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Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí hóa học quốc tế đề tài : Identifying type and determinants of missing items in quality of life questionnaires: Application to the SF-36 French version of the 2003 Decennial Health Survey | Peyre et al. Health and Quality of Life Outcomes 2010 8 16 http www.hqlo.eom content 8 1 16 HEALTH AND QUALITY of life outcomes RESEARCH Open Access Identifying type and determinants of missing items in quality of life questionnaires Application to the SF-36 French version of the 2003 Decennial Health Survey Hugo Peyre1 2 Joel Coste1 2 Alain Leplège2 3 Abstract Background Missing items are common in quality of life QoL questionnaires and present a challenge for research in this field. The development of sound strategies of replacement and prevention requires accurate knowledge of their type and determinants. Methods We used the 2003 French Decennial Health Survey of a representative sample of the general population - including 22 620 adult subjects who completed the SF-36 questionnaire- to test various socio-demographic health status and QoL variables as potential predictors of missingness. We constructed logistic regression models for each SF-36 item to identify independent predictors and classify them according to Little and Rubin missing completely at random missing at random and missing not at random . Results The type of missingness was missing at random for half of the items of the SF-36 and missing not at random for the others. None of the items were missing completely at random. Independent predictors of missingness were age female sex low scores on the SF-36 subscales and in some cases low educational level occupation nationality and poor health status. Conclusion This study of the SF-36 shows that imputation of missing items is necessary and emphasizes several factors for missingness that should be considered in prevention strategies of missing data. Similar methodologies could be applied to item missingness in other QoL questionnaires. Background In the field of quality of life QoL as in other research fields missing data reduce the statistical power of studies and may cause selection biases if observations with missing values are excluded from the .
