This report provides a review of the multiple imputation methodology used by the Sponsor (Acorn Cardiovascular, Inc.) to handle missing data in the analysis of the primary endpoint of its pivotal clinical trial of the CorCap Cardiac Support Device (CorCap), as well as an evaluation of the impact of multiple imputation on the results of that analysis. This included a completely blinded imputatio...

Fractional hot deck imputation, considered in Fuller and Kim (2005), is extended to multivariate missing data. The joint distribution of the study items is nonparametrically estimated using a discrete approximation, where the discrete transformation also serves to define imputation cells. The procedure first estimates the probabilities for the cells and then imputes real observations for missin...

Variance estimation for estimators of state, county, and school district quantities derived from the Census 2000 long form are discussed. The variance estimator must account for (1) uncertainty due to imputation, and (2) raking to census population controls. An imputation procedure that imputes more than one value for each missing item using donors that are neighbors is described and the proced...

MI is usually performed under the assumption that the mechanism causing the missing data is ‘Missing At Random’. We discuss the practical implications of this elsewhere (Carpenter and Kenward, 2008). Here we note that although this assumption may be plausible, it cannot be verified from the data at hand, and therefore the analysis of a partially observed data set under the MAR assumption can ne...

Visual simulation systems that utilize a terrain database often do not require total geo-specific accuracy. Instead, a believable “geo-approximate” representation can be utilized. For example, given an area containing a forest, it is usually not necessary to have each tree accurately represented in the imagery. Therefore, a database modeler will typically apply a repeatable texture of a tree ca...

In studies to assess the accuracy of a screening test, often definitive disease assessment is too invasive or expensive to be ascertained on all the study subjects. Although it may be more ethical or cost effective to ascertain the true disease status with a higher rate in study subjects where the screening test or additional information is suggestive of disease, estimates of accuracy can be bi...

Imputation estimates based on imputed values obtained from neural network models used in an 'impute first-aggregate next' approach, have been computed from Norwegian population census and administrative register data. The imputation estimates were compared with simple unbiased estimates obtained by the traditional 'aggregate first estimate next' approach and found to be superior for estimating ...

We explore the relationship between directed generative models and reinforcement learning by developing a new approach to data imputation that combines ideas from both areas. We address data imputation by defining an MDP for which we construct policies parametrized by (reasonably) large neural networks. We then show how to train these policies using a form of (self) Guided Policy Search (Levine...

In most surveys, we encounter missing data of one or more types. Some sample units leave some data items blank--item nonresponse. The usual approach is to impute all missing, inconsistent, or otherwise invalid items. This paper considers the effect on the estimates of variance from treating imputed values as if they were reported, and compares strategies to address it. Data processing for many ...