CURRENT demographic projections for the twenty-first

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1 Journal of Gerontology: MEDICAL SCIENCES Vol. 53A. No. I. M39-M46 Copyright 1998 by The Gerontological Society of America Versus Volunteer Selection for a Community-Based Study Mary Ganguli, 12 Mary E. Lytle, 2 Maureen D. Reynolds, 1 and Hiroko H. Dodge 2 'Division of Geriatrics and Neuropsychiatry, Department of Psychiatry, School of Medicine, and department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania. Background. Selection methods vary greatly in ease and cost-effectiveness. The effects of selection factors associated with subjects' recruitment into studies can introduce bias and seriously limit the generalizability of results. Methods. For an epidemiologic study, we recruited an age-stratified random sample of 1,422 community-dwelling individuals aged 65+ years from the voter registration lists in a rural area of southwestern Pennsylvania. The first 1,366 of these were accrued through intensive recruitment efforts; the last 56 of them responded to a single mailing. To increase sample size for future risk factor analyses, we also recruited by direct advertisement a sample of 259 volunteers from the same area. The three groups were compared on selected baseline characteristics and subsequent mortality. Results. The two subgroups of the random sample were not significantly different on any of the variables we examined. Compared to the random sample, in cross-sectional analyses, volunteers were significantly more likely to be women, more educated, and less likely to have used several health and human services. also had higher cognitive test scores and Instrumental Activities of Daily Living (IADL) ability. Over 6-8 years (10,861 person-years) of follow-up, volunteers had significantly lower mortality rates than randomly selected subjects. Conclusions. Health-related studies with populations composed partly or entirely of volunteers should take potential volunteer bias into account when analyzing and interpreting data. CURRENT demographic projections for the twenty-first century anticipate a dramatic increase in the population over age 65 (1). Planning for the service needs for a growing elderly population, recommendations for specific interventions, as well as the development of etiopathogenic hypotheses about aging-related disorders, will be based on results from descriptive and experimental studies of the elderly. Such studies employ a variety of sampling and recruitment methods, but empirical data are largely lacking on how these methods affect the interpretation of study results. Selection methods vary greatly in ease and costeffectiveness, yet the effects of selection factors associated with subjects' inclusion in studies can seriously limit the generalizability of results. or otherwise representative samples are considerably more difficult and expensive to recruit than are volunteer samples. To the extent that volunteers differ from nonvolunteers, however, parameters estimated from volunteer samples may be seriously biased, particularly in survey research (2). We conducted a survey of the elderly members of a rural community as part of an effort to establish a population-based dementia registry (3). The current article presents data from the three randomly selected and volunteer subgroups of the sample who participated in the survey. We predicted that there would be significant differences among the random and volunteer groups, postulating that these differences would help indicate the nature of volunteer bias. Our particular concerns were with variables relevant to dementia, such as demographics, cognitive and functional ability, and the use of health services. METHODS Sampling and Recruitment The survey was conducted in the mid-monongahela Valley of southwestern Pennsylvania, an economically depressed rural area approximately 25 miles south of Pittsburgh. This population is largely blue-collar and of European descent. The project was named the Monongahela Valley Independent Elders Survey (MoVIES). Entry criteria were: (a) age 65+ years at the time of study entry; (b) being community-dwelling (i.e., not in a nursing home) at the time of study entry; (c) fluency in English; and (d) at least a grade 6 education the last two requirements being related to interpretability of neuropsychologic (cognitive) test data. General information about the study and its objectives was provided to the community through newspaper articles, flyers, talk radio, information booths at local fairs, and presentations to various religious and secular community groups. A series of 1:13 age-stratified (65-74, 75+) random subsamples was drawn from a master sampling frame based on the voter registration lists for the study area. Because these communities have very stable populations, the electoral rolls were believed to be the most complete and least biased sampling frame for the area, as they included subjects who had voted not just in the most recent election but in past elections as well. Selected subjects were mailed letters informing them that they had been randomly chosen to participate in the study, asking them to mail back a form indicating the most convenient times for them to be con- M39

2 M40 GANGULIETAL tacted, and informing them that they would receive a follow-up telephone call. Subjects who responded agreeing to participate were scheduled for a home visit, during which the study was explained in greater detail and informed consent was obtained. Subjects who could not be contacted by letter or telephone were visited directly ("door knocking"), so that an effort could be made to recruit them in person. In some cases, several calls and/or "door knocking" visits were made before contact could be made and consent to participate was either obtained or denied. In some cases, positive word-of-mouth publicity from those who had entered the study was helpful in recruiting individuals who had initially been reluctant to participate. When it was felt that maximal recruitment had been reached with a particular random subsample, another random subsample was drawn from the master list, this process being repeated until an adequate total sample size was attained. Thus, recruitment of the random sample was a labor- and time-intensive process. Further details have been reported previously (3). Using the methods described above, a total age-stratified random sample of 1,366 individuals was recruited, and designated for this article as the random sample (RS). When it was determined that an adequate random sample had been accrued, recruitment was officially closed. However, recruiting letters had already been mailed out for the next 1:13 random subsample. Each individual in this final random subsample received only this single letter inviting participation in the study; no follow-up recruiting letters, calls, or visits were made. Fifty-six subjects, designated as random volunteers (RV) for the purposes of this article, responded spontaneously and entered the study. We have no information about the individuals who were sent, but did not respond to, the final mailing. A general recruiting drive was then publicized in the local media, asking for volunteers. The project was then deluged with calls and letters from persons who had not been randomly selected but wanted to participate. A total of 259 such individuals who met study entry criteria were recruited into the study before time ran out and recruitment had to be closed. They are designated as direct volunteers for this article. We were concerned that randomly selected individuals who agreed to participate after receiving a single letter might resemble the volunteers more than the rest of the random sample. Choosing to err on the side of caution, we initially demarcated the RS, RV, and DV as distinct subgroups of the Mo VIES cohort. Our intention was to pool the RV and DV groups with the RS group for risk factor determination but not for prevalence or incidence estimation. Data Collection Subjects were interviewed at their own residences unless they expressed a preference to be interviewed at the project offices. After obtaining informed consent, trained interviewers administered a battery of cognitive tests (lasting approximately 25 min in an intact subject), which have been described in detail elsewhere (3,4) and which included a general mental status test, the Mini Mental State Examination (MMSE) (5). Subjects were also asked to provide information about a variety of characteristics, including demographics, living arrangements, functional (Instrumental Activities of Daily Living, IADL) ability (6), use of health and human services, and use of prescription drugs (7). The MoVIES survey has since become a prospective cohort study, as part of which the project has followed all surviving and consenting participants through visits, telephone calls, and letters. Mortality is tracked through these means as well as through daily scanning of obituaries in six local newspapers. Dates, places, and listed causes of deaths are eventually confirmed through abstraction of death certificates. Statistical Methods Cross-sectional analyses. sample (RS) and direct volunteer subjects were compared to identify differences, if any, between subjects randomly sampled from the voter registration lists (RS) and subjects who volunteered in response to advertisements. sample (RS) and random volunteer (RV) groups were compared to identify differences, if any, between randomly sampled subjects who agreed to participate with minimal recruitment efforts (RV), and the randomly sampled subjects in the more intensively recruited prevalence/ incidence cohort (RS). Between-group differences in proportions with selected characteristics at study entry were examined univariately between RS and RV samples, and between RS and DV samples. The variables examined were as follows: demographics: age, sex, education, race; social characteristics: living arrangements (living alone, living at home with others, group living situations), marital status, previous occupation; health service utilization: usual source of health care, hospitalization during the past 6 months, personal care or nursing home use in the past, use of professional health care at home in the past year, current use of social services, number of current prescription drugs; functional abilities: IADL total score (6), possible score ranging from 11 to 45 (dichotomized as no deficit vs any deficit, or 11 vs 12+, and also treated as a continuous variable); whether still driving a motor vehicle; cognitive functioning: MMSE scores, possible scores ranging from 0 to 30, (dichotomized as 0 to 23 and 24+, also treated as a continuous variable). The chi-square test for association, or Fisher's exact test if the expected values for any cell was less than 5, was used to assess statistical significance defined as p <.05. For variables that were continuous in nature (age, IADL, and MMSE scores), we calculated mean, standard deviation (SD), and median. Pairwise between-group comparisons of continuous variables were performed using the nonparametric Wilcoxon rank sum tests, as these continuous variables had highly skewed distributions. Because the large number of statistical tests performed in this exploratory study increases the Type I error rate for each individual test, the reader may choose to interpret p values using a different criterion. The establishment of an appropriate value for reporting statistical significance is difficult, if not impossible, because many unreported exploratory comparisons were performed while examining these data. However, based on the number of comparisons

3 RANDOM VS VOLUNTEER SELECTION M41 reported in this article, a Bonferroni adjustment would yield a criterion of p = Longitudinal analysis. Given the similarity on all variables between RS and RV groups (see below), they were combined for longitudinal comparisons between random (RS + RV) and volunteer groups. To examine differences in mortality between random and volunteer groups, first, we examined age-specific and overall mortality rates per 100 person-years for each group. Second, we used the proportional hazards model (8), for which time was measured from the date of study entry to his or her death. If subjects were still alive, or had dropped out of the survey, time from the first contact until the dates when the subjects were last observed were included as censored cases. Control variables included in the model were age (continuous variable), gender, and education (at least high school versus less than high school), because these variables are known to be significant predictors of mortality and (as reported below) the above cross-sectional analyses showed that DV were more likely than RS to be younger, female, and to have higher educational levels. A second proportional hazards model was also fitted including interaction terms between DV*age, DV*gender, and DV*education, in addition to participant status (random vs volunteer). A third model, similar to the first, used all-cause attrition (i.e., not restricted to mortality) as the outcome variable. RESULTS For the cross-sectional analyses, three groups were compared: random sample (RS) (n = 1,366), random volunteer sample (RV) (n = 56), and direct volunteer sample (n = 259). Tables 1-4 show the proportions of each group with various selected characteristics, and the results of tests for significant differences in proportions between (a) RS and RV groups and (b) RS and DV groups. The tables show p values <.05, and also indicate p values that are significant after adjustment for multiple comparisons at/? < Demographic characteristics (age, gender, education, race) of the three groups are shown in Table 1. There were no statistically significant differences in proportions between RS and RV samples. Compared to RS subjects, DV subjects were significantly more likely to be in a younger age category; age distribution was also significantly (p =.002, Wilcoxon rank sum test) different between RS and DV (not shown in the table). RS, RV, and DV subjects had mean (SD) ages of 73.1 (6.0), 73.9 (5.8), and 71.8 (5.3) years, respectively. DV were more likely to be female and better educated. Social characteristics (marital status, living arrangements, previous occupation) of the three groups are shown in Table 2. These variables also showed no significant differences between RS and RV. Male DV were significantly more likely than male RS to have previously held professional/technical positions; occupational class differences among women were not detectable, probably because the majority had been (and still were) housewives. Cognitive (MMSE) (5), functional (IADL) (6), and driving status are shown in Table 3. While there were no significant differences between RS and RV group, DV were significantly more likely than RS to have MMSE scores above 24. However, mean (SD) MMSE scores in the RS, RV, and DV groups were 27.1 (3.0), 27.3 (2.1), and 27.5 (2.6); MMSE distributions were not significantly different (Wilcoxon rank sum test) between RV and DV groups (p =.318). Functional (IADL) scores, when categorized, were not significantly different between RS and RV groups, while the DV group had significantly better IADL scores than the RS group. The distribution of IADL scores was also significantly different (p =.016, Wilcoxon test) between DV and RS groups, even though mean (SD) IADL scores were close among RS, RV, and DV groups: 12.5 (4.5), 12.0 (3.0), and 11.5 (1.7). With respect to automobile driving, a much larger proportion of women than men reported never having driven. However, among both men and women who did previously drive, significantly greater proportions of RS than DV had quit driving before study entry. Table 1. Demographic Characteristics: Percentage of Subjects in Each Group (column %) Subject Characteristics (/V= 1,681) Age < Sample (RS) (n= 1,366) (RV) (n = 56) Direct (n = 259) RS vs RV P RS vs DV 11* Gender Men Women ** Education < High school grad High school grad ** Race Caucasian Other *p <.05; **p <.0014 (Bonferroni adjustment for multiple comparisons).

4 M42 GANGULIETAL Table 2. Social Characteristics: Percentage of Subjects in Each Group (column %) Subject Characteristics (W = 1,681) Marital status (n = 1,681) Married Other Sample (RS) (n= 1,366) (RV) (n = 56) Direct (n = 259) RS vs RV RS vs DV Living arrangements (n = 1,679)' Alone With others at home Group living situation Previous occupation (n = 1,678)' Men (n = 708) Professional/technical Other Women (n = 970) Professional/technical Other (includes housewife) * 'Analyses based on n < 1,681 on variables with missing or incomplete data. *p <.05; **p <.0014 (Bonferonni adjustment for multiple comparisons). Table 3. Cogitive and Instrumental Functioning: Percentage of Subjects in Each Group (column %) Subject Characteristics (N= 1,681) MMSE score 2 (n= 1,671)' < Sample (RS) (n= 1,366) (RV) (n = 56) Direct (n = 259) RS vs RV RS vs DV 04* IADL score 3 (n= 1,674)' * Driving automobile (n = 1,673)' Men (n = 707) Never drove Quit driving Still driving Women (n = 966) Never drove Quite driving Still driving * 01** 'Analyses based on n < 1,681 on variables with missing or incomplete data. : Mini Mental State exam (5). 'Instrumental Activities of Daily Living (6). *p <.05; **p <.0014 (Bonferroni adjustment for multiple comparisons). Table 4 shows the utilization of health and human services and the regular use of prescription medications among the three groups. Again, there were no statistically significant differences between RS and RV groups. DV subjects were significantly less likely than RS subjects to report no usual source of health care (no identified primary care physician), to have been hospitalized in the previous 6 months, to have ever been in a personal care home or nursing home, to have had home health care in the previous year, and to be currently using social services. There was,, however, no significant difference with respect to number of regularly used prescription drugs. Longitudinal Analyses For the mortality analyses, two groups were compared: random (RS + RV) and volunteer. Between the beginning of the survey (April 1987) and July 1996 (10,861 total person-years), we observed 531 total deaths: 495 random subjects (481 RS and 14 RV) and 36 volunteers. Table 5 shows age-specific and overall mortality rates per 100 person-years in each group. Overall mortality rates were significantly lower (p <.001) among the volunteers than among randomly selected subjects. In a Cox proportional hazards model, risk of dying (risk ratio 0.484, p =.0001) was significantly lower among vol-

5 RANDOM VS VOLUNTEER SELECTION M43 Table 4. Recent Use of Health Services and Current Use of Prescription Drugs: Percentage of Subjects in Each Group (column %) Subject Characteristics (N= 1,681)' Usual source of health care (n = 1,677)' No doctor All other Sample (RS) («= 1,366) (RV) (n = 56) Direct (n =259) RS vs RV RS vs DV 34* Hospitalization past 6 months (n = 1,677)' Yes No * Previous (ever) stay in nursing or personal care home(n = 1,672)' Yes No * Home health care previous year(/j = 1,677)' Yes No * Current social services (n = 1,675)' Yes No * Current prescription drug use(/i = 1,681) * 'Analyses based on n < 1,681 on variables with missing or incomplete data. *p <.05; **p <.0014 (Bonferroni adjustment for multiple comparisons). Age Table 5. Total Mortality Rates per 100 Person-Years (95% CI) Overall Subjects (RS + RV) Mortality Rate (95% CI) 2.9 (2.4, 3.5) 4.5 (3.8, 5.4) 7.8 (6.5, 9.3) 15.9(13.4, 18.9) 5.4 (5.0, 5.9) Mortality Rate (95% CI) 1.3(0.6,2.3) 3.0(1.6,5.0) 1.6(0.4,4.0) 6.0(, 12.5) 2.1 (1.5,2.9) p Value p <.001 /?<.O4 p<.00l p<.00\ p<m\ unteer than among random subjects. Of the control variables, factors predicting mortality were greater age (for each year, risk ratio 1.1, p ) and male gender (risk ratio 1.68, p =.0001), but not education. When the model was rerun including interactions between DV status and age, gender, and education, no interactions were significant; that is, the effects of age, gender, and education on mortality were similar among volunteer and random samples. We also examined a proportional hazards model in which the outcome variable was dropouts for any reason (not restricted to death). This added a further 169 dropouts to the 531 deaths, bringing the total to 700 events. Results were similar to those of the mortality model: significant predictors of dropout were age (risk ratio 1.1, p =.0001) and male gender (risk ratio 1.56, p =.0001); volunteer status reduced the risk of dropout (risk ratio = 0.553, p =.0001). DISCUSSION Summary of Findings In a cohort drawn from a rural community, we have compared two subgroups who were selected by random sampling, with greater and lesser ease of recruitment, and one subgroup that was composed entirely of volunteers. Our major finding from the univariate analyses is that, compared to the random sample, the volunteers were significantly younger, better educated, more likely to be women, with higher functional and perhaps cognitive ability, and lesser use of health and human services. (We note that after adjusting for the multiple comparisons reported here, only the differences in gender and education met the more stringent test of significance.) Several of these differences, as well as the significantly greater survival rates we found among our volunteers, suggest that, in general, those who volunteer for research are a healthier group than the general population from which they come. As would be expected, those who volunteered in the first place were less likely to drop out of the study for any reason. Volunteer "Bias" All consenting research participants are "volunteers" to a greater or lesser extent, but may differ in the degree of bias they introduce into a study. In their classic volume, Rosenthai and Rosnow stated (2, p. 1): "There is a growing suspicion among behavioral researchers that those human subjects who find their way into the role of research subject may not be entirely representative of humans in general."

6 M44 GANGULIETAL They summarized the results of several studies showing that research volunteers tend to be younger and female, with higher educational and occupational attainment. Two decades later, many cohort and case-control studies still have to resort to recruiting volunteers because of the sometimes prohibitive cost of other strategies. We are not aware of other published attempts to compare random samples with volunteer samples. Our findings regarding volunteer bias parallel many, but not all, of those studies examining response bias by comparing elderly study responders with nonresponders. Norton et al. (9) found initial nonresponders (who later agreed to participate) to have lesser education and lower cognitive (MMSE) scores. Hebert et al. (10) found nonresponders to be more cognitively impaired, more disabled, and to have a higher 1-year mortality rate than responders. Koval et al. (11) found nonresponders to be older, more likely to be women, more likely to be married, to have had less education, and to be less likely to have had a white-collar job. Launer et al. (12) found nonresponders to be more likely to be unmarried, with lesser education and lower cognitive status, and to report higher rates of physical and mental illness. The similarities among characteristics of responders (as compared to nonresponders) in the literature and volunteers (as compared to randomly selected subjects) in our study intuitively suggests a continuum of responsiveness from outright volunteers to "draftees" to refusers. An interesting contrast is provided by Ives et al. (13), who identified three groups initially selected for a trial of preventive health services: participants, refusers, and individuals who could not be reached. In that study, refusers appeared to be the healthiest, followed by participants, followed by unreachables. In experimental studies, including intervention trials, the selection bias inherent in volunteer- or referral-based samples is typically countered by randomization and matching to controls, who for the most part are also volunteers. Confounding, unlike bias, cannot be countered as readily. Poon et al. (14) pointed out that confounding variables are not randomly distributed among cases and controls, nor can they be randomized. Nonvolunteer controls, such as hospital controls, are subject to Berkson's bias. The growing recognition of the importance of random sampling and community-based studies lies in their perceived ability to overcome selection bias. However, it is well known that research response rates in the elderly decrease with age (15), and recruitment of a random sample can be difficult, expensive, and of low yield. Thus, many well-known studies (16,17) of the elderly have relied on volunteer samples. Potential Bias in Dementia Studies The ongoing Mo VIES study is focused primarily on prevalence, incidence, risk factors, and outcome in dementia. In our sample, on the cognitive functioning measure (MMSE), significant differences between random and volunteer groups were found when the scores were dichotomized as 0-23 and 24+ (the conventional clinical cutpoint), but not when MMSE was treated as a continuous variable so that its overall distribution could be compared between the groups. The mean and median MMSE scores were almost identical between groups, but the skewness of the MMSE score distributions made it difficult simply to compare means. The discrepancy in results between continuous and categorical treatment of the MMSE score is most likely a function of dichotomous classification (above or below a semiarbitrary screening cutpoint). Thus, although volunteer subjects may not be less impaired in a general sense, they would be less likely than randomly selected subjects to fall below this predetermined cutpoint. The consequences would depend on the specific purpose of cognitive screening in a given study; for example, in the Mo VIES study, volunteers would have been less likely than random sample subjects to be selected for the detailed clinical assessment designed to determine whether or not they were prevalent cases of dementia. Thus, the Mo VIES study could not have included the volunteer sample in analyses involving characteristics at the time of recruitment (e.g., for prevalence estimation), because of the expected bias; for example, individuals with cognitive impairment are less likely to be able or willing to volunteer. We recruited the volunteer sample only to enlarge the overall cohort for future analyses of risk factors for conditions that were absent at the time of recruitment. Thus, in a combined cohort of random plus volunteer subjects who were not demented at study entry, we could prospectively determine risk factors for (future) incident dementia. However, concern is still raised by the fact that several of the characteristics (lesser age, greater education, higher cognitive and functional ability, lower mortality) of volunteers are known to be inversely associated with the presence of dementia (15,18-21). Thus, by including volunteers in our sample for risk factor analyses, we might have increased power at the expense of introducing a systematic bias. Rosenthal and Rosnow (2) observed that researchers often suspect bias but are unable to quantify it. One solution for our cohort is to pool the volunteer and random subsamples for risk factor analyses, but to stratify the analyses by subjects' participant type (volunteer vs random status) so as to account for potential bias. Another possibility is to include participant status in the model and look for interactions between participant status and suspected risk factors; this approach has been demonstrated in the mortality analyses reported in this article. Much of what is known about risk/protective factors for Alzheimer's and other dementias is based on retrospective case-control studies of patients referred (or volunteering) for dementia research for example, in Alzheimer centers and memory disorder clinics at major urban centers. For example, at the Mayo Clinic in Rochester, Minnesota (22), patients with Alzheimer's disease who lived locally and received their primary care at the Mayo Clinic had dementia of later onset, were less educated, more often lived alone, and were more often institutionalized than were those who were referred to the clinic from farther away specifically for the evaluation of dementia. Alzheimer's disease patients referred to the University of Washington Alzheimer Center were significantly younger, more educated, more severely impaired, had less comorbidity, and even had different gene frequencies than Alzheimer's disease patients identified within the HMO-based dementia registry at the same center (19,23). Thus, studies that are based entirely on volunteers

7 RANDOM VS VOLUNTEER SELECTION M45 might find systematically higher or lower rates of dementia, and/or of risk factors for dementia, and their results may not be as generalizable to the community at large as those based on representative samples. These types of bias pose more of a concern for cross-sectional studies in which prevalent cases, that is, subjects demented at study entry, must be detected, than for prospective studies, in which dementia-free subjects are recruited and incident cases of dementia are identified over time. For results of risk factor analyses to be biased by the inclusion of volunteers, the relationship between the outcome (e.g., mortality or incident dementia) and the risk factor would have to be different between random and volunteer subgroups. The data presented here show that this is not the case with respect to mortality in the Mo VIES cohort. Cost-Effectiveness The cost-effectiveness of random selection vs volunteer recruitment must be considered depending on the objectives of the given study. For example, the scientific costs of inadequate sample sizes must be weighed against the diminishing returns of open-ended recruitment; Silagy et al. (24) suggest that the pressure felt by investigators to obtain high response rates may lead to the use of a variety of low-yield recruiting measures, which can substantially increase the cost of the study without proportionately increasing its value. Based on experience in the Duke Longitudinal Study, Maddox (16) concluded that for clinical studies and prospective studies, representative samples would be ideal but impractical. Schleser et al. (25) observed that, although successful in meeting the requirements of individual studies, the absolute cost of incentive programs prohibits largescale recruitment and may be effective in attracting only a certain subset of the population. Describing attempts to recruit volunteers as normal controls, Shtasel et al. (26) reported that, of 1,670 persons responding to newspaper advertisements for "healthy people ages 18-45," only 9% met inclusion criteria and were willing to participate. A secondary finding of our study, which should be tempered by the recognition of the small sample size on which it is based, is that the RV subsample, those who were randomly selected but immediately agreed to participate, was not significantly different from the larger random sample on any of the baseline characteristics we measured. The fact that no statistically significant differences were detected between the RS and RV samples may be construed as reflecting largely lack of power resulting from the relatively small size of the RV sample. However, it is notable that the percentages of each characteristic in Tables 1-4 are extremely close between the RS and RV samples, suggesting that the groups are indeed similar in all these aspects. This finding has led to our decision to pool the two random samples for future incidence as well as risk factor analyses. A potential methodologic implication is, however, worth discussing, given the effort required to obtain "maximum" response rates in random samples, and the potential for diminishing returns as recruitment proceeds. Our data suggest that a sample similar in characteristics to the intensively recruited random sample can be recruited by enrolling the most willing members of a random subsample. Hypothetically, perhaps an entire cohort of the desired size can be accrued, with minimal selection bias as well as minimal cost, by drawing a series of random subsamples, recruiting those who respond immediately, and moving on to the next random subsample. This hypothesis would need to be tested with a larger "random volunteer" sample than that which we recruited. While to some extent this is de facto what occurs in most studies, we have attempted a systematic post hoc examination of its effects. Although some degree of bias is inevitable with such an approach, this strategy may represent a cost-effective middle road between conventional random sampling and volunteer selection. ACKNOWLEDGMENTS The authors gratefully acknowledge the contributions of Jill Bennett, Rebecca Veschio, Faith Gallioto, Norma Maatta, and Diane Beley for recruitment and data collection; Mary Marcini for mortality tracking and on-site clerical services; Deborah Echement, Wilma Furlong, and Meribeth Riccio for data management; Eric C. Seaberg for assistance with statistical analyses; and Dr. Steven Belle for overall supervision of data management and analysis. The cooperation of the Mon Valley Community Health Center is also appreciated. The work reported here was supported in part by grants AG00312, AG06872, and AG07562 from the National Institute on Aging, U.S. Department of Health and Human Services. Address correspondence to Dr. Mary Ganguli, WPIC, 3811 O'Hara Street, Pittsburgh, PA gangulim@vms.cis.pitt.edu REFERENCES 1. Kinsella K. Demographic imperative. Cancer Control 1995; Rosenthal R, Rosnow RL. The volunteer subject. New York: John Wiley, Ganguli M, Belle S, Ratcliff G, et al. Sensitivity and specificity for dementia of population-based criteria for cognitive impairment: the MoVIES project. J Gerontol 1993;48:M Ganguli M, Ratcliff G, Huff FJ, et al. Effects of age, gender, and education on cognitive tests in a rural elderly community sample: norms from the Monongahela Valley Independent Elders Survey. Neuroepidemiology 1991;10: Folstein MF, Folstein SE, McHugh PR. Mini-Mental State: a practical method for grading the cognitive state of patients for the clinician. 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8 M46 GANGULIETAL 16. Maddox GL. Goals and methods. Selected methodological issues. In: Palmore E., ed. Reports from the Duke Longitudinal Study, Durham, NC: Duke University Press, 1970; Katzman R, Aronson M, Fuld P, et al. Development of dementing illnesses in an 80-year-old volunteer cohort. Ann Neurol 1989;25: Perls TT, Morris JN, Ooi WL, Lipsitz LA. The relationship between age, gender and cognitive performance in the very old: the effect of selective survival. J Am Geriatr Soc 1993;41: Tsuang D, Kukull W, Sheppard L, et al. Impact of sample selection on APOE e4 allele frequency: a comparison of two Alzheimer's disease samples. J Am Geriatr Soc 1996;44: Williams DE, Vitiello MV, Ries RK, Bokan J, Prinz PN. Successful recruitment of elderly community-dwelling subjects for Alzheimer disease research. J Gerontol 1988;43:M Norris FH. Characteristics of older nonrespondents over five waves of a panel study. J Gerontol 1985;40: Kokmen E, Ozsarfati Y, Beard CM, O'Brien PC, Rocca WA. Impact of referral bias on clinical and epidemiological studies of Alzheimer's disease. J Clin Epidemiol 1996;49: Barnhart RL, Belle G, Edland SD, et al. Geographically overlapping Alzheimer's disease registries: comparisons and implications. J Geriatr Psychiat Neurol 1995;8: Silagy CA, Campion K, McNeil JJ, Worsam B, Donnan GA, Tonkin AM. Comparison of recruitment strategies for a large-scale clinical trial in the elderly. J Clin Epidemiol 1991 ;44: Schleser R, West RL, Boatwright LK. A comparison of recruiting strategies for increasing older adults' initial entry and compliance in a memory training program. Int J Aging Hum Dev ,24: Shtasel DL, Gur RE, Mozley D, et al. for biomedical research. Recruitment and screening of normal controls. Arch Gen Psychiat 1991;48: Received January 30, 1997 Accepted May 23, 1997 DIRECTOR, KUNIN-LUNENFELD CLINICAL RESEARCH UNIT (Associate to Full Professor Level) Reporting to the Vice President, Research, the Director will be responsible for the scientific leadership and management of a newly-developed and expanding unit which focuses on clinical research designed to enhance the quality of care and life of the elderly. Research in the Unit functions at the interface between basic and applied science and clinical epidemiology, and involves both the evaluation of the validity or current treatment programs and the development of new management and treatment procedures. The Unit includes fully-funded scientists, postdoctoral fellows, and technical support staff. This unit also includes the Katz Centre for Gerontological Social Work, a joint Centre with the University of Toronto and the Faculty of Social Work. Baycrest Centre for Geriatric Care is a leader in the care of the elderly, and is fully affiliated with the University of Toronto as a teaching and research organization. Its unique campus includes a 300-bed geriatric hospital, 372-bed home for the aged, a 204-unit seniors' supported apartment residence, a 14-unit group home, and a range of ambulatory and community outreach programs. The Rotman Research Institute is also an integral part of the Research Division at the Centre. The Director will be expected to provide a vision for the future development of the Unit, scientific leadership, promotion of training for postdoctoral fellows and graduate students, and a commitment to excellence. The successful candidate will be a PhD and MD with proven leadership abilities and a strong background in clinical research with a specific focus on the elderly, including a record of grant awards and publications. Research strengths could include clinical trials, theoretically driven clinical research, evaluation, and/or clinical epidemiology. An Assistant Director will provide support and compliment the Director's skills. The Director will be eligible for a status-only appointment at the University of Toronto at the Associate or full Professor level. The salary offered will be commensurate with that offered by the University. Baycrest and the University of Toronto encourage applications from qualified women and men, members of visible minorities, aboriginal peoples, and persons with disabilities. In accordance with Canadian immigration requirements, this advertisement is directed firstly to Canadian citizens and permanent residents. Applicants should submit a C.V., together with the names of three references, by March 31,1998, to: Baycrest Centre for Geriatric Care Vice-President, Human Resources Baycrest Centre for Geriatric Care 3560 Bathurst Street Toronto, Ontario M6A 2E1 Canada Fax:(416)