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. Author manuscript; available in PMC: 2022 Mar 4.
Published in final edited form as: Prev Med. 2021 Mar 4;144:106438. doi: 10.1016/j.ypmed.2021.106438

Table 1.

Step-by-step health decision analysis for cervical cancer control.

Step Components of a state-transition model
1. Build and test a model: a.Understand the etiology of cervical cancer.
b.Identify the necessary intermediate states leading from a normal cervix to cancer.
c.Define the corollary transitions between the causal states.
d.Define population- and individual-level variables that meaningfully modify the transition.
e.Directly estimate transition probabilities from longitudinal data in a representative population and reckon how confident we are of each transition probability.
f.Identify uncertain transition probabilities that cannot be directly estimated.
g.Calibrate uncertain transition probabilities (when data are lacking) using epidemiologic data targets from a population of interest (e.g., to produce realistic matches to empirical type- and age-specific prevalence of HPV and precancer; cervical cancer incidence).
h.Validate the state-transition model to determine adequacy of model fit to data from different, independent populations that were not used to derive transition probabilities.
2. Estimate intervention impact (costs and health outcomes): a.Identify the available and soon-to-be-available prevention methods.
b.Determine population- and individual-level variables that meaningfully modify performance of the prevention methods.
c.Directly estimate the performance of prevention methods (i.e., HPV vaccination; screening; treatment of precancer) based on where each interrupts the causal pathway.
d.Anticipate likely combinations of the prevention methods into alternative strategies.
e.Measure effective coverage and costs of strategies specific to different regions.
3. Perform the health decision modeling analysis and compare alternative strategies. a.Run the natural history model to project cost and health outcomes in the absence of any intervention.
b.Simulate each prevention strategy to project cost and health outcomes.
c.Compare strategies incrementally, eliminating strategies that are more costly and less effective than other strategies (i.e., strong dominance) or less costly and less cost-effective than more effective strategies (i.e., extended dominance).
d.Perform extensive scenario and sensitivity analysis on uncertain factors.