def __init__(self, diagnostic):
self.diagnostic = diagnostic
self.probInfectedRVG = None # list of dirichlet distributions for moving out of the infected state
self.probAlphaRVG = None # list of beta distributions for the alpha probability if the diagnostic test is NSB
self.weeklyStateCostRVG = [
] # list of gamma distributions for the annual cost of states
self.singleStateCostRVG = [
] # list of gamma distributions for the single cost of states
# create Dirichlet distributions for transition probabilities
self.probInfectedRVG = RVGs.Dirichlet(a=[
Data.P_INFECTED_CLEARED, Data.P_INFECTED_TBD, Data.P_INFECTED_TBM
])
# crate beta distribution for the alpha probability
if self.diagnostic == Diagnostic.SOC:
fit_output = MM.get_beta_params(mean=Data.P_DX_SOC,
st_dev=Data.P_DX_SOC / 5)
else:
fit_output = MM.get_beta_params(mean=Data.P_DX_NSB,
st_dev=Data.P_DX_NSB / 5)
self.probAlphaRVG = RVGs.Beta(a=fit_output["a"], b=fit_output["b"])
# create gamma distributions for annual state cost
for cost in Data.WEEKLY_STATE_COST:
# if cost is zero, add a constant 0, otherwise add a gamma distribution
if cost == 0:
self.weeklyStateCostRVG.append(RVGs.Constant(value=0))
else:
# find shape and scale of the assumed gamma distribution
# no data available to estimate the standard deviation, so we assumed st_dev=cost / 5
fit_output = MM.get_gamma_params(mean=cost, st_dev=cost / 5)
# append the distribution
self.weeklyStateCostRVG.append(
RVGs.Gamma(a=fit_output["a"],
loc=0,
scale=fit_output["scale"]))
# create gamma distributions for single state cost
if self.diagnostic == Diagnostic.SOC:
single_cost = Data.SOC_ONE_TIME_COST
else:
single_cost = Data.NSB_ONE_TIME_COST
for cost in single_cost:
# if cost is zero, add a constant 0, otherwise add a gamma distribution
if cost == 0:
self.singleStateCostRVG.append(RVGs.Constant(value=0))
else:
# find shape and scale of the assumed gamma distribution
# no data available to estimate the standard deviation, so we assumed st_dev=cost / 5
fit_output = MM.get_gamma_params(mean=cost, st_dev=cost / 5)
# append the distribution
self.singleStateCostRVG.append(
RVGs.Gamma(a=fit_output["a"],
loc=0,
scale=fit_output["scale"]))
def __init__(self, therapy):
self.therapy = therapy
self.probMatrixRVG = [
] # list of dirichlet distributions for transition probabilities
self.lnRelativeRiskRVG = None # normal distribution for the natural log of the treatment relative risk
self.annualStateCostRVG = [
] # list of gamma distributions for the annual cost of states
self.annualStateUtilityRVG = [
] # list of beta distributions for the annual utility of states
# create Dirichlet distributions for transition probabilities
j = 0
for prob in Data.PROB_MATRIX:
self.probMatrixRVG.append(RVGs.Dirichlet(a=prob[j:]))
j += 1
# treatment relative risk
rr_ci = [1.2, 3] # confidence interval of the treatment relative risk
# find the mean and st_dev of the normal distribution assumed for ln(RR)
# sample mean ln(RR)
mean_ln_rr = math.log(Data.TREATMENT_RR)
# sample standard deviation of ln(RR)
std_ln_rr = \
(math.log(rr_ci[1]) - math.log(rr_ci[0])) / (2 * stat.norm.ppf(1 - 0.05 / 2))
# create a normal distribution for ln(RR)
self.lnRelativeRiskRVG = RVGs.Normal(loc=mean_ln_rr, scale=std_ln_rr)
# create gamma distributions for annual state cost
for cost in Data.ANNUAL_STATE_COST_NO:
# if cost is zero, add a constant 0, otherwise add a gamma distribution
if cost == 0:
self.annualStateCostRVG.append(RVGs.Constant(value=0))
else:
# find shape and scale of the assumed gamma distribution
# no data available to estimate the standard deviation, so we assumed st_dev=cost / 5
fit_output = MM.get_gamma_params(mean=cost, st_dev=cost / 5)
# append the distribution
self.annualStateCostRVG.append(
RVGs.Gamma(a=fit_output["a"],
loc=0,
scale=fit_output["scale"]))
# create beta distributions for annual state utility
for utility in Data.ANNUAL_STATE_UTILITY_0:
# if utility is zero, add a constant 0, otherwise add a beta distribution
if utility == 0:
self.annualStateCostRVG.append(RVGs.Constant(value=0))
else:
# find alpha and beta of the assumed beta distribution
# no data available to estimate the standard deviation, so we assumed st_dev=cost / 4
fit_output = MM.get_beta_params(mean=utility,
st_dev=utility / 4)
# append the distribution
self.annualStateUtilityRVG.append(
RVGs.Beta(a=fit_output["a"], b=fit_output["b"]))
