I. Setup

II. Datasets

load("~/Desktop/Blueprints/9.7/Cleaned_9.7.24.RData")

# Full Sample (N = 292)
dim(remir_all)

## [1] 292 350

# Analysis Sample (N = 240)
tabyl(remir_all$country)

remir_us <- remir_all %>% filter(country == "USA")
dim(remir_us)

## [1] 240 350

# Sub-Analysis Sample (N = 100)
tabyl(remir_all$method)

remir_us_subgroup <- remir_all %>% filter(country == "USA" & !is.na(method))
dim(remir_us_subgroup)

## [1] 100 350

III. Table 1: Prevalence of Culturally Tailored EBPIs

a. Column I

## Full Sample (N = 292; Column 1, Table 1)

tabyl(remir_all, target_any)

tabyl(remir_all, target_race)

tabyl(remir_all, target_ethnic)

tabyl(remir_all, target_gender)

tabyl(remir_all, target_ses)

tabyl(remir_all, target_urban)

tabyl(remir_all, target_rural)

b. Column II

## Analysis Sample (N = 240; Column 2, Table 1)

tabyl(remir_us, target_any)

tabyl(remir_us, target_race)

tabyl(remir_us, target_ethnic)

tabyl(remir_us, target_gender)

tabyl(remir_us, target_ses)

tabyl(remir_us, target_urban)

tabyl(remir_us, target_rural)

c. Column III

## Sub-analysis Sample (N = 100; Column 2, Table 1)
tabyl(remir_us_subgroup, target_any)

tabyl(remir_us_subgroup, target_race)

tabyl(remir_us_subgroup, target_ethnic)

tabyl(remir_us_subgroup, target_gender)

tabyl(remir_us_subgroup, target_ses)

tabyl(remir_us_subgroup, target_urban)

tabyl(remir_us_subgroup, target_rural)

IV. Table 2: Proportions Testing for Subgroup Effects in U.S. Sample (n = 240 reports) by Nine Subgroups

a. Panel I

b. Panel II

## USE US SAMPLE ONLY 

## PANEL II (RQ 2): How often did evaluation studies of EBPIs test for differences in program effects by race, ethnicity, gender, sexual identity, economic disadvantages, location (urban, rural, suburban) and nativity subgroups?

#Column 1
tabyl(remir_us$race_examine_final_exact)

#Column 2
tabyl(remir_us$race_examine_final_mixed)

#Column 3
tabyl(remir_us$ethnic_examine_final_exact)

#Column 4
tabyl(remir_us$ethnic_examine_final_mixed)

#Column 5
tabyl(remir_us$gender_examine_final)

#Column 6
tabyl(remir_us$sex_examine_final)

#Column 7
tabyl(remir_us$ses_examine_final)

#Column 8
tabyl(remir_us$loc_examine_final)

#Column 9
tabyl(remir_us$nat_examine_final)

c. Panel III

## PANEL III: (RQ 3) How prevalent were evaluation studies of EBPIs showing beneficial effects for race, ethnicity, gender, sexual identity, economic disadvantages, location (urban, rural, suburban) and nativity subgroups?*

#Column 1
tabyl(remir_us$race_benefit_final_exact)

#Column 2
tabyl(remir_us$race_benefit_final_mixed)

#Column 3
tabyl(remir_us$ethnic_benefit_final_exact)

#Column 4
tabyl(remir_us$ethnic_benefit_final_mixed)

#Column 5
tabyl(remir_us$gender_benefit_final)

#Column 6
tabyl(remir_us$sex_benefit_final)

#Column 7
tabyl(remir_us$ses_benefit_final)

#Column 8
tabyl(remir_us$loc_benefit_final)

#Column 9
tabyl(remir_us$nat_benefit_final)

V. Online Supplemental Table 1: Ns and Proportions for Characteristics of Sample EBPI Evaluation Reports

a. Column I

## Full: U.S. and non-U.S. reports (N = 292, Column 1, Online Supplement Table 1)

tabyl(remir_all, design)

tabyl(remir_all, "Published.In.Journal.")

tabyl(remir_all, "Certified.")

## Age
tabyl(remir_all, age_infant)

tabyl(remir_all, age_preschool)

tabyl(remir_all, age_elementary)

tabyl(remir_all, age_middle)

tabyl(remir_all, age_high)

tabyl(remir_all, age_youngadult)

tabyl(remir_all, age_adult)

## Place
tabyl(remir_all$pl_comm)

tabyl(remir_all$pl_corr)

tabyl(remir_all$pl_home)

tabyl(remir_all$pl_med)

tabyl(remir_all$pl_online)

tabyl(remir_all$pl_school)

tabyl(remir_all$pl_service)

## Outcome
tabyl(remir_all$out_adult)

tabyl(remir_all$out_educ)

tabyl(remir_all$out_emot)

tabyl(remir_all$out_phys)

tabyl(remir_all$out_posrel)

tabyl(remir_all$out_problem)

b. Column II

## Analysis: U.S. Reports (N = 240; Column 2, Online Supplement Table 1)

tabyl(remir_us, design)

tabyl(remir_us, "Published.In.Journal.")

tabyl(remir_us, "Certified.")

## Age
tabyl(remir_us, age_infant)

tabyl(remir_us, age_preschool)

tabyl(remir_us, age_elementary)

tabyl(remir_us, age_middle)

tabyl(remir_us, age_high)

tabyl(remir_us, age_youngadult)

tabyl(remir_us, age_adult)

## Place
tabyl(remir_us$pl_comm)

tabyl(remir_us$pl_corr)

tabyl(remir_us$pl_home)

tabyl(remir_us$pl_med)

tabyl(remir_us$pl_online)

tabyl(remir_us$pl_school)

tabyl(remir_us$pl_service)

## Outcome
tabyl(remir_us$out_adult)

tabyl(remir_us$out_educ)

tabyl(remir_us$out_emot)

tabyl(remir_us$out_phys)

tabyl(remir_us$out_posrel)

tabyl(remir_us$out_problem)

c. Column III

## SUBGROUP SAMPLE 

tabyl(remir_us_subgroup, design)

tabyl(remir_us_subgroup, "Published.In.Journal.")

tabyl(remir_us_subgroup, "Certified.")

## Age
tabyl(remir_us_subgroup, age_infant)

tabyl(remir_us_subgroup, age_preschool)

tabyl(remir_us_subgroup, age_elementary)

tabyl(remir_us_subgroup, age_middle)

tabyl(remir_us_subgroup, age_high)

tabyl(remir_us_subgroup, age_youngadult)

tabyl(remir_us_subgroup, age_adult)

## Place
tabyl(remir_us_subgroup$pl_comm)

tabyl(remir_us_subgroup$pl_corr)

tabyl(remir_us_subgroup$pl_home)

tabyl(remir_us_subgroup$pl_med)

tabyl(remir_us_subgroup$pl_online)

tabyl(remir_us_subgroup$pl_school)

tabyl(remir_us_subgroup$pl_service)

## Outcome
tabyl(remir_us_subgroup$out_adult)

tabyl(remir_us_subgroup$out_educ)

tabyl(remir_us_subgroup$out_emot)

tabyl(remir_us_subgroup$out_phys)

tabyl(remir_us_subgroup$out_posrel)

tabyl(remir_us_subgroup$out_problem)

VI. Online Supplemental Table 2: Ns, Proportions, and Descriptive Statistics for Characteristics of Sample EBPI Evaluation Reports

a. Column I

## FULL SAMPLE

## Studies That Reported Sample Distribution
tabyl(remir_all, ReportRace)

tabyl(remir_all, ReportHisp)

tabyl(remir_all, ReportGend)

tabyl(remir_all, ReportEcon)

## Note. Remir 1.0 measured if program targeted location (rural and urban)
## but did not measure if sample distribution was reported 
## or percentage of sample in rural and urban locations

## Sample statistics for studies that report characteristic: 
describe <- psych::describe
describe(remir_all$asian_pc)

describe(remir_all$black_pc)

describe(remir_all$native_pc)

describe(remir_all$pacif_pc)

describe(remir_all$white_pc)

describe(remir_all$multi_pc)

describe(remir_all$none_pc)

describe(remir_all$hisp_pc)

describe(remir_all$female_pc)

describe(remir_all$econ_pc)

b. Column II

## US SAMPLE 

## Studies That Reported Sample Distribution
tabyl(remir_us, ReportRace)

tabyl(remir_us, ReportHisp)

tabyl(remir_us, ReportGend)

tabyl(remir_us, ReportEcon)

## Sample statistics for studies that report characteristic: 
describe(remir_us$asian_pc)

describe(remir_us$black_pc)

describe(remir_us$native_pc)

describe(remir_us$pacif_pc)

describe(remir_us$white_pc)

describe(remir_us$multi_pc)

describe(remir_us$none_pc)

describe(remir_us$hisp_pc)

describe(remir_us$female_pc)

describe(remir_us$econ_pc)

c. Column III

## US SUBGROUP SAMPLE 

## Studies That Reported Sample Distribution
tabyl(remir_us_subgroup, ReportRace)

tabyl(remir_us_subgroup, ReportHisp)

tabyl(remir_us_subgroup, ReportGend)

tabyl(remir_us_subgroup, ReportEcon)

## Sample statistics for studies that report characteristic: 
describe(remir_us_subgroup$asian_pc)

describe(remir_us_subgroup$black_pc)

describe(remir_us_subgroup$native_pc)

describe(remir_us_subgroup$pacif_pc)

describe(remir_us_subgroup$white_pc)

describe(remir_us_subgroup$multi_pc)

describe(remir_us_subgroup$none_pc)

describe(remir_us_subgroup$hisp_pc)

describe(remir_us_subgroup$female_pc)

describe(remir_us_subgroup$econ_pc)

VII. Online Supplemental Table 3: Proportions Testing for Subgroup Effects by Culturally Tailored Program (Yes or No) by Subgroup

a. Row I: Race

################ RACE ##################

## Test for Relationship Between Examined and Culturally Tailored
tabyl(remir_us$target_race)

tabyl(remir_us,examine12_race_short, target_race ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us$examine12_race_short, remir_us$target_race, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us$examine12_race_short and remir_us$target_race
## X-squared = 59.018, df = 2, p-value = 1.529e-13

b. Row II: Ethnicity

################ ETHNICTY ##################

## Test for Relationship Between Examined and Culturally Tailored
tabyl(remir_us$target_ethnic)

tabyl(remir_us,examine12_ethnic_short, target_ethnic ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us$examine12_ethnic_short, remir_us$target_ethnic, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us$examine12_ethnic_short and remir_us$target_ethnic
## X-squared = 15.904, df = 2, p-value = 0.000352

c. Row III: Gender

########
################ GENDER ##################

## Test for Relationship Between Examined and Culturally Tailored
tabyl(remir_us$target_gender)

tabyl(remir_us,examine12_gender_short, target_gender ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us$examine12_gender_short, remir_us$target_gender, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us$examine12_gender_short and remir_us$target_gender
## X-squared = 209.74, df = 2, p-value < 2.2e-16

d. Row IV: Sex

################ SEX ##################

## No Culturally Tailored Program for Sex

## Test for Relationship Between Examined and Culturally Tailored
## Assume all citations are not culturally tailed
tabyl(remir_us$examine12_sex_short)

e. Row V: Socioeconomic Status

################ SES ##################

## Test for Relationship Between Examined and Culturally Tailored
tabyl(remir_us$target_ses)

tabyl(remir_us,examine12_ses_short, target_ses ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us$examine12_ses_short, remir_us$target_ses, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us$examine12_ses_short and remir_us$target_ses
## X-squared = 12.094, df = 2, p-value = 0.002365

f. Row VI: Location (Rural, Urban)

################ Location ##################

## Test for Relationship Between Examined/Benefitted and Culturally Tailored
tabyl(remir_us,examine12_loc_short, target_loc ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us$examine12_loc_short, remir_us$target_loc, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us$examine12_loc_short and remir_us$target_loc
## X-squared = 0.25199, df = 1, p-value = 0.6157

g. Row VII: Nativity

################ Nativity ##################

## No culturally tailored programs

########################################################################
############## CHECK FOR CULTURALLY TAILORED AND BENEFITTED #############
###################### NOT LISTED IN TABLES ##############################

## The results for benefitted differ only trivially from those above for
## examined. Benefitted differs from examined in the presence of an 'Other' benefitted category. But the runs below show no cases in the other for culturally tailed programs. Thus, the comparison of culturally tailored programs for benefitted differes only trivially from that for examined. 

## Show That Among culturally Tailored Programs, There are No other Benefits
tabyl((remir_us$target_race))

remir_us_temp <- remir_us %>% filter(target_race == 1)
tabyl(remir_us_temp, inter_benefit12_race_cat, subg_signif12_race_cat)

remir_us_temp <- remir_us %>% filter(target_ethnic == 1)
tabyl(remir_us_temp, inter_benefit12_ethnic_cat, subg_signif12_ethnic_cat)

remir_us_temp <- remir_us %>% filter(target_gender == 1)
tabyl(remir_us_temp, inter_benefit12_gender_cat, subg_signif12_gender)

remir_us_temp <- remir_us %>% filter(target_ses == 1)
tabyl(remir_us_temp, inter_benefit12_ses_cat, subg_signif12_ses)

rm(remir_us_temp)

VIII. Online Supplemental Table 4: Proportions Testing for Subgroup Effects by Period of Time (Early Period and Late Period) by Subgroup

a. Columns I & II

################ FULL SAMPLE ##################

## RACE
## Test for Relationship Between Examined and Year 
tabyl(remir_all,examine12_race_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_all$examine12_race_short, remir_all$dyear, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  remir_all$examine12_race_short and remir_all$dyear
## X-squared = 7.425, df = 2, p-value = 0.02442

## ETHNICTY 
## Test for Relationship Between Examined and Year
tabyl(remir_all,examine12_ethnic_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_all$examine12_ethnic_short, remir_all$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_all$examine12_ethnic_short and remir_all$dyear
## X-squared = 16.151, df = 2, p-value = 0.0003111

## GENDER 
## Test for Relationship Between Examined and Year
tabyl(remir_all,examine12_gender_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_all$examine12_gender_short, remir_all$dyear, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  remir_all$examine12_gender_short and remir_all$dyear
## X-squared = 2.9677, df = 2, p-value = 0.2268

## SEX 
## Test for Relationship Between Examined and Year
tabyl(remir_all,examine12_sex_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_all$examine12_sex_short, remir_all$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_all$examine12_sex_short and remir_all$dyear
## X-squared = 3.19, df = 1, p-value = 0.07409

## SES 
## Test for Relationship Between Examined and Year
tabyl(remir_all,examine12_ses_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_all$examine12_ses_short, remir_all$dyear, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  remir_all$examine12_ses_short and remir_all$dyear
## X-squared = 0.19515, df = 2, p-value = 0.907

## LOCATION 
## Test for Relationship Between Examined and Year
tabyl(remir_all,examine12_loc_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_all$examine12_loc_short, remir_all$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_all$examine12_loc_short and remir_all$dyear
## X-squared = 2.5602, df = 1, p-value = 0.1096

## NATIVITY
## Test for Relationship Between Examined and Year
tabyl(remir_all, examine12_nat_short, dyear) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_all$examine12_nat_short, remir_all$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_all$examine12_nat_short and remir_all$dyear
## X-squared = 0.63345, df = 1, p-value = 0.4261

b. Columns III & IV

################ US SAMPLE ##################

## RACE
## Test for Relationship Between Examined and Year 
tabyl(remir_us,examine12_race_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us$examine12_race_short, remir_us$dyear, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us$examine12_race_short and remir_us$dyear
## X-squared = 7.4228, df = 2, p-value = 0.02444

## ETHNICTY 
## Test for Relationship Between Examined and Year
tabyl(remir_us,examine12_ethnic_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us$examine12_ethnic_short, remir_us$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us$examine12_ethnic_short and remir_us$dyear
## X-squared = 16.564, df = 2, p-value = 0.000253

## GENDER
## Test for Relationship Between Examined and Year
tabyl(remir_us,examine12_gender_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us$examine12_gender_short, remir_us$dyear, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us$examine12_gender_short and remir_us$dyear
## X-squared = 1.5242, df = 2, p-value = 0.4667

## SEX 
## Test for Relationship Between Examined and Year
tabyl(remir_us,examine12_sex_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us$examine12_sex_short, remir_us$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us$examine12_sex_short and remir_us$dyear
## X-squared = 3.1879, df = 1, p-value = 0.07419

## SES 
## Test for Relationship Between Examined and Year
tabyl(remir_us,examine12_ses_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us$examine12_ses_short, remir_us$dyear, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us$examine12_ses_short and remir_us$dyear
## X-squared = 1.5377, df = 2, p-value = 0.4635

## LOCATION
## Test for Relationship Between Examined and Year
tabyl(remir_us,examine12_loc_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us$examine12_loc_short, remir_us$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us$examine12_loc_short and remir_us$dyear
## X-squared = 2.5735, df = 1, p-value = 0.1087

## NATIVITY
## Test for Relationship Between Examined and Year
tabyl(remir_us, examine12_nat_short, dyear) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us$examine12_nat_short, remir_us$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us$examine12_nat_short and remir_us$dyear
## X-squared = 0.6353, df = 1, p-value = 0.4254

c. Columns V & VI

################ US SUBGROUP SAMPLE ##################

## RACE 
## Test for Relationship Between Examined and Year 
tabyl(remir_us_subgroup,examine12_race_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us_subgroup$examine12_race_short, remir_us_subgroup$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us_subgroup$examine12_race_short and remir_us_subgroup$dyear
## X-squared = 6.0621, df = 2, p-value = 0.04826

## ETHNICTY
## Test for Relationship Between Examined and Year
tabyl(remir_us_subgroup,examine12_ethnic_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us_subgroup$examine12_ethnic_short, remir_us_subgroup$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us_subgroup$examine12_ethnic_short and remir_us_subgroup$dyear
## X-squared = 4.2895, df = 2, p-value = 0.1171

## GENDER 
## Test for Relationship Between Examined and Year
tabyl(remir_us_subgroup,examine12_gender_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us_subgroup$examine12_gender_short, remir_us_subgroup$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us_subgroup$examine12_gender_short and remir_us_subgroup$dyear
## X-squared = 4.7829, df = 2, p-value = 0.0915

## SEX 
## Test for Relationship Between Examined and Year
tabyl(remir_us_subgroup,examine12_sex_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us_subgroup$examine12_sex_short, remir_us_subgroup$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us_subgroup$examine12_sex_short and remir_us_subgroup$dyear
## X-squared = 2.7053, df = 1, p-value = 0.1

## SES 
## Test for Relationship Between Examined and Year
tabyl(remir_us_subgroup,examine12_ses_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us_subgroup$examine12_ses_short, remir_us_subgroup$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us_subgroup$examine12_ses_short and remir_us_subgroup$dyear
## X-squared = 1.3173, df = 2, p-value = 0.5176

## Location 
## Test for Relationship Between Examined and Year
tabyl(remir_us_subgroup,examine12_loc_short, dyear ) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us_subgroup$examine12_loc_short, remir_us_subgroup$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us_subgroup$examine12_loc_short and remir_us_subgroup$dyear
## X-squared = 3.1433, df = 1, p-value = 0.07624

## Nativity 
## Test for Relationship Between Examined and Year
tabyl(remir_us_subgroup, examine12_nat_short, dyear) %>% 
  adorn_percentages("col") %>% adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us_subgroup$examine12_nat_short, remir_us_subgroup$dyear, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us_subgroup$examine12_nat_short and remir_us_subgroup$dyear
## X-squared = 0.76201, df = 1, p-value = 0.3827

IX. Online Supplemental Table 5: Proportions Registering Prospectively and Retrospectively with and without Subgroup Registration for Sub-Analysis Sample

## COMPARE PREREGISTRATION BY CULTURALLY TAILORED FOR SUBSAMPLE
tabyl(remir_us_subgroup$prereg_all)

# Registered Prospectively with Subgroup = 0.10

#Registered Prospectively without Subgroup = 0.10

#Registered Retrospectively with Subgroup = 0.060

#Registered Retrospectively without Subgroup = 0.150

#Not registered = 0.770

## See Text: No significant differences in registration for culturally 
## tailored proograms and non-culturally grounded programs.
tabyl(remir_us_subgroup, target_any, prereg_all) %>% 
  adorn_percentages("all") %>% 
  adorn_pct_formatting(rounding = "half up", digits = 1)

chisq.test(remir_us_subgroup$target_any, remir_us_subgroup$prereg_all, correct=FALSE)

## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  remir_us_subgroup$target_any and remir_us_subgroup$prereg_all
## X-squared = 4.4618, df = 4, p-value = 0.3471

X. Figure 1

# Included: Full Sample: U.S/Non-US Programs
length(unique(remir_all$Program.ID))

## [1] 111

# Included: Full Sample: U.S/Non-US Studies
length(unique(remir_all$Study.ID))

## [1] 172

# Included: Full Sample: U.S/Non-US Citations
length(unique(remir_all$Citation.ID))

## [1] 292

## Screening: Records excluded
1598-111

## [1] 1487

2955-172

## [1] 2783

4176-292

## [1] 3884

## Included: Analysis Sample: U.S. Programs
length(unique(remir_us$Program.ID))

## [1] 97

## Included: Analysis Sample: U.S. Studies 
length(unique(remir_us$Study.ID))

## [1] 140

## Included: Analysis Sample: U.S. Citations
length(unique(remir_us$Citation.ID))

## [1] 240

## Non-US Studies (values for information-purposes only - not included in diagram)
111-97

## [1] 14

172-140

## [1] 32

292-240

## [1] 52

## Included: Sub-Analysis Sample
## Included: Sub-Analysis Sample of U.S. Programs
length(unique(remir_us_subgroup$Program.ID))

## [1] 49

## Included: Sub-Analysis Sample of U.S. Studies
length(unique(remir_us_subgroup$Study.ID))

## [1] 63

## Included: Sub-Analysis Sample of U.S. Citations
length(unique(remir_us_subgroup$Citation.ID))

## [1] 100

# U.S. Studies Not Examining Subgroup Effects (Excluded)
97-52

## [1] 45

140-49

## [1] 91

240-63

## [1] 177

REMIR - Data Analysis

Buckley et al. (2024)

2024-09-11

I. Setup

II. Datasets

III. Table 1: Prevalence of Culturally Tailored EBPIs

a. Column I

b. Column II

c. Column III

IV. Table 2: Proportions Testing for Subgroup Effects in U.S. Sample (n = 240 reports) by Nine Subgroups

a. Panel I

b. Panel II

c. Panel III

V. Online Supplemental Table 1: Ns and Proportions for Characteristics of Sample EBPI Evaluation Reports

a. Column I

b. Column II

c. Column III

VI. Online Supplemental Table 2: Ns, Proportions, and Descriptive Statistics for Characteristics of Sample EBPI Evaluation Reports

a. Column I

b. Column II

c. Column III

VII. Online Supplemental Table 3: Proportions Testing for Subgroup Effects by Culturally Tailored Program (Yes or No) by Subgroup

a. Row I: Race

b. Row II: Ethnicity

c. Row III: Gender

d. Row IV: Sex

e. Row V: Socioeconomic Status

f. Row VI: Location (Rural, Urban)

g. Row VII: Nativity

VIII. Online Supplemental Table 4: Proportions Testing for Subgroup Effects by Period of Time (Early Period and Late Period) by Subgroup

a. Columns I & II

b. Columns III & IV

c. Columns V & VI

IX. Online Supplemental Table 5: Proportions Registering Prospectively and Retrospectively with and without Subgroup Registration for Sub-Analysis Sample

X. Figure 1