Hua Jin

Diffusion tensor imaging: serial quantitation of white matter tract maturity in premature newborns.

Magnetic resonance diffusion tensor imaging (DTI) enables the discrimination of white matter pathways before myelination is evident histologically or on conventional MRI. In this investigation, 14 premature neonates with no evidence of white matter abnormalities by conventional MRI were studied with DTI. A custom MR-compatible incubator with a novel high sensitivity neonatal head coil and improved acquisition and processing techniques were employed to increase image quality and spatial resolution. The technical improvements enabled tract-specific quantitative characterization of maturing white matter, including several association tracts and subcortical projection tracts not previously investigated in neonates by MR. Significant differences were identified between white matter pathways, with earlier maturing commissural tracts of the corpus callosum, and deep projection tracts of the cerebral peduncle and internal capsule exhibiting lower mean diffusivity (Dav) and higher fractional anisotropy (FA) than later maturing subcortical projection and association pathways. Maturational changes in white matter tracts included reductions in Dav and increases in FA with age due primarily to decreases in the two minor diffusion eigenvalues (lambda2 and lambda3). This work contributes to the understanding of normal white matter development in the preterm neonatal brain, an important step toward the use of DTI for the improved evaluation and treatment of white matter injury of prematurity.

Comparing microstructural and macrostructural development of the cerebral cortex in premature newborns: Diffusion tensor imaging versus cortical gyration

This study assessed microstructural development in four regions of the human cerebral cortex during preterm maturation using diffusion tensor imaging (DTI), compared to the macrostructural development of cortical gyration evaluated using three-dimensional volumetric T1-weighted MR imaging. Thirty-seven premature infants of estimated gestational age (EGA) ranging from 25 to 38 weeks were prospectively enrolled and imaged in an MR-compatible neonatal incubator with a high-sensitivity neonatal head coil. Cortical gyration was measured quantitatively as the ratio of gyral height to width on the volumetric MR images in four regions bilaterally (superior frontal, superior occipital, precentral, and postcentral gyri). Mean diffusivity (D(av)), fractional anisotropy (FA-the fraction of D(av) that is anisotropic), and the three DTI eigenvalues (components of diffusivity radial and tangential to the pial surface of cortex) were measured in the same cortical regions. Cortical gyration scores, FA, and radial diffusivity were all significantly correlated with EGA (P < 0.0001). However, in multivariate analysis, no significant relationship (P > 0.05) was found between DTI parameters and cortical gyration beyond their common association with estimated gestational age. Pre- and postcentral gyri had significantly lower anisotropy than the superior occipital and superior frontal gyri (P < 0.05), indicating that DTI is sensitive to regional heterogeneity in cortical development. Maturational changes in the DTI eigenvalues of cortical gray matter were found to differ from those that have previously been described in developing white matter, with a significant age-related decline in the radial diffusivity (P < 0.0001) but not in the tangential diffusivities (P > 0.05).

Classification algorithms for hip fracture prediction based on recursive partitioning methods.

This article presents 2modifications to the classification and regression tree. The authors improved the robustness of a split in the test sample approach and developed a cost-saving classification algorithm by selecting noninferior to the optimum splits from variables with lower cost or being used in parent splits. The new algorithmwas illustrated by 43 predictive variables for 5-year hip fracture previously documented in the Study of Osteoporotic Fractures. The authors generated the robust optimum classification rule without consideration of classification variable costs and then generated an alternative cost-saving rulewith equivalent diagnostic utility. A6-fold cross-validation study proved that the cost-saving alternative classification is statistically noninferior to the optimal one. Their modified classification and regression tree algorithm can be useful in clinical applications. A dual X-ray absorptiometry hip scan and information from clinical examinations can identify subjects with elevated 5-year hip fracture risk without loss of efficiency to more costly and complicated algorithms.

Alternative tree-structured survival analysis based on variance of survival time.

Tree-structured survival analysis (TSSA) is a popular alternative to the Cox proportional hazards regression in medical research of survival data. Several methods for constructing a tree of different survival profiles have been developed, including TSSA based on log-rank statistics, martingale residuals, Lp Wasserstein metrics between Kaplan-Meier survival curves, and a method based on a weighted average of the within-node impurity of the death indicator and the within-node loss function of follow-up times. Lu and others used variance of restricted mean lifetimes as an index of degree of separation (DOS) to measure the efficiency in separations of survival profiles by a classification method. Like tree-based regression analysis that uses variance as a criterion for node partition and pruning, the variance of restricted mean lifetimes between different groups can be an alternative index to log-rank test statistics in construction of survival trees. In this article, the authors explore the use of DOS in TSSA. They propose an algorithm similar to the least square regression tree for survival analysis based on the variance of the restricted mean lifetimes. They apply the proposed method to prospective cohort data from the Study of Osteoporotic Fracture that motivated the research and then compare their classification rule to those rules based on the conventional TSSA mentioned above. A limited simulation study suggests that the proposed algorithm is a competitive alternative to the log-rank or martingale residual-based TSSA approaches.

A new adaptive design based on Simon's two-stage optimal design for phase II clinical trials

Phase II clinical trials are conducted to determine whether a new agent or drug regimen has sufficient promise in treating cancer to merit further testing in larger groups of patients. Both ethical and practical considerations often require early termination of phase II trials if early results clearly indicate that the new regimen is not active or worthy of further investigation. Simons two-stage designs (1989) are common methods for conducting phase II studies investigating new cancer therapies. Banerjee and Tsiatis (2006) proposed an adaptive two-stage design which allows the sample size at the second stage to depend on the results at the first stage. Their design is more flexible than Simons, but it is somewhat counter-intuitive: as the response in the first stage increases, the second-stage sample size increases till a certain point and then abruptly becomes zero. In this paper, based on Simons two-stage optimal design, we propose a new adaptive one which depends on the first stage results using the restrict conditions the conditional type I error and the conditional power. Comparisons are made between Banerjee and Tsiatis results and our new adaptive designs.

A non-inferiority test of areas under two parametric ROC curves

Equivalence or non-inferiority in diagnostic accuracy of two medical diagnostic tests is a common medical problem. Statistical tests of non-inferiority of diagnostic tests have long been a subject of interest in medicine and biostatistical research. Accuracy of a continuous diagnostic test can be evaluated by the area under a receiver operating characteristic (ROC) curve. A conventional non-inferiority test for areas of two parametric ROC curves has been proposed by Zhou, Obuchowski, and McClish [Zhou XH, Obuchowski NA, McClish DK. Statistical Methods in Diagnostic Medicine. New York: John Wiley; 2002]. In this paper, we showed that this conventional approach is anti-conservative and proposed a new statistical test based on restricted maximum likelihood estimator to achieve designed type I error rate. Simulation studies supported this new test to be appropriate for small sample size or highly correlated diagnostic modalities.

Reduction of sampling bias of odds ratios for vertebral fractures using propensity scores.

IntroductionAssessment of the predictive power of a newly introduced diagnostic technique with regard to fracture risk is frequently limited by the enormous costs and long time periods required for prospective studies. A preliminary estimate of predictive power usually relies on cross-sectional case-control studies in which bone measurements of normal and fractured subjects are compared. The measured discriminatory power is taken as an estimate of predictive power. Because of possible sample selection bias, study participants may have different bone mineral density (BMD) values, and fractured patients may have fractures of different severity levels. The same diagnostic techniques for the measured discriminatory power, expressed as odds ratios, will differ among studies with different patient and control populations.MethodsIn this paper, we propose a weighted logistic regression approach to adjust the odds ratio in order to reduce the effect of sampling bias. The weight is derived from age, deformity severity, BMD, and the interactions of these, using the propensity score theory and reference population data.ResultsSimulation examples using data from the Osteoporosis and Ultrasound Study (OPUS) demonstrate that such a procedure can effectively reduce the estimation bias of odds ratios introduced by sampling differences, such as for dual x-ray absorptiometry (DXA) scans of the spine and hip as well as various quantitative ultrasound techniques. The derived estimated odds ratios are substantially less biased, and the corresponding 95% confidence intervals contain the true odds ratios from the population data.ConclusionsWe conclude that a statistical correction procedure based on propensity scores and weighted logistic regression can effectively reduce the effect of sampling bias on the odds ratios calculated from cross-sectional case-control studies. For a new diagnostic technique, hip BMD and deformity severity information are necessary and likely sufficient to derive the propensity scores required to adjust the measured standardized odds ratios.

Cost-Saving Tree-Structured Survival Analysis for Hip Fracture of Study of Osteoporotic Fractures Data

It is important to predict osteoporotic fracture risk accurately in order to select high-risk patients for treatment. Previous tree-structured survival analysis (TSSA) methods focused on optimization in statistical performance in construction of survival trees. However, they did not take into account the cost of the predictive variables. Because of the high cost of some predictors, the derived algorithm may have only limited application in practice. In this article, the authors consider the cost-effectiveness in TSSA and propose a cost-saving TSSA (denoted as CSTSSA) to construct the survival tree for identifying subjects at high risk of hip fracture based on the data from Study of Osteoporotic Fractures. The new rule is compared with the optimum classification based on log-rank test statistics using the noninferiority test by Lu and others. The comparison results suggest that, for identifying patients at high risk of hip fracture, the CSTSSA is a good alternative to the optimum classification.

On Comparison of Two Classification Methods with Survival Endpoints

Publisher Summary This chapter introduces an index degree of separation (DOS) to measure the efficiency in prognostic separation by a classification method based on restricted mean lifetimes. This index is used to compare the efficiency of two classification methods with survival time as the endpoint. Comparison of the methods efficiency in separating patients according to survival profiles is necessary. When comparing two classification rules, the common approach utilizes the Cox regression model with both classification methods as ordinal variables. It may be assumed that the first method contains all information of the second if the type-3 p -value for the second method is above a pre-specified significance level but the same p -value for the first method is below that level. On the other hand, if both methods are significant in the presence of the other, they are considered complementary. The mathematical relationships between partial ordering of DOS and classification methods are presented and estimating and testing procedures of two DOSs based on paired data are proposed, the goal being the development of a statistical procedure for comparing the efficiency of two classification methods. The chapter uses simulation to evaluate the distribution properties of the proposed test statistics under finite sample size.