Hazard models with varying coefficients for multivariate failure time data

Statistical estimation and inference for marginal hazard models with varying coefficients for multivariate failure time data are important subjects in survival analysis. A local pseudo-partial likelihood procedure is proposed for estimating the unknown coefficient functions. A weighted average estimator is also proposed in an attempt to improve the efficiency of the estimator. The consistency and asymptotic normality of the proposed estimators are established and standard error formulas for the estimated coefficients are derived and empirically tested. To reduce the computational burden of the maximum local pseudo-partial likelihood estimator, a simple and useful one-step estimator is proposed. Statistical properties of the one-step estimator are established and simulation studies are conducted to compare the performance of the one-step estimator to that of the maximum local pseudo-partial likelihood estimator. The results show that the one-step estimator can save computational cost without compromising performance both asymptotically and empirically and that an optimal weighted average estimator is more efficient than the maximum local pseudo-partial likelihood estimator. A data set from the Busselton Population Health Surveys is analyzed to illustrate our proposed methodology.Comment: Published at http://dx.doi.org/10.1214/009053606000001145 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

BIO regulates the ex vivo expansion and function of hematopoietic stem cells by inhibiting GSK-3β

Hematopoietic stem cells (HSCs) have been applied in clinic settings for treating hematologic diseases, including leukemic disorders, immune deficiencies, and hemoglobinopathies. Umbilical cord blood(UCB) is an important source of HSCs. However, the low frequency of HSCs per unit of UCB remains a big hurdle to their wider applications. Wnt/β-catenin pathway plays important roles in the self-renewal of HSCs in vivo, but the roles of Wnt/β-catenin signaling on ex vivo expansion of HSCs remains controversial. GSK3β is the major regulator of Wnt pathway. Here, we evaluate the effects of 6-bromoindirubin-3’-oxime (BIO), a GSK3β inhibitor, on ex vivo expansion characteristics and regenerative potential of (UCB)-derived CD34+ cells. Please click Additional Files below to see the full abstract

Recent progresses in outcome-dependent sampling with failure time data

An outcome-dependent sampling (ODS) design is a retrospective sampling scheme where one observes the primary exposure variables with a probability that depends on the observed value of the outcome variable. When the outcome of interest is failure time, the observed data are often censored. By allowing the selection of the supplemental samples depends on whether the event of interest happens or not and oversampling subjects from the most informative regions, ODS design for the time-to-event data can reduce the cost of the study and improve the efficiency. We review recent progresses and advances in research on ODS designs with failure time data. This includes researches on ODS related designs like case–cohort design, generalized case–cohort design, stratified case–cohort design, general failure-time ODS design, length-biased sampling design and interval sampling design

Effects of Disorder On Thouless Pumping In Higher-Order Topological Insulators

We investigate the effects of random onsite disorder on higher-order Thouless pumping of noninteracting fermionic Benalcazar-Bernevig-Hughes (BBH) model. The interplay of disorderinduced topological phase transition and delocalization-localization transition is extensively explored. The higher-order Thouless pumping is characterized by the quantized corner-to-corner charge transport and nonzero Chern number, and the delocalization-localization transition is analyzed by utilizing both inverse participation ratio and energy-level statistics. The results show that the quantized corner-to-corner charge transport is broken in the strong disorder, where the instantaneous bulk energy gap is closed due to effects of disorder. While, although the instantaneous eigenstates are localized, the charge transport remains quantized. This is attributed to delocalized Floquet states caused by the periodic driving. Furthermore, the phase transition from the quantized charge transport to topologically trivial pumping is accompanied by the disorder-induced delocalization-localization transition of Floquet states.Comment: 8 pages, 7figure

Outcome-dependent sampling design and inference for Cox’s proportional hazards Model

We propose a cost-effective outcome-dependent sampling design for the failure time data and develop an efficient inference procedure for data collected with this design. To account for the biased sampling scheme, we derive estimators from a weighted partial likelihood estimating equation. The proposed estimators for regression parameters are shown to be consistent and asymptotically normally distributed. A criteria that can be used to optimally implement the ODS design in practice is proposed and studied. The small sample performance of the proposed method is evaluated by simulation studies. The proposed design and inference procedure is shown to be statistically more powerful than existing alternative designs with the same sample sizes. We illustrate the proposed method with an existing real data from the Cancer Incidence and Mortality of Uranium Miners Study

Estimated Pseudopartial-Likelihood Method for Correlated Failure Time Data with Auxiliary Covariates

As biological studies become more expensive to conduct, statistical methods that take advantage of existing auxiliary information about an expensive exposure variable are desirable in practice. Such methods should improve the study efficiency and increase the statistical power for a given number of assays. In this paper, we consider an inference procedure for multivariate failure time with auxiliary covariate information. We propose an estimated pseudo-partial likelihood estimator under the marginal hazard model framework and develop the asymptotic properties for the proposed estimator. We conduct simulation studies to evaluate the performance of the proposed method in practical situations and demonstrate the proposed method with a data set from the Studies of Left Ventricular Dysfunction (SOLVD,1991)

Semiparametric inference for data with a continuous outcome from a two-phase probability-dependent sampling scheme

Multi-phased designs and biased sampling designs are two of the well recognized approaches to enhance study efficiency. In this paper, we propose a new and cost-effective sampling design, the two-phase probability dependent sampling design (PDS), for studies with a continuous outcome. This design will enable investigators to make efficient use of resources by targeting more informative subjects for sampling. We develop a new semiparametric empirical likelihood inference method to take advantage of data obtained through a PDS design. Simulation study results indicate that the proposed sampling scheme, coupled with the proposed estimator, is more efficient and more powerful than the existing outcome dependent sampling design and the simple random sampling design with the same sample size. We illustrate the proposed method with a real data set from an environmental epidemiologic study

Additive–multiplicative rates model for recurrent events

Recurrent events are frequently encountered in biomedical studies. Evaluating the covariates effects on the marginal recurrent event rate is of practical interest. There are mainly two types of rate models for the recurrent event data: the multiplicative rates model and the additive rates model. We consider a more flexible additive–multiplicative rates model for analysis of recurrent event data, wherein some covariate effects are additive while others are multiplicative. We formulate estimating equations for estimating the regression parameters. The estimators for these regression parameters are shown to be consistent and asymptotically normally distributed under appropriate regularity conditions. Moreover, the estimator of the baseline mean function is proposed and its large sample properties are investigated. We also conduct simulation studies to evaluate the finite sample behavior of the proposed estimators. A medical study of patients with cystic fibrosis suffered from recurrent pulmonary exacerbations is provided for illustration of the proposed method

Marginal hazard regression for correlated failure time data with auxiliary covariates

In many biomedical studies, it is common that due to budget constraints, the primary covariate is only collected in a randomly selected subset from the full study cohort. Often, there is an inexpensive auxiliary covariate for the primary exposure variable that is readily available for all the cohort subjects. Valid statistical methods that make use of the auxiliary information to improve study efficiency need to be developed. To this end, we develop an estimated partial likelihood approach for correlated failure time data with auxiliary information. We assume a marginal hazard model with common baseline hazard function. The asymptotic properties for the proposed estimators are developed. The proof of the asymptotic results for the proposed estimators is nontrivial since the moments used in estimating equation are not martingale-based and the classical martingale theory is not sufficient. Instead, our proofs rely on modern empirical theory. The proposed estimator is evaluated through simulation studies and is shown to have increased efficiency compared to existing methods. The proposed methods are illustrated with a data set from the Framingham study