An Alternative Exact Solution of the Two-Stage Clonal Growth Model of Cancer

The approximate solution of the two-stage clonal expansion model of cancer may substantially deviate from the exact solution, and may therefore lead to erroneous conclusions in particular applications. However, for time-varying parameters the exact solution (method of characteristics) is not easy to implement, hampering the accessibility of the model to nonmathematicians. Based on intuitive reasoning, Clewell et al. (1995) proposed an improved approximate solution that is easy to implement whatever time-varying behavior the parameters may have. Here we provide the mathematical foundation for the approximation suggested by Clewell et al. (1995) and show that, after a slight modification, it is in fact an exact solution for the case of time-constant parameters. We were not able to prove that it is an exact solution for time-varying parameters as well. However, several computer simulations showed that the numerical results do not differ from the exact solution as proposed by Moolgavkar and Luebeck (1990). The advantage of this alternative solution is that the hazard rate of the first malignant cell can be evaluated by numerically integrating a single differential equation.     

An Improved Approximation to the Exact Solution of the Two-Stage Clonal Growth Model of Cancer

Multistage clonal growth models are of interest for cancer risk assessment because they can explicitly incorporate data on cell replication. Both approximate and exact formulations of the two stage growth model have been described. The exact solution considers the conditional probability of tumors arising in previously tumor-free animals; the approximate solution estimates total probability of tumor formation. The exact solution is much more computationally intensive when time-dependent cell growth parameters are included. The approximate solution deviates from the exact solution at high incidences and probabilities of tumor. This report describes a computationally tractable,'improved approximation'to the exact solution. Our improved approximation includes a correction term to adjust the unconditional expectation of intermediate cells based on the time history of formation of intermediate cells by mutation of normal cells (recruitment) or by cell division in the intermediate cell population (expansion). The improved approximation provided a much better match to the exact solution than the approximate solution for a wide range of parameter values. The correction term also appears to provide insight into the biological factors that contribute to the variance of the expectation for the number of intermediate cells over time.     

On the Exact Hazard and Survival Functions of the MVK Stochastic Carcinogenesis Model

The MVK two-stage carcinogenesis model is one of the most widely accepted mechanistic models in carcinogenesis modeling. However, due to a perceived difficulty in obtaining analytic solutions for the hazard and survival functions, approximations and numerical methods have been used to calculate these two fundamental quantities. This paper focuses on a special case of the homogeneous MVK model where the number of normal cells is constant. The probability generating function (pgf) for the number of tumor cells is derived, and the exact analytic solutions to the hazard and survival functions are obtained from the pgf.     

A Nonidentifiability Aspect of the Two-Stage Model of Carcinogenesis

This paper discusses identifiability of the two-stage birth-death-mutation model of carcinogenesis. It is shown that the homogeneous version of the model is nonidentifiable; the same is all the more evident for its nonhomogeneous versions. This result implies that the model parameters cannot be uniquely estimated from time-to-tumor observations.     

The Two-Stage Model of Carcinogenesis: Overcoming the Nonidentifiability Dilemma

The two-stage mathematical model of carcinogenesis has been shown to be nonidentifiable whenever tumor incidence data alone is used to fit the model (Hanin and Yakovlev, 1996). This lack of identifiability implies that more than one parameter vector satisfies the optimization criteria for parameter estimation, e.g., maximum likelihood estimation. A question of greater concern to persons using the two-stage model of carcinogenesis is under what conditions can identifiable parameters be obtained from the observed experimental data. We outline how to obtain identifiable parameters for the two-stage model.     

Fitting the Two-Stage Clonal Expansion Model Based on Exact Hazard to the ED01 Data Using SAS NLIN

The two-stage clonal expansion model is a popular model for carcinogenesis data. One common form of this model is based on the approximate hazard function. In certain situations, this formulation is not appropriate, and the exact hazard should be applied. However, the difficulty of implementing the model based on the exact hazard has deterred many from using it. This paper presents a program implementing the exact hazard model for piecewise constant dosing using SAS, a package that is readily available to most that are interested in this type of analysis. Also, an analysis of the ED₀₁ data is presented using this program, and comparisons are made to an earlier analysis based on the approximate hazard. By allowing for an independent background tumor mechanism, an excellent fit to the bladder tumor incidence data was obtained.     

An Exact Analysis of the Multistage Model Explaining Dose-Response Concavity

The traditional multistage (MS) model of carcinogenesis implies several empirically testable properties for dose-response functions. These include convex (linear or upward-curving) cumulative hazards as a function of dose; symmetric effects on lifetime tumor probability of transition rates at different stages; cumulative hazard functions that increase without bound as stage-specific transition rates increase without bound; and identical tumor probabilities for individuals with identical parameters and exposures. However, for at least some chemicals, cumulative hazards are not convex functions of dose. This paper shows that none of these predicted properties is implied by the mechanistic assumptions of the MS model itself. Instead, they arise from the simplifying "rare-tumor" approximations made in the usual mathematical analysis of the model. An alternative exact probabilistic analysis of the MS model with only two stages is presented, both for the usual case where a carcinogen acts on both stages simultaneously, and also for idealized initiation-promotion experiments in which one stage at a time is affected. The exact two-stage model successfully fits bioassay data for chemicals (e.g., 1,3-butadiene) with concave cumulative hazard functions that are not well-described by the traditional MS model. Qualitative properties of the exact two-stage model are described and illustrated by least-squares fits to several real datasets. The major contribution is to show that properties of the traditional MS model family that appear to be inconsistent with empirical data for some chemicals can be explained easily if an exact, rather than an approximate model, is used. This suggests that it may be worth using the exact model in cases where tumor rates are not negligible (e.g., in which they exceed 10%). This includes the majority of bioassay experiments currently being performed.     

Analytic Bayesian Solution of the Two-Stage Poisson-Type Problem in Probabilistic Risk Analysis

The basic purpose of probabilistic risk analysis is to make inferences about the probabilities of various postulated events, with an account of all relevant information such as prior knowledge and operating experience with the specific system under study, as well as experience with other similar systems. Estimation of the failure rate of a Poisson-type system leads to an especially simple Bayesian solution in closed form if the prior probability implied by the invariance properties of the problem is properly taken into account. This basic simplicity persists if a more realistic prior, representing order of magnitude knowledge of the rate parameter, is employed instead. Moreover, the more realistic prior allows direct incorporation of experience gained from other similar systems, without need to postulate a statistical model for an underlying ensemble. The analytic formalism is applied to actual nuclear reactor data.     

两阶段生产系统的DEA效率评价模型

数据包络分析(DEA)作为一种数学规划方法,已经被广泛用来评价一个决策单元相对于其它决策单元的效率。经典的DEA模型把决策单元看作一个“黑箱”,对决策单元的内部运行机制不作深入的研究。本文以一个两阶段生产系统为例,从生产系统的内部过程出发,提出一个基于DEA的模型以合理评估该决策单元的相对效率。本文提出的模型实质上是一类特殊的网络DEA模型,其评价原理有别于已有的研究成果,但确实有助于管理者确定生产过程(如供应链)的非有效来源及其效率改进方向。     

Extending the Stochastic Two-Stage Model of Carcinogenesis to Include Self-Regulation of the Nonmalignant Cell Population

One of the challenges of introducing greater biological realism into stochastic models of cancer induction is to find a way to represent the homeostatic control of the normal cell population over its own size without complicating the analysis too much to obtain useful results. Current two-stage models of carcinogenesis typically ignore homeostatic control. Instead, a deterministic growth path is specified for the population of "normal" cells, while the population of "initiated" cells is assumed to grow randomly according to a birth-death process with random immigrations from the normal population. This paper introduces a simple model of homeostatically controlled cell division for mature tissues, in which the size of the nonmalignant population remains essentially constant over time. Growth of the nonmalignant cell population (normal and initiated cells) is restricted by allowing cells to divide only to fill the "openings" left by cells that die or differentiate, thus maintaining the constant size of the nonmalignant cell population. The fundamental technical insight from this model is that random walks, rather than birth-and-death processes, are the appropriate stochastic processes for describing the kinetics of the initiated cell population. Qualitative and analytic results are presented, drawn from the mathematical theories of random walks and diffusion processes, that describe the probability of spontaneous extinction and the size distribution of surviving initiated populations when the death/differentiation rates of normal and initiated cells are known. The constraint that the nonmalignant population size must remain approximately constant leads to much simpler analytic formulas and approximations, flowing directly from random walk theory, than in previous birth-death models.(ABSTRACT TRUNCATED AT 250 WORDS)     

资源约束型两阶段生产系统的DEA效率评价模型

经典的数据包络分析(DEA)模型将决策单元看作"黑箱",忽视决策单元的内部过程,必然会高估决策单元的效率。本文研究了一种资源约束型两阶段生产系统的DEA效率评价方法,针对此类生产过程的内部过程,研究其内部运行机制对整体效率的影响。本文提出的模型实质上是一类特殊的网络DEA模型,其评价原理有别于已有的研究成果,但更有助于管理者确定生产过程的非有效来源及其效率改进方向。实例证实本文方法的合理性。     

Limits of Applicability for the Deterministic Approximation of the Two-Step Clonal Expansion Model

Motivated by a hypothesis published recently, the limits of applicability of the deterministic approximation of the two-step clonal expansion model are investigated. The approximate hazard increases unlimited, while the exact hazard is approaching a constant value. The approximate solution becomes inapplicable for hazards in the order of that constant rate. When the initiation rate is much larger than the cell division rate, the survival rate is small when the approximation becomes inapplicable. The simplicity of the exact solutions suggests using them in all situations.     

存在网络外部性下的两阶段圆周模型

本文分析了存在网络外部性下的两阶段圆周模型。网络外部性的存在使厂商有动机降低产品价格以获得更大的市场份额,模型的子博弈精炼纳什均衡表明消费者剩余和社会净福利水平得到改进,同时行业内产品的差异程度偏少。特别地,在强网络外部性下,垄断结构能使社会净福利最大化。     

A Stochastic Two-Stage Model for Cancer Risk Assessment. II. The Number and Size of Premalignant Clones

Mathematical expressions are derived, under different dosing patterns, for the number and size of premalignant clones within the framework of a two-mutation model for carcinogenesis, which has previously been shown to be consistent with a large body of epidemiologic and experimental data.     

Using the Biological Two-Stage Model to Assess Risk from Short-Term Exposures

Two assumptions used in risk assessment are investigated: (1) the assumption of fraction of lifetime dose rate assumes that the risk from a fractional lifetime exposure at a given dose rate is equal to the risk from full lifetime exposure at that same fraction of the given dose rate; (2) the assumption of fraction of lifetime risk assumes that the risk from a fractional lifetime exposure at a given dose rate is equal to that same fraction of the risk from full lifetime exposure at the same dose rate. These two assumptions are equivalent when risk is a linear function of dose. Thus both can be thought of as generalizations of the assumption that cancer risk is proportional to the total accumulated lifetime dose (or average daily dose), which is often made to assess the risk from short-term exposures. In this paper, the age-specific cumulative hazard functions are derived using the two-stage model developed by Moolgavkar, Venzon, and Knudson for situations when the exposure occurs during a single period or a single instant. The two assumptions described above are examined for three types of carcinogens, initiator, completer, and promoter, in the context of the model. For initiator and completer, these two assumptions are equivalent in the low-dose region; for a promoter, using the fraction of lifetime risk assumption is generally more conservative than that of the fraction of lifetime dose rate assumption. Tables are constructed to show that the use of either the fraction of lifetime dose rate assumption or the fraction lifetime risk assumption can both underestimate and overestimate the true risk for the three types of carcinogens.     

基于理想决策单元参照求解策略的DEA交叉效率评价模型

在已有关于DEA交叉效率评价模型中,激进型模型和仁慈型模型会因评价结果不一致而导致实际应用中难以对它们予以抉择的难题;中立型模型虽在形式上规避了前述问题,但其本身存在着理论偏差。针对上述问题,基于TOPSIS的理想点构造方法,提出了一种关于DEA交叉效率评价的新模型,即基于理想决策单元参照求解策略的DEA交叉效率评价模型。该模型不仅具有理论的严谨性,可以规避激进型模型与仁慈型模型之间的选择难题,而且相对于它们而言能够更好地坚持DEA最有利于被评价决策单元的基本思想。数值模拟分析表明新模型具有解决实际问题的较好适用性。     

基于两阶段博弈模型的钢铁行业碳强度减排机制研究

减少温室气体排放,高耗能行业将承担主要的减排任务,不同的减排机制对减排任务在不同技术水平的企业间分配和减排成本影响不同,而且对企业竞争力和社会福利有影响。本文构造了一个两阶段动态博弈模型,以两个代表性钢铁企业(东部和西部)为研究对象,来考察在完成一定减排目标前提下,政府设置统一碳税和差异化碳税对减排成本、社会经济福利、企业竞争力等的影响。实证结果表明:统一碳税下,钢铁行业产量降幅较小,行业减排任务分解的更加平均,西部钢铁企业竞争力损失较小;差异税下,实现既定减排目标的减排成本较小;社会经济福利损失较小,且随着减排目标的不断升高,两种碳税下社会经济福利损失之差有拉大的趋势;东部钢铁企业竞争力提高幅度较大,但对西部钢铁企业的负面影响十分明显。因此,在碳税机制设计时应充分考虑不同税率模式对减排任务分解、钢铁行业产量、社会经济福利以及企业竞争力的具体影响,需要在社会成本较低和个体企业的竞争力受影响较小之间进行权衡。     

A Stochastic Two-Stage Model for Cancer Risk Assessment. I. The Hazard Function and the Probability of Tumor

We present a mathematical treatment of a two-mutation model for carcinogenesis with time-dependent parameters. This model has previously been shown to be consistent with epidemiologic and experimental data. An approximate hazard function used in previous papers is critically evaluated.     

Uncertainties in the CIIT Model for Formaldehyde-Induced Carcinogenicity in the Rat: A Limited Sensitivity Analysis–I

Scientists at the CIIT Centers for Health Research (Conolly et al., 2000, 2003; Kimbell et al., 2001a, 2001b) developed a two-stage clonal expansion model of formaldehyde-induced nasal cancers in the F344 rat that made extensive use of mechanistic information. An inference of their modeling approach was that formaldehyde-induced tumorigenicity could be optimally explained without the role of formaldehyde's mutagenic action. In this article, we examine the strength of this result and modify select features to examine the sensitivity of the predicted dose response to select assumptions. We implement solutions to the two-stage cancer model that are valid for nonhomogeneous models (i.e., models with time-dependent parameters), thus accounting for time dependence in variables. In this reimplementation, we examine the sensitivity of model predictions to pooling historical and concurrent control data, and to lumping sacrificed animals in which tumors were discovered incidentally with those in which death was caused by the tumors. We found the CIIT model results were not significantly altered with the nonhomogeneous solutions. Dose-response predictions below the range of exposures where tumors occurred in the bioassays were highly sensitive to the choice of control data. In the range of exposures where tumors were observed, the model attributed up to 74% of the added tumor probability to formaldehyde's mutagenic action when our reanalysis restricted the use of the National Toxicology Program (NTP) historical control data to only those obtained from inhalation exposures. Model results were insensitive to hourly or daily temporal variations in DNA protein cross-link (DPX) concentration, a surrogate for the dose-metric linked to formaldehyde-induced mutations, prompting us to utilize weekly averages for this quantity. Various other biological and mathematical uncertainties in the model have been retained unmodified in this analysis. These include model specification of initiated cell division and death rates, and uncertainty and variability in the dose response for cell replication rates, issues that will be considered in a future paper.