stata$in$the$everyday$life$of$a$ health$economist · blog •...

Stata in the everyday life of a health economist

Pedro Pita Barros

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Stata and me: what I do?

•  Research ar7cles •  Books •  Data management •  Blog and other things

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Blog •  Need treatment of simple data, usually small samples

•  Problem: they are in a variety of formats •  Solu7on: import from Excel files has become the easier solu7on – most data sources now have this possibility – Sta7s7cs Portugal, Pordata, AMECO / Eurostat, OECD Health Data

•  Problem: formaOng changes from 7me to 7me •  Solu7on: import to “my” Excel format, which is read by Stata easily

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•  Next step: export outputs •  Graph tools from Stata are great – high degree of flexibility; standard “schemes” are fine (though beYer in color than black/white)

•  Export format is ok (png works well with wordpress)

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8

500

1000

1500

2000

Jan-12 Jul-12 Jan-13 Jul-13 Jan-14 Jul-14 Jan-15 Jul-15Mês-Ano

Dívida Dez2011/Mai2012Jan/Out2013 Dez2013/Ago2014Set/Nov2014 Dez2014/Fev2015Fev/Jul2015

•  Expor7ng tables of regressions – could be easier – user-‐made commands help, but are not perfect for html/blog

•  Print screens look nice graphically but not easy to read

•  Wish: have direct export to html (the copy table as html does not work well in my Mac)

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•  Version:0.9

•  StartHTML:0000000105

•  EndHTML:0000001132

•  StartFragment:0000000136

•  EndFragment:0000001099

•  <HTML><BODY><!-‐-‐StartFragment-‐-‐><TABLE BORDER><tr><td>. esYab m1 m2, b not

•  (1) (2) •  divida_epe divida_epe

•  tend1 80.42*** 80.42*** •  tend2 -‐190.5*** -‐190.5*** •  tend3 34.07*** •  tend4 28.47* •  tend5 -‐8.370 •  tend6 41.56 •  tend7 -‐1.611 •  ct1 554.7 554.7 •  ct2 5406.7*** 5406.7*** •  ct3 -‐152.8 -‐96.77 •  ct4 825.0*** 825.0*** •  ct5 -‐430.7 -‐583.9* •  ct6 1206.6 941.9 •  ct7 -‐1472.3 -‐1004.6** •  ct8 613.8 674.7 •  tend346 32.20*** •  tend57 -‐2.737

•  N 44 44

•  * p<0.05, ** p<0.01, *** p<0.001 •  </td></tr></TABLE><!-‐-‐EndFragment-‐-‐></BODY></HTML>

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Data management

•  Management of large data sets – Volume of data – 3,3 million observa7ons per month, 24 months – using cycles in “do files”, size of joint database is very large (each file about 168-‐180 MB)

–  Joining repeated cross-‐sec7ons – Ex: Standard Income and Living Condi7ons – Eurostat

– Merging different data sets (ex: individual hospital admissions with residen7al data)

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Average price per day, each month from a different file All data in a single file makes it very slow (close to 2,5 GB)

Research papers

•  Use of regression models available •  Flexibility for new likelihood func7ons •  New commands available •  User-‐built commands

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Example

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-‐  Health – latent variable – 5 states -‐  Pharma consump7on (yes/no) -‐  Visits to doctor – number of visits – count variable

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About 11,000 observa7ons; 2 itera7ons takes roughly 5 hours

Results

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Our interest: role of subsystems – impact on number of visits, but not pharma or health directly

•  Problems: a “do file” that runs in Stata 13 does not in Stata 14 – not easy to find out why…

•  Look for more efficient coding to reduce 7me to solve the problem

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New commands

•  Example: – Treatment effects – matching es7mator (new command)

– Krls – user command for machine-‐learning technique to fit non-‐linear func7on

– Tradi7onal regression •  Suicides during the crisis period

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What happened

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46

810

12suicidio_ine

1980 1990 2000 2010 2020ano

Aver 2004 (5 years each side)

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46

810

12suicidio_ine

1980 1990 2000 2010 2020ano

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89

1011

12

2004 2006 2008 2010 2012 2014ano

Linear prediction s2suicidio_ine

Non-‐significant differences – the three methods are roughly giving the same info

Go back 10 years: since 1994

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46

810

12suicidio_ine

1980 1990 2000 2010 2020ano

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Impact of crisis – there are differences but not too different

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-10

010

2030

1995 2000 2005 2010 2015ano

Linear prediction s2_93suicidio_ine

We look at predicted effect – the krls is wildly non-‐linear (unreasonable so)

Stata in the life of a health economist

•  Helpful in many ways •  Could be improved to interact with new social media

•  Works very well for management of large data sets (speed can be an issue)

•  Flexible enough to accommodate new models / likelihood func7ons

•  Great to test different technique using both official and community-‐based commands

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stata$in$the$everyday$life$of$a$ health$economist · blog •...

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