Comparison of random-effects meta-analysis models for the relative risk in the case of rare events: A simulation study

Pooling the relative risk (RR) across studies investigating rare events, for example, adverse events, via meta-analytical methods still presents a challenge to researchers. The main reason for this is the high probability of observing no events in treatment or control group or both, resulting in an undefined log RR (the basis of standard meta-analysis). Other technical challenges ensue, for example, the violation of normality assumptions, or bias due to exclusion of studies and application of continuity corrections, leading to poor performance of standard approaches. In the present simulation study, we compared three recently proposed alternative models (random-effects [RE] Poisson regression, RE zero-inflated Poisson [ZIP] regression, binomial regression) to the standard methods in conjunction with different continuity corrections and to different versions of beta-binomial regression. Based on our investigation of the models' performance in 162 different simulation settings informed by meta-analyses from the Cochrane database and distinguished by different underlying true effects, degrees of between-study heterogeneity, numbers of primary studies, group size ratios, and baseline risks, we recommend the use of the RE Poisson regression model. The beta-binomial model recommended by Kuss (2015) also performed well. Decent performance was also exhibited by the ZIP models, but they also had considerable convergence issues. We stress that these recommendations are only valid for meta-analyses with larger numbers of primary studies. All models are applied to data from two Cochrane reviews to illustrate differences between and issues of the models. Limitations as well as practical implications and recommendations are discussed; a flowchart summarizing recommendations is provided.     

Identification of novel prognostic alternative splicing signature in papillary renal cell carcinoma

Papillary renal cell carcinoma (pRCC) is a heterogeneous disease containing multifocal or solitary tumors with an aggressive phenotype. Increasing evidence has indicated the involvement of aberrant splicing variants in renal cell cancer, while systematic profiling of aberrant alternative splicing (AS) in pRCC was lacking and largely unknown. In the current study, comprehensive profiling of AS events were performed based on the integration of pRCC cohort from the Cancer Genome Atlas database and SpliceSeq software. With rigorous screening and univariate Cox analysis, a total of 2077 prognoses AS events from 1642 parent genes were identified. Then, stepwise least absolute shrinkage and selection operator method-penalized Cox regression analyses with 10-fold cross-validation followed by multivariate Cox regression were used to construct the prognostic AS signatures within each AS type. And a final 21 AS event-based signature was proposed which showed potent prognostic capability in stratifying patients into low- and high-risk subgroups (P < .0001). Furthermore, time-dependent receiver operating characteristics curves confirmed that the final AS signature was effective and robust in predicting overall survival for pRCC patients with the area under the curve above 0.9 from 1 to 5 years. In addition, splicing correlation network was built to uncover the potential regulatory pattern among prognostic splicing factors and candidate AS events. Besides, gene set enrichment analysis revealed the involvement of these candidates AS events in tumor-related pathways including extracellular matrix organization, oxidative phosphorylation, and P53 signaling pathways. Taken together, our results could contribute to elucidating the underlying mechanism of AS in the oncogenesis process and broaden the novel field of prognostic and clinical application of molecule-targeted approaches in pRCC.     

Physical maturity in common bottlenose dolphins (Tursiops truncatus) from Sarasota Bay,Florida

Cetacean physical maturity is defined by growth cessation and complete fusion of epiphyses to vertebral bodies indicated by invisible sutures. Many studies have shown epiphyseal fusion is highly variable among individuals. In-depth examinations into fusion variability are lacking. We analyzed vertebrae of 37 (n = 21 female, n = 16 male) stranded common bottlenose dolphins (Tursiops truncatus) from the well-studied Gulf of Mexico, Sarasota Bay community. For each specimen, vertebrae were examined by vertebral region for degree of fusion anteriorly and posteriorly of each centrum and categorized from unfused to fused in five degrees. An ordinal logistic regression was used to estimate degree of fusion probability for each epiphysis. The model had fixed effects for age, number of offspring, sex, sexual maturity, and a random effect for epiphysis. Results show that age/reproductive status significantly explains an individual's degree of fusion. Adult females with fewer calves had more fusion than those with more reproductive experience across multiple ages. Access to long-term observational and sample data on the dolphins residing in the area served by Mote Marine Laboratory's Stranding Investigations Program offers a unique opportunity to examine the relationship between energetic demands of reproduction (calcium production/reproductive output) versus preconceived definitions of physical maturity (skeletal fusion) more closely.     

A design criterion for symmetric model discrimination based on flexible nominal sets

Experimental design applications for discriminating between models have been hampered by the assumption to know beforehand which model is the true one, which is counter to the very aim of the experiment. Previous approaches to alleviate this requirement were either symmetrizations of asymmetric techniques, or Bayesian, minimax, and sequential approaches. Here we present a genuinely symmetric criterion based on a linearized distance between mean-value surfaces and the newly introduced tool of flexible nominal sets. We demonstrate the computational efficiency of the approach using the proposed criterion and provide a Monte-Carlo evaluation of its discrimination performance on the basis of the likelihood ratio. An application for a pair of competing models in enzyme kinetics is given.     

Comparison of complex modeling strategies for prediction of a binary outcome based on a few,highly correlated predictors

Motivated by a clinical prediction problem, a simulation study was performed to compare different approaches for building risk prediction models. Robust prediction models for hospital survival in patients with acute heart failure were to be derived from three highly correlated blood parameters measured up to four times, with predictive ability having explicit priority over interpretability. Methods that relied only on the original predictors were compared with methods using an expanded predictor space including transformations and interactions. Predictors were simulated as transformations and combinations of multivariate normal variables which were fitted to the partly skewed and bimodally distributed original data in such a way that the simulated data mimicked the original covariate structure. Different penalized versions of logistic regression as well as random forests and generalized additive models were investigated using classical logistic regression as a benchmark. Their performance was assessed based on measures of predictive accuracy, model discrimination, and model calibration. Three different scenarios using different subsets of the original data with different numbers of observations and events per variable were investigated. In the investigated setting, where a risk prediction model should be based on a small set of highly correlated and interconnected predictors, Elastic Net and also Ridge logistic regression showed good performance compared to their competitors, while other methods did not lead to substantial improvements or even performed worse than standard logistic regression. Our work demonstrates how simulation studies that mimic relevant features of a specific data set can support the choice of a good modeling strategy.     

A prognostic eight-gene expression signature for patients with breast cancer receiving adjuvant chemotherapy

Breast cancer is a popularly diagnosed malignant tumor. Genomic profiling studies suggest that breast cancer is a disease with heterogeneity. Chemotherapy is one of the chief means to treat breast cancer, while its responses and clinical outcomes vary largely due to the conventional clinicopathological factors and inherent chemosensitivity of breast cancer. Using the least absolute shrinkage and selection operator (LASSO) Cox regression model, our study established a multi-mRNA-based signature model and constructed a relative nomogram in predicting distant-recurrence-free survival for patients receiving surgery and following chemotherapy. We constructed a signature of eight mRNAs (IPCEF1, SYNDIG1, TIGIT, SPESP1, C2CD4A, CLCA2, RLN2, and CCL19) with the LASSO model, which was employed to separate subjects into groups with high- and low-risk scores. Obvious differences of distant-recurrence-free survival were found between these two groups. This eight-mRNA-based signature was independently associated with the prognosis and had better prognostic value than classical clinicopathologic factors according to multivariate Cox regression results. Receiver operating characteristic results demonstrated excellent performance in diagnosing 3-year distant-recurrence by the eight-mRNA signature. A nomogram that combined both the eight-mRNA-based signature and clinicopathological risk factors was constructed. Comparing with an ideal model, the nomograms worked well both in the training and validation sets. Through the results that the eight-mRNA signature effectively classified patients into low- and high-risk of distant recurrence, we concluded that this eight-mRNA-based signature played a promising predictive role in prognosis and could be clinically applied in breast cancer patients receiving adjuvant chemotherapy.     

Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer

In this study, we purpose to investigate a novel five-gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan-Meier curve and Log-rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five-gene signature through Cox survival analysis. Patients were divided into low- and high-risk groups depending on the median risk score, and a significant difference of the 5-year overall survival was found between these two groups (P < .05). ROC curves verified that this five-gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five-gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.     

Equivalence of regression curves sharing common parameters

In clinical trials, the comparison of two different populations is a common problem. Nonlinear (parametric) regression models are commonly used to describe the relationship between covariates, such as concentration or dose, and a response variable in the two groups. In some situations, it is reasonable to assume some model parameters to be the same, for instance, the placebo effect or the maximum treatment effect. In this paper, we develop a (parametric) bootstrap test to establish the similarity of two regression curves sharing some common parameters. We show by theoretical arguments and by means of a simulation study that the new test controls its significance level and achieves a reasonable power. Moreover, it is demonstrated that under the assumption of common parameters, a considerably more powerful test can be constructed compared with the test that does not use this assumption. Finally, we illustrate the potential applications of the new methodology by a clinical trial example.     

Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated “The Cancer Proteome Atlas” (TCPA), which contains reverse-phase protein arrays–based high-quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.     

In vitro nemato-toxic potential of some leaf extracts on Juvenile mortality of Meloidogyne incognita race-3

Abstract

Aqueous leaf extracts of four commonly growing weeds namely Ageratum conyzoides, Elephantopus scaber, Lantana camara and Xanthium strumarium were used to evaluate their nematicidal activity on second stage juvenile of Meloidogyne incognita race-3. The juveniles were exposed to various concentration of leaf extract namely 250, 500, 1000 and 2000?ppm for 12, 24 and 48?h, respectively. All leaf extracts showed the nematicidal property in concentration and time-dependent manner. The maximum juvenile mortality was recorded in E. scaber throughout the incubation period followed by X. strumarium, L. camara and A. conyzoides. The regression and correlation of regression revealed the best concentration-dependent effect of aqueous leaf extracts on nematode mortality in E. scaber (R²?=?.751) followed by X. strumarium (R²?=?.749), A. conyzoides (R²?=?.687) and L. camara (R²?=?.756). Aqueous leaves extracts of these aforementioned weeds showed nematicidal properties, therefore, may be used as a key component of integrated disease management programme.