Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Changes in serum lipids with change and fluctuation in body mass index and body fat distribution in Mexican Americans with type 2 diabetes

This dissertation was written in the format of three journal articles. Paper 1 examined the influence of change and fluctuation in body mass index (BMI) over an eleven-year period, on changes in serum lipid levels (total, HDL, and LDL cholesterol, triglyceride) in a population of Mexican Americans with type 2 diabetes. Linear regression models containing initial lipid value, BMI and age, BMI change (slope of BMI), and BMI fluctuation (root mean square error) were used to investigate associations of these variables with change in lipids over time. Increasing BMI over time was associated with gains in total and LDL cholesterol and triglyceride levels in women. Fluctuation of BMI was not associated with detrimental lipid profiles. These effects were independent of age and were not statistically significant in men. In Mexican-American women with type 2 diabetes, weight reduction is likely to result in more favorable levels of total and LDL cholesterol and triglyceride, without concern for possible detrimental effects of weight fluctuation. Weight reduction may not be as effective in men, but does not appear to be harmful either. ^ Paper 2 examined the associations of upper and total body fat with total cholesterol, HDL and LDL cholesterol, and triglyceride levels in the same population. Multilevel analysis was used to predict serum lipid levels from total body fat (BMI and triceps skinfold) and upper body fat (subscapular skinfold), while controlling for the effects of sex, age and self-correlations across time. Body fat was not strikingly associated with trends in serum lipid levels. However, upper body fat was strongly associated with triglyceride levels. This suggests that loss of upper body fat may be more important than weight loss in management of the hypertriglyceridemia commonly seen in type 2 diabetes. ^ Paper 3 was a review of the literature reporting associations between weight fluctuation and lipid levels. Few studies have reported associations between weight fluctuation and total, LDL, and HDL cholesterol and triglyceride levels. The body of evidence to date suggests that weight fluctuation does not strongly influence levels of total, LDL and HDL cholesterol and triglyceride. ^

Temporal variations of dyspnea, fatigue, and lung function in chronic lung disease

Background. Research investigating symptom management in patients with chronic obstructive pulmonary disease (COPD) largely has been undertaken assuming the homeostatic construct, without regard to potential roles of circadian rhythms. Temporal relations among dyspnea, fatigue, peak expiratory flow rate (PEFR) and objective measures of activity/rest have not been reported in COPD. ^ Objectives. The specific aims of this study were to (1) explore the 24-hour patterns of dyspnea, fatigue, and PEFR in subjects with COPD; (2) examine the relations among dyspnea, fatigue, and PEFR in COPD; and (3) examine the relations among objective measures of activity/rest and dyspnea, fatigue, and PEFR in COPD. ^ Methods. The repeated-measures design involved 10 subjects with COPD who self-assessed dyspnea and fatigue by 100 mm visual analog scales, and PEFR by peak flow meter in their home 5 times a day for 8 days. Activity/rest was measured by wrist actigraphy. Single and population mean cosinor analyses and correlations were computed for dyspnea, fatigue, and PEFR; correlations were done among these variables and activity/rest. ^ Results. Circadian rhythms were documented by single cosinor analysis in 40% of the subjects for dyspnea, 60% for fatigue, and 60% for PEFR. The population cosinor analysis of PEFR yielded a significant rhythm (p < .05). The 8-day 24-hour means of dyspnea and fatigue was moderately correlated (r = .48, p < .01). Dyspnea and PEFR, and fatigue and PEFR, were weakly correlated in a negative way (r = −.11, p < .05 and r = −.15, p < .01 respectively). Weak to moderate correlations (r = .12–.34, p < .05) were demonstrated between PEFR and mean activity level measured up to 4 hours before PEFR measurement. ^ Conclusions. The findings suggest that (1) the dyspnea and fatigue experienced by COPD patients are moderately related, (2) there is a weak to modest positive relation between PEFR and activity levels, and (3) temporal variation in lung function may not affect the dyspnea and fatigue experienced by patients with COPD. Further research, examining the relations among dyspnea, fatigue, PEFR, and activity/rest is needed. Replication of this study is suggested with a larger sample size. ^