activetb_qaly_cost: activetb_qaly_cost
In n8thangreen/ltbiScreenLite: ltbiScreenLite

For the Population model, calculate various QALYs and costs accounting for active TB progression in non-cured cohort subset.

1	activetb_qaly_cost(dectree_res, interv, cohort, folders = NA)

`dectree_res`	Output of `parallel_decision_tree()`. This contains the probability of being cured of LTBI via screening.
`interv`	list of fixed model run parameter values
`cohort`	dataframe of individual level data
`folders`	list of strings locations for data and plots

QALY.statusquo: For each scenario a vector of total QALYs without screening programme, length number of sims. These are all the same because population QALYs are not varied for the cohort.
QALY.screened: For each scenario a vector of total QALYs with screening programme, length number of sims.
E_cost_screened: For each scenario single expected cost with screening programme.
cost.screened_person: For each scenario a vector of QALYs per person with screening programme, length number of sims.
cost.statusquo_person: For each scenario a vector of costs per person without screening programme, length number of sims.
cost_incur: For each scenario a vector of incurred costs by screening programme, length number of sims.
cost.statusquo: For each scenario a vector of total costs without screening programme, length number of sims. The are not identical because TB costs are randomly sampled.
cost.screened: For each scenario a vector of total costs with screening programme, length number of sims.
E_QALY_screened: For each scenario single expected QALYs with screening programme.
QALY.screened_person: For each scenario a vector of QALYs per person with screening programme, length number of sims.
QALY.statusquo_person: For each scenario a vector of QALYs per person without screening programme, length number of sims. These are all the same.
QALYgain: For each scenario a vector of total QALYs gained with screening programme as the difference between screening and status-quo, length number of sims.
cost_incur_person: For each scenario a vector of total costs incured per person with screening programme as the difference between screening and status-quo, length number of sims.
E_cost_incur: For each scenario the expectedd total cost incured with screening programme as the difference between screening and status-quo.
E_cost_incur_person: For each scenario the expected total cost incured per person with screening programme as the difference between screening and status-quo.
QALYgain_person: For each scenario a vector of total QALY gained per person with screening programme as the difference between screening and status-quo.
E_QALYgain: For each scenario the expected total QALY gained with screening programme as the difference between screening and status-quo.
E_QALYgain_person: For each scenario the expected total QALy gained per person with screening programme as the difference between screening and status-quo.