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.     

Tagging SNPs in the ERCC4 gene are associated with gastric cancer risk

ERCC4 plays an essential role in the nucleotide excision repair (NER) pathway, which is involved in the removal of a wide variety of DNA lesions. To determine whether the ERCC4 tagging SNPs (tSNPs) are associated with risk of gastric cancer, we conducted a hospital-based case-control study of 350 cases and 468 cancer-free controls. In the logistic regression (LR) analysis, we found a significantly decreased risk of gastric cancer associated with the rs744154 GC/CC genotypes [adjusted odds ratio (OR) = 0.56, 95% confidence interval (CI) = 0.42–0.75, false discovery rate (FDR) P = 0.003] compared with the wild-type GG genotype. Haplotype-based association study revealed that the CGC haplotype that containing the rs744154 C allele can decrease the risk of gastric cancer compared with the most common haplotype GGT (adjusted OR = 0.61, 95% CI = 0.46–0.81). Using the multifactor dimensionality reduction (MDR) analysis, we identified that the SNP rs744154 and smoking status were the best two predictive factors for gastric cancer with a testing accuracy of 55.76% and a perfect cross-validation consistency (CVC) of 10 (P = 0.001). Furthermore, the smokers with the rs744154 GC/CC genotypes showed a decreased risk of gastric cancer (adjusted OR = 0.55, 95% CI = 0.35–0.85) compared with the smokers with the GG genotype using multivariate LR analysis. The above findings consistently suggested that genetic variants in the ERCC4 gene may play a protective role in the etiology of gastric cancer, even in the smokers.     

International Society of Chronobiology: Membership Information

Most variables of interest in laboratory medicine show predictable changes with several frequencies in the span of time investigated. The waveform of such nonsinusoidal rhythms can be well described by the use of multiple components rhythmometry, a method that allows fitting a linear model with several cosine functions. The method, originally described for analysis of longitudinal time series, is here extended to allow analysis of hybrid data (time series sampled from a group of subjects, each represented by an individual series). Given k individual series, we can fit the same linear model with m different frequencies (harmonics or not from one fundamental period) to each series. This fit will provide estimations for 2m + 1 parameters, namely, the amplitude and acrophase of each component, as well as the rhythm-adjusted mean. Assuming that the set of parameters obtained for each individual is a random sample from a multivariate normal population, the corresponding population parameter estimates can be based on the means of estimates obtained from individuals in the sample. Their confidence intervals depend on the variability among individual parameter estimates. The vari-ance-covariance matrix can then be estimated on the basis of the sample covariances. Confidence intervals for the rhythm-adjusted mean, as well as for the amplitude-acrophase pair, of each component can then be computed using the estimated covariance matrix. The p-values for testing the zero-amplitude assumption for each component, as well as for the global model, can finally be derived using those confidence intervals and the t and F distributions. The method, validated by a simulation study and illustrated by an example of modeling the circadian variation of heart rate, represents a new step in the development of statistical procedures in chronobiology.     

Effect of increasing GnRH and PGF2α dose during Double-Ovsynch on ovulatory response,luteal regression,and fertility of lactating dairy cows

Ovsynch-type synchronization of ovulation protocols have suboptimal synchronization rates due to reduced ovulation to the first GnRH treatment and inadequate luteolysis to the prostaglandin F_2α (PGF_2α) treatment before timed artificial insemination (TAI). Our objective was to determine whether increasing the dose of the first GnRH or the PGF_2α treatment during the Breeding-Ovsynch portion of Double-Ovsynch could improve the rates of ovulation and luteolysis and therefore increase pregnancies per artificial insemination (P/AI). In experiment 1, cows were randomly assigned to a two-by-two factorial design to receive either a low (L) or high (H) doses of GnRH (Gonadorelin; 100 vs. 200 μg) and a PGF_2α analogue (cloprostenol; 500 vs. 750 μg) resulting in the following treatments: LL (n = 263), HL (n = 277), LH (n = 270), and HH (n = 274). Transrectal ultrasonography and serum progesterone (P4) were used to assess ovulation to GnRH1, GnRH2, and luteal regression after PGF_2α during Breeding-Ovsynch in a subgroup of cows (n = 651 at each evaluation). Pregnancy status was assessed 29, 39, and 74 days after TAI. In experiment 2, cows were randomly assigned to LL (n = 220) or HH (n = 226) treatment as described for experiment 1. For experiment 1, ovulation to GnRH1 was greater (P = 0.01) for cows receiving H versus L GnRH (66.6% [217/326] vs. 57.5% [187/325]) treatment, but only for cows with elevated P4 at GnRH1. Cows that ovulated to GnRH1 had increased (P < 0.001) fertility compared with cows that did not ovulate (52.2% vs. 38.5%); however, no effect of higher dose of GnRH on fertility was detected. The greater PGF_2α dose increased luteal regression primarily in multiparous cows (P = 0.03) and tended to increase fertility (P = 0.05) only at the pregnancy diagnosis 39 days after TAI. Overall, P/AI was 47.0% at 29 days and 39.7% at 74 days after TAI; P/AI did not differ (P = 0.10) among treatments at 74 days (LL, 34.6%; HL, 40.8%; LH, 42.2%; HH, 40.9%) and was greater (P < 0.001) for primiparous cows than for multiparous cows (46.1% vs. 33.8%). For experiment 2, P/AI did not differ (P = 0.21) between H versus L treatments (44.2% [100/226] vs. 40.5% [89/220]). Thus, despite an increase in ovulatory response to GnRH1 and luteal regression to PGF_2α, there were only marginal effects of increasing dose of GnRH or PGF_2α on fertility to TAI after Double-Ovsynch.     

Effect of vegetation on density thresholds of adult desert locust gregarization from survey data in Mauritania

In the desert locust, Schistocerca gregaria (Forskål) (Orthoptera: Acrididae), the threshold density inducing the gregarization phenomenon has never been determined under natural conditions. The influence of environmental factors on this phenomenon has been studied mostly in controlled environments. Based on data collected during several years by the survey teams of the National Center for Locust Control in Mauritania, we analyzed the influence of locust density, vegetation cover, and vegetation status on the probability of observing gregarious locusts. We assumed that a probability to observe gregarious locusts of 0.5 corresponded to the density threshold of gregarization. The results showed in detail the change in the threshold of gregarization according to the cover and status of the vegetation. Low cover and dry vegetation led to a low density threshold of gregarization probably due to high probability of individuals to touch each other. Dense and green vegetation favored a high threshold of gregarization probably due to a dispersion of the individuals and a low probability of individual encounters. These findings should help the management of locusts and decision making during control operations.