Multiple Imputation Techniques Research Articles

THE INCIDENCE OF CYSTIC FIBROSIS

We develop a statistical model for estimating cystic fibrosis (CF) incidence among infants born in the U.S.A. that accounts for under-diagnosis due to death prior to diagnosis and we apply it to the Cystic Fibrosis Foundation Patient Registry data for the years 1989 to 1991. The resulting estimate of incidence relative to live births among whites is 1:3419 while that among non-whites is 1:12,163. As a by-product of the modelling approach, estimates of the underlying average diagnosis age given survival to diagnosis are 4.09 years for whites and 4.55 years for non-whites, but this difference was not statistically significant and appears to demonstrate that diagnosis efforts may be approximately the same for whites and non-whites. Also, as another by-product of the modelling approach, CF mortality was estimated as more severe for females than males and marginally more severe for non-whites than whites. A variety of statistical methods underlie achievement of these results, including semi-parametric maximum likelihood, survival analysis, multiple imputation and bootstrap techniques.

Estimating the distribution of times from HIV seroconversion to aids using multiple imputation

Multiple imputation is a model based technique for handling missing data problems. In this application we use the technique to estimate the distribution of times from HIV seroconversion to AIDS diagnosis with data from a cohort study of 4954 homosexual men with 4 years of follow-up. In this example the missing data are the dates of diagnosis with AIDS. The imputation procedure is performed in two stages. In the first stage, we estimate the residual AIDS-free time distribution as a function of covariates measured on the study participants with data provided by the participants who were seropositive at study entry. Specifically, we assume the residual AIDS-free times follow a log-normal regression model that depends on the covariates measured at enrolment on the seropositive participants. In the second stage we impute the date of AIDS diagnosis for the participants who seroconverted during the course of the study and are AIDS-free with use of the log-normal distribution estimated in the first stage and the covariates from each seroconverter's latest visit. The estimated proportions developing AIDS within 4 and within 7 years of seroconversion are 15 and 36 per cent respectively, with associated 95 per cent confidence intervals of (10, 21) and (26, 47) per cent. We discuss the Bayesian foundations of the multiple imputation technique and the statistical and scientific assumptions.

Multiple Imputation for Interval Estimation from Simple Random Samples with Ignorable Nonresponse

Abstract Several multiple imputation techniques are described for simple random samples with ignorable nonresponse on a scalar outcome variable. The methods are compared using both analytic and Monte Carlo results concerning coverages of the resulting intervals for the population mean. Using m = 2 imputations per missing value gives accurate coverages in common cases and is clearly superior to single imputation (m = 1) in all cases. The performances of the methods for various m can be predicted well by linear interpolation in 1/(m — 1) between the results for m = 2 and m = ∞. As a rough guide, to assure coverages of interval estimates within 2% of the nominal level when using the preferred methods, the number of imputations per missing value should increase from 2 to 3 as the nonresponse rate increases from 10% to 60%.