Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study

<p>Abstract</p> <p>Background</p> <p>The appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model.</p> <p>Methods</p> <p>Observed data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500 replications. Five levels of missingness (ranging from 5% to 75%) were imposed on three covariates using a missing at random (MAR) mechanism. Five missing data methods were applied; a) complete case analysis (CC) b) single imputation using regression switching with predictive mean matching (SI), c) multiple imputation using regression switching imputation, d) multiple imputation using regression switching with predictive mean matching (MICE-PMM) and e) multiple imputation using flexible additive imputation models. A Cox proportional hazards model was fitted to each dataset and estimates for the regression coefficients and model performance measures obtained.</p> <p>Results</p> <p>CC produced biased regression coefficient estimates and inflated standard errors (SEs) with 25% or more missingness. The underestimated SE after SI resulted in poor coverage with 25% or more missingness. Of the MI approaches investigated, MI using MICE-PMM produced the least biased estimates and better model performance measures. However, this MI approach still produced biased regression coefficient estimates with 75% missingness.</p> <p>Conclusions</p> <p>Very few differences were seen between the results from all missing data approaches with 5% missingness. However, performing MI using MICE-PMM may be the preferred missing data approach for handling between 10% and 50% MAR missingness.</p

Imputation strategies for missing binary outcomes in cluster randomized trials

<p>Abstract</p> <p>Background</p> <p>Attrition, which leads to missing data, is a common problem in cluster randomized trials (CRTs), where groups of patients rather than individuals are randomized. Standard multiple imputation (MI) strategies may not be appropriate to impute missing data from CRTs since they assume independent data. In this paper, under the assumption of missing completely at random and covariate dependent missing, we compared six MI strategies which account for the intra-cluster correlation for missing binary outcomes in CRTs with the standard imputation strategies and complete case analysis approach using a simulation study.</p> <p>Method</p> <p>We considered three within-cluster and three across-cluster MI strategies for missing binary outcomes in CRTs. The three within-cluster MI strategies are logistic regression method, propensity score method, and Markov chain Monte Carlo (MCMC) method, which apply standard MI strategies within each cluster. The three across-cluster MI strategies are propensity score method, random-effects (RE) logistic regression approach, and logistic regression with cluster as a fixed effect. Based on the community hypertension assessment trial (CHAT) which has complete data, we designed a simulation study to investigate the performance of above MI strategies.</p> <p>Results</p> <p>The estimated treatment effect and its 95% confidence interval (CI) from generalized estimating equations (GEE) model based on the CHAT complete dataset are 1.14 (0.76 1.70). When 30% of binary outcome are missing completely at random, a simulation study shows that the estimated treatment effects and the corresponding 95% CIs from GEE model are 1.15 (0.76 1.75) if complete case analysis is used, 1.12 (0.72 1.73) if within-cluster MCMC method is used, 1.21 (0.80 1.81) if across-cluster RE logistic regression is used, and 1.16 (0.82 1.64) if standard logistic regression which does not account for clustering is used.</p> <p>Conclusion</p> <p>When the percentage of missing data is low or intra-cluster correlation coefficient is small, different approaches for handling missing binary outcome data generate quite similar results. When the percentage of missing data is large, standard MI strategies, which do not take into account the intra-cluster correlation, underestimate the variance of the treatment effect. Within-cluster and across-cluster MI strategies (except for random-effects logistic regression MI strategy), which take the intra-cluster correlation into account, seem to be more appropriate to handle the missing outcome from CRTs. Under the same imputation strategy and percentage of missingness, the estimates of the treatment effect from GEE and RE logistic regression models are similar.</p

Calpain system protein expression in carcinomas of the pancreas, bile duct and ampulla

Background: Pancreatic cancer, including cancer of the ampulla of Vater and bile duct, is very aggressive and has a poor five year survival rate; improved methods of patient stratification are required. Methods: We assessed the expression of calpain-1, calpain-2 and calpastatin in two patient cohorts using immunohistochemistry on tissue microarrays. The first cohort was composed of 68 pancreatic adenocarcinomas and the second cohort was composed of 120 cancers of the bile duct and ampulla. Results: In bile duct and ampullary carcinomas an association was observed between cytoplasmic calpastatin expression and patient age (P = 0.036), and between nuclear calpastatin expression and increased tumour stage (P = 0.026) and the presence of vascular invasion (P = 0.043). In pancreatic cancer, high calpain-2 expression was significantly associated with improved overall survival (P = 0.036), which remained significant in multivariate Cox-regression analysis (hazard ratio = 0.342; 95% confidence interva l = 0.157-0.741; P = 0.007). In cancers of the bile duct and ampulla, low cytoplasmic expression of calpastatin was significantly associated with poor overall survival (P = 0.012), which remained significant in multivariate Cox-regression analysis (hazard ratio = 0.595; 95% confidence interval = 0.365-0.968; P = 0.037). Conclusion: The results suggest that calpain-2 and calpastatin expression is important in pancreatic cancers, influencing disease progression. The findings of this study warrant a larger follow-up study. Keywords: Calpain, Calpastatin, Pancreas, Ampulla, Bile duct, Cance

Development of the rhopalial nervous system in Aurelia sp.1 (Cnidaria, Scyphozoa)

We examined the development of the nervous system in the rhopalium, a medusa-specific sensory structure, in Aurelia sp.1 (Cnidaria, Scyphozoa) using confocal microscopy. The rhopalial nervous system appears primarily ectodermal and contains neurons immunoreactive to antibodies against tyrosinated tubulin, taurine, GLWamide, and FMRFamide. The rhopalial nervous system develops in an ordered manner: the presumptive gravity-sensing organ, consisting of the lithocyst and the touch plate, differentiates first; the “marginal center,” which controls swimming activity, second; and finally, the ocelli, the presumptive photoreceptors. At least seven bilaterally arranged neuronal clusters consisting of sensory and ganglion cells and their neuronal processes became evident in the rhopalium during metamorphosis to the medusa stage. Our analysis provides an anatomical framework for future gene expression and experimental studies of development and functions of scyphozoan rhopalia

Purification and Structural Characterization of Siderophore (Corynebactin) from Corynebacterium diphtheriae

During infection, Corynebacterium diphtheriae must compete with host iron-sequestering mechanisms for iron. C. diphtheriae can acquire iron by a siderophore-dependent iron-uptake pathway, by uptake and degradation of heme, or both. Previous studies showed that production of siderophore (corynebactin) by C. diphtheriae is repressed under high-iron growth conditions by the iron-activated diphtheria toxin repressor (DtxR) and that partially purified corynebactin fails to react in chemical assays for catecholate or hydroxamate compounds. In this study, we purified corynebactin from supernatants of low-iron cultures of the siderophore-overproducing, DtxR-negative mutant strain C. diphtheriae C7(β) ΔdtxR by sequential anion-exchange chromatography on AG1-X2 and Source 15Q resins, followed by reverse-phase high-performance liquid chromatography (RP-HPLC) on Zorbax C8 resin. The Chrome Azurol S (CAS) chemical assay for siderophores was used to detect and measure corynebactin during purification, and the biological activity of purified corynebactin was shown by its ability to promote growth and iron uptake in siderophore-deficient mutant strains of C. diphtheriae under iron-limiting conditions. Mass spectrometry and NMR analysis demonstrated that corynebactin has a novel structure, consisting of a central lysine residue linked through its α- and ε- amino groups by amide bonds to the terminal carboxyl groups of two different citrate residues. Corynebactin from C. diphtheriae is structurally related to staphyloferrin A from Staphylococcus aureus and rhizoferrin from Rhizopus microsporus in which d-ornithine or 1,4-diaminobutane, respectively, replaces the central lysine residue that is present in corynebactin

Characterization of Engineered Actin Binding Proteins That Control Filament Assembly and Structure

Eukaryotic cells strictly regulate the structure and assembly of their actin filament networks in response to various stimuli. The actin binding proteins that control filament assembly are therefore attractive targets for those who wish to reorganize actin filaments and reengineer the cytoskeleton. Unfortunately, the naturally occurring actin binding proteins include only a limited set of pointed-end cappers, or proteins that will block polymerization from the slow-growing end of actin filaments. Of the few that are known, most are part of large multimeric complexes that are challenging to manipulate.We describe here the use of phage display mutagenesis to generate of a new class of binding protein that can be targeted to the pointed-end of actin. These proteins, called synthetic antigen binders (sABs), are based on an antibody-like scaffold where sequence diversity is introduced into the binding loops using a novel "reduced genetic code" phage display library. We describe effective strategies to select and screen for sABs that ensure the generated sABs bind to the pointed-end surface of actin exclusively.From our set of pointed-end binders, we identify three sABs with particularly useful properties to systematically probe actin dynamics: one protein that caps the pointed end, a second that crosslinks actin filaments, and a third that severs actin filaments and promotes disassembly

Testing of a Model with Latino Patients That Explains the Links Among Patient-Perceived Provider Cultural Sensitivity, Language Preference, and Patient Treatment Adherence

Introduction Disparities in treatment adherence based on race and ethnicity are well documented but poorly understood. Specifically, the causes of treatment nonadherence among Latino patients living in the USA are complex and include cultural and language barriers. Purpose The purpose of this study was to examine whether patients’ perceptions in patient-provider interactions (i.e., trust in provider, patient satisfaction, and patient sense of interpersonal control in patient-provider interactions) mediate any found association between patient-perceived provider cultural sensitivity (PCS) and treatment adherence among English-preferred Latino (EPL) and Spanish-preferred Latino (SPL) patients. Methods Data from 194 EPL patients and 361 SPL patients were obtained using questionnaires. A series of language-specific structural equation models were conducted to test the relationship between patient-perceived PCS and patient treatment adherence and the examined mediators of this relationship among the Latino patients. Results No significant direct effects of patient-perceived PCS on general treatment adherence were found. However, as hypothesized, several significant indirect effects emerged. Preferred language appeared to have moderating effects on the relationships between patient-perceived PCS and general treatment adherence. Conclusion These results suggest that interventions to promote treatment adherence among Latino patients should likely include provider training to foster patient-defined PCS, trust in provider, and patient satisfaction with care. Furthermore, this training needs to be customized to be suitable for providing care to Latino patients who prefer speaking Spanish and Latino patients who prefer speaking English