Biomedical subjects

Guo-Liang Tian

Publications and source records attributed to Guo-Liang Tian.

6 recordsLinked to original sources

PubMed2006

A multivariate random-effects model with restricted parameters: application to assessing radiation therapy for brain tumours.

In clinical studies, multiple endpoints are often measured for each patient longitudinally. The multivariate random-effects or random coefficient model has been a useful method for analysis. However, medical research problems may impose restrictions on the model parameters of interests. For example, in a paediatric brain tumour study on radiation therapy, there is a natural ordering in the white matter relaxation time of brain tissues among different regions surrounding the primary tumour, i.e. the closer a specific region of brain tissues is to the centre of primary tumour, the shorter is the relaxation time. Such parameter constraints should be accounted for in the analysis. This article proposes a class of multivariate random coefficient models with restricted parameters and derives its maximum likelihood estimates (MLE). We propose a modified EM algorithm for the quadratic optimalization with linear inequality constraints necessary in deriving the MLE. The method is applied to analysing the paediatric brain tumour study.

Adolescent↗

PubMed2005

Repeated-measures models with constrained parameters for incomplete data in tumour xenograft experiments.

In cancer drug development, xenograft experiments (models) where mice are grafted with human cancer cells are used to elucidate the mechanism of action and/or to assess efficacy of a promising compound. Demonstrated activity in this model is an important step to bring a promising compound to humans. A key outcome variable in these experiments is tumour volumes measured over a period of time, while mice are treated with an anticancer agent following certain schedules. However, a mouse may die during the experiment or may be sacrificed when its tumour volume quadruples and then incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumour shrinkage (<0.01 cm3) or random truncation. In addition, if no treatment were given to the tumour-bearing mice, the tumours would keep growing until the mice die or are sacrificed. This intrinsic growth of tumour in the absence of treatment constrains the parameters in the regression and causes further difficulties in statistical analysis. We develop a maximum likelihood method based on the expectation/conditional maximization (ECM) algorithm to estimate the dose-response relationship while accounting for the informative censoring and the constraints of model parameters. A real xenograft study on a new anti-tumour agent temozolomide combined with irinotecan is analysed using the proposed method.

Algorithms↗

PubMed2004

Hierarchical models for tumor xenograft experiments in drug development.

In cancer drug development, demonstrated anticancer activity in animal models is an important step to bring a promising compound to clinic. Proper design and analysis of experiments using laboratory animals have received increasing attention recently. These experiments involve informatively censored longitudinal data with small samples. The problem is further complicated because of order constraints due to the intrinsic growth of control tumors without treatment. This article proposes a Bayesian hierarchical model to analyze informatively censored longitudinal data while accounting for the parameter constraints and providing valid small sample inference. We adopt a noniterative sampling approach, the inverse Bayes formulae (IBF) sampler, to generate independent posterior samples, which avoids convergence problems associated with Markov chain Monte-Carlo methods. To effectively deal with the restricted parameter problem, we use a linear transformation to simplify the constraints and exploit the IBF method to generate random samples from truncated multivariate normal distributions. Because diffuse priors are used, the posterior modes approximate the maximum likelihood estimates well, and the hierarchical model can be considered as an extended mixed-effects model. A real xenograft experiment on a new treatment is analyzed by using the proposed method.

Algorithms↗

PubMed2003

Experimental design and sample size determination for testing synergism in drug combination studies based on uniform measures.

In anticancer drug development, the combined use of two drugs is an important strategy to achieve greater therapeutic success. Often combination studies are performed in animal (mostly mice) models before clinical trials are conducted. These experiments on mice are costly, especially with combination studies. However, experimental designs and sample size derivations for the joint action of drugs are not currently available except for a few cases where strong model assumptions are made. For example, Abdelbasit and Plackett proposed an optimal design assuming that the dose-response relationship follows some specified linear models. Tallarida et al. derived a design by fixing the mixture ratio and used a t-test to detect the simple similar action. The issue is that in reality we usually do not have enough information on the joint action of the two compounds before experiment and to understand their joint action is exactly our study goal. In this paper, we first propose a novel non-parametric model that does not impose such strong assumptions on the joint action. We then propose an experimental design for the joint action using uniform measure in this non-parametric model. This design is optimal in the sense that it reduces the variability in modelling synergy while allocating the doses to minimize the number of experimental units and to extract maximum information on the joint action of the compounds. Based on this design, we propose a robust F-test to detect departures from the simple similar action of two compounds and a method to determine sample sizes that are economically feasible. We illustrate the method with a study of the joint action of two new anticancer agents: temozolomide and irinotecan.

Animals↗

PubMed2002

Comparison of energy prediction equations with measured resting energy expenditure in children with sickle cell anemia.

OBJECTIVE: To determine the accuracy of energy prediction equations when compared with measured resting energy expenditure (REE) in children with sickle cell anemia. To develop a modified equation that more accurately estimates the energy needs of children with sickle cell anemia and to cross-validate these on a different set of patients (test patients). DESIGN: REE was measured in children using indirect calorimetry and compared with predicted values using the Harris-Benedict and the Food and Agriculture Organization/World Health Organization/United Nations University equations (WHO). SUBJECTS/SETTING: Eighteen patients participated in the original sample that compared predicted with measured energy expenditure. The modified equations were developed using the original 18 patients. A test population of 20 different patients was used to validate the modified equations. STATISTICAL ANALYSIS: Wilcoxon signed-rank test was performed to compare measured with predicted REE. The correlation analysis method and multiple linear regression method were used to develop 2 modified versions for the Harris-Benedict and WHO prediction equations. RESULTS: When compared with the mean predicted REE using the Harris-Benedict and WHO equations, the mean measured REE was 14% and 12% greater than both (P=.005 and P=.014, respectively). Two modified equations were developed from the Harris-Benedict and WHO equations. Based on the data from the test patients, the mean measured REE was 15% greater than the mean predicted REE based on the Harris-Benedict and WHO equations (P=.0001 for both). When the modified Harris-Benedict and WHO equations were used, there was almost no difference in the mean measured REE and the mean predicted REE (mean difference using Harris-Benedict = 14, P = .9273; mean difference using WHO = -13, P = .6215). CONCLUSION: Both energy prediction equations underestimated REE in children with sickle cell anemia. The 2 modified versions of the energy prediction equations that we propose predicted the energy needs of these children much more accurately; however, the modified equations need to be validated through application to other children with sickle cell anemia.

Analysis of Variance↗

PubMed2002

Small-sample inference for incomplete longitudinal data with truncation and censoring in tumor xenograft models.

In cancer drug development, demonstrating activity in xenograft models, where mice are grafted with human cancer cells, is an important step in bringing a promising compound to humans. A key outcome variable is the tumor volume measured in a given period of time for groups of mice given different doses of a single or combination anticancer regimen. However, a mouse may die before the end of a study or may be sacrificed when its tumor volume quadruples, and its tumor may be suppressed for some time and then grow back. Thus, incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumor shrinkage (<0.01 cm3) or random truncation. Because of the small sample sizes in these models, asymptotic inferences are usually not appropriate. We propose two parametric test procedures based on the EM algorithm and the Bayesian method to compare treatment effects among different groups while accounting for informative censoring. A real xenograft study on a new antitumor agent, temozolomide, combined with irinotecan is analyzed using the proposed methods.

Algorithms↗