Multiple imputation for body mass index: lessons from the Australian Longitudinal Study on Women's Health

In large epidemiological studies missing data can be a problem, especially if information is sought on a sensitive topic or when a composite measure is calculated from several variables each affected by missing values. Multiple imputation is the method of choice for 'filling in' missing data based on associations among variables. Using an example about body mass index from the Australian Longitudinal Study on Women's Health, we identify a subset of variables that are particularly useful for imputing values for the target variables. Then we illustrate two uses of multiple imputation. The first is to examine and correct for bias when data are not missing completely at random. The second is to impute missing values for an important covariate; in this case omission from the imputation process of variables to be used in the analysis may introduce bias. We conclude with several recommendations for handling issues of missing data. Copyright (C) 2004 John Wiley Sons, Ltd.

Differences in mail and telephone responses to self-rated health: use of multiple imputation in correcting for response bias

Objectives: To estimate differences in self-rated health by mode of administration and to assess the value of multiple imputation to make self-rated health comparable for telephone and mail. Methods: In 1996, Survey 1 of the Australian Longitudinal Study on Women's Health was answered by mail. In 1998, 706 and 11,595 mid-age women answered Survey 2 by telephone and mail respectively. Self-rated health was measured by the physical and mental health scores of the SF-36. Mean change in SF-36 scores between Surveys 1 and 2 were compared for telephone and mail respondents to Survey 2, before and after adjustment for socio-demographic and health characteristics. Missing values and SF-36 scores for telephone respondents at Survey 2 were imputed from SF-36 mail responses and telephone and mail responses to socio-demographic and health questions. Results: At Survey 2, self-rated health improved for telephone respondents but not mail respondents. After adjustment, mean changes in physical health and mental health scores remained higher (0.4 and 1.6 respectively) for telephone respondents compared with mail respondents (-1.2 and 0.1 respectively). Multiple imputation yielded adjusted changes in SF-36 scores that were similar for telephone and mail respondents. Conclusions and Implications: The effect of mode of administration on the change in mental health is important given that a difference of two points in SF-36 scores is accepted as clinically meaningful. Health evaluators should be aware of and adjust for the effects of mode of administration on self-rated health. Multiple imputation is one method that may be used to adjust SF-36 scores for mode of administration bias.

Diagnosis of diabetes mellitus: case of different cutoff values: A data mining approach

Objective: An estimation of cut-off points for the diagnosis of diabetes mellitus (DM) based on individual risk factors. Methods: A subset of the 1991 Oman National Diabetes Survey is used, including all patients with a 2h post glucose load >= 200 mg/dl (278 subjects) and a control group of 286 subjects. All subjects previously diagnosed as diabetic and all subjects with missing data values were excluded. The data set was analyzed by use of the SPSS Clementine data mining system. Decision Tree Learners (C5 and CART) and a method for mining association rules (the GRI algorithm) are used. The fasting plasma glucose (FPG), age, sex, family history of diabetes and body mass index (BMI) are input risk factors (independent variables), while diabetes onset (the 2h post glucose load >= 200 mg/dl) is the output (dependent variable). All three techniques used were tested by use of crossvalidation (89.8%). Results: Rules produced for diabetes diagnosis are: A- GRI algorithm (1) FPG>=108.9 mg/dl, (2) FPG>=107.1 and age>39.5 years. B- CART decision trees: FPG >=110.7 mg/dl. C- The C5 decision tree learner: (1) FPG>=95.5 and 54, (2) FPG>=106 and 25.2 kg/m2. (3) FPG>=106 and =133 mg/dl. The three techniques produced rules which cover a significant number of cases (82%), with confidence between 74 and 100%. Conclusion: Our approach supports the suggestion that the present cut-off value of fasting plasma glucose (126 mg/dl) for the diagnosis of diabetes mellitus needs revision, and the individual risk factors such as age and BMI should be considered in defining the new cut-off value.