Cardiovascular Disease Epidemiological Survey Data To Fill In The Missing Comparison And Simulation Research Methods

ObjectiveCardiovascular disease is a serious disease to human health worldwide. Recent studies have shown that the incidence and mortality were increasing in developing countries. For this chronic disease, many large-scale epidemiological researches carried out,and provided new clues and evidence of a large sample for the prevention of cardiovascular disease. However, due to the social and psychological characteristics of people, there was a number of incomplete data in the scientific information, named missing data. For the proportion of missing data within a certain range, the past approach was deleting the data directly. While simple, but it will reduce sample of observations, and affect the test power of results. In recent years, the imputation methods were recognized by more experts, and developed rapidly. In this study, single and multiple imputation methods are applied for handling missing data, focused on the differences between many multiple imputation methods, and we expect to find appropriate methods and strategies for chronic epidemiological studies.MethodsWe took Jmte Carlo techniques to simulate the different types of single variable (including continuous variables, binary variables, ordinal variables and nominal variables) missing at random, two variables jmotone missing, or two variables random missing at5%,10%,20%, and30%missing proportions, based on a large sample of cardiovascular disease and multivariate data sets. We simulated500times in each scenario deletion. In each simulation, were used delete method, a single imputation method, joint modeling multiple imputation method, and FCS multiple imputation method for missing data set after processing. Then, collected evaluated values of different methods in each time, and compared treatment effects.ResultsFor single variable missing, the joint modeling multiple imputation method can get overall mean value closed to complete data set if it was single continuous variable missing; If it was a single nominal variable missing, jmotone joint modeling imputation method may get the highest correct rate for the missing individual. But FCS multiple imputation method can get greater accuracy and smaller parameter deviation for single continuous variable missing, and the same to a single binary variable missing. For a single categorical variable, the discriminant analysis method was better than the logistic regression imputation method. To multiple imputation times, the imputation15times were the best, but more than10times the effect enhanced limited for single continuous variable missing; single missing binary variables and nominal variables,5times were best.For jmotone multivariate missing, joint modeling multiple imputation method was better than FCS multiple imputation method. In binary variable and continuous variable, ordinal variable and continuous variable, nominal variable and continuous variable imputation, FCS multiple imputation method had higher accuracy than joint modeling multiple imputation method for continuous variable, but joint modeling imputation multiple imputation method had higher correct rate to another categorical variable.For random multivariate missing, in continuous variables and nominal variables missing imputation, regpmm and discrim associated had high accuracy for continuous variables and nominal variable. For four kinds of situations,5times FCS (regpmm+discrim) imputation were best.ConclusionIn our study, we used simulation methods to construct different types of variable missing. For a single variable missing, joint modeling multiple imputation method was suitable for nominal variables, and FCS multiple imputation method adapt to binary variables and continuous variables; for jmotone multiple continuous variables missing, jmotone joint modeling imputation can get higher accuracy; for both continuous variables and discrete variables missing, joint modeling multiple imputation applied to continuous variable and FCS multiple imputation method was suitable for discrete variables; for multivariate random missing, FCS multiple imputation can get higher precision.