Is Personality a Key Predictor of Missing Study Data? An Analysis From a Randomized Controlled Trial
Abstract Purpose Little is known regarding the effects of psychological factors on data collection in research studies. We examined whether Five Factor Model (FFM) personality factors—Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness—predicted missing data in a randomized con...
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Published in | Annals of family medicine Vol. 7; no. 2; pp. 148 - 156 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
United States
American Academy of Family Physicians
01.03.2009
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Subjects | |
Online Access | Get full text |
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Summary: | Abstract Purpose Little is known regarding the effects of psychological factors on data collection in research studies. We examined whether Five Factor Model (FFM) personality factors—Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness—predicted missing data in a randomized controlled trial (RCT). Methods Individuals (N = 415) aged 40 years and older with various chronic conditions, plus basic activity impairment, depressive symptoms, or both, were recruited from a primary care network and enrolled in a 6-week RCT of an illness self-management intervention, delivered by means of home visits or telephone calls or usual care. Random effects logistic regression modeling was used to examine whether FFM factors predicted missing illness management self-efficacy data at any scheduled follow-up (2, 4, and 6 weeks, and 6 and 12 months), controlling for disease burden, study arm, and sociodemographic characteristics. Results Across all follow-up points, the missing data rate was 4.5%. Higher levels of Openness (adjusted odds ratio [AOR] for 1-SD increase = 0.24; 95% CI, 0.12-0.46; P <.001), Agreeableness (AOR = 0.29; CI 0.14-0.60; P = .001), and Conscientiousness (AOR = 0.24; CI 0.15-0.50; P <.001) were independently associated with fewer missing data. Accuracy of the missing data prediction model increased when personality variables were added (change in area under the receiver operating characteristic curve from 0.71 to 0.77; χ1 = 6.6; P = .01). Conclusions Personality was a powerful predictor of missing study data in this RCT. Assessing personality could inform efforts to enhance data completion and adjust analyses for bias caused by missing data. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 CORRESPONDING AUTHOR: Anthony Jerant, MD, Department of Family and Community Medicine, UC Davis School of Medicine, 4860 Y St, Suite 2300, Sacramento, CA 95817, afjerant@ucdavis.edu Trial Registration: Homing in on Health: Study of a Home Delivered Chronic Disease Self Management Program. www.ClincalTrials.gov identifier NCT00263939. Conflicts of interest: none reported Funding support: Funded in part by Agency for Healthcare Research and Quality grant number R01HS013603, and National Institute of Health grant numbers T32 MH073452 and K24MH072712. |
ISSN: | 1544-1709 1544-1717 |
DOI: | 10.1370/afm.920 |