Predictive margins with survey data

In the analysis of covariance, the display of adjusted treatment means allows one to compare mean (treatment) group outcomes controlling for different covariate distributions in the groups. Predictive margins are a generalization of adjusted treatment means to nonlinear models. The predictive margin for group r represents the average predicted response if everyone in the sample had been in group r. This paper discusses the use of predictive margins with complex survey data, where an important consideration is the choice of covariate distribution used to standardize the predictive margin. It is suggested that the textbook formula for the standard error of an adjusted treatment mean from the analysis of covariance may be inappropriate for applications involving survey data. Applications are given using data from the 1992 National Health Interview Survey (NHIS) and the Epidemiologic Followup Study to the first National Health and Nutrition Examination Survey (NHANES I).     

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.     

Colon cancer prediction with genetic profiles using intelligent techniques

Micro array data provides information of expression levels of thousands of genes in a cell in a single experiment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. In our present study we have used the benchmark colon cancer data set for analysis. Feature selection is done using t‐statistic. Comparative study of class prediction accuracy of 3 different classifiers viz., support vector machine (SVM), neural nets and logistic regression was performed using the top 10 genes ranked by the t‐statistic. SVM turned out to be the best classifier for this dataset based on area under the receiver operating characteristic curve (AUC) and total accuracy. Logistic Regression ranks as the next best classifier followed by Multi Layer Perceptron (MLP). The top 10 genes selected by us for classification are all well documented for their variable expression in colon cancer. We conclude that SVM together with t-statistic based feature selection is an efficient and viable alternative to popular techniques.     

Carbon pool densities and a first estimate of the total carbon pool in the Mongolian forest‐steppe

The boreal forest biome represents one of the most important terrestrial carbon stores, which gave reason to intensive research on carbon stock densities. However, such an analysis does not yet exist for the southernmost Eurosiberian boreal forests in Inner Asia. Most of these forests are located in the Mongolian forest‐steppe, which is largely dominated by Larix sibirica. We quantified the carbon stock density and total carbon pool of Mongolia's boreal forests and adjacent grasslands and draw conclusions on possible future change. Mean aboveground carbon stock density in the interior of L. sibirica forests was 66 Mg C ha^?1, which is in the upper range of values reported from boreal forests and probably due to the comparably long growing season. The density of soil organic carbon (SOC, 108 Mg C ha^?1) and total belowground carbon density (149 Mg C ha^?1) are at the lower end of the range known from boreal forests, which might be the result of higher soil temperatures and a thinner permafrost layer than in the central and northern boreal forest belt. Land use effects are especially relevant at forest edges, where mean carbon stock density was 188 Mg C ha^?1, compared with 215 Mg C ha^?1 in the forest interior. Carbon stock density in grasslands was 144 Mg C ha^?1. Analysis of satellite imagery of the highly fragmented forest area in the forest‐steppe zone showed that Mongolia's total boreal forest area is currently 73 818 km², and 22% of this area refers to forest edges (defined as the first 30 m from the edge). The total forest carbon pool of Mongolia was estimated at ~ 1.5?1.7 Pg C, a value which is likely to decrease in future with increasing deforestation and fire frequency, and global warming.     

采用数据挖掘技术对湖北省人类狂犬病开展生物信息学研究

狂犬病作为一种急性人畜共患疾病,其致死率接近100％.而湖北省作为我国中部狂犬病高发地之一,开展该省狂犬病的流行病学调查不仅有助于了解我国当前面临的狂犬病疫情风险,还能为当地及全国狂犬病防控工作提供有效的参考意见.首先运用描述性分析发现湖北省狂犬病发病人数随时间呈下降趋势,而狂犬病暴露人数则呈上升趋势,且近年来中西部地...     

Multikink quantile regression for longitudinal data with application to progesterone data analysis

Motivated by investigating the relationship between progesterone and the days in a menstrual cycle in a longitudinal study, we propose a multikink quantile regression model for longitudinal data analysis. It relaxes the linearity condition and assumes different regression forms in different regions of the domain of the threshold covariate. In this paper, we first propose a multikink quantile regression for longitudinal data. Two estimation procedures are proposed to estimate the regression coefficients and the kink points locations: one is a computationally efficient profile estimator under the working independence framework while the other one considers the within-subject correlations by using the unbiased generalized estimation equation approach. The selection consistency of the number of kink points and the asymptotic normality of two proposed estimators are established. Second, we construct a rank score test based on partial subgradients for the existence of the kink effect in longitudinal studies. Both the null distribution and the local alternative distribution of the test statistic have been derived. Simulation studies show that the proposed methods have excellent finite sample performance. In the application to the longitudinal progesterone data, we identify two kink points in the progesterone curves over different quantiles and observe that the progesterone level remains stable before the day of ovulation, then increases quickly in 5 to 6 days after ovulation and then changes to stable again or drops slightly.     

Development of a risk scoring system for patients with papillary thyroid cancer

As the importance of personalized therapeutics in aggressive papillary thyroid cancer (PTC) increases, accurate risk stratification is required. To develop a novel prognostic scoring system for patients with PTC (n = 455), we used mRNA expression and clinical data from The Cancer Genome Atlas. We performed variable selection using Network‐Regularized high‐dimensional Cox‐regression with gene network from pathway databases. The risk score was calculated using a linear combination of regression coefficients and mRNA expressions. The risk score and clinical variables were assessed by several survival analyses. The risk score showed high discriminatory power for the prediction of event‐free survival as well as the presence of metastasis. In multivariate analysis, the risk score and presence of metastasis were significant risk factors among the clinical variables that were examined together. In the current study, we developed a risk scoring system that will help to identify suitable therapeutic options for PTC.     

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.     

Using real-world data to predict health outcomes—The prediction design: Application and sample size planning

The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time-consuming, and not always practicable. At the same time, huge quantities of available patient-level control condition data in analyzable format of (former) RCTs or real-world data (RWD) are neglected. Therefore, alternative study designs are desirable. The design presented here consists of setting up a prediction model for determining treatment effects under the control condition for future patients. When a new treatment is intended to be tested against a control treatment, a single-arm trial for the new therapy is conducted. The treatment effect is then evaluated by comparing the outcomes of the single-arm trial against the predicted outcomes under the control condition. While there are obvious advantages of this design compared to classical RCTs (increased efficiency, lower cost, alleviating participants’ fear of being on control treatment), there are several sources of bias. Our aim is to investigate whether and how such a design—the prediction design—may be used to provide information on treatment effects by leveraging external data sources. For this purpose, we investigated under what assumptions linear prediction models could be used to predict the counterfactual of patients precisely enough to construct a test and an appropriate sample size formula for evaluating the average treatment effect in the population of a new study. A user-friendly R Shiny application (available at: https://web.imbi.uni-heidelberg.de/PredictionDesignR/ ) facilitates the application of the proposed methods, while a real-world application example illustrates them.