def try_to_update_cur_outcome(self):
n1, m1, sd1, n2, m2, sd2 = self.ma_unit.get_raw_data_for_groups(self.cur_groups)
se1, se2 = self._get_float(0, 3), self._get_float(1, 3)
# here we check whether or not we have sufficient data to compute an outcome
if not any([self.no_val(x) for x in [n1, m1, sd1, n2, m2, sd2 ]]) or \
not any([self.no_val(x) for x in [m1, se1, m2, se2]]) and self.cur_effect=="MD" or \
not any([self.no_val(x) for x in [n1, m1, sd1]]) and self.cur_effect in CONTINUOUS_ONE_ARM_METRICS:
est_and_ci_d = None
if self.cur_effect in CONTINUOUS_TWO_ARM_METRICS:
est_and_ci_d = meta_py_r.continuous_effect_for_study(n1, m1, sd1, se1=se1, \
n2=n2, m2=m2, sd2=sd2, se2=se2,\
metric=self.cur_effect, conf_level=meta_py_r.get_global_conf_level())
else:
# continuous, one-arm metric
est_and_ci_d = meta_py_r.continuous_effect_for_study(n1, m1, sd1, \
two_arm=False, metric=self.cur_effect, conf_level=meta_py_r.get_global_conf_level())
display_est, display_low, display_high = est_and_ci_d["display_scale"]
self.ma_unit.set_display_effect_and_ci(self.cur_effect, self.group_str, display_est, display_low, display_high)
est, low, high = est_and_ci_d["calc_scale"] # calculation (e.g., log) scale
self.ma_unit.set_effect_and_ci(self.cur_effect, self.group_str, est, low, high)
self.set_current_effect()
def try_to_update_cur_outcome(self):
n1, m1, sd1, n2, m2, sd2 = self.ma_unit.get_raw_data_for_groups(
self.cur_groups)
se1, se2 = self._get_float(0, 3), self._get_float(1, 3)
# here we check whether or not we have sufficient data to compute an outcome
if not any([self.no_val(x) for x in [n1, m1, sd1, n2, m2, sd2 ]]) or \
not any([self.no_val(x) for x in [m1, se1, m2, se2]]) and self.cur_effect=="MD" or \
not any([self.no_val(x) for x in [n1, m1, sd1]]) and self.cur_effect in CONTINUOUS_ONE_ARM_METRICS:
est_and_ci_d = None
if self.cur_effect in CONTINUOUS_TWO_ARM_METRICS:
est_and_ci_d = meta_py_r.continuous_effect_for_study(
n1,
m1,
sd1,
se1=se1,
n2=n2,
m2=m2,
sd2=sd2,
se2=se2,
metric=self.cur_effect,
conf_level=self.conf_level)
else:
# continuous, one-arm metric
est_and_ci_d = meta_py_r.continuous_effect_for_study(
n1,
m1,
sd1,
two_arm=False,
metric=self.cur_effect,
conf_level=self.conf_level)
est, low, high = est_and_ci_d[
"calc_scale"] # calculation (e.g., log) scale
self.ma_unit.set_effect_and_ci(self.cur_effect,
self.group_str,
est,
low,
high,
mult=self.mult)
self.set_current_effect()
def update_outcome_if_possible(self, study_index):
"""
Checks the parametric study to ascertain if enough raw data has been
entered to compute the outcome. If so, the outcome is computed and
displayed.
"""
est, lower, upper = None, None, None
data_type = self.get_current_outcome_type(get_str=False)
if self.raw_data_is_complete_for_study(study_index):
if data_type == BINARY:
e1, n1, e2, n2 = self.get_cur_raw_data_for_study(study_index)
est, lower, upper = meta_py_r.effect_for_study(e1, n1, e2, n2)
elif data_type == CONTINUOUS:
n1, m1, se1, n2, m2, se2 = self.get_cur_raw_data_for_study(study_index)
est, lower, upper = meta_py_r.continuous_effect_for_study(n1, m1, se1, n2, m2, se2)
ma_unit = self.get_current_ma_unit_for_study(study_index)
# now set the effect size & CIs
ma_unit.set_effect_and_ci(self.current_effect, est, lower, upper)
