def __init__(self, seed, therapy):
# initializing the base class
_Parameters.__init__(self, therapy)
self._rng = Random.RNG(
seed
) # random number generator to sample from parameter distributions
self._hivProbMatrixRVG = [
] # list of dirichlet distributions for transition probabilities
self._lnRelativeRiskRVG = None # random variate generator for the natural log of the treatment relative risk
self._annualStateCostRVG = [
] # list of random variate generators for the annual cost of states
self._annualStateUtilityRVG = [
] # list of random variate generators for the annual utility of states
# HIV transition probabilities
j = 0
for prob in Data.TRANS_MATRIX:
self._hivProbMatrixRVG.append(Random.Dirichlet(prob[j:]))
j += 1
# treatment relative risk
# find the mean and st_dev of the normal distribution assumed for ln(RR)
if self._therapy == Therapies.MONO:
sample_mean_lnRR = math.log(Data.Treatment_RR_GC)
sample_std_lnRR = \
(math.log(Data.Treatment_RR_G_CI[1])-math.log(Data.Treatment_RR_G_CI[0]))/(2*stat.norm.ppf(1-0.05/2))
self._lnRelativeRiskRVG = Random.Normal(loc=sample_mean_lnRR,
scale=sample_std_lnRR)
else:
sample_mean_lnRR = math.log(Data.Treatment_RR_GC)
sample_std_lnRR = \
(math.log(Data.Treatment_RR_GC_CI[1]) - math.log(Data.Treatment_RR_GC_CI[0])) / (
2 * stat.norm.ppf(1 - 0.05 / 2))
self._lnRelativeRiskRVG = Random.Normal(loc=sample_mean_lnRR,
scale=sample_std_lnRR)
# annual state cost
for cost in Data.MONTHLY_STATE_COST:
# find shape and scale of the assumed gamma distribution
estDic = Est.get_gamma_params(mean=cost, st_dev=cost / 4)
# append the distribution
self._annualStateCostRVG.append(
Random.Gamma(a=estDic["a"], loc=0, scale=estDic["scale"]))
# annual state utility
for utility in Data.MONTHLY_STATE_UTILITY:
# find alpha and beta of the assumed beta distribution
estDic = Est.get_beta_params(mean=utility, st_dev=utility / 4)
# append the distribution
self._annualStateUtilityRVG.append(
Random.Beta(a=estDic["a"], b=estDic["b"]))
# resample parameters
self.__resample()
def test_normal(rnd, mean, sigma):
#normal random variate generator
normal_dist = RVGs.Normal(mean, sigma)
# obtain samples
samples = get_samples(normal_dist, rnd)
# report mean and variance
print_test_results('Normal', samples,
expectation=mean,
variance=sigma**2
)
def __init__(self, seed, therapy):
# initializing the base class
_Parameters.__init__(self, therapy)
self._rng = Random.RNG(
seed
) # random number generator to sample from parameter distributions
self._hivProbMatrixRVG = [
] # list of dirichlet distributions for transition probabilities
self._lnRelativeRiskRVG = None # random variate generator for the treatment relative risk
self._annualStateCostRVG = [
] # list of random variate generators for the annual cost of states
self._annualStateUtilityRVG = [
] # list of random variate generators for the annual utility of states
# HIV transition probabilities
j = 0
for prob in Data.TRANS_MATRIX:
self._hivProbMatrixRVG.append(Random.Dirichlet(prob[j:]))
j += 1
# treatment relative risk
# find the mean and st_dev of the normal distribution assumed for ln(RR)
sample_mean_lnRR = math.log(Data.TREATMENT_RR)
sample_std_lnRR = (Data.TREATMENT_RR_CI[1] - Data.TREATMENT_RR_CI[0]
) / (2 * stat.norm.ppf(1 - 0.05 / 2))
self._lnRelativeRiskRVG = Random.Normal(mean=sample_mean_lnRR,
st_dev=sample_std_lnRR)
# annual state cost
for cost in Data.ANNUAL_STATE_COST:
# find shape and scale of the assumed gamma distribution
shape, scale = Est.get_gamma_parameters(mean=cost, st_dev=cost / 4)
# append the distribution
self._annualStateCostRVG.append(Random.Gamma(shape, scale))
# annual state utility
for utility in Data.ANNUAL_STATE_UTILITY:
# find alpha and beta of the assumed beta distribution
a, b = Est.get_beta_parameters(mean=utility, st_dev=utility / 5)
# append the distribution
self._annualStateUtilityRVG.append(Random.Beta(a, b))
# resample parameters
self.__resample()
