Juha Karvanen1, Hanna Tolonen2, Tommi Härkänen2, Pekka Jousilahti2, Kari Kuulasmaa2. 1. Department of Mathematics and Statistics, University of Jyvaskyla, P.O. Box (MaD), FI-40014, Jyväskylä, Finland. Electronic address: juha.t.karvanen@jyu.fi. 2. Department of Health, National Institute for Health and Welfare, P.O. Box 30, FI-00271, Helsinki, Finland.
Abstract
OBJECTIVE: One of the main goals of health examination surveys is to provide unbiased estimates of health indicators at the population level. We demonstrate how multiple imputation methods may help to reduce the selection bias if partial data on some nonparticipants are collected. STUDY DESIGN AND SETTING: In the FINRISK 2007 study, a population-based health study conducted in Finland, a random sample of 10,000 men and women aged 25-74 years were invited to participate. The study included a questionnaire data collection and a health examination. A total of 6,255 individuals participated in the study. Out of 3,745 nonparticipants, 473 returned a simplified questionnaire after a recontact. Both the participants and the nonparticipants were followed up for death and hospitalizations. The follow-up data allowed to check the assumptions on the missing data mechanism, and tailored multiple imputation methods were used to handle the missing data. RESULTS: Nonparticipation is a strong predictor for mortality in the five-year follow-up. However, the recontact response does not predict mortality or morbidity among the nonparticipants when adjusted for age and sex. The result suggests that the recontact respondents can be used as proxy for all nonparticipants. A comparison of raw estimates and estimates adjusted for selection bias reveals clear differences in the estimated population prevalences of smoking and heavy alcohol usage. CONCLUSION: All efforts to collect data on nonparticipants are likely to be useful even if the response rate for the recontact remains low. Statistical analysis of the recontact respondents provides an indication of the extent of the selection bias, even in studies where follow-up data are not available to check the assumptions.
OBJECTIVE: One of the main goals of health examination surveys is to provide unbiased estimates of health indicators at the population level. We demonstrate how multiple imputation methods may help to reduce the selection bias if partial data on some nonparticipants are collected. STUDY DESIGN AND SETTING: In the FINRISK 2007 study, a population-based health study conducted in Finland, a random sample of 10,000 men and women aged 25-74 years were invited to participate. The study included a questionnaire data collection and a health examination. A total of 6,255 individuals participated in the study. Out of 3,745 nonparticipants, 473 returned a simplified questionnaire after a recontact. Both the participants and the nonparticipants were followed up for death and hospitalizations. The follow-up data allowed to check the assumptions on the missing data mechanism, and tailored multiple imputation methods were used to handle the missing data. RESULTS: Nonparticipation is a strong predictor for mortality in the five-year follow-up. However, the recontact response does not predict mortality or morbidity among the nonparticipants when adjusted for age and sex. The result suggests that the recontact respondents can be used as proxy for all nonparticipants. A comparison of raw estimates and estimates adjusted for selection bias reveals clear differences in the estimated population prevalences of smoking and heavy alcohol usage. CONCLUSION: All efforts to collect data on nonparticipants are likely to be useful even if the response rate for the recontact remains low. Statistical analysis of the recontact respondents provides an indication of the extent of the selection bias, even in studies where follow-up data are not available to check the assumptions.
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