def __init__(self,
n=None,
epsilon=None,
stdev=None,
known_stdev=None,
alpha=None,
beta=None,
power=None,
hypothesis=None):
if n is not None:
is_integer(n, 'N')
self.n = n
else:
self.n = None
is_numeric(epsilon, 'epsilon')
self.epsilon = epsilon
is_positive(stdev, 'stdev')
self.stdev = stdev
self.theta = self.epsilon / self.stdev
if known_stdev is not None:
is_boolean(known_stdev, 'known_stdev')
else:
known_stdev = True
self.known_stdev = known_stdev
# Initialize the remaining arguments through the parent.
super(MeansBase, self).__init__(alpha=alpha,
power=power,
beta=beta,
hypothesis=hypothesis)
def __init__(self,
n=None,
alpha=None,
power=None,
beta=None,
hypothesis=None,
margin=None,
hazard_ratio=None,
proportion_visible=None,
control_proportion=None,
treatment_proportion=None):
is_positive(hazard_ratio, 'Hazard Ratio')
is_in_0_1(treatment_proportion, 'Treatment Proportion')
is_in_0_1(control_proportion, 'Control Proportion')
is_in_0_1(proportion_visible, 'Proportion Visible')
epsilon = math.log(hazard_ratio)
stdev = treatment_proportion * control_proportion * proportion_visible
stdev = 1 / math.sqrt(stdev)
super(Cox, self).__init__(n=n,
mu=epsilon,
mu_0=0,
stdev=stdev,
known_stdev=True,
alpha=alpha,
beta=beta,
power=power,
margin=margin,
hypothesis=hypothesis)
def __init__(self,
n=None,
mu=None,
stdev=None,
hypothesis=None,
margin=None,
alpha=None,
beta=None,
power=None,
known_stdev=None):
if n is not None:
if isinstance(n, int):
self.n = n
else:
raise ValueError('`n` should be of type Int.')
else:
self.n = None
for value in mu:
is_numeric(value, 'value')
mu = [float(value) for value in mu]
self.mu = mu
self.n_groups = len(self.mu)
if self.n_groups % 2 == 0:
# If the number of groups is even, the Williams design is
# equivalent to a n_groups by n_groups crossover design
self.k = self.n_groups
else:
# Otherwise, it is equal to a 2*n_groups by n_groups crossover
# design
self.k = self.n_groups * 2
is_positive(stdev, 'stdev')
self.stdev = stdev
if known_stdev is not None:
is_boolean(known_stdev, 'known_stdev')
else:
known_stdev = True
self.known_stdev = known_stdev
if margin is not None:
is_numeric(margin, 'margin')
else:
margin = 0
self.margin = float(margin)
# Initialize the remaining arguments (alpha, beta, power)
# through the parent.
super(MultiSampleWilliams, self).__init__(alpha=alpha,
power=power,
beta=beta,
hypothesis=hypothesis)
def __init__(self,
n=None,
mu=None,
stdev=None,
comparison=None,
known_stdev=None,
alpha=None,
beta=None,
power=None):
if n is not None:
if isinstance(n, int):
self.n = n
else:
raise ValueError('`n` should be of type Int.')
else:
self.n = None
for value in mu:
is_numeric(value, 'value')
self.mu = mu
self.mean_mu = statistics.mean(self.mu)
self.n_groups = len(self.mu)
# The number of comparisons of interest for pairwise comparisons
self.tau = self.n_groups * (self.n_groups - 1) / 2.0
is_positive(stdev, 'stdev')
self.stdev = stdev
if comparison in ['simultaneous', 'pairwise']:
self.comparison = comparison
elif comparison is None:
self.comparison = 'pairwise'
else:
raise ValueError(("`comparison` should be in ['simultaneous', "
"'pairwise']"))
if known_stdev is not None:
is_boolean(known_stdev, 'known_stdev')
else:
known_stdev = True
self.known_stdev = known_stdev
# Initialize the remaining arguments (alpha, beta, power)
# through the parent.
super(OneWayAnova, self).__init__(alpha=alpha,
power=power,
beta=beta,
hypothesis='equality')
def __init__(self,
n_1=None,
n_2=None,
ratio=1,
alpha=None,
power=None,
beta=None,
hypothesis=None,
margin=None,
control_hazard=None,
treatment_hazard=None,
gamma=None,
trial_time=None,
accrual_time=None):
is_positive(accrual_time, 'Accrual Time')
is_positive(trial_time, 'Trial Time')
if gamma is None:
gamma = 1
else:
is_non_negative(gamma, 'Gamma')
g = gamma
t = trial_time
t_0 = accrual_time
variance = []
is_positive(control_hazard, 'Control Hazard')
is_positive(treatment_hazard, 'Treatment Hazard')
for hazard in [control_hazard, treatment_hazard]:
if g == 0:
EVNum = (math.exp(-hazard * t) - math.exp(-hazard * (t - t_0)))
EVDenom = hazard * t_0
else:
EVNum = (g * math.exp(-1 * hazard * t) * (1 - math.exp(
(hazard - g) * t_0)))
EVDenom = (hazard - g) * (1 - math.exp(-g * t_0))
EV = 1 + EVNum / EVDenom
variance.append(hazard**2 / EV)
self.stdev_control = math.sqrt(variance[0])
self.stdev_treatment = math.sqrt(variance[1])
# Initialize the remaining arguments through the parent.
super(Exponential, self).__init__(
n_1=n_1,
n_2=n_2,
ratio=ratio,
mu_1=control_hazard,
mu_2=treatment_hazard,
stdev=1, # dummy value
known_stdev=True,
alpha=alpha,
beta=beta,
power=power,
margin=margin,
hypothesis=hypothesis)
def __init__(self,
n=None,
alpha=None,
beta=None,
power=None,
p_11=None,
p_12=None,
p_21=None,
p_22=None,
gamma=None,
stdev_1=None,
stdev_2=None):
if gamma is None:
is_in_0_1(p_11, 'p_11 should be in [0, 1].')
is_in_0_1(p_12, 'p_12 should be in [0, 1].')
is_in_0_1(p_21, 'p_21 should be in [0, 1].')
is_in_0_1(p_22, 'p_22 should be in [0, 1].')
gamma = (math.log(p_11 / (1 - p_11)) + math.log(p_12 /
(1 - p_12)) -
math.log(p_21 / (1 - p_21)) - math.log(p_22 / (1 - p_22)))
else:
is_numeric(gamma, '`gamma` should be a number.')
if n is not None:
is_integer(n, '`n` should be of type Int.')
self.n = n
is_positive(stdev_1, 'stdev_1')
self.stdev_1 = stdev_1
is_positive(stdev_2, 'stdev_2')
self.stdev_2 = stdev_2
self.theta = gamma / math.sqrt(stdev_1**2 + stdev_2**2)
# Set remaining variables.
super(CarryOverEffect, self).__init__(alpha=alpha,
beta=beta,
power=power)
def __init__(self,
delta=None,
stdev_wr=None,
stdev_wt=None,
stdev_br=None,
stdev_bt=None,
theta_BE=None,
m_plus=None,
alpha=None,
power=None,
beta=None):
is_numeric(delta, 'delta')
self.delta = delta
is_positive(stdev_wr, 'stdev_wr')
self.stdev_wr = stdev_wr
is_positive(stdev_wt, 'stdev_wt')
self.stdev_wt = stdev_wt
is_positive(stdev_bt, 'stdev_bt')
self.stdev_bt = stdev_bt
is_positive(stdev_br, 'stdev')
self.stdev_br = stdev_br
if theta_BE is None:
theta_BE = 1
else:
is_positive(theta_BE, 'theta_BE')
self.theta_BE = theta_BE
if m_plus is None:
m_plus = MAX_ITERATIONS
else:
is_integer(m_plus, 'm_plus')
self.m_plus = m_plus
# Initialize the remaining arguments through the parent.
super(InVitro, self).__init__(hypothesis="equivalence",
alpha=alpha,
beta=beta,
power=power)
def __init__(self,
delta=None,
stdev_wr=None,
stdev_wt=None,
stdev_br=None,
stdev_bt=None,
rho=None,
theta_IBE=None,
alpha=None,
power=None,
beta=None):
is_numeric(delta, 'delta')
self.delta = delta
is_positive(stdev_wr, 'stdev_wr')
self.stdev_wr = stdev_wr
is_positive(stdev_wt, 'stdev_wt')
self.stdev_wt = stdev_wt
is_positive(stdev_bt, 'stdev_br')
self.stdev_br = stdev_br
is_positive(stdev_bt, 'stdev_bt')
self.stdev_bt = stdev_bt
is_in_0_1(rho, 'rho')
self.rho = rho
var_d = self.stdev_bt**2 + self.stdev_br**2 - 2 * self.rho * self.stdev_bt**2 * self.stdev_bt**2
self.stdev_d = math.sqrt(var_d)
if theta_IBE is None:
theta_IBE = 1.74
else:
is_positive(theta_IBE, 'theta_IBE')
self.theta_IBE = theta_IBE
# Initialize the remaining arguments through the parent.
super(Individual, self).__init__(hypothesis="equivalence",
alpha=alpha,
beta=beta,
power=power)
def __init__(self,
n=None,
odds_ratio=None,
stdev=None,
hypothesis=None,
margin=None,
alpha=None,
beta=None,
power=None):
is_positive(odds_ratio, '`odds_ratio` should be greater than 0')
log_odds = math.log(odds_ratio)
# Initialize the remaining arguments through the parent.
super(RelativeRiskCrossover, self).__init__(n=n,
mu_1=log_odds,
mu_2=0,
stdev=stdev,
known_stdev=True,
alpha=alpha,
beta=beta,
power=power,
margin=margin,
hypothesis=hypothesis)
def __init__(self, delta=None, l=None, stdev_11=None, stdev_tt=None,
stdev_tr=None, stdev_bt=None, stdev_br=None, rho=None,
theta_PBE=None, alpha=None, power=None, beta=None):
if delta is None:
delta = 0
else:
is_numeric(delta, 'delta')
self.delta = delta
is_numeric(l, 'l')
self.l = l
is_positive(stdev_11, 'stdev_11')
self.stdev_11 = stdev_11
is_positive(stdev_tt, 'stdev_tt')
self.stdev_tt = stdev_tt
is_positive(stdev_tr, 'stdev_tr')
self.stdev_tr = stdev_tr
is_positive(stdev_bt, 'stdev_bt')
self.stdev_bt = stdev_bt
is_positive(stdev_br, 'stdev')
self.stdev_br = stdev_br
is_in_0_1(rho, 'rho')
self.rho = rho
if theta_PBE is None:
theta_PBE = 1.74
else:
is_positive(theta_PBE, 'theta_PBE')
self.theta_PBE = theta_PBE
# Initialize the remaining arguments through the parent.
super(Population, self).__init__(hypothesis="equivalence",
alpha=alpha, beta=beta, power=power)
def __init__(self,
n=None,
m=None,
stdev_1=None,
stdev_2=None,
similarity_limit=None,
hypothesis=None,
alpha=None,
beta=None,
power=None):
if n is not None:
if isinstance(n, int):
self.n = n
else:
raise ValueError('`n` should be of type Int.')
else:
self.n = None
if m is not None:
if isinstance(m, int):
self.m = m
else:
raise ValueError('`m` should be of type Int.')
else:
raise ValueError('`m` must be provided.')
is_positive(stdev_1, 'stdev_1')
self.stdev_1 = stdev_1
is_positive(stdev_2, 'stdev_2')
self.stdev_2 = stdev_2
# _alpha and _beta are adjusted to be used as cutpoints.
# alpha and beta are to be used for display
# We use _beta to prevent floating point errors when beta is passed on
if hypothesis == 'equality':
if alpha is not None:
self._alpha = alpha / 2
else:
self._alpha = None
if power is not None:
self._beta = 1 - power
elif beta is not None:
self._beta = beta
else:
self._beta = None
similarity_limit = 1
sigma_ratio = stdev_2 / stdev_1
elif hypothesis == 'superiority':
if alpha is not None:
self._alpha = alpha
else:
self._alpha = None
if power is not None:
self._beta = 1 - power
elif beta is not None:
self._beta = beta
else:
self._beta = None
if similarity_limit is None:
raise ValueError('A similarity_limit must be provided')
sigma_ratio = stdev_2 / (stdev_1 * similarity_limit)
elif hypothesis == 'equivalence':
if alpha is not None:
self._alpha = 1 - alpha
else:
self._alpha = None
if power is not None:
self._beta = (power) / 2
elif beta is not None:
self._beta = (1 - beta) / 2
else:
self._beta = None
if similarity_limit is None:
raise ValueError('A similarity_limit must be provided')
sigma_ratio = stdev_2 / (stdev_1 * similarity_limit)
self.similarity_limit = similarity_limit
self.sigma_ratio = sigma_ratio
# Initialize the remaining arguments through the parent.
super(VarianceBase, self).__init__(alpha=alpha,
power=power,
beta=beta,
hypothesis=hypothesis)
