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Presented by Gulzat

Barbara Guardabascio and Marco Ventura ” Estimating the dose–response function through a generalized linear model approach” The Stata Journal (2014) 14 , Number 1, pp. 141–158. Presented by Gulzat. Background.

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Presented by Gulzat

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  1. Barbara Guardabascio and Marco Ventura”Estimating the dose–response function through a generalized linear model approach” The Stata Journal (2014)14, Number 1, pp. 141–158 Presented by Gulzat

  2. Background • Rosenbaum and Rubin (1983a) -binary treatment (pscore.ado, psmatch2.ado) • Hirano and Imbens (2004)-continuous treatment normally distributed (gpscore.ado, doseresponse.ado) • Guardabascio and Ventura (2014) - continuous treatment, not necessarily normally distributed (glmgpscore.ado, glmdose.ado)

  3. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.3

  4. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.4 • What is a dose-response function? • It is a relationship between treatment and an outcome variable e.g.: birth weight, employment, bank debt, etc

  5. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.5

  6. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.8

  7. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.9

  8. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.10

  9. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.11

  10. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.12

  11. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.13

  12. Practical implementation of GPS • Estimate • (1) r(t,x) • (2) • (3) for

  13. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.19

  14. Example: The Imbens–Rubin–Sacerdote lottery sample • Survey of Massachusetts lottery winners. • The goal: to analyze the effect of the prize amount on subsequent labor earnings (from social security records). • The sample is the “winners” sample of 237 individuals who won a major prize in the lottery.

  15. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.25

  16. The Imbens–Rubin–Sacerdote lottery sample. Summary

  17. The Imbens–Rubin–Sacerdote lottery sample. • Choose the quantiles of the treatment variable to divide the sample into three groups, [0-23], (23-80] and (80-485]: • qui generate cut=23 if prize<=23 • qui replace cut=80 if prize>23 & prize<=80 • qui replace cut=485 if prize>80 • egen max_p=max(prize) • g fraction=prize/max_p • qui generate cut1=23/max_p if fraction<=23/max_p • qui replace cut1=80/max_p if fraction>23/max_p & fraction<=80/max_p • qui replace cut1=485/max_p if fraction>80/max_p • glmgpscore male ownhs owncoll tixbot workthen yearw yearm1 yearm2, t(fraction) gpscore(gpscore_fr) predict(y_hat_fr) sigma(sd_fr) cutpoints(cut1) index(mean) nq_gps(5) family(binomial) link(logit) detail • mat def tp1=(0.10\0.20\0.30\0.40\0.50\0.60\0.70\0.80) • glmdose male ownhs owncoll tixbot workthen yearw yearm1 yearm2, t(fraction) gpscore(gps_flog) predict(y_hat_fl) sigma(sd_fl) cutpoints(cut1) index(mean) nq_gps(5) family(binomial) link(logit) outcome(year6) dose_response(doseresp_fl) tpoints(tp1) delta(0.1) reg_type_t(quadratic) reg_type_gps(quadratic) interaction(1) bootstrap(yes) boot_reps(10) analysis(yes) detail filename("output_flog") graph("graphflog.eps")

  18. The Imbens–Rubin–Sacerdote lottery sample.

  19. Flogit glmgpscore output

  20. Flogit glmgpscore output

  21. Flogit glmgpscore output

  22. Flogit glmgpscore output

  23. Flogit glmgpscore output

  24. Flogit glmdose output

  25. Flogit glmdose output

  26. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.26

  27. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.28

  28. Source: PPT by Barbara Guardabascio, Marco Ventura “ESTIMATING THE DOSE-RESPONSE FUNCTION THROUGH THE GLM APPROACH”, Italian National Institute of Statistics, 7th June 2013, Potsdam, p.30

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