ADVANCED STATISTICAL METHODS: PART 1: …

1 ACS Outcomes Research Course ADVANCED STATISTICAL methods 1 ADVANCED STATISTICAL methods : part 1: introduction TO propensity SCORES IN STATA Learning objectives: To understand the use of propensity scores as a means for controlling for selection bias in observational studies of treatment effects. To learn how to create propensity scores apply them in a variety of analytic approaches in STATA. To use propensity scores to evaluate the outcomes of open versus laparoscopic appendectomy in the NSQIP data provided. propensity SCORES IN STATA: Open the dataset and describe the data For this analysis, we will use NSQIP data for patients undergoing appendectomy (2005-2007).

2 This dataset contains 21,475 observations (patients) and 86 variables. The first group (2-56) includes pre-operative patient characteristics and medical conditions. The second group (57-83) includes peri-operative outcomes. The variables morbprob (var #84) and mortprob (var #85) are probabilities of morbidity and mortality, respectively, based on the NSQIP risk models. Finally, the variable treatment (var #86) tells you whether the appendectomy was performed open or laparoscopically. Type the command: describe, number STATA output: Contains data from D:\nbirkmey\Desktop\Desktop\ACS Course 2010\Mark propensity \Appendicitis_Propensity\ obs: 21,475 vars: 90 27 Oct 2010 13:11 size: 4,123,200 ( of memory free) ---------------------------------------- ---------------------------------------- --------- variable storage display value name type format label variable label ---------------------------------------- ---------------------------------------- --------- 1.

3 Caseid long % Patient ID 2. sex byte % sex Patient Sex 3. race str32 %32s Patient Race 4. race1 byte % race==American Indian or Alaska Native 5. race2 byte % race==Asian or Pacific Islander 6. race3 byte % race==Black, Not of Hispanic Origin 7. race4 byte % race==Hispanic, Black 8. race5 byte % race==Hispanic, Color Unknown 9. race6 byte % race==Hispanic, White 10. race7 byte % race==Unknown 11. race8 byte % race==White, Not of Hispanic Origin 12.

4 White byte % whitelab White/Non-White Race 13. white1 byte % white==White 14. white2 byte % white==Non-white 15. white3 byte % white==Unknown 16. prncptx str25 %25s Principal CPT ACS Outcomes Research Course ADVANCED STATISTICAL methods 2 17. cpt str5 %5s CPT code 18. inout byte % inout Inpatient/Outpatient Status 19. age byte % Patient Age 20. operyr int % Year of Operation 21. height byte % Patient Height 22. weight int % Patient Weight 23.

5 Bmi float % BMI 24. bmi40 float % Morbid Obesity 25. bmicat byte % 4 quantiles of bmi 26. diabetes byte % diabetes DM/oral or insulin 27. diabet~1 byte % diabetes==No 28. diabet~2 byte % diabetes==Oral Med 29. diabet~3 byte % diabetes==Insulin 30. smoke byte % yesno Current (Within 1 YR) Smoker 31. etoh byte % yesno Current (Within 2 Weeks) Drinker (>2 per Day) 32. dyspnea byte % dyspnea Dyspnea 33. dyspnea1 byte % dyspnea==No 34. dyspnea2 byte % dyspnea==Moderate Exertion 35.

6 Dyspnea3 byte % dyspnea==At Rest 36. dnr byte % yesno DNR Status 37. fnsta~s2 byte % fnstatus2 Functional Status Prior to Surgery 38. fnsta~21 byte % fnstatus2==Independent 39. fnsta~22 byte % fnstatus2==Partially Dependent 40. fnsta~23 byte % fnstatus2==Totally Dependent 41. hxcopd byte % yesno History of Severe COPD 42. ascites byte % yesno Ascites 43. hxchf byte % yesno CHF within 30 days 44. hxmi byte % yesno MI within 6 mths 45. hypermed byte % yesno HTN requiring medication 46.

7 Dialysis byte % yesno On dialysis pre-op 47. pregna~y byte % yesno Pregnant at time of surgery 48. rupture byte % Rupture 49. emergncy byte % yesno Emergency Case 50. asa byte % asa ASA Class 51. asa1 byte % asaclas==1-No Disturb 52. asa2 byte % asaclas==2-Mild Disturb 53. asa3 byte % asaclas==3-Severe Disturb 54. asa4 byte % asaclas==4-Life Threat 55. asa5 byte % asaclas==5-Moribund 56. asa6 byte % asaclas==NULL 57.

8 Tothlos int % LOS 58. supinfec byte % yesno Superficial surgical site infection 59. wndinfd byte % yesno Deep Incisional SSI 60. orgspc~i byte % yesno Organ Space SSI 61. dehis byte % yesno Wound Disruption 62. oupneumo byte % yesno Pneumonia 63. reintub byte % yesno Unplanned Reintubation 64. pulembol byte % yesno PE 65. failwean byte % yesno Ventilator>48 Hours 66. renainsf byte % yesno Progressive Renal Insufficiency 67. oprenafl byte % yesno Acute Renal Failure 68. urninfec byte % yesno UTI 69.

9 Cnscva byte % yesno Stroke/CVA 70. cnscoma byte % yesno Coma>24 Hours 71. neurodef byte % yesno Peripheral Nerve Injury 72. cdarrest byte % yesno Cardiac Arrest req CPR 73. cdmi byte % yesno MI 74. othbleed byte % yesno Bleeding req. >4 units 75. othdvt byte % yesno DVT 76. othsysep byte % yesno Sepsis 77. othses~k byte % yesno Septic Shock 78. convert byte % yesno Convert Lap to Open 79. return byte % yesno Return to OR 80. died byte % diedlab Peri-op Death 81.

10 Anycomp float % Any Complication 82. mincomp float % Minor Complication ACS Outcomes Research Course ADVANCED STATISTICAL methods 3 83. majcomp float % Major Complication 84. morbprob float % Morbidity Risk 85. mortprob float % Mortality Risk 86. treatment byte % treatment Open/Lap Treatment ---------------------------------------- ---------------------------------------- -------------------Sorted by: r Exploring the relationship between patient pre-operative characteristics and conditions and treatment We will first explore the relationship between patient pre-operative characteristics and treatment with either open or laparoscopic appendectomy in this dataset.