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Tobacco products can lead to chronic health effects that can take decades to manifest (e.g., lung cancer can take 20+ years), which would require long-term studies to assess. Population models and simulations provide a desirable alternative for making estimates and predictions of likely impact on morbidity/mortality at the population level in the absence of empirical data. Mathematical, computational, and simulation models can also help guide regulatory activities such as new product authorizations and policy development. Such models take into consideration both users and nonusers of tobacco products and include cohort cohort models, agentagent-based models, deterministic deterministic and stochastic systemic dynamic models, and static and dynamic social network models.The objective of population modeling is to study the impact of tobacco products on the population as whole. Input parameters include demographic information, tobacco use transition probability and mortality and/or morbidity. The input parameters are typically derived from population-level sources (e.g., census data or other population-level surveys). However, depending on the objective, other sources of data may be used. When using these other sources, steps should be taken to ensure they are representative of the population.
Outputs of the model may include projections on morbidity/mortality and prevalence of use resulting from the impact of the desired objective of the model (such as new product authorizations or regulatory policy development).

For example, the use of System dynamic modeling (SDM) in tobacco research and regulation has a long history with models developed to study different aspects of the tobacco landscape via population dynamics. In the early 2000’s, SDMs were developed in which the dynamic of the population was projected based on a system of difference equations (discrete time). Those early models – involving a small number of compartments – were developed to investigate the impact of user behaviors (initiation, cessation, relapse) of a single tobacco product (such as cigarettes) on prevalence and mortality. Research and regulatory activities at CTP have opened the door to a new class of models in which it is fundamental to account for the impact of multiple tobacco products on the dynamic of the population in relation to user behaviors – including poly-user – and health outcomes known to be causally related to the use of tobacco products, including mortality. For example, it is important to understand how potential behavioral responses to the introduction of a new MR product (e.g., initiation, switching from cigarettes, dual use) will impact use patterns and tobacco-related disease and mortality.

Selection of initial model input parameters is an important step during model development. Input parameters are typically chosen to represent characteristics of the entire population: transitional probabilities (initiation, cessation, quitting, switching) and health outcome (mortality and/or morbidity rates). The initial values of these parameters are typically estimated from sources representative of the population, such complex probability population survey and census.    

baseline input parameters

• We are standardizing content for these sections
  • 6.6 Population Modeling or Analysis

Standards for these sections will not be developed at this time, but can be referenced:

6.1 Tabular Listing of All Population Health Studies
6.2 Tobacco Product Perception and Intention Study 
6.3 Behavioral Epidemiology (Observational) Study
6.4 Biomarker Epidemiology (Observational) Study
6.5 Health Risk Epidemiology (Observational) Study
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6.7 Postmarket Surveillance and Postmarket Study Plan or Protocol
6.8 Population Health Literature Review
6.9 Other Documents Relating to Research [911(d)(5)] or 910(b)(1)]
6.10 Referenced Literature

Tobacco products can lead to chronic health effects that can take decades to manifest (e.g., lung cancer can take 20+ years), which would require long-term studies to assess. Population models and simulations provide a desirable alternative for making estimates and predictions of likely impact on morbidity/mortality at the population level in the absence of empirical data. Mathematical, computational, and simulation models can also help guide regulatory activities such as new product authorizations and policy development. Such models take into consideration both users and nonusers of tobacco products and include cohort models, agent-based models, deterministic and stochastic systemic dynamic models, and static and dynamic social network models.
The objective of population modeling is to study the impact of tobacco products on the population as whole. Input parameters include demographic information, tobacco use transition probability and mortality and/or morbidity. The input parameters are typically derived from population-level sources (e.g., census data or other population-level surveys). However, depending on the objective, other sources of data may be used. When using these other sources, steps should be taken to ensure they are representative of the population.
Outputs of the model may include projections on morbidity/mortality and prevalence of use resulting from the impact of the desired objective of the model (such as new product authorizations or regulatory policy development).

In this section, these models are discussed with regard to inputs to, and outputs from the models, and how these models contribute to studies of tobacco products, and how they are represented in CDISC standards for submission to a regulatory authority.

Unknown User (a.paredes) will work on additional language to introduce the concept of "parameters" in the context of population modeling.

An clear description of what is in scope

We need to be clear in this section on scope - "what it is and what it is not" - Scope clarified to baseline input parameters used to support downstream modeling only

To be included in a subsequent subsection:

...

static and dynamic social network models.