PT  - JOURNAL ARTICLE
AU  - Jerant, Anthony
AU  - Chapman, Benjamin P.
AU  - Duberstein, Paul
AU  - Franks, Peter
TI  - Is Personality a Key Predictor of Missing Study Data? An Analysis From a Randomized Controlled Trial
AID  - 10.1370/afm.920
DP  - 2009 Mar 01
TA  - The Annals of Family Medicine
PG  - 148--156
VI  - 7
IP  - 2
4099  - http://www.annfammed.org/content/7/2/148.short
4100  - http://www.annfammed.org/content/7/2/148.full
SO  - Ann Fam Med2009 Mar 01; 7
AB  - 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; χ21=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.