Motivational interviewing and feedback for college drinkers: Handling missing values with complete case analysis and multiple imputation

Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis. ^ Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.^ Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results. ^ Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.^

The role of the conserved N -terminal domain of STAT3 in STAT3 dephosphorylation and nuclear export

Rapid redistribution of STAT subcellular localization is an essential feature of cytokine signaling. To elucidate the molecular basis of STAT3 function, which plays a critical role in controlling innate immune responses in vivo, we initiated studies to determine the mechanisms controlling STAT3 nuclear trafficking. We found that STAT3 is transported to the nucleus in the absence of cytokine treatment, as judged by indirect immunofluorescence studies in the presence of leptomycin B, an inhibitor of CRM1-dependent nuclear export, suggesting that the non-phosphorylated STAT3 protein contains a functional nuclear import signal. An isoform lacking the STAT3 N-terminal domain (Δ133STAT3) retains the ability to undergo constitutive nuclear localization, indicating that this region is not essential for cytokine-independent nuclear import. Δ133STAT3 is also transported to the nucleus following stimulation with interleukin-6 (IL-6). Interestingly, IL-6-dependent tyrosine phosphorylation of Δ133STAT3 appears to be prolonged and the nuclear export of the protein delayed in cells expressing endogenous STAT3, consistent with defective Δ133STAT3 dephosphorylation. Endogenous STAT3 does not promote the nuclear export of Δ133STAT3, although dimerization between endogenous Stat3 and Δ133STAT3 is detected readily. Thus, the STAT3 N-terminal domain is not required for dimerization with full-length STAT3, yet appears to play a role in proper export of Stat3 from the nucleus following cytokine stimulation. STAT3-deficient cells reconstituted with Δ133STAT3 show enhanced and prolonged Stat1 signaling in response to IL-6, suggesting that induction of the STAT3-dependent negative regulator SOCS3 is impaired. In fact, Δ133STAT3 fails to induce SOCS3 mRNA efficiently. These studies collectively indicate that the STAT3 N-terminal region may be important for IL-6-dependent target gene activation and nuclear dephosphorylation, while dispensable for nuclear import. STAT3 is an oncogene. STAT3 is constitutively activated in primary tumors of many types. Thus far, research in the design of STAT3 protein inhibitors has focused on the SH2 and DNA-binding domains of STAT3. Interference with these domains eliminates all signaling through STAT3. If the N-terminal domain is involved in tetramerization on a subset of target genes, inhibition of this region may lead to a more selective inhibition of some STAT3 functions while leaving others intact. ^