How to use χ2 test correctly——the analysis of common relative risk for the data of a multiway table and the implementation of SAS software / 四川精神卫生

The purpose of the paper was to introduce the relative risk analysis method of g×2×2 table data and the calculation method based on SAS software. The contents included the following aspects： firstly， the homogeneity test of the data in the g×2×2 table was performed； secondly， when the data met the homogeneity requirements， the point estimation and confidence interval estimation of the common relative risk based on the correction method were implemented； thirdly， when the data did not meet the requirements of homogeneity， the common relative risk RRDL and its 95% confidence interval were estimated， based on the DerSimonian-Laird method （DL method for short）； fourthly， when the data met and did not meet the requirements of homogeneity， the hypothesis of "whether the common relative risk equals to 1" was tested. Combining two examples and based on SAS software， the paper completed the following three tasks： ① the homogeneity test for the relative risk； ② the point estimation of the common relative risk； ③the confidence interval estimation of the common relative risk. The last two tasks were performed under the conditions of the data met and did not meet the homogeneity requirements， respectively. The output results of SAS software were explained， and the statistical and professional conclusions were made.

Comparison of Two Meta-Analysis Methods: Inverse-Variance-Weighted Average and Weighted Sum of Z-Scores

The meta-analysis has become a widely used tool for many applications in bioinformatics, including genome-wide association studies. A commonly used approach for meta-analysis is the fixed effects model approach, for which there are two popular methods: the inverse variance-weighted average method and weighted sum of z-scores method. Although previous studies have shown that the two methods perform similarly, their characteristics and their relationship have not been thoroughly investigated. In this paper, we investigate the optimal characteristics of the two methods and show the connection between the two methods. We demonstrate that the each method is optimized for a unique goal, which gives us insight into the optimal weights for the weighted sum of z-scores method. We examine the connection between the two methods both analytically and empirically and show that their resulting statistics become equivalent under certain assumptions. Finally, we apply both methods to the Wellcome Trust Case Control Consortium data and demonstrate that the two methods can give distinct results in certain study designs.

Estimation of Pharmacokinetic Parameters Using Nonlinear Fixed Effects One Compartment Open Model.

Pharmacokinetics (PK) is the science of the kinetics of drug absorption, distribution and elimination. Statistical methods are usually used for PK parameter estimation producing nonlinear responses where drug effect mechanism is modeled using compartmental approach. In the present study, PK parameters were estimated with nonlinear fixed effects one compartment open model where drug dose and sampling time are covariates and the plasma drug concentration is dependent variable. The PK parameters namely absorption rate constant (a), elimination rate constant (b) and apparent volume of distribution (V) were estimated using nonlinear least square method for each individual separately and for all individuals collectively with longitudinal or multiple response plasma drug concentration-time data obtained from 24 healthy human volunteers with reference drug product of Atorvastatin. The estimates for combined data were â =0.13±0.13hr-1, 􀜾􀷡 =0.49±0.13hr-1, 􀜸􀷡 =248±0.05L. All the individuals were again stratified into three categories based on Body Mass Index (BMI) and the PK parameters were estimated for each stratum accordingly (stratum-normal: â=0.12±0.17hr-1, 􀜾 􀷠 =0.47±0.17hr-1, 􀜸 􀷠 =250.24±0.07L; stratum-overweight: â =0.15±0.24hr-1, 􀜾􀷡 =0.47±0.25hr-1, 􀜸􀷡 =267.25±0.09L; stratumunderweight: â =0.13±0.13hr-1, 􀜾 􀷠 =0.49±0.13hr-1, 􀜸 􀷠=245±0.05L).