def __init__(self, id, parameters):
self.id = id
self.rng = RVGs.RNG(seed=id)
self.params = parameters
self.gillespie = Markov.Gillespie(
transition_rate_matrix=parameters.rateMatrix)
self.stateMonitor = PatientStateMonitor(parameters=parameters)
def __init__(self, err_sigma):
""""
:param err_sigma is the standard deviation of noise term
"""
SimModel.__init__(self)
# create a normal distribution to model noise
self._err = RVGs.Normal(loc=0, scale=err_sigma)
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"]))
def __init__(self, id, parameters):
""" initiates a patient
:param id: ID of the patient
:param parameters: an instance of the parameters class
"""
self.id = id
self.rng = RVGs.RNG(
seed=id) # random number generator for this patient
self.params = parameters
# gillespie algorithm
self.gillespie = Markov.Gillespie(
transition_rate_matrix=parameters.rateMatrix)
self.stateMonitor = PatientStateMonitor(
parameters=parameters) # patient state monitor
def get_obj_value(self, x, seed_index=0):
""" returns one realization from x^2+noise """
# create a random number generator
rng = RVGs.RNG(seed=seed_index)
accum_penalty = 0 # accumulated penalty
# test the feasibility
if x[1] < 1:
accum_penalty += self._penalty * pow(x[1] - 1, 2)
x[1] = 1
return (x[0] + 1) * (x[0] + 1) + x[1] * x[1] + self._err.sample(
rng) + accum_penalty
