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is there a way to report rows percentage and confidence interval using table1 template? I want my table to look like this example.
Has used sleeping pills in the last month ________________________________________________________________________ Yes No ________________________________________________________________________ ________%_______95% CI_____________________________%_______95% CI______ Female 77.64 [75.22, 79.89] ____________ 22.36 [20.11, 24.78] Male 89.67 [88.15, 91.03] ____________ 10.33 [8.97, 11.85] _______________________________________________________________________
I have not personally used table1 in my research. Can you advise me if it is appropriate to report confidence intervals for categorical variables?
Yes, you can report the confidence interval for the proportion. I can get it when I use the command:
svy:tab variable1 variable2, ci percent
but it it doesn’t show up when use asdocx, I guess it’s not supported.
It would look like this: https://media.discordapp.net/attachments/1068130821283786793/1068130857040228433/image.png?width=806&height=473
I have added the requested feature. First update asdocx withasdocx_update
. Here is an example.
* Create a toy example *generate random number of observations clear set obs 200 * create variable for using sleeping pills gen use_pills = round(runiform(0,1)) label define use_pills 0 "Yes" 1 "No" label val use_pills use_pills label var use_pills "Used sleeping pills in the last month" *create variable for male and female gen gender = round(runiform(0,1)) label define gender 0 "Female" 1 "Male" label val gender gender replace use_pills = 1 if gender == 1 & use_pills == 0 & runiform()<0.7 replace use_pills = 0 if gender == 0 & use_pills == 1 & runiform()<0.3
Row percentage with confidence intervals
* if row percentages are requiredasdocx tab use_pills gender, template(table1) row cat(% ci) replace
Table 1: DemographicsVariables Yes (n=79) No (n=121) Total (200) Pvalue gender 0.000 Female64.444% (53.921  73.735) 35.556% (26.265  46.079) 100.000% Male19.091% (12.731  27.622) 80.909% (72.378  87.269) 100.000% Pvalues by ttest for continuous variables and Chi2 test for binary/categorical variables. * If the total column is not required, use option nototal * Add option table_layout(auto) for auto adjust columns for contents asdocx tab use_pills gender, template(table1) row cat(% ci) /// replace nototal table_layout(auto)
Table 1: DemographicsVariables Yes (n=79) No (n=121) Pvalue gender 0.000 Female64.444% (53.921  73.735) 35.556% (26.265  46.079) Male19.091% (12.731  27.622) 80.909% (72.378  87.269) Pvalues by ttest for continuous variables and Chi2 test for binary/categorical variables. Column percentages
* If col percentages asdocx tab use_pills gender, template(table1) col categorical(% ci) replace
Table 1: DemographicsVariables Yes (n=79) No (n=121) Total (200) Pvalue gender 0.000 Female73.418% (62.455  82.097) 26.446% (19.294  35.097) 45.0% (38.203  51.989) Male26.582% (17.903  37.545) 73.554% (64.903  80.706) 55.0% (48.011  61.797) Pvalues by ttest for continuous variables and Chi2 test for binary/categorical variables. Thank you, Professor. This new feature will be extremely beneficial for not only myself but also a significant number of users. It will greatly aid in my research.
One more thing, can you look if this can work with the svy command. It doesn’t seem to work for me.
Support for confidence intervals with survey data has been added. Try this example after updating.
asdocx_update * Results from the Stata command svy: tabulate diabetes race , row ci  Diabetes  Race status  White Black Other Total + Not diab  .8812 .0931 .0257 1  [.8437,.9107] [.0705,.122] [.0111,.0586]  Diabetic  .8203 .1647 .0151 1  [.7476,.8755] [.1203,.2213] [.0033,.0668]  Total  .8791 .0955 .0253 1  [.8407,.9093] [.0725,.1249] [.0108,.0585]  * In asdocx, we need to write the treatment variable first asdocx svy: tabulate race diabetes, row ci template(table1) replace cat(% ci)
Table 1: DemographicsVariables White (n=9065) Black (n=1086) Other (n=200) Pvalue Diabetes status 0.000 Not diabetic88.121% (84.370 – 91.068) 9.308% (7.045 – 12.201) 2.571% (1.106 – 5.862) Diabetic82.028% (74.756 – 87.553) 16.467% (12.027 – 22.134) 1.505% (0.325 – 6.680) Pvalues by ttest for continuous variables and Chi2 test for binary/categorical variables. * Designbased Pearson Chi2 test pvalue 
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