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Power Analysis Template for Binary Outcomes
# *-----------------------------------------------------------------
# | PROGRAM NAME: Power Analysis
# | DATE: 09/30/21
# | PROJECT FILE:       
# *----------------------------------------------------------------
# | PURPOSE: power calculation and sample size scenarios for binary outcomes
# *----------------------------------------------------------------
import pandas as pd
import numpy as np
import statsmodels
import statsmodels.stats.power as smp
import statsmodels.stats.api as sms
from statsmodels.stats.proportion import proportion_effectsize
# basic power calculation
# this is full compliance or TOT level (as if everyone that gets the email takes action regardless of clickthrough)
p1 = .25 # baseline outcome proportion
p2 = .30 # this is the observed proportion we need to see in the treatment group to meet our definition of success
# effect size
h = sms.proportion_effectsize(p2,p1)
pwr = .80 # desired level of fidelity/power
fp = .10 # specify alpha or our acceptable 'false positive' rate
# Calculate the required sample size
# while R has a power calculation package specific to proportions, python relies on the z-test equivalent
# Parameters and Assumptions
# NormalIndPower.solve_power(effect_size=None, nobs1=None, alpha=None, power=None, ratio=1.0, alternative='two-sided')
# solve for any one parameter of the power of a two sample z-test
# exactly one needs to be None, all others need numeric values
# effect_size# standardized effect size
# nobs1: number of observations of sample 1. The number of observations of sample two is ratio times the size of sample 1, i.e. nobs2 = nobs1 * ratio ratio can be set to zero in order to get the power for a one sample test.
# alpha:float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true.
# power:float in interval (0,1) power of the test, e.g. 0.8, is one minus the probability of a type II error. Power is the probability that the test correctly rejects the Null Hypothesis if the Alternative Hypothesis is true.
# ratio:ratio of the number of observations in sample 2 relative to sample 1. see description of nobs1 The default for ratio is 1; to solve for ration given the other arguments it has to be explicitly set to None.
# alternative:str, ‘two-sided’ (default), ‘larger’, ‘smaller’
# extra argument to choose whether the power is calculated for a two-sided (default) or one sided test.
# The one-sided test can be either ‘larger’, ‘smaller’.
result = smp.NormalIndPower().solve_power(effect_size =h,nobs1=None, power=pwr, alpha= fp, alternative='two-sided',
                                          ratio = 1)
print('Sample Size Per Group: %.3f' % result)
# adjustments for non-compliance
# considering that many will not see the emails or open to actually be 'nudged' by our design
# only a few people will be driving the result - that means the observed difference we expect
# to see at the test vs control level will be diluted
# (i.e. a 3% impact on those that are nudged by engaging content will show up as a very small
# diluted effect when comparing the difference between the randomized test and control group
# overall)
# for these reasons we have to adjust sample sizes for expected engagement
# Reference: Friedman LM, Furberg CD, DeMets DL. Fundamentals of Clinical Trials. 3. New York, NY: Springer; 1998.
#  Adjusting sample size to compensate for nonadherence; pp. 107–108
# "Using Randomization in Development Economics Research: A Toolkit",
# Duflo, Esther and Glennerster, Rachel and Kremer, Michael,
# National Bureau of Economic Research, Working Paper,Technical Working Paper Series,
# No. 333", 2006.
N = 985 # required sample size per group with full engagement
Eng = .03 # expected level of engagement (in this case click-through rates)
Ro = (1- Eng) # expected proportion of dropouts or subjects that fail to engage
Ri = 0 # expected proportion of 'drop ins' or subjects in the control group that somehow get treatment
N_ITT = N/((1-Ro-Ri)**2) # adjusted N
print(N_ITT) # sample size given non-compliance
# note this is the same as 1/k^2 implied in the Duflo guide to field experiments assuming one sided non-compliance
# where k = level of engagement
# sample size scenarios
p1 = .25  # baseline outcome
p2 = np.arange(.26, .36, 0.01) # hypothetical range of treatment group proportions
pwr = .80 # desired level of power
fp = .10  # desired false positive rate (alpha)
# define effect size function (calcualtes effect sizes for given treatment and control scenarios)
def effect_sizes(p1,p2):
    return 2 * (np.arcsin(np.sqrt(p2)) - np.arcsin(np.sqrt(p1)))
# define sample size function (calculates sample size given effect size 'd' and required power and alpha)
def sample_size(d):
    return smp.NormalIndPower().solve_power(d,nobs1=None, power=pwr, alpha=fp, alternative='two-sided', ratio = 1)
d = [effect_sizes(p1,i) for i in p2] # calculate range of effect sizes for given baseline and treatment scenarios
nSizes = [sample_size(i) for i in d] # calcuate range of required sample sizes per group
# adjustment for non-compliance / engagement
# define function to adjust for one-sided non-compliance (requires entering expected engagement)
def adj_sizes(eng,Ri,n):
    # eng = level or proportion of engagement
    # Ri = proportion of control group that gets treatment (or similar treatment)
    Ro = (1- eng) # proportion of dropouts or non-engagers in the treatment goup
    return n/((1-Ro-Ri)**2) # sample size adjustment
nSizesAdj = [adj_sizes(.03,0,i) for i in nSizes] # adjusted for engagement level (!!!check manually entered engagment level!!)
df = pd.DataFrame(columns=['Control','Treatment','Difference','EffectSize','RequiredNPerGroup','Engagement','AdjustedN'])
df['Control'] = [p1 for i in range(len(d))]
df['Treatment'] = p2
df['Difference'] =  df['Treatment'] - df['Control']
df['EffectSize'] = np.round(d,2)
df['RequiredNPerGroup'] = np.round(nSizes)
df['Engagement'] = .03 # specify expected engagement
df['AdjustedN'] = np.round(nSizesAdj)
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