Consequences of an Extended Recruitment on Participation in the Follow-Up of a Child Study: Results from the German IDEFICS Cohort.

BACKGROUND: Declining response proportions in population-based studies are often countered by extended recruitment efforts at baseline that may, however, result in higher attrition in a subsequent follow-up. This study analysed the effect of extended recruitment efforts on attrition at the first follow-up of a child cohort. METHODS: We used paradata (i.e. information about the process of data collection) from the German IDEFICS cohort investigating dietary- and life style-induced health effects on children to quantify recruitment effort and classify respondents as completing the recruitment early vs. late for baseline and follow-up separately. Multilevel logistic regression models were used to investigate the association between recruitment effort and attrition at follow-up (loss to follow-up) adjusted for sociodemographic and health related variables. RESULTS: Individuals who were late respondents at baseline and early respondents at the follow-up had a higher chance of attrition (odds ratio 1.65, 95% confidence interval (CI) 1.19, 2.28) as compared to other groups. An investigation of reasons for non-participation revealed that members of this group were more likely to be not reachable by phone. CONCLUSIONS: An extended recruitment effort at baseline of a child cohort study is not per se associated with a higher chance of attrition at follow-up. Much care should be taken to collect valid telephone numbers.

Multiple imputation of missing data under missing at random: compatible imputation models are not sufficient to avoid bias if they are mis-specified.

OBJECTIVES: Epidemiological studies often have missing data, which are commonly handled by multiple imputation (MI). Standard (default) MI procedures use simple linear covariate functions in the imputation model. We examine the bias that may be caused by acceptance of this default option and evaluate methods to identify problematic imputation models, providing practical guidance for researchers. STUDY DESIGN AND SETTING: Using simulation and real data analysis, we investigated how imputation model mis-specification affected MI performance, comparing results with complete records analysis (CRA). We considered scenarios in which imputation model mis-specification occurred because (i) the analysis model was mis-specified or (ii) the relationship between exposure and confounder was mis-specified. RESULTS: Mis-specification of the relationship between outcome and exposure, or between exposure and confounder, can cause biased CRA and MI estimates (in addition to any bias in the full-data estimate due to analysis model mis-specification). MI by predictive mean matching can mitigate model mis-specification. Methods for examining model mis-specification were effective in identifying mis-specified relationships. CONCLUSION: When using MI methods that assume data are MAR, compatibility between the analysis and imputation models is necessary, but not sufficient to avoid bias. We propose a step-by-step procedure for identifying and correcting mis-specification of imputation models.

Study invitations with envelopes made from recycled paper do not increase likelihood of active responses or study participation in the German National Cohort.

OBJECTIVE: We conducted a trial embedded within the German National Cohort comparing the responses to study invitations sent in recycled envelopes of grey color vs. envelopes of white color. We analyzed paradata for reactions to the invitation letters by potential subjects, the duration between mailing date of the invitation and active responses, and study participation. RESULTS: Grey envelopes only slightly increased the chance of active responses (OR 1.16, 95% CI 0.83, 1.62) to the invitation letter. Potential study subjects with German nationality (OR 3.75, 95% CI 2.07, 7.66) and age groups above 50 years (50-59: OR 1.78, 95% CI 1.19, 2.69; 60-69: OR 2.25, 95% CI 1.48, 3.43) were more likely to actively respond to the invitation letter. The duration between mailing date of the invitation and active response was not associated with envelope color, sex, nationality, or age. Our trial replicates previous observations that the color of the envelope of a study invitation does not influence the likelihood of an active response or study participation.

Attrition in the European Child Cohort IDEFICS/I.Family: Exploring Associations Between Attrition and Body Mass Index.

Attrition may lead to bias in epidemiological cohorts, since participants who are healthier and have a higher social position are less likely to drop out. We investigated possible selection effects regarding key exposures and outcomes in the IDEFICS/I.Family study, a large European cohort on the etiology of overweight, obesity and related disorders during childhood and adulthood. We applied multilevel logistic regression to investigate associations of attrition with sociodemographic variables, weight status, and study compliance and assessed attrition across time regarding children's weight status and variations of attrition across participating countries. We investigated selection effects with regard to social position, adherence to key messages concerning a healthy lifestyle, and children's weight status. Attrition was associated with a higher weight status of children, lower children's study compliance, older age, lower parental education, and parent's migration background, consistent across time and participating countries. Although overweight (odds ratio 1.17, 99% confidence interval 1.05-1.29) or obese children (odds ratio 1.18, 99% confidence interval 1.03-1.36) were more prone to drop-out, attrition only seemed to slightly distort the distribution of children's BMI at the upper tail. Restricting the sample to subgroups with different attrition characteristics only marginally affected exposure-outcome associations. Our results suggest that IDEFICS/I.Family provides valid estimates of relations between socio-economic position, health-related behaviors, and weight status.