using samples of convenience and also on clinical case-control studies. The objective of this report is to describe examples of such bias, and to identify probably mechanisms underlying it. A series of cases was recruited from among inpatients at a general hospital in Calgary, Canada. This case-series consisted of consenting subjects with current episodes of major depression according to a structured diagnostic interview. Comparison subjects consisted of non-depressed (according to the structured interview) individuals admitted to the same units and a sample of community subjects scoring negatively on a major depression predictor. Bayesian calculations using ancillary census and national survey data were used to estimate the selection probabilities underlying bias apparent in several of the odds ratio estimates. Neither a cross-sectional analysis incorporating all of the clinical subjects, the use of a community comparison group nor a case-control analysis using a subset of the clinical subjects resulted in valid estimation. This study confirms that the probability of selection of clinical subjects can be conditionally dependent on diagnosis and other variables in ways that create a substantial vulnerability to selection bias. © 2000 Elsevier Science Inc. All rights reserved. Keywords: Cross-sectional studies; Case-control studies; Depressive disorders; Mental disorders; Epidemiology; Bias (epidemiology)
1. Introduction Comprehensive coverage of the wide range of potential biopsychosocial risk factors for depressive disorders has not yet been achieved by the large scale community surveys that have been prominent in the psychiatric epidemiological literature in the past 15 years. The use of clinical subjects may facilitate research in this area by providing economical access to large numbers of subjects with depressive disorders. In fact, studies comparing depressed and non-depressed subjects in clinical settings are frequently published [1–3]. Also, comparisons of clinical subjects to normal controls are sometimes made [4,5]. Often, these studies are cross-sectional and their samples may be regarded as samples of convenience. However, case-control studies of major depression using clinical subjects have also been conducted [6–8]. While cross-sectional studies using samples of convenience and case-control studies can be vulnerable to selection bias, estimates of association (such as odds ratios) deriving from such studies are not necessarily biased. When
the odds of exposure to a variable is measured in subjects with a disease or outcome and in subjects without a disease or outcome, selection bias will distort the odds ratio only when the risk factor is related to the probability of selection in a way that differs between the comparison groups [9]. In a case-control study, the ratio of selection probabilities for exposed and non-exposed cases (selection odds for the cases) and the ratio of selection probabilities for exposed and nonexposed controls (selection odds for the controls), must be equal for unbiased estimation of the population odds ratio. The objective of this study was to utilize data collected from clinical subjects in a general hospital psychiatric unit, along with ancillary data about the underlying population, in order to determine whether selection bias would distort estimates of association between major depression and certain variables known to be associated with major depression. Another objective was to illustrate possible mechanisms underlying such bias. 2. Methods
* Corresponding author. Population Health Investigator, The Alberta Heritage Foundation for Medical Research. Assistant Professor, Departments of Community Health Sciences and Psychiatry, Faculty of Medicine, The University of Calgary. 3330 Hospital Drive N.W., Calgary, Alberta, Canada T2N-4N1. Tel: (403) 220-8752; fax: (403) 270-7307. E-mail address: [email protected]; Website: http://www.ucalgary.ca/ ⵑpatten
2.1. Subject selection 2.1.1. Clinical cases Between October 1996 and March 1998 a preliminary consent form was made available to clinical staff for distribution to psychiatric inpatients at the Calgary General Hos-
0895-4356/00/$ – see front matter © 2000 Elsevier Science Inc. All rights reserved. PII: S0895-4356(99)00 2 1 5 - 2
352
S.B. Patten / Journal of Clinical Epidemiology 53 (2000) 351–357
pital. Patients signing a preliminary consent form were approached by a research assistant who provided a description of the study, and a detailed consent form, in order to obtain informed consent. After consent was obtained, each clinical subject was administered the demographic and mood disorders section from the automated Composite International Diagnostic Interview (CIDI-A) [10]. Cases consisted of subjects meeting DSM-IV criteria for Major Depressive Disorder. 2.1.2. Non-depressed clinical subjects These subjects were selected using the same procedure as the clinical cases, except that these subjects did not have a depressive disorder according to the CIDI-A. Although only the mood disorders section of the CIDI-A was administered, additional diagnoses were recorded from clinical records at the time of admission. The most common diagnoses among the clinical subjects without major depressive disorders were psychotic conditions (24.7%), bipolar disorders (23.6%), substance use disorders (21.3%) and other non-psychotic mental disorders (16.9%). There were 12 (13.5%) subjects who were admitted for a diagnostic evaluation. Clinical diagnoses recorded from the hospital chart were used to evaluate the impact of selecting a subset (consisting of individuals with schizophrenia, other non-affective psychoses, or manic, or mixed bipolar episodes) of nondepressed clinical subjects as controls, as could be carried out in a case-control study (see below). This allowed an exploration of the impact of this restriction on the occurrence of bias. 2.1.3. Non-depressed community subjects Community controls were selected by random digit dialling (RDD) using a modification of the Mitofsky-Waksberg procedure [11]. Initially, three-digit prefixes were randomly selected from among those in service within the Calgary telephone system. Next, four-digit suffixes were randomly generated and assigned to each randomly selected prefix. Each resulting seven-digit telephone number was called and, if it was determined to be a residential number, 10 additional numbers consisting of the same first five digits, but with a different randomly generated final two digits were called. From each household, one resident over the age of 17 was selected using the next birthday method. Data were collected by telephone interview between January 1997 and March 1998. These RDD data were incorporated into the analysis in an unweighted form. To confirm the absence of a depressive disorder, the community subjects were interviewed using a short form of the CIDI [12].
Form in order to facilitate comparisons with census data. This was essential to the procedure employed for estimating selection probabilities. In fact, the variables specified were chosen specifically because census data were available, facilitating an evaluation of the occurrence of, and apparent mechanisms underlying, selection bias (see below). 2.3. Other data sources The analysis required estimates of the frequency of the various exposures in the underlying population (see below). Frequencies applicable to the local population deriving from the 1996 Canadian Census [13,14] were used in this study. This were obtained by data request from Statistics Canada. Also, estimates of the prevalence of major depression in various exposure groups within the underlying population were needed. These estimates were obtained from the first wave of the Canadian National Population Health Survey (NPHS), which was conducted in 1994. Statistics Canada guidelines for analysis of this data set [15] were followed in making the estimates, which are summarised in Table 1. The NPHS used a diagnostic predictor for major depression that was developed, based on the CIDI, by Kessler and Mroczek for use in the American National Health Interview Survey [12]. The prevalence of major depression observed in the NPHS, and the associations of major depression with gender, age, and martial status were all consistent with estimates from elsewhere in the literature [16]. Because age was classified in five-year intervals in the NPHS data set, all relevant analyses were restricted to subjects over the age of 19.
Table 1 Major depression 12-month period prevalence in the Canadian population over the age of 19 by age, sex, marital status, employment status, income adequacy, and educationa Variable
Exposure group
Estimated prevalence (%)
Age
⬍30 30–39 40⫹ Female Male Separated/divorced/widowed Never married Marriedb Not employed Employedc Low income Greater than low income threshold Less than high school graduation High school graduate College or university (at least some)
6.8 6.2 4.4 7.1 3.4 8.7 7.3 4.1 6.4 4.6 8.1 5.2 5.2 4.8 5.5
Gender Marital status
Employment status Income
2.2. Data collection
Education level
Each subject was interviewed using a questionnaire to classify certain variables of interest to the study: age, gender, marital status, employment status, family income, and educational status. The questions focussing on these variables were taken verbatim from the 1996 Canadian Census
a Data from the 1994 Canadian National Population Health Survey (NPHS). b Including subjects with a common-law partner. c Subjects who reported that their main activity was working for pay or profit.
S.B. Patten / Journal of Clinical Epidemiology 53 (2000) 351–357
It was sometimes necessary to approximate categories across the different data sources. In the analysis concerned with employment status, subjects reporting no hours of paid employment were classified as not employed using the census data (and the data collected from the study subjects, where the census questions were used). In the NPHS data, subjects who reported that they were “currently working” were classified as employed, and all other responding subjects were classified as not employed. In the analysis of income data, subjects were categorized in a low income category if they had ⬍$10,000 in household income for single person households and ⭐$20,000 in household income for households consisting of ⭓2 persons. These cut-points were somewhat arbitrary, but were necessary for congruous integration of data from the various data sources employed.
3. Analysis The occurrence of selection bias, and its magnitude, depend upon the probabilities associated with the selection of subjects in relation to their disease and exposure status [9]. In the current study, selection probabilities were conceptualized from a population perspective: the population was conceptually divided into four compartments corresponding to the four exposure-disease contingencies in an exposuredisease 2 ⫻ 2 table. The probability of a subject in a specific compartment being selected into the study was the selection probability. Generally, it is not possible to estimate selection probabilities since a direct calculation would require access to disease and exposure information for the entire population. In this analysis, Bayesian calculations were used to estimate the selection fractions within each disease and exposure category: p(selection|disease status) = p(disease status|selection) p(selection) -------------------------------------------------------------------------------------------p(disease status) The conditional probability in the numerator on the right side of the equation could be evaluated within each disease and exposure group using the study data. The overall probability of selection (the second term in the numerator) was the proportion of subjects in the population recruited into the study and the denominator of the right side of the equation was the overall prevalence of major depression in the exposure group in question. Estimates of the latter proportions were taken from the NPHS. Selection probabilities for the RDD subjects were estimated as follows. Using the reported prevalence from the NPHS, the number of nondepressed females and males in the Calgary population was estimated by multiplying the total population in these groups by the compliment of the prevalence, resulting in an estimated non-depressed population of 279,173 males and 281,811 females over the age of 19 in the city of Calgary. The number of subjects recruited was divided by this value.
353
Selection probabilities calculated as described above, and using data from all available subjects, would assist in the description of bias as this might appear in a cross-sectional study using a sample of convenience consisting of a clinical or community comparison group. In the case-control context, efforts to avoid selection bias centre upon the selection of a control group. Where subjects from a clinical setting, such as a hospital comprise the case group, it is often reasonable to assume that patients admitted to the same hospital as the cases are members of the same (secondary) study base [17]. However, this approach can easily violate other principles underlying control selection in case-control studies. Specifically, if there is a relationship between the exposure(s) and the diagnoses used to determine inclusion of controls, bias may result [17]. Therefore, a subset of the non-depressed subjects comprising the sample of convenience described above was defined in order to evaluate whether this subgroup could be used to generate valid estimates in a case-control analysis. This control group consisted of those subjects who were diagnosed with schizophrenia or manic episodes, both highly heritable conditions [18]. As there may be an overlap in the demographic and risk factor profiles associated with unipolar and bipolar mood disorders, it may have been preferable not to include manic subjects in this group. However, the size of the sample did not permit the use of smaller subsets of subjects in the analysis. 4. Results Because of the sensitive nature of the inpatient psychiatric population, no data collection was possible without informed consent. As a result, the proportion of subjects who received a consent form and the proportion of these who provided consent in the clinical setting was unknown. However, previous experience at the same hospital with the same consent procedure was determined to achieve informed consent from approximately 25% of new admissions [19,20]. Of 440 residential households contacted in the RDD, 256 indicated acceptance so that the household response rate was 58.2%. Two hundred twenty (220) eligible subjects were successfully contacted (85.9%), and 177 (80.5%) of these participated in the data collection. The age, gender, marital status, employment status, family income, and educational status of the study subjects are summarized in Table 2, the same variables in the Calgary population according to the 1996 census are summarized in Table 3. Note that in all estimates, the 1996 Census and the NPHS were restricted to the ⬎19-year-old age group (except for census data pertaining to education and employment status where, for reasons of data availability, the tables show figures applicable to members of the population over the age of 15). In the analysis concerned with gender, using all of the hospitalized subjects in the analysis as a sample of convenience, the observed odds ratio for female gender was 2.17
354
S.B. Patten / Journal of Clinical Epidemiology 53 (2000) 351–357
Table 2 Socio-demographic features of the study subjects
Exposure level Age group
Gender Marital status
Employment status Incomec Education level
Hospitalized, no major depression, n ⫽ 101 (%)
Hospitalized, no major depression, psychotic & bipolar, n ⫽ 43 (%)
52 (29.5) 55 (31.3) 69 (39.2) 82 (46.6) 94 (53.4) 56 (31.8)
33 (32.7) 30 (29.7) 38 (37.6) 29 (28.7) 72 (71.3) 41 (40.6)
14 (36.2) 13 (30.2) 16 (37.2) 16 (37.2) 27 (62.8) 20 (46.5)
29 (20.3) 43 (30.1) 71 (49.7) 82 (57.3) 61 (42.7) 32 (22.4)
72 (40.9) 48 (27.3) 113 (64.2) 63 (35.8) 25 (19.0) 107 (81.1) 40 (22.7)
47 (46.5) 13 (12.9) 76 (75.2) 25 (24.8) 17 (23.9) 54 (76.1) 36 (35.6)
20 (46.5) 3 (7.0) 32 (74.4) 11 (25.6) 11 (34.4) 21 (65.6) 14 (32.6)
34 (23.8) 77 (53.8) 42 (29.4) 101 (70.6) 2 (1.9) 101 (98.1) 13 (9.1)
62 (35.2) 74 (42.0)
26 (25.7) 39 (38.6)
12 (27.9) 17 (39.5)
32 (22.4) 98 (68.5)
Hospitalized, major depression, n ⫽ 176 (%) ⬍30 30–39 40⫹ Female Male Separated/divorced/ widowed Never married Marrieda Not employedb Employed Below threshold Above threshold Less than high school graduate High school graduate Some ⬎high school
Community subjects, no major depression, n ⫽ 143 (%)
a
Including common-law. Includes those not in the labour force. c Missing data due to refusal for 44 cases, 30 hospital controls and 40 community controls. b
with a 95% confidence interval of 1.25–3.80. This point estimate is consistent with what has previously been reported from community surveys (see discussion, below) including the NPHS, suggesting that there was not a major impact of
Table 3 Demographic features of the Calgary population [13,14]a Exposure level Age
Gender Marital status
Employment status Income
Education level
a
⬍30 30–39 40⫹ Female Male Never married Marriedb Separated/widowed/ divorced Employed Not employedc Low income family members ⬎Low income threshold Less than high school graduation High school graduate Some trade, college, or university
Number
Percent
124,675 163,345 304,220 300,510 291,730 125,290 382,575 84,385
21.1% 27.6% 51.4% 50.7% 49.3% 21.2% 64.6% 14.2%
441,575 197,460 66,330d
69.1% 30.9% 11.2%
525,900d 172,600
88.8% 27.0%
72,965 393,480
11.4% 61.6%
Age, gender, and marital status have been restricted to the members of the population over the age of 19 (n ⫽ 592,240). For reasons of data availability, employment and education status refer to members of the population over the age of 15. b Including common-law. c Includes labour force non-participants. d An estimate calculated by multiplying the proportion of families in the two income groups by the total population over the age of 19.
selection bias on the odds ratio calculated from this crosssectional analysis. However, when the community subjects were used in the calculation, the odds ratio was 0.65 (95% CI: 0.41–1.04) which is inconsistent with estimates deriving from previous studies, suggesting a considerable impact of bias. The estimated probability of disease (major depression) given selection for females was 82/111 (see Table 2). The unconditional probability of selection for females was 111/300,510 (see Tables 2 and 3) and the unconditional probability of major depression from the NPHS data for females was .071 (see Table 1). This led to an estimated selection fraction of 3.84 ⫻ 10⫺3. For males, analogous calculations led to a selection fraction of 9.48 ⫻ 10⫺3. Hence, the selection odds [9] were ⵑ0.41 for the cases. For nondepressed subjects, selection fractions for females and males were 1.04 ⫻ 10⫺4 and 2.55 ⫻ 10⫺4, respectively, leading to an identical selection odds of 0.41. In the RDD selection of non-depressed community subjects, 82 females and 61 males were selected, this resulting in estimated selection fractions of 2.94 ⫻ 10⫺4 and 2.16 ⫻ 10⫺4 respectively, and a selection odds of 1.36. Using these calculations, the apparent absence of bias in the estimated odd’s ratio for female gender when clinical cases and controls were used, and the bias apparent in the estimate incorporating community control is conceptualized as follows: The probability of selection of clinical subjects depended on gender (males were more likely to be selected than females in the clinical population employed here, and using the selection procedures described), but this tendency did not differ between depressed and non-depressed subjects in the sample of convenience so that there was no se-
S.B. Patten / Journal of Clinical Epidemiology 53 (2000) 351–357
355
Table 4 Estimated Selection Odds for the Clinical and Community Subjects
Selection odds (cases)
Selection odds (clinical w/o MDE)
Selection odds (psychotic/ bipolar)
Selection odds (community subjects w/o MDE)
Odds ratio (calculated with...)
Exposure level Age group Gender Marital status
Employment Incomec Education level
⬍30 30–39 Female Separated/divorced/ widowed Never marrieda Not employedb Below threshold ⬍High school graduation High school graduation
Clinical subjects w/o MDE
psychotic & bipolar inpatients w/o MDE
Community subjects w/o MDE
0.87 1.01 2.17 0.37
0.86 0.98 1.47 0.18
1.85 1.32 0.65 2.81
1.19 1.05 0.41 2.49
2.17 1.50 0.41 15.02
2.19 1.54 0.60 31.75
1.02 1.15 1.36 1.98
0.41 0.59 0.74 0.59 1.26
0.23 0.62 0.45 0.66 1.19
3.40 4.31 11.80 4.07 2.57
2.57 2.88 1.19 1.30 5.18
11.42 6.93 2.57 2.10 3.57
21.06 6.63 4.28 1.87 3.78
1.39 0.95 0.16 0.30 1.75
a
Including common-law. Includes those not in the labour force. c Missing data due to refusal for 44 cases, 30 hospital controls, and 40 community controls. b
lection bias. In the RDD selection of non-depressed community dwelling subjects, females rather than males had a higher probability of selection, such that the odds of female gender was systematically greater in the community controls that in the cases and the odd’s ratio for female gender was biased as a result. Calculations pertinent to other variables are summarized in Table 4. The apparent impact of bias (and the effect of method of control selection on the occurrence of bias) was uneven for different variables, with dramatic distortions in various directions being evident. The association of younger age with major depression was underestimated in the sample of convenience, but not when the depressed clinical subjects were compared to community subjects. The association of divorced, widowed, or separated marital status with major depression was underestimated when depressed clinical subjects were compared to non-depressed clinical subjects and overestimated when comparisons to community subjects were used. Being married appeared to alter the chance of selection by various extents in the various groups compared. A consistent pattern was observed for employment status, income and education level in the sample of convenience. These variables appeared to increase the probability of selection of clinical subjects (generally to a greater degree in non-depressed than depressed subjects) and to diminish the probability of selection of non-depressed community subjects. As a result, there was a tendency to overestimate associations when comparisons were made to community subjects and to underestimate these when comparisons to non-depressed clinical subjects were made. Restriction of the comparison group to subjects with schizophrenia or manic or mixed bipolar episodes did not substantially change the pattern of distortion, see Table 4.
5. Discussion These findings confirm that strong distortions in estimates of association can occur in psychiatric epidemiological studies using clinical subjects. General principles of control selection in case-control studies have been articulated by Wacholder et al. [21]. The concept of a study base was central to the formulation of these principles. A study base is the set of persons, or person-time, in which diseased subjects become cases. A study base may be primary or secondary [22]. In this study, the cases were individuals admitted to a particular hospital with major depression, hence the study base was a secondary one. A valid set of controls would consist of individuals without major depression but representative of the same base of experience. In a study of this type, the control group should consist of individuals who would have become study cases had they developed the outcome of interest [22], major depression. In conducting a hospital based case-control study, selection of controls from the same clinical services as the cases is sometimes undertaken. When controls are selected from a hospital, validity depends on the assumption that the distribution of exposure in this group is the same as that of the study base, or differs by measurable factors [21]. This assumption seems reasonable in circumstances where a hospital has identical catchment populations for case and control conditions (individuals admitted for one set of conditions would have been admitted for the other) or where exposure is independent of admission [21]. Since neither condition was met by the procedures employed here, nor by many other case-control investigations in the psychiatric literature, considerable vulnerability to bias is evident. The impact of exposure status on the probability of selection in a hospital-based case-control study could occur as a result of a direct impact on the probability of admission or
356
S.B. Patten / Journal of Clinical Epidemiology 53 (2000) 351–357
by an effect of exposure on the probability of development of one of the mental disorders for which controls were admitted. The latter possibility was evaluated in this study by defining a group of controls with conditions (schizophrenia, other non-affective psychotic conditions, and manic or mixed bipolar episodes) that are generally not thought to be etiologically related to the specific exposures evaluated. Distortion due to selection bias remained evident, however. The most probable explanation is that although the exposures evaluated in this project did not cause the conditions characterizing the control group, the exposures were nevertheless related to these conditions either by an impact on the probability of admission, or as a result of the control subjects’ mental disorders on the variables themselves. However, since an overlap of risk factors may exist for depressive disorders and bipolar disorders, it is possible that the inclusion of bipolar disorders in the control group caused a negative bias for some of the associations examined. For example, since bipolar disorders have a younger age of onset than unipolar depression [23], a negative bias may have been introduced into the estimate of the odds ratio representing the ⬍30-year-old category. Certain other factors must be considered in the interpretation of these results. First, the NPHS did not use the full CIDI to identify cases of major depression. Rather, a screener was used (the same screener [24] used to exclude cases of major depression from the RDD control sample in this study). Since this brief instrument may misclassify some subjects, the NPHS estimates, which were used as a standard to detect bias in this study, may have been distorted by misclassification bias. However, the set of associations identified in the NPHS major depression data [16] is very consistent with the rest of the epidemiological literature concerned with major depression. Second, the NPHS data refer to the Canadian national population (more specifically, household residents), whereas the census data used in this analysis pertained only to the city of Calgary. However, there is no evidence that the epidemiology of major depression in Calgary differs from that of the national population. The analysis presented here was possible because information about associations between several demographic variables and major depression were available from a large national survey, and additional necessary information was available from the 1996 Canadian census. This state of affairs is not likely to apply in real world situations where investigators wish to evaluate undetermined associations between potential risk factors and major depression. However, the observation that severe bias can result from selective pressures occurring with the use of clinical subjects indicates that the methodological spectre of selection bias cannot be lightly dismissed in this context. While the theoretical possibility of making valid estimates of population parameters using clinically recruited subjects exists, it remains to be confirmed that any practical procedure for subject selection can provide confidence about the validity of the resulting estimates.
Acknowledgments Author is Population Health Investigator with The Alberta Heritage Foundation for Medical Research; and Assistant Professor in the Departments of Community Health Sciences and Psychiatry of The University of Calgary Faculty of Medicine. This study was supported by a grant from the Alberta Heritage Foundation for Medical Research. References [1] Barry KL, Fleming MF, Manwell LB, Copeland LA, Appel S. Prevalence of and factors associated with current and lifetime depression in older adult primary care patients. Fam Med 1998;30:366–71. [2] Langs G, Quehenberger F, Fabisch K, Klug G, Fabisch H, Zapotocsky HG. Prevalence, patterns and role of personality disorders in panic disorder patients with and without comorbid (lifetime) major depression. Acta Psychiatr Scand 1998;98:116–23. [3] Soskolne V, Bonne O, Denour AK, Shalev AY. Depressive symptoms in hospitalized patients: a cross-sectional survey. Int J Psychiatry Med 1996;26:271–85. [4] Roy A. Is introversion a risk factor for suicidal behaviour in depression? Psychol Med 1998;28:1457–61. [5] Hodges S, Marks M. Cognitive characteristics of seasonal affective disorder: a preliminary investigation. J Affect Disord 1998;50:59–64. [6] Roy A. Five risk factors for depression. Br J Psychiatry 1987;150: 536–541. [7] Roy A. A case-control study of social risk factors for depression in American patients. Can J Psychiatry 1997;42:307–9. [8] Shapira B, Zislin J, Gelfin Y, Osher Y, Gorfine M, Souery D, Mendlewicz J, Lerer B. Social adjustment and self-esteem in remitted patients with unipolar and bipolar affective disorder: a case-control analysis. Compr Psychiatry 1999;40:24–30. [9] Kleinbaum DG, Morgenstern H, Kupper LL. Selection bias in epidemiological studies. Am J Epidemiol 1981;113:452–63. [10] Wittchen HU. Reliability and validity studies of the WHO-Composite International Diagnostic Interview (CIDI): A critical review. J Psychiatr Res 1994;28(1):57–84. [11] Potthoff RF. Telephone sampling in epidemiological research: to reap the benefits, avoid the pitfalls. Am J Epidemiol 1994;139:967–78. [12] Mroczek, DK, and Kessler, RC. Final CIDI screening scales with timing estimates. Ann Arbor, Michigan: The University of Michigan Survey Research Center, 1994. [13] Diskette Product 93F0022XDB96000. Age, sex, marital status and common-law status families: number, type and structure, structural type of dwelling and household size. Ottawa: Statistics Canada, 1998. [14] Profiles of the component municipalities for the CMA of Calgary. Ottawa, Canada: Statistics Canada, 1998. [15] Statistics Canada Health Statistics Division. National Population Health Survey, 1994–95, Public Use Microdata Files. Ottawa, Canada: Minister of Industry, 1995. [16] Beaudet MP. Depression. Health Reports 1996;7(4):11–24. [17] Wacholder S, Silverman DT, McLaughlin JK, Mandel JS. Selection of controls in case-control studies. II. Types of controls. Am J Epidemiol 1992;135:1029–41. [18] Cardno AG, Marshall EJ, Coid B, Macdonald AM, Ribchester TR, Davies NJ, Venturi P, Jones LA, Lewis SW, Sham PC, et al. Heritability estimates for psychotic disorders. The Maudsley twin psychosis series. Arch Gen Psychiatry 1999;56:162–8. [19] Patten SB, Williams JVA, Haynes L, McCruden J, Arboleda-Florez J. The incidence of delirium in psychiatric inpatient units. Can J Psychiatry 1997;42:858–63. [20] Patten SB. Integrating administrative and research data in pharmacoepidemiological research. Can J Clin Pharmacol 1998;5:92–7.
S.B. Patten / Journal of Clinical Epidemiology 53 (2000) 351–357 [21] Wacholder S, McLaughlin JK, Silverman DT, Mandel JS. Selection of controls in case-control studies. I. Principles. Am J Epidemiol 1992;135:1019–28. [22] Miettinen OS. Design of the study base. [Chapter 3] In: Design of the study base. Chapter 3, Theoretical epidemiology: principles of occurrence research in medicine. New York, NY, USA: John Wiley & Sons, Inc., 1985, pp. 46–68.
357
[23] Blazer DG, Kaplan HI, Sadock BJ, Editors. In: Mood disorders, epidemiology. Comprehensive Textbook of Psychiatry/VI. 6th ed. Baltimore, MD, USA: Williams and Wilkins, 1995, pp. 1079–88. [24] Kessler, R, and Mroczek, D. An update on the development of mental health screening scales for the U.S. National Health Interview Scales. Ann Arbor, MI, USA: The University of Michigan Survey Research Center, 1994.