Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Multiple independent genetic factors at NOS1AP modulate the QT interval in a multi-ethnic population.

Extremes of electrocardiographic QT interval are associated with increased risk for sudden cardiac death (SCD); thus, identification and characterization of genetic variants that modulate QT interval may elucidate the underlying etiology of SCD. Previous studies have revealed an association between a common genetic variant in NOS1AP and QT interval in populations of European ancestry, but this finding has not been extended to other ethnic populations. We sought to characterize the effects of NOS1AP genetic variants on QT interval in the multi-ethnic population-based Dallas Heart Study (DHS, n = 3,072). The SNP most strongly associated with QT interval in previous samples of European ancestry, rs16847548, was the most strongly associated in White (P = 0.005) and Black (P = 3.6 x 10(-5)) participants, with the same direction of effect in Hispanics (P = 0.17), and further showed a significant SNP x sex-interaction (P = 0.03). A second SNP, rs16856785, uncorrelated with rs16847548, was also associated with QT interval in Blacks (P = 0.01), with qualitatively similar results in Whites and Hispanics. In a previously genotyped cohort of 14,107 White individuals drawn from the combined Atherosclerotic Risk in Communities (ARIC) and Cardiovascular Health Study (CHS) cohorts, we validated both the second locus at rs16856785 (P = 7.63 x 10(-8)), as well as the sex-interaction with rs16847548 (P = 8.68 x 10(-6)). These data extend the association of genetic variants in NOS1AP with QT interval to a Black population, with similar trends, though not statistically significant at P<0.05, in Hispanics. In addition, we identify a strong sex-interaction and the presence of a second independent site within NOS1AP associated with the QT interval. These results highlight the consistent and complex role of NOS1AP genetic variants in modulating QT interval.

14-3-3zeta overexpression in multiple human cancers plays a critical role in cancer progression

14-3-3 is a family of highly conserved and ubiquitously expressed proteins in eukaryotic organisms. 14-3-3 isoforms bind in a phospho-serine/threonine-dependent manner to a host of proteins involved in essential cellular processes including cell cycle, signal transduction and apoptosis. We fortuitously discovered 14-3-3 zeta overexpression in many human primary cancers, such as breast, lung, and sarcoma, and in a majority of cancer cell lines. To determine 14-3-3 zeta involvement in breast cancer progression, we used immunohistochemical analysis to examine 14-3-3 zeta expression in human primary invasive breast carcinomas. High 14-3-3 zeta expression was significantly correlated with poor prognosis of breast cancer patients. Increased expression of 14-3-3 zeta was also significantly correlated with elevated PKB/Akt activation in patient samples. Thus, 14-3-3 zeta is a marker of poor prognosis in breast cancers. Furthermore, up-regulation of 14-3-3 zeta enhanced malignant transformation of cancer cells in vitro. ^ To determine the biological significance of 14-3-3 zeta in human cancers, small interfering RNAs (siRNA) were used to specifically block 14-3-3 zeta expression in cancer cells. 14-3-3 zeta siRNA inhibited cellular proliferation by inducing a G1 arrest associated with up-regulation of p27 KIP1 and p21CIP1 cyclin dependent kinase inhibitors. Reduced 14-3-3 zeta inhibited PKB/Akt activation while stimulating the p38 signaling pathway. Silencing 14-3-3 zeta expression also increased stress-induced apoptosis by caspase activation. Notably, 14-3-3 zeta siRNA inhibited transformation related properties of breast cancer cells in vitro and inhibited tumor progression of breast cancer cells in vivo. 14-3-3 zeta may be a key regulatory factor controlling multiple signaling pathways leading to tumor progression. ^ The data indicate 14-3-3 zeta is a major regulator of cell growth and apoptosis and may play a critical role in the development of multiple cancer types. Hence, blocking 14-3-3 zeta may be a promising therapeutic approach for numerous cancers. ^