A novel Approach for Analysis of the Log-Linear Age-period-Cohort Model: Application to Lung Cancer Incidence

Mdzinarishvili, T. G.; Gleason, Michael X.; Sherman, Simon

doi:10.4137/cin.s3572

Cited by 8 publications

(28 citation statements)

References 17 publications

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“…To estimate the values of a cancer hazard function in aging, the recently proposed method can be utilized 4,10. This method allows one to correct the observed age-specific incidence rates I ( t ) for time period and cohort effects.…”

Section: Definitions and Mathematical Statement Of The Problemmentioning

confidence: 99%

“…In practice, the observed values of I ( t ) are presented as I i , j , c ( t i ), where t i is a given age interval, j —a time period interval of observation and c —indicates a given categorical risk factor (for example, gender, race, etc .). The procedure allows one: (i) to separate the problem of estimating the time period and birth cohort coefficients from the problem of estimating the unknown hazard function; (ii) to resolve the identifiability problem by an assumption that neighboring cohorts almost equally influence the I i , j , c ( t i ) and by anchoring the time period and birth cohort effects to the selected time period and cohort; and (iii) after obtaining the time period and birth cohort coefficients, to estimate values of the hazard function,

h_{c}^{*} false(t_{i} false)

, and their standard errors, SE i , in each age interval, t i 4,10. Here and below estimates of statistical parameters as well as hazard function values are designated by asterisk (*).…”

Section: Definitions and Mathematical Statement Of The Problemmentioning

confidence: 99%

“…Data preparation and filtration were performed by the previously described protocol 4. LC incidence rates, expressed per 100,000 person per year, were age-adjusted by the direct method to the 2000 United States standard population 26.…”

Section: Weibull-like Model Of the Lung Cancer Presentation In Agingmentioning

confidence: 99%

“…We utilized observational data on cancer incidence rates collected in the Surveillance Epidemiology and End Results (SEER) database. These incidence rates were used to estimate a cancer hazard function by the log-linear age-period-cohort (LLAPC) model that accounts for age, time period and birth-cohort effects 1–4…”

Section: Introductionmentioning

confidence: 99%

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Weibull-like Model of Cancer Development in Aging

Mdzinarishvili

Sherman

2010

Cancer Inform

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Mathematical modeling of cancer development is aimed at assessing the risk factors leading to cancer. Aging is a common risk factor for all adult cancers. The risk of getting cancer in aging is presented by a hazard function that can be estimated from the observed incidence rates collected in cancer registries. Recent analyses of the SEER database show that the cancer hazard function initially increases with the age, and then it turns over and falls at the end of the lifetime. Such behavior of the hazard function is poorly modeled by the exponential or compound exponential-linear functions mainly utilized for the modeling. In this work, for mathematical modeling of cancer hazards, we proposed to use the Weibull-like function, derived from the Armitage-Doll multistage concept of carcinogenesis and an assumption that number of clones at age t developed from mutated cells follows the Poisson distribution. This function is characterized by three parameters, two of which (r and λ) are the conventional parameters of the Weibull probability distribution function, and an additional parameter (C0) that adjusts the model to the observational data. Biological meanings of these parameters are: r—the number of stages in carcinogenesis, λ—an average number of clones developed from the mutated cells during the first year of carcinogenesis, and C0—a data adjustment parameter that characterizes a fraction of the age-specific population that will get this cancer in their lifetime. To test the validity of the proposed model, the nonlinear regression analysis was performed for the lung cancer (LC) data, collected in the SEER 9 database for white men and women during 1975–2004. Obtained results suggest that: (i) modeling can be improved by the use of another parameter A- the age at the beginning of carcinogenesis; and (ii) in white men and women, the processes of LC carcinogenesis vary by A and C0, while the corresponding values of r and λ are nearly the same. Overall, the proposed Weibull-like model provides an excellent fit of the estimates of the LC hazard function in aging. It is expected that the Weibull-like model can be applicable to fit estimates of hazard functions of other adult cancers as well.

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Section: Definitions and Mathematical Statement Of The Problemmentioning

confidence: 99%

h_{c}^{*} false(t_{i} false)

, and their standard errors, SE i , in each age interval, t i 4,10. Here and below estimates of statistical parameters as well as hazard function values are designated by asterisk (*).…”

Section: Definitions and Mathematical Statement Of The Problemmentioning

confidence: 99%

Section: Weibull-like Model Of the Lung Cancer Presentation In Agingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Weibull-like Model of Cancer Development in Aging

Mdzinarishvili

Sherman

2010

Cancer Inform

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“…In this paper we introduce a simple, computationally effective method to solve this identifiability problem. The proposed solution of this problem is analogous to one that we recently utilized for accounting APC effects on cancer incidence rates 10,11…”

Section: Introductionmentioning

confidence: 99%

Extension of Cox Proportional Hazard Model for Estimation of Interrelated Age-period-Cohort Effects on Cancer Survival

et al. 2011

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In the frame of the Cox proportional hazard (PH) model, a novel two-step procedure for estimating age-period-cohort (APC) effects on the hazard function of death from cancer was developed. In the first step, the procedure estimates the influence of joint APC effects on the hazard function, using Cox PH regression procedures from a standard software package. In the second step, the coefficients for age at diagnosis, time period and birth cohort effects are estimated. To solve the identifiability problem that arises in estimating these coefficients, an assumption that neighboring birth cohorts almost equally affect the hazard function was utilized. Using an anchoring technique, simple procedures for obtaining estimates of interrelated age at diagnosis, time period and birth cohort effect coefficients were developed.As a proof-of-concept these procedures were used to analyze survival data, collected in the SEER database, on white men and women diagnosed with LC in 1975–1999 and the age at diagnosis, time period and birth cohort effect coefficients were estimated. The PH assumption was evaluated by a graphical approach using log-log plots. Analysis of trends of these coefficients suggests that the hazard of death from LC for a given time from cancer diagnosis: (i) decreases between 1975 and 1999; (ii) increases with increasing the age at diagnosis; and (iii) depends upon birth cohort effects.The proposed computing procedure can be used for estimating joint APC effects, as well as interrelated age at diagnosis, time period and birth cohort effects in survival analysis of different types of cancer.

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