Types Of Missingness Research Articles

Transformation Structural Equation Models With Highly Nonnormal and Incomplete Data

As useful multivariate techniques, structural equation models have attracted significant attention from various fields. Most existing statistical methods and software for analyzing structural equation models have been developed based on the assumption that the response variables are normally distributed. Several recently developed methods can partially address violations of this assumption, but still encounter difficulties in analyzing highly nonnormal data. Moreover, the presence of missing data is a practical issue in substantive research. Simply ignoring missing data or improperly treating nonignorable missingness as ignorable could seriously distort statistical influence results. The main objective of this article is to develop a Bayesian approach for analyzing transformation structural equation models with highly nonnormal and missing data. Different types of missingness are discussed and selected via the deviance information criterion. The empirical performance of our method is examined via simulation studies. Application to a study concerning people’s job satisfaction, home life, and work attitude is presented.

Choosing the analysis population in non-inferiority studies: per protocol or intent-to-treat

For superiority trials, the intent-to-treat population (ITT) is considered the primary analysis population because it tends to avoid the over-optimistic estimates of efficacy that results from a per-protocol (PP) population. However, the roles of the ITT population and PP population in non-inferiority studies are not clearly defined as in superiority trials. In this paper, a simulation study is conducted to systematically investigate the impact of different types of missingness and protocol violations on the conservatism or anticonservatism of analyses based on the ITT and the PP population in non-inferiority trials. We find that conservatism or anticonservatism of the PP or ITT analysis depends on many factors, including the type of protocol deviation and missingness, the treatment trajectory (for longitudinal study) and the method of handling missing data in ITT population. The requirement that non-inferiority be shown for both PP and ITT populations does not necessarily guarantee the validity of a non-inferiority conclusion and a sufficiently powered PP analysis is not necessarily powered for ITT analysis. It is important to assess the potential types and rates of protocol deviation and missingness that might occur in a non-inferiority trial and to obtain some prior knowledge regarding the treatment trajectory of the test treatment versus the active control at the design stage so that a proper analysis plan and appropriate power estimation can be carried out. In general, for the types of protocol violations and missingness considered, we find that hybrid ITT/PP analysis, which excludes non-compliant patients as in the PP analysis and properly addresses the impact of non-trivial missing data as in the MLE-based ITT analysis, is more promising by way of providing reliable non-inferiority tests.

Identifying the types of missingness in quality of life data from clinical trials

Identifying the types of missingness in quality of life data from clinical trials

P80 Identifying the types of missingness in longitudinal quality of life data from clinical trials

P80 Identifying the types of missingness in longitudinal quality of life data from clinical trials

An approach to estimating exposure-specific rates of breast cancer from a two-stage case-control study within a cohort.

The Breast Cancer Detection and Demonstration Project (BCDDP) included a large cohort of women followed for incidence of breast cancer and from whom an initial case-control sample was drawn and standard risk factors obtained. In order to study the effect of mammographic features on breast cancer risk, a nested subsample of cases and controls was drawn. Therefore, these data can be viewed as two-stage case-control data within a cohort, or as cohort data with two nested levels of missingness, since basic characteristics like age were measured on all members of the cohort, standard risk factors were elicited only in the initial case-control sample, and mammographic features were assessed only in the nested subsample of cases and controls. We present a Poisson pseudo-likelihood approach to estimating age- and exposure-specific breast cancer incidence rates based on the three types of variables. This approach takes into account the nested missingness as well as two other type of missingness namely, that for basic variables and standard risk factors, some levels (i) were omitted by design in the nested subsample of case and controls or (ii) were empty because of the sparsity of the data in that subsample. Estimates of standard errors are obtained from a parametric bootstrap. The approach seems to be efficient when applied to the BCDDP data and is flexible for modelling breast cancer rates and taking the special missingness features of these data into account.