Genetic analysis of tumor progression susceptibility in the mouse skin model

In the field of chemical carcinogenesis the use of animal models has proved to be a useful tool in dissecting the multistage process of tumor formation. In this regard the outbred SENCAR mouse has been the strain of choice in the analysis of skin carcinogenesis given its high sensitivity to the chemically induced acquisition of premalignant lesions, papillomas, and the later progression of these lesions into squamous cell carcinomas (SCC).^ The derivation of an inbred strain from the SENCAR stock called SSIN, that in spite of a high sensitivity to the development of papillomas lack the ability to transform these premalignant lesions into SCC, suggested that tumor promotion and progression were under the genetic control of different sets of genes.^ In the present study the nature of susceptibility to tumor progression was investigated. Analysis of F1 hybrids between the outbred SENCAR and SSIN mice suggested that there is at least one dominant gene responsible for susceptibility to tumor progression.^ Later development of another inbred strain from the outbred SENCAR stock, that had sensitivity to both tumor promotion and progression, allowed the formulation of a more accurate genetic model. Using this newly derived line, SENCAR B/Pt. and SSIN it was determined that there is one dominant tumor progression susceptibility gene. Linkage analysis showed that this gene maps to mouse chromosome 14 and it was possible to narrow the region to a 16 cM interval.^ In order to better characterize the nature of the progression susceptibility differences between these two strains, their proliferative pattern was investigated. It was found that SENCAR B/Pt, have an enlarged proliferative compartment with overexpression of cyclin D1, p16 and p21. Further studies showed an aberrant overexpression of TGF-$\beta$ in the susceptible strain, an increase in apoptosis, p53 protein accumulation and early loss of connexin 26. These results taken together suggest that papillomas in the SENCAR B/Pt. mice have higher proliferation and may have an increase in genomic instability, these two factors would contribute to a higher sensitivity to tumor progression. ^

The number and timing of time -dependent covariate measurements for the Cox regression model

The problem of analyzing data with updated measurements in the time-dependent proportional hazards model arises frequently in practice. One available option is to reduce the number of intervals (or updated measurements) to be included in the Cox regression model. We empirically investigated the bias of the estimator of the time-dependent covariate while varying the effect of failure rate, sample size, true values of the parameters and the number of intervals. We also evaluated how often a time-dependent covariate needs to be collected and assessed the effect of sample size and failure rate on the power of testing a time-dependent effect.^ A time-dependent proportional hazards model with two binary covariates was considered. The time axis was partitioned into k intervals. The baseline hazard was assumed to be 1 so that the failure times were exponentially distributed in the ith interval. A type II censoring model was adopted to characterize the failure rate. The factors of interest were sample size (500, 1000), type II censoring with failure rates of 0.05, 0.10, and 0.20, and three values for each of the non-time-dependent and time-dependent covariates (1/4,1/2,3/4).^ The mean of the bias of the estimator of the coefficient of the time-dependent covariate decreased as sample size and number of intervals increased whereas the mean of the bias increased as failure rate and true values of the covariates increased. The mean of the bias of the estimator of the coefficient was smallest when all of the updated measurements were used in the model compared with two models that used selected measurements of the time-dependent covariate. For the model that included all the measurements, the coverage rates of the estimator of the coefficient of the time-dependent covariate was in most cases 90% or more except when the failure rate was high (0.20). The power associated with testing a time-dependent effect was highest when all of the measurements of the time-dependent covariate were used. An example from the Systolic Hypertension in the Elderly Program Cooperative Research Group is presented. ^

Left, right and interval bivariate censored data: Evaluating screening mammography in the presence of lead -time bias, length bias and over-detection

Of the large clinical trials evaluating screening mammography efficacy, none included women ages 75 and older. Recommendations on an upper age limit at which to discontinue screening are based on indirect evidence and are not consistent. Screening mammography is evaluated using observational data from the SEER-Medicare linked database. Measuring the benefit of screening mammography is difficult due to the impact of lead-time bias, length bias and over-detection. The underlying conceptual model divides the disease into two stages: pre-clinical (T0) and symptomatic (T1) breast cancer. Treating the time in these phases as a pair of dependent bivariate observations, (t0,t1), estimates are derived to describe the distribution of this random vector. To quantify the effect of screening mammography, statistical inference is made about the mammography parameters that correspond to the marginal distribution of the symptomatic phase duration (T1). This shows the hazard ratio of death from breast cancer comparing women with screen-detected tumors to those detected at their symptom onset is 0.36 (0.30, 0.42), indicating a benefit among the screen-detected cases. ^

Analysis of interval -censored events in hypertension studies: Evaluation of treatment of coronary heart disease

Many statistical studies feature data with both exact-time and interval-censored events. While a number of methods currently exist to handle interval-censored events and multivariate exact-time events separately, few techniques exist to deal with their combination. This thesis develops a theoretical framework for analyzing a multivariate endpoint comprised of a single interval-censored event plus an arbitrary number of exact-time events. The approach fuses the exact-time events, modeled using the marginal method of Wei, Lin, and Weissfeld, with a piecewise-exponential interval-censored component. The resulting model incorporates more of the information in the data and also removes some of the biases associated with the exclusion of interval-censored events. A simulation study demonstrates that our approach produces reliable estimates for the model parameters and their variance-covariance matrix. As a real-world data example, we apply this technique to the Systolic Hypertension in the Elderly Program (SHEP) clinical trial, which features three correlated events: clinical non-fatal myocardial infarction, fatal myocardial infarction (two exact-time events), and silent myocardial infarction (one interval-censored event). ^