Medical Clinical Studies Research Articles

Asymptotic Sample Size for Common Test of Relative Risk Ratios in Stratified Bilateral Data

In medical clinical studies, various tests usually relate to the sample size. This paper proposes several methods to calculate sample sizes for a common test of relative risk ratios in stratified bilateral data. Under the prespecified significant level and power, we derive some explicit formulae and an algorithm of the sample size. The sample sizes of the stratified intra-class model are obtained from the likelihood ratio, score, and Wald-type tests. Under pooled data, we calculate sample size based on the Wald-type test and its log-transformation form. Numerical simulations show that the proposed sample sizes have empirical power close to the prespecified value for given significance levels. The sample sizes from the iterative method are more stable and effective.

Homogeneity Test of Many-to-One Relative Risk Ratios in Unilateral and Bilateral Data with Multiple Groups

In medical clinical studies, we often encounter paired organs’ unilateral or bilateral data. For bilateral data, there exists an intraclass correlation between paired organs. Under an intraclass correlation model, this paper proposes asymptotic statistics for testing the equality of many-to-one relative risk ratios in combined unilateral and bilateral data. Furthermore, we calculate the explicit expressions of these statistics. Moreover, these procedures are adequate to solve the hypothesis problems of unilateral or bilateral data. Through comparison, the simulation results show that the score test has a robust empirical type-I error rate and sufficient power. We provide a clinical trial of acute otitis media to illustrate our proposed methods.

Testing the common risk difference of proportions for stratified uni‐ and bilateral correlated data

In medical clinical studies, uni‐ and bilateral data naturally occurs if each patient contributes either one or both of paired organ measurements in a stratified design. This paper mainly proposes a common test of risk differences between proportions for stratified uni‐ and bilateral correlated data. Likelihood ratio, score, and Wald‐type test statistics are constructed using global, unconstrained, and constrained maximum likelihood estimations of parameters. Simulation studies are conducted to evaluate the performance of these test procedures in terms of type I error rates and powers. Empirical results show that the likelihood ratio test is more robust and powerful than other statistics. A real example is used to illustrate the proposed methods.

Statistical Inference for Odds Ratio of Two Proportions in Bilateral Correlated Data

Bilateral correlated data frequently arise in medical clinical studies such as otolaryngology and ophthalmology. Based on an equal correlation coefficient model, this paper mainly aimed to investigate the statistical inference for the odds ratio of two proportions in bilateral correlated data, including not only three test procedures but also four confidence interval (CI) constructions. Through iterative algorithms, all unknown parameters are estimated in order to construct the likelihood ratio, score and Wald-type tests. Furthermore, the profile likelihood CI, score CI, and Wald-type CI are obtained by the bisection root-finding algorithm. We provided another Wald-type CI based on an asymptotic normality property. The performance of the proposed tests were investigated with regard to empirical type I error rate and power, and CI methods were compared in terms of mean coverage probability and mean interval width. Numerical simulations show that the score test is more robust, and has higher power than other tests. The score CI also has a shorter interval width, and its coverage probability is closer to 0.95. A real example is used to illustrate the proposed methods.

Homogeneity test of relative risk ratios for stratified bilateral data under different algorithms

Medical clinical studies about paired body parts often involve stratified bilateral data. The correlation between responses from paired parts should be taken into account to avoid biased or misleading results. This paper aims to test if the relative risk ratios across strata are equal under the optimal algorithms. Based on different algorithms, we obtain the desired global and constrained maximum likelihood estimations (MLEs). Three asymptotic test statistics (i.e. , and ) are proposed. Monte Carlo simulations are conducted to evaluate the performance of these algorithms with respect to mean square errors of MLEs and convergence rate. The empirical results show Fisher scoring algorithm is usually better than other methods since it has effective convergence rate for global MLEs, and makes mean-square error lower for constrained MLEs. Three test statistics are compared in terms of type I error rate (TIE) and power. Among these statistics, is recommended according to its robust TIEs and satisfactory power.