Hello Statalist,
I have survey data that collected individual level use of a bed net against mosquitoes. Individuals residing in districts were randomly sampled and asked whether they used a net the night before (Y/N). These individuals came from 76 districts which are in turn nested within 14 regions. Furthermore, there were 5 annual rounds of this survey. In the example data below, data from 2016 are presented (first 100 out of 3000 overall).
My objective is to combine district-level and region-level data in a way that allows me to obtain estimates of district-specific rates that minimize the error across all districts within the same region. When there are fewer data in a district, I would like to rely more heavily on the regional data.
I was thinking that I could achieve this using a mixed logistic regression model. In this approach, I think I can model the observed outcome in each district as a function of fixed effects for both region and year and with random effects for district. I suppose that estimates for each district would then be shrunk toward the regional average, with districts having more observations shrunk less.
I am just not sure of how to best implement this model in Stata.
I am just not sure which of the models above would be most appropriate or if a different model would be more appropriate. I was wondering if someone with experience with these kinds of models would be able to comment on whether any of the above computations would give me what I want.
Thank you
I have survey data that collected individual level use of a bed net against mosquitoes. Individuals residing in districts were randomly sampled and asked whether they used a net the night before (Y/N). These individuals came from 76 districts which are in turn nested within 14 regions. Furthermore, there were 5 annual rounds of this survey. In the example data below, data from 2016 are presented (first 100 out of 3000 overall).
My objective is to combine district-level and region-level data in a way that allows me to obtain estimates of district-specific rates that minimize the error across all districts within the same region. When there are fewer data in a district, I would like to rely more heavily on the regional data.
I was thinking that I could achieve this using a mixed logistic regression model. In this approach, I think I can model the observed outcome in each district as a function of fixed effects for both region and year and with random effects for district. I suppose that estimates for each district would then be shrunk toward the regional average, with districts having more observations shrunk less.
I am just not sure of how to best implement this model in Stata.
Code:
clear input str12 id int year byte(region district netuse) " 21" 2016 1 9 0 " 421" 2016 1 10 0 " 51" 2016 1 9 1 " 59" 2016 1 9 1 " 51" 2016 1 9 1 " 52" 2016 1 9 0 " 522" 2016 1 9 0 " 73" 2016 1 9 1 " 723" 2016 1 9 0 " 76" 2016 1 9 0 " 716" 2016 1 9 1 " 722" 2016 1 9 1 " 8 20" 2016 1 31 0 " 9 2" 2016 1 31 0 " 9 2" 2016 1 31 0 " 9 5" 2016 1 31 0 " 9 15" 2016 1 31 1 " 9 22" 2016 1 31 0 " 11 17" 2016 1 55 0 " 11 18" 2016 1 55 0 " 12 18" 2016 1 45 1 " 12 22" 2016 1 45 1 " 13 13" 2016 1 13 0 " 14 21" 2016 1 61 0 " 14 21" 2016 1 61 0 " 16 2" 2016 1 61 0 " 17 17" 2016 1 61 1 " 17 18" 2016 1 61 1 " 17 18" 2016 1 61 1 " 18 2" 2016 1 61 0 " 19 2" 2016 1 23 0 " 19 2" 2016 1 23 0 " 19 6" 2016 1 23 0 " 19 7" 2016 1 23 0 " 19 7" 2016 1 23 0 " 19 17" 2016 1 23 0 " 22 5" 2016 2 72 1 " 22 9" 2016 2 72 0 " 22 14" 2016 2 72 1 " 23 10" 2016 2 3 1 " 24 4" 2016 2 16 1 " 24 9" 2016 2 16 1 " 24 9" 2016 2 16 1 " 24 17" 2016 2 16 1 " 25 16" 2016 2 16 1 " 25 17" 2016 2 16 0 " 25 17" 2016 2 16 0 " 25 20" 2016 2 16 0 " 25 22" 2016 2 16 0 " 26 3" 2016 2 52 1 " 26 6" 2016 2 52 0 " 26 8" 2016 2 52 1 " 29 15" 2016 2 76 1 " 29 20" 2016 2 76 0 " 30 3" 2016 2 76 0 " 30 6" 2016 2 76 0 " 30 11" 2016 2 76 0 " 30 15" 2016 2 76 0 " 31 5" 2016 2 76 1 " 31 8" 2016 2 76 0 " 31 8" 2016 2 76 1 " 31 10" 2016 2 76 1 " 32 3" 2016 2 76 0 " 32 6" 2016 2 76 0 " 32 10" 2016 2 76 0 " 32 12" 2016 2 76 0 " 32 12" 2016 2 76 0 " 32 14" 2016 2 76 1 " 32 14" 2016 2 76 1 " 33 12" 2016 2 76 1 " 33 13" 2016 2 76 1 " 33 13" 2016 2 76 1 " 33 17" 2016 2 76 0 " 33 22" 2016 2 76 1 " 34 9" 2016 2 76 1 " 34 15" 2016 2 76 0 " 34 18" 2016 2 76 0 " 35 17" 2016 3 2 1 " 36 2" 2016 3 17 0 " 36 4" 2016 3 17 1 " 36 17" 2016 3 17 0 " 36 18" 2016 3 17 0 " 37 12" 2016 3 17 1 " 37 20" 2016 3 17 0 " 40 8" 2016 3 17 0 " 40 16" 2016 3 17 1 " 40 18" 2016 3 17 0 " 40 19" 2016 3 17 0 " 40 22" 2016 3 17 0 " 41 3" 2016 3 74 1 " 41 7" 2016 3 74 0 " 41 17" 2016 3 74 0 " 42 3" 2016 3 74 0 " 43 1" 2016 3 44 0 " 43 16" 2016 3 44 1 " 43 18" 2016 3 44 0 " 44 11" 2016 3 74 0 " 44 14" 2016 3 74 0 " 44 20" 2016 3 74 0 " 45 10" 2016 3 74 0 end label values region region label def region 1 "dakar", modify label def region 2 "ziguinchor", modify label def region 3 "diourbel", modify label values district district_id label def district_id 2 "Bambey", modify label def district_id 3 "Bignona", modify label def district_id 9 "Dakar-nord", modify label def district_id 10 "Dakar-ouest", modify label def district_id 13 "Diameniadio", modify label def district_id 16 "Diouloulou", modify label def district_id 17 "Diourbel", modify label def district_id 23 "Guédiawaye", modify label def district_id 31 "Keur Massar", modify label def district_id 44 "Mbacké", modify label def district_id 45 "Mbao", modify label def district_id 52 "Oussouye", modify label def district_id 55 "Pikine", modify label def district_id 61 "Rufisque", modify label def district_id 72 "Thionck-Essyl", modify label def district_id 74 "Touba", modify label def district_id 76 "Ziguinchor", modify melogit netuse year i.region || district: melogit netuse i.year i.region || district: melogit netuse year i.region || district: year
Thank you
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