Is personality a key predictor of missing study data? an analysis from a randomized controlled trial

Anthony F Jerant, Enjamin P. Chapman, Paul Duberstein, Peter Franks

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

PURPOSE Little is known regarding the effects of psychological factors on data collection in research studies. We examined whether Five Factor Model (FFM) per- sonality factors-Neuroticism, Extraversion, Openness, Agreeableness, and Consci- entiousness-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-effcacy data at any scheduled follow-up (2, 4, and 6 weeks, and 6 and 12 months), con- trolling 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 asso- ciated 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; 21 = 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.

Original languageEnglish (US)
Pages (from-to)148-156
Number of pages9
JournalAnnals of Family Medicine
Volume7
Issue number2
DOIs
StatePublished - Mar 2009

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Personality
Randomized Controlled Trials
Odds Ratio
House Calls
Self Care
Telephone
ROC Curve
Primary Health Care
Logistic Models
Depression
Psychology
Research

Keywords

  • Bias (Epidemiology)
  • Data interpretation, Statistical
  • Patient compliance
  • Personality
  • Randomized controlled trials

ASJC Scopus subject areas

  • Family Practice

Cite this

Is personality a key predictor of missing study data? an analysis from a randomized controlled trial. / Jerant, Anthony F; Chapman, Enjamin P.; Duberstein, Paul; Franks, Peter.

In: Annals of Family Medicine, Vol. 7, No. 2, 03.2009, p. 148-156.

Research output: Contribution to journalArticle

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abstract = "PURPOSE Little is known regarding the effects of psychological factors on data collection in research studies. We examined whether Five Factor Model (FFM) per- sonality factors-Neuroticism, Extraversion, Openness, Agreeableness, and Consci- entiousness-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-effcacy data at any scheduled follow-up (2, 4, and 6 weeks, and 6 and 12 months), con- trolling 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 asso- ciated 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; 21 = 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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AB - PURPOSE Little is known regarding the effects of psychological factors on data collection in research studies. We examined whether Five Factor Model (FFM) per- sonality factors-Neuroticism, Extraversion, Openness, Agreeableness, and Consci- entiousness-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-effcacy data at any scheduled follow-up (2, 4, and 6 weeks, and 6 and 12 months), con- trolling 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 asso- ciated 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; 21 = 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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