see attahched file
Original Analyses – Variable Selections In Week 4 students will perform original analyses of Heart dataset variables*. Students should either select one row of pre-approved variables or use the Heart CSV Codebook to create a proposal for your instructor to review. · Heart Pre-Approved Variables · Heart CSV Codebook * Instructor Approval Required Prior to Analyses to Ensure Students Have Unique Variable Results. NOTE: Student that select a row with a restriction will need to add a where statement to their code on all data lines. EXAMPLE: proc logistic data=SASHELP.HEART (where= (SEX ='Female')); model status (event='dead') = weight; run; Optio n # Student Name Variable #1 Dichotomous Variable #2 Continuous Restriction Variable: CURSMOKE; Variable Label: Currently smoking cigarettes; Values & Value Labels 0 = No, 1 = Yes 1. CURSMOKE AGE N/A 2. CURSMOKE AGE Female Only 3. CURSMOKE AGE Males Only 4. CURSMOKE BMI N/A 5. CURSMOKE BMI Female Only 6. CURSMOKE BMI Males Only 7. CURSMOKE GLUCOSE N/A 8. CURSMOKE GLUCOSE Female Only 9. CURSMOKE GLUCOSE Males Only Variable: DIABETES; Variable Label: Diabetes (glucose > 200 mg/dL or on treatment); Values & Value Labels 0 = No, 1 = Yes 10. DIABETES AGE N/A 11. DIABETES AGE Female Only 12. DIABETES AGE Males Only 13. DIABETES BMI N/A 14. DIABETES BMI Female Only 15. DIABETES BMI Males Only 16. DIABETES GLUCOSE N/A 17. DIABETES GLUCOSE Female Only 18. DIABETES GLUCOSE Males Only 19. DIABETES AGE N/A 20. DIABETES AGE Female Only 21. DIABETES AGE Males Only 22. DIABETES CIGPDAY N/A 23. DIABETES CIGPDAY Female Only 24. DIABETES CIGPDAY Males Only Variable: BPMEDS; Variable Label: Use of anti-hypertensive medication; Values & Value Labels 0 = No, 1 = Yes 25. BPMEDS AGE N/A 26. BPMEDS AGE Female Only 27. BPMEDS AGE Males Only 28. BPMEDS BMI N/A 29. BPMEDS BMI Female Only 30. BPMEDS BMI Males Only 31. BPMEDS GLUCOSE N/A 32. BPMEDS GLUCOSE Female Only 33. BPMEDS GLUCOSE Males Only 34. BPMEDS AGE N/A 35. BPMEDS AGE Female Only 36. BPMEDS AGE Males Only 37. BPMEDS CIGPDAY N/A 38. BPMEDS CIGPDAY Female Only 39. BPMEDS CIGPDAY Males Only Original Analyses – Variable Selections In Week 4 students will perform original analyses of Heart dataset variables*. Students should either select one row of pre-approved variables or use the Heart CSV Codebook to create a proposal for your instructor to review. · Heart Pre-Approved Variables · Heart CSV Codebook * Instructor Approval Required Prior to Analyses to Ensure Students Have Unique Variable Results. NOTE: Student that select a row with a restriction will need to add a where statement to their code on all data lines. EXAMPLE: proc logistic data=SASHELP.HEART (where= (SEX ='Female')); model status (event='dead') = weight; run; Optio n # Student Name Variable #1 Dichotomous Variable #2 Continuous Restriction Variable: CURSMOKE; Variable Label: Currently smoking cigarettes; Values & Value Labels 0 = No, 1 = Yes 1. CURSMOKE AGE N/A 2. CURSMOKE AGE Female Only 3. CURSMOKE AGE Males Only 4. CURSMOKE BMI N/A 5. CURSMOKE BMI Female Only 6. CURSMOKE BMI Males Only 7. CURSMOKE GLUCOSE N/A 8. CURSMOKE GLUCOSE Female Only 9. CURSMOKE GLUCOSE Males Only Variable: DIABETES; Variable Label: Diabetes (glucose > 200 mg/dL or on treatment); Values & Value Labels 0 = No, 1 = Yes 10. DIABETES AGE N/A 11. DIABETES AGE Female Only 12. DIABETES AGE Males Only 13. DIABETES BMI N/A 14. DIABETES BMI Female Only 15. DIABETES BMI Males Only 16. DIABETES GLUCOSE N/A 17. DIABETES GLUCOSE Female Only 18. DIABETES GLUCOSE Males Only 19. DIABETES AGE N/A 20. DIABETES AGE Female Only 21. DIABETES AGE Males Only 22. DIABETES CIGPDAY N/A 23. DIABETES CIGPDAY Female Only 24. DIABETES CIGPDAY Males Only Variable: BPMEDS; Variable Label: Use of anti-hypertensive medication; Values & Value Labels 0 = No, 1 = Yes 25. BPMEDS AGE N/A 26. BPMEDS AGE Female Only 27. BPMEDS AGE Males Only 28. BPMEDS BMI N/A 29. BPMEDS BMI Female Only 30. BPMEDS BMI Males Only 31. BPMEDS GLUCOSE N/A 32. BPMEDS GLUCOSE Female Only 33. BPMEDS GLUCOSE Males Only 34. BPMEDS AGE N/A 35. BPMEDS AGE Female Only 36. BPMEDS AGE Males Only 37. BPMEDS CIGPDAY N/A 38. BPMEDS CIGPDAY Female Only 39. BPMEDS CIGPDAY Males Only The Framingham Heart Study - Integrative Exercises with Solutions Essentials of Biostatistics in Public Health, Third Edition Lisa M. Sullivan Integrative Exercises (Edited for COH 602 Biostatistics) Copyright © 2018 by Jones & Bartlett Learning, LLC, an Ascend Learning Company 1 The Framingham Heart Study—Integrative Exercises with Solutions Background The National Heart, Lung, and Blood Institute (NHLBI)1 created a teaching dataset that includes real but anonymized data collected as part of the Framingham Heart Study. The Framingham Heart Study is one of the most influential and longest running epidemiological studies of risk factors for cardiovascular disease ever run. The study started in 1948 and continues today to collect extensive data from original participants, their children, and their children’s children. Much of what we know about cardiovascular disease was discovered by investigators involved in the Framingham Heart Study. In fact, studies to date using data collected in the Framingham Heart study have resulted in over 3000 publications in high impact, peer-reviewed medical journals. The Framingham Heart Study has been widely discussed in the media. WGBH in Boston produced a video documentary for PBS entitled “The Hidden Epidemic: Heart Disease in America” that details the history of heart disease in this country and highlights the Framingham Heart Study.2 In 2007, CBS News did a story on the study, its participants, and its impact.3 Additionally, research results from the Framingham Heart Study are communicated widely, most recently highlighting the discovery of a gene that may promote obesity4 and new data showing declining rates of dementia.5 Interested readers can visit the Framingham Heart Study website for a detailed history of this incredible study and its many contributions to preventive medicine.6 1 http://www.nhlbi.nih.gov/ 2 http://www.pbs.org/wgbh/takeonestep/heart/ 3 http://www.cbsnews.com/videos/landmark-heart-study/ 4 http://www.cbsnews.com/news/how-a-fat-gene-may-influence-your-weight/ 5 http://www.cbsnews.com/news/dementia-alzheimers-risk-signs-of-hope-study/ 6 https://www.framinghamheartstudy.org/about-fhs/history.php/ Essentials of Biostatistics in Public Health, Third Edition Lisa M. Sullivan Integrative Exercises (Edited for COH 602 Biostatistics) Copyright © 2018 by Jones & Bartlett Learning, LLC, an Ascend Learning Company 2 Dataset for Analyses NHLBI created a longitudinal teaching dataset includes clinical, laboratory, and outcome data on n = 4434 participants. Each participant has between one and three observations—which represent examinations held approximately 6 years apart. A detailed description of the Framingham Heart Study dataset and other public use datasets available from NHLBI are available on the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) website. The dataset available for analysis includes only data collected at the first examination for each participant (n = 4434). The dataset is available as comma separated values (.csv) file. Variables The following variables are available in each dataset for analysis (extracted from the complete documentation file, available on the NHLBI BioLINCC website 7). Variable Name Description Coding Details/Range RANDID Unique identification number for each participant 2248-9999312 SEX Participant sex 1 = Male, 2 = Female PERIOD Exam cycle 1, 2, 3 TIME Number of days since first (baseline) exam 0–4854 AGE Age at exam, years 32–81 SYSBP Systolic blood pressure, mmHg 83–295 DIABP Diastolic blood pressure, mmHg 30–150 BPMEDS Use of anti-hypertensive medication 0 = No, 1 = Yes CURSMOKE Currently smoking cigarettes 0 = No, 1 = Yes CIGPDAY Number of cigarettes smoked per day 0 (non-smoker)–90 TOTCHOL Total serum cholesterol, mg/dL 107–696 HDLC* High density lipoprotein cholesterol, mg/dL 10–189 LDLC* Low density lipoprotein cholesterol, mg/dL 20–565 BMI Body mass index = weight (kg)/height (m)2 14–57 GLUCOSE Serum glucose, mg/dL 39–478 DIABETES Diabetes (glucose > 200 mg/dL or on treatment) 0 = No, 1 = Yes HEARTRTE Heart rate, beats/minute 37–220 PREVAP Prevalent angina pectoris 0 = No, 1 = Yes PREVCHD Prevalent coronary heart disease (CHD) 0 = No, 1 = Yes PREVMI Prevalent myocardial infarction (MI) 0 = No, 1 = Yes 7 https://biolincc.nhlbi.nih.gov/static/studies/teaching/framdoc.pdf?link_time=2016-07-06_14:21:55.514359 Essentials of Biostatistics in Public Health, Third Edition Lisa M. Sullivan Integrative Exercises (Edited for COH 602 Biostatistics) Copyright © 2018 by Jones & Bartlett Learning, LLC, an Ascend Learning Company 3 PREVSTRK Prevalent stroke 0 = No, 1 = Yes PREVHYP Prevalent hypertension 0 = No, 1 = Yes The following are outcome events coded 1 if the event occurred during the follow-up (only the first event is recorded). ANGINA Angina pectoris 0 = No, 1 = Yes HOSPMI Hospitalized for MI 0 = No, 1 = Yes MI_FCHD Hospitalized for MI or fatal CHD 0 = No, 1 = Yes ANYCHD Any coronary heart disease event 0 = No, 1 = Yes STROKE Stroke 0 = No, 1 = Yes CVD Cardiovascular disease 0 = No, 1 = Yes HYPERTEN Hypertension 0 = No, 1 = Yes DEATH Death from any cause 0 = No, 1 = Yes The following are numbers of days from the first (baseline) exam to the first event during the follow-up. If no event occurred, time is end of follow-up, death, or last known contact date. TIMEAP Time from baseline to first angina TIMEMI Time from baseline to first myocardial infarction TIMEMIFC Time from baseline to first MI or fatal CHD TIMECHD Time from baseline to first CHD TIMESTRK Time from baseline to first stroke TIMECVD Time from baseline to first cardiovascular disease TIMEHYP