A multivariate frailty model for disease recurrences and survival

A multivariate frailty hazard model is developed for joint-modeling of three correlated time-to-event outcomes: (1) local recurrence, (2) distant recurrence, and (3) overall survival. The term frailty is introduced to model population heterogeneity. The dependence is modeled by conditioning on a shared frailty that is included in the three hazard functions. Independent variables can be included in the model as covariates. The Markov chain Monte Carlo methods are used to estimate the posterior distributions of model parameters. The algorithm used in present application is the hybrid Metropolis-Hastings algorithm, which simultaneously updates all parameters with evaluations of gradient of log posterior density. The performance of this approach is examined based on simulation studies using Exponential and Weibull distributions. We apply the proposed methods to a study of patients with soft tissue sarcoma, which motivated this research. Our results indicate that patients with chemotherapy had better overall survival with hazard ratio of 0.242 (95% CI: 0.094 - 0.564) and lower risk of distant recurrence with hazard ratio of 0.636 (95% CI: 0.487 - 0.860), but not significantly better in local recurrence with hazard ratio of 0.799 (95% CI: 0.575 - 1.054). The advantages and limitations of the proposed models, and future research directions are discussed. ^

Survival analysis of longevity in siblings

Do siblings of centenarians tend to have longer life spans? To answer this question, life spans of 184 siblings for 42 centenarians have been evaluated. Two important questions have been addressed in analyzing the sibling data. First, a standard needs to be established, to which the life spans of 184 siblings are compared. In this report, an external reference population is constructed from the U.S. life tables. Its estimated mortality rates are treated as baseline hazards from which the relative mortality of the siblings are estimated. Second, the standard survival models which assume independent observations are invalid when correlation within family exists, underestimating the true variance. Methods that allow correlations are illustrated by three different methods. First, the cumulative relative excess mortality between siblings and their comparison group is calculated and used as an effective graphic tool, along with the Product Limit estimator of the survival function. The variance estimator of the cumulative relative excess mortality is adjusted for the potential within family correlation using Taylor linearization approach. Second, approaches that adjust for the inflated variance are examined. They are adjusted one-sample log-rank test using design effect originally proposed by Rao and Scott in the correlated binomial or Poisson distribution setting and the robust variance estimator derived from the log-likelihood function of a multiplicative model. Nether of these two approaches provide correlation estimate within families, but the comparison with the comparison with the standard remains valid under dependence. Last, using the frailty model concept, the multiplicative model, where the baseline hazards are known, is extended by adding a random frailty term that is based on the positive stable or the gamma distribution. Comparisons between the two frailty distributions are performed by simulation. Based on the results from various approaches, it is concluded that the siblings of centenarians had significant lower mortality rates as compared to their cohorts. The frailty models also indicate significant correlations between the life spans of the siblings. ^

The INO80 chromatin remodeling complex is involved in the nucleotide excision repair pathway

The genomic DNA of eukaryotic cells is well organized into chromatin structures. However, these repressed structures present barriers that block the access of regulatory factors to the genome during various nuclear events. To overcome the obstacle, two major cellular processes, post-modification of histone tails and ATP-dependent chromatin remodeling, are involved in reconfiguring chromatin structure and creating accessible DNA. Despite the current research progress, much remains to be explored concerning the relationship between chromatin remodeling and DNA repair. Recently, one member of the ATP-dependent chromatin remodeling complexes, INO80, has been found to play a crucial role in DNA damage repair. However, the functions of this complex in higher eukaryotes have yet to be determined. The goal of my study is to generate a human somatic INO80 conditional knockout model and investigate the functions of Ino80 in damage repair.^ By homologous targeting of the INO80 locus in human HCT116 colon epithelial cells, I established a human somatic INO80 conditional knockout model. I have demonstrated that the conditional INO80 cells exhibited a sufficiently viable period when the INO80 protein is removed. Moreover, I found that loss of INO80 resulted in deficient UV lesion repair in response to UV while the protein levels of the NER factors such as XPC, XPA, XPD were not affected. And in vitro repair synthesis assay showed that the NER incision and repair synthesis activities were intact in the absence of INO80. Examination on the damage recognition factor XPC showed its recruitment to damage sites was impaired in the INO80 mutant cells. Loss of INO80 also led to reduced enrichment of XPA at the site of UV lesions. Despite the reduced recruitment of XPC and XPA observed in INO80 mutants, no direct interaction was detected. Meanwhile, direct interaction between INO80 and DDB1, the initial UV lesion detector, was detected by coimmunoprecipitation. UV-induced chromosome relaxation was reduced in cells devoid of INO80. These results demonstrate the INO80 complex may participates in the NER by interacting with DDB1 and having a critical role of in creating DNA accessibility for the nucleotide excision pathway. ^

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^

Detection and sequence analysis of a nickel-induced mutation in a retroviral model system

We have developed a novel way to assess the mutagenicity of environmentally important metal carcinogens, such as nickel, by creating a positive selection system based upon the conditional expression of a retroviral transforming gene. The target gene is the v-mos gene in MuSVts110, a murine retrovirus possessing a growth temperature dependent defect in expression of the transforming gene due to viral RNA splicing. In normal rat kidney cells infected with MuSVts110 (6m2 cells), splicing of the MuSVts110 RNA to form the mRNA from which the transforming protein, p85$\sp{\rm gag-mos}$, is translated is growth-temperature dependent, occurring at 33 C and below but not at 39 C and above. This splicing "defect" is mediated by cis-acting viral sequences. Nickel chloride treatment of 6m2 cells followed by growth at 39 C, allowed the selection of "revertant" cells which constitutively express p85$\sp{\rm gag-mos}$ due to stable changes in the viral RNA splicing phenotype, suggesting that nickel, a carcinogen whose mutagenicity has not been well established, could induce mutations in mammalian genes. We also show by direct sequencing of PCR-amplified integrated MuSVts110 DNA from a 6m2 nickel-revertant cell line that the nickel-induced mutation affecting the splicing phenotype is a cis-acting 70-base duplication of a region of the viral DNA surrounding the 3$\sp\prime$ splice site. These findings provide the first example of the molecular basis for a nickel-induced DNA lesion and establish the mutagenicity of this potent carcinogen. ^

An empirical model to estimate the demand for primary care in urban settings

Objective. To measure the demand for primary care and its associated factors by building and estimating a demand model of primary care in urban settings.^ Data source. Secondary data from 2005 California Health Interview Survey (CHIS 2005), a population-based random-digit dial telephone survey, conducted by the UCLA Center for Health Policy Research in collaboration with the California Department of Health Services, and the Public Health Institute between July 2005 and April 2006.^ Study design. A literature review was done to specify the demand model by identifying relevant predictors and indicators. CHIS 2005 data was utilized for demand estimation.^ Analytical methods. The probit regression was used to estimate the use/non-use equation and the negative binomial regression was applied to the utilization equation with the non-negative integer dependent variable.^ Results. The model included two equations in which the use/non-use equation explained the probability of making a doctor visit in the past twelve months, and the utilization equation estimated the demand for primary conditional on at least one visit. Among independent variables, wage rate and income did not affect the primary care demand whereas age had a negative effect on demand. People with college and graduate educational level were associated with 1.03 (p < 0.05) and 1.58 (p < 0.01) more visits, respectively, compared to those with no formal education. Insurance was significantly and positively related to the demand for primary care (p < 0.01). Need for care variables exhibited positive effects on demand (p < 0.01). Existence of chronic disease was associated with 0.63 more visits, disability status was associated with 1.05 more visits, and people with poor health status had 4.24 more visits than those with excellent health status. ^ Conclusions. The average probability of visiting doctors in the past twelve months was 85% and the average number of visits was 3.45. The study emphasized the importance of need variables in explaining healthcare utilization, as well as the impact of insurance, employment and education on demand. The two-equation model of decision-making, and the probit and negative binomial regression methods, was a useful approach to demand estimation for primary care in urban settings.^

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^

Bcr: A conditional inhibitor of Bcr -Abl oncoprotein

Chronic myelogenous leukemia (CML) is characterized cytogenetically by the presence of the Philadelphia chromosome and clinically by the clonal expansion of the hematopoietic stem cells and the accumulation of large numbers of myeloid cells. Philadelphia chromosome results from the reciprocal translocation between chromosomes 9 and 22 [t(9;22)(324;q11)], which fuses parts of the ABL proto-oncogene to 5′ portions of the BCR gene. The product of the fused gene is Bcr-Abl oncoprotein. Bcr-Abl oncoprotein has elevated protein tyrosine kinase activity, and is the cause of Philadelphia chromosome associated leukemias. The Bcr sequence in the fusion protein is crucial for the activation of Abl kinase activity and transforming phenotype of Bcr-Abl oncoprotein. Although the Bcr-Abl oncoprotein has been studied extensively, its normal counterpart, the Bcr protein, has been less studied and its function is not well understood. At this point, Bcr is known to encode a novel serine/threonine protein kinase. In Bcr-Abl positive leukemia cells, we found that the serine kinase activity of Bcr is impaired by tyrosine phosphorylation. Both the Bcr protein sequences within Bcr-Abl and the normal cellular Bcr protein lack serine/threonine kinase activity when they become phosphorylated on tyrosine residues by Bcr-Abl. Therefore, the goal of this study was to investigate the role of Bcr in Bcr-Abl positive leukemia cells. We found that overexpression of Bcr can inhibit Bcr-Abl tyrosine kinase activity, and the inhibition is dependent on its intact serine/threonine kinase function. Using the tet repressible promoter system, we demonstrated that Bcr when induced in Bcr-Abl positive leukemia cells inhibited the Bcr-Abl oncoprotein tyrosine kinase. Furthermore, induction of Bcr also increased the number of cells undergoing apoptosis and inhibited the transforming ability of Bcr-Abl. In contrast to the wild-type Bcr, the kinase-inactive mutant of Bcr (Y328F/Y360F) had no effects on Bcr-Abl tyrosine kinase in cells. Results from other experiments indicated that phosphoserine-containing Bcr sequences within the first exon, which are known to bind to the Abl SH2 domain, are responsible for observed inhibition of the Bcr-Abl tyrosine kinase. Several lines of evidence suggest that the phosphoserine form of Bcr, which binds to the Abl SH2 domain, strongly inhibits the Abl tyrosine kinase domain of Bcr-Abl Previously published findings from our laboratory have also shown that Bcr is phosphorylated on tyrosine residue 177 in Bcr-Abl positive cells and that this form of Bcr recruits the Grb2 adaptor protein, which is known to activate the Ras pathway. These findings implicate Bcr as an effector of Bcr-Abl's oncogenic activity. Therefore based on the findings presented above, we propose a model for dual Function of Bcr in Bcr-Abl positive leukemia cells. Bcr, when active as a serine/threonine kinase and thus autophosphorylating its own serine residues, inhibits Bcr-Abl's oncogenic functions. However, when Ber is tyrosine phosphorylated, its Bcr-Abl inhibitory function is neutralized thus allowing Bcr-Abl to exert its full oncogenic potential. Moreover, tyrosine phosphorylated Bcr would compliment Bcr-Abl's neoplastic effects by the activation of the Ras signaling pathway. ^