生物识别与生物统计学杂志

With the recent release of FDA draft guidance (2010), adaptive designs, including adaptive randomization (e.g. response-adaptive (RA) randomization) has become popular in clinical trials because of its advantages of flexibility and efficiency gains, which also have the significant ethical advantage of assigning fewer patients to treatment arms with inferior outcomes. In this paper, we presented a general overview of adaptive randomization designs for clinical trials, including Bayesian and frequentist approaches as well as response-adaptive randomization. Examples were used to demonstrate the procedure for design parameters calibration and operating characteristics. Both advantages and disadvantages of adaptive randomization were discussed in the summary from practical perspective of clinical trials.

Objectives: This study explored associations of environmental factors with percent trunk fat (PTF) and body mass index (BMI), using quantile regression to explain variability in these traits at percentiles of the distributions.
Methods: Using a sample of 1695 adults from Newfoundland and Labrador, multiple and quantile regression models were used to analyse the significance of environmental factors on the average population and upper percentiles of the BMI and PTF distributions.
Results: Higher physical activity was associated with significantly lower PTF and BMI in the average population and upper percentiles, regardless of age. Both genders in percentiles closer to the median of PTF had more benefit with increased physical activity compared to higher percentiles. Interestingly, adults in higher percentiles of BMI distribution seem to benefit more with increased physical activity compared to percentiles closer to the median.
Conclusion: Using quantile regression as a robust approach toward violation of normality assumptions and outliers, variations in PTF and BMI for individuals across upper percentiles of the distributions based on some lifestyle factors were described. This method may be used to estimate the impact of certain lifestyle on different percentiles of BMI and PTF, rather than average population.

In this paper, the problem of estimating the regression parameters is considered in a multiple regression model Y= X α + u hen the multicollinearity is present. Two suggested methods of finding the ridge regression parameter k are investigated and evaluated in terms of Mean Square Error (MSE) by simulation techniques. A number of factors that may affect the properties of these methods have been varied. Results of a simulation study indicate that with respect to MSE criteria, the suggested estimators perform better than both the ordinary least squares (OLS) estimator and the other estimators discussed here.

Accordingly to FDA draft guidance (2010), adaptive randomization (e.g. response-adaptive (RA) randomization) has become popular in clinical research because of its flexibility and efficiency, which also have the advantage of assigning fewer patients to inferior treatment arms. However, these designs lack a mechanism to actively control the imbalance of prognostic factors, i.e. covariates that substantially affect the study outcome. Improving the balance of patient characteristics among the treatment arms could potentially increases the statistical power of the trial. We propose a randomization procedure that is response-adaptive and that also actively balances the covariates across treatment arms. We then incorporate this method into a sequential RA randomization design such that the resulting design skews the allocation probability to the better treatment arm, and also controls the imbalance of the prognostic factors across the arms. The proposed method extends the existing randomization where Ning and Huang (2010) approach requires polytomizing continuous covariates and Yuan (2011) approach uses fixed allocation probability to adjust covariates imbalance.

Invasive breast cancer is a heterogeneous disease. The analysis of one or a group of specific gene expression profiles may not be enough to understand molecular activities in cancer cells. Therefore, a method which gives us the opportunity to compare similar up and down regulated gene expression profiles, is needed. The main purpose of our work is to sort the extreme high and low expressed genes and extract, compare and cluster them. Expression profiles of 598 samples of invasive breast cancer and 48 samples of normal tissue have been analysed to create a new algorithm called Extreme Gene Expression Family (EGEF). The EGEF algorithm sorted, grouped and compared the highest and the lowest expressed genes (n = 100). According to the hierarchical clustering result, dense and light memberships of gene families are detected. The resulting analysis allows us to predict which genes would show similar expression signatures in invasive breast cancer and to us to recognize specific biological activities and processes. EGEF algorithm can be used to detect expression signatures in other cancers and biological processes.

A class of scale mixtured normal distribution with lifetime probability distributions has been proposed in this article. Different moments, characteristic function and shape characteristics of these proposed probability distributions have also been provided. The scale mixture of normal distribution is extensively used in Biostatistical field. In this article, some new lifetime probability distributions have been proposed which is the scale mixture of normal distribution. Different properties such as moments, characteristic function, shape characteristics of these probability distributions have also been mentioned.

Biplot simulation using factor analysis rotation promax kapa 90 was conducted to determine the growth rate of body dimension in female Bali duck of 0-16 week-old. The result of the biplot simulation showed that the body dimension of female Bali ducks that belongs to slow growth rate was in quadran II including the length of radius ulna, femur, tarsal and humerus. The body dimensions of female Bali ducks with the moderate growth rates were in quadrant I such as the length of carpal, chest circumference, body weight, the length necks, the length of digital 1 and the length of head. The body dimension with fast growth rate such as head circumference, neck circumference, abdominal circumference, and the length of digital 2, 3 and 4, and the length of tibia-fibula. Based on the ages, the coordinates distances in two dimension Eigen vector space were as follows. At the most distance position was at the age of 0-2 weeks, followed by the age of 2-4 weeks, and finally with closest distance was at the age of maturity.

Oligodendrogliomas are characterized by 1p/19q co-deletion, which generally correlates with subjective morphologic features like nuclei circularity ratio and texture features of the cancer nuclei. As cost control becomes more of an issue in medicine, upfront reflex molecular diagnostic testing for lesions like 1p/19q co-deletion may not be appropriate. This paper aims to develop a rigorous, unbiased digital imaging segmentation algorithm and statistical models that can accurately predict the likelihood of 1p/19q co-deletion based on morphology and texture, which would greatly improve cost-effectiveness in clinic trail. In this study, totally 28 gliomas of haematoxylin and eosin stained slides are comprised in this test cohort. Selected areas that had high tumour cell density were digitally analysed with a high-throughput image segmentation algorithm to automatically delineate the boundaries of the cell nuclei. Then we extracted the morphologic features and texture features based on the segmentation result, and applied them in to Lasso-logistic regression to build the correlation between these features with 1p/19q co-deletion status. As a comparison, we also used PAM (Prediction Analysis of Microarrays), RPA (Recursive Partitioning Analysis) to compare the predication performance. We find out that the circularity ratio of the cell, the variance of cell area for each patient, and parts of texture features effect the 1p/19q co-deletion status, and the false prediction rate of leave one out cross validation is at most around 10%. Moreover, we conduct survival analysis and find out two morphologic features and one texture features are significant influential to patients’ survival time.