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Edited
Jun 16, 2023
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import {cea_functions, cell} from "@falk-lieder/cea_library"
import {cost_effectiveness_of_intervention_bs} from "@falk-lieder/cea-baumsteiger"
prosocialbehavioroverview1 = FileAttachment("ProsocialBehaviorOverview@1.png").image()
step1 = FileAttachment("Step1.png").image()
viewof P_g_new = cell(`p_g_new = p_g_old`, P_g_old)
viewof P_g_old = cell(`
p_g_old_mixture = mx(effects_shin_lee,effects_menting, additional_studies_mesurado, effect_baumsteiger, nr_interventions)
p_g_old = quantile(p_g_old_mixture,0.05) to quantile(p_g_old_mixture,0.95)
`, effect_sizes,{distributionChartSettings: {showSummary: true}})
viewof effect_sizes = cell(`
//Studies reviewed by Shin and Lee
nr_studies_shin_lee = 38
mean_shin_lee = 0.442
ci_shin_lee = [0.240, 0.644]
standard_error_shin_lee = (ci_shin_lee[1]-ci_shin_lee[0])/(2*1.96)
std_shin_lee = pow(nr_studies_shin_lee,1/2)*standard_error_shin_lee
effects_shin_lee = normal(mean_shin_lee,std_shin_lee)
//Effect size for the Incredible Years program reviewed by Menting et al.
effects_menting = normal_from_ci(0.15,0.31)
//Additional studies reviewed by Mesurado et al.
additional_studies_mesurado = mx(normal(0.058,0.278), normal(0.038,0.287), normal(0.679,0.152), normal(0.54,0.313))
//mean scores on the self-report measure of prosocial behavior.
//m stands for mean, post stands for post-test, pre stands for pre-test, t stands for the treatment group, c stands for the control group
m_post_t =4.80
m_pre_t = 4.08
m_post_c = 4.84
m_pre_c = 4.62
//standard deviations of the pre-test scores
sd_pre_t = 1.28
sd_pre_c = 1.41
// number of participants in the treatment group (n_t) and the control group (n_c)
n_t = 43
n_c = 32
// correlation between pre-test scores and post-test scores
rho = 0.47
effect_size_estimate_baumsteiger = effect_size_ppc(m_post_t,m_pre_t,m_post_c,m_pre_c,sd_pre_t,sd_pre_c,n_t,n_c)
se_effect_size_estimate = se_effect_size_ppc(m_post_t,m_pre_t,m_post_c,m_pre_c,sd_pre_t,sd_pre_c,n_t,n_c,rho)
effect_baumsteiger = normal(effect_size_estimate_baumsteiger,se_effect_size_estimate)
//The number of interventions covered by each of the three meta-analyses
nr_interventions = [33,1,4,1]
/*
g_max_mesurado1 = max(normal(0.058,0.278), normal(0.038,0.287))
g_max_mesurado2 = max(normal(0.679,0.152), normal(0.54,0.313))
g_max_mesurado = max(g_mesurado1,g_mesurado2)
g_max_menting_shin = max(normal(mean_shin_lee,std_shin_lee), effects_menting)
g_max_prosocial = max(g_max_mesurado, g_max_menting_shin)
*/
`, cea_functions,{distributionChartSettings: {showSummary: true}})
step2 = FileAttachment("Step2.png").image()
viewof initial_freq_of_prosocial_behavior = cell(`
initial_increase_in_freq_of_prosocial_behavior_per_day = p_g_new * std_freq_of_prosocial_behavior_in_acts_per_day
`, [P_g_new,cea_functions,std_freq_prosocial,half_life],{distributionChartSettings: {showSummary: true}})
viewof effect_on_prosocial_behavior = cell(`
increase_in_nr_acts_of_kindness_per_person = sum_over_time(initial_increase_in_freq_of_prosocial_behavior_per_day,half_life_of_effect_on_prosocial_behavior_in_days)
`, [initial_freq_of_prosocial_behavior,half_life,cea_functions],{distributionChartSettings: {showSummary: true}})
viewof std_freq_prosocial = cell(`
// estimate of the standard deviation reported in the article
std_estimate = 1.64
n=77
std_freq_of_prosocial_behavior_in_acts_per_day = p_std_from_estimate(std_estimate,n)
`,cea_functions)
viewof half_life = cell(`
half_life_mixture = mx(half_life_bs,half_life_caparara)
half_life_of_effect_on_prosocial_behavior_in_days = quantile(half_life_mixture,0.05) to quantile(half_life_mixture,0.95)
`, [half_life_bs_samples,half_life_caparara_samples],{distributionChartSettings: {showSummary: true}})
half_life_bs_json = FileAttachment("half_life_bs.json").json()
viewof half_life_bs_samples=cell(`half_life_bs=SampleSet.fromList($half_life_bs)`,undefined,{jsImports: {half_life_bs: half_life_bs_json}, distributionChartSettings: {showSummary: true}})
half_lifeCaparara = FileAttachment("half_life.caparara.json").json()
viewof half_life_caparara_samples=cell(`half_life_caparara=SampleSet.fromList($half_life)`,undefined,{jsImports: {half_life: half_lifeCaparara}, distributionChartSettings: {showSummary: true}})
step31 = FileAttachment("Step3@1.png").image()
viewof wellbeing = cell(`
increase_in_happiness = increase_in_nr_acts_of_kindness_per_person * benefit_of_one_kind_act()
`
,[effect_on_prosocial_behavior,cea_functions],{distributionChartSettings: {showSummary: true}})
viewof cost_effectiveness_of_intervention = cell(`
ce_new = cost_effectiveness(increase_in_happiness,cost_of_online_intervention_per_person_reached())
`, [cea_functions,wellbeing],{distributionChartSettings: {showSummary: true}})
viewof cost_effectiveness_of_research = cell(`
//How cost-effective would it be to conduct this research?
cost_effectiveness_of_research = cost_effectiveness(net_effect_of_research_on_promoting_prosocial_purpose,cost_of_research)
`, [benefit_of_research,cost_of_research,cea_functions],{distributionChartSettings: {showSummary: true}})
viewof cost_of_research = cell(`
// cost of the research and R&D project's required to develop a new intervention in USD
cost_of_research = 250000 to 1.5M
`,undefined,{distributionChartSettings: {showSummary: true, tickFormat: "$,f"}, })
viewof benefit_of_research = cell(`
// total amount of money that will be invested into the cause area of promoting well-being according to EA principles
budget = 10M to 1B
// How much money would be invested into the new intervention if were is superior?
usd_deployable_to_new_intervention = investment_in_new_intervention(scalability_in_usd,budget,cost_of_research)
// Cost-Effectiveness of Strong Mind's task-shifted group CBT for perinatal depression
// for women in developing countries, according to McGuire & Plant (2021).
ce_StrongMinds = normal_from_ci(50,138)
//ce_best_preexisting_intervention = max(ce_StrongMinds->SampleSet.fromDist, ce_new->SampleSet.fromDist) ce_best_preexisting_intervention = ce_StrongMinds // max(ce_StrongMinds,ce_prev_prosocial_behavior)
// What would be the research's net effect on the total well-being of humanity?
net_effect_of_research_on_promoting_prosocial_purpose = net_effect_of_research(ce_new,ce_StrongMinds,usd_deployable_to_new_intervention,cost_of_research)
net_effect_of_research_in_WELBY = hours_of_happiness_to_WELLBYs(net_effect_of_research_on_promoting_prosocial_purpose)
`,[cost_effectiveness_of_intervention,cea_functions,cost_of_research,scalability,cost_effectiveness_of_intervention_bs],{distributionChartSettings: {showSummary: true}})
viewof scalability = cell(`
//The target group of the new intervention is emerging adults.
//We assume that this corresponds to people of ages 15 to 35. We estimate that this
//demographic constitutes between 50% and 70% of all active internet users.
//source: https://www.statista.com/statistics/272365/age-distribution-of-internet-users-worldwide/
proportion_internet_users_between_15_and_35 = 0.5 to 0.7
// What is the maximal amount of money that the intervention could absorb?
scalability_in_usd = scalability_of_online_interventions(proportion_internet_users_between_15_and_35)
`, [cea_functions],{distributionChartSettings: {showSummary: true}})
viewof ce_ratio = cell(`
//How does the cost-effectiveness of this research compare to the cost-effectiveness of the best existing intervention?
cost_effectiveness_ratio = cost_effectiveness_of_research / ce_best_preexisting_intervention
`, [cost_effectiveness_of_research,benefit_of_research],{distributionChartSettings: {showSummary: true}})
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