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Seminar #21

Seminar #21. Karen Jakubowski. Finding 95% Confidence Intervals:. An approximate 95% confidence interval for unknown population proportion p is based on sample proportion p-hat from a random sample of size n = sample proportion +/- 2 standard deviations.

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Seminar #21

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  1. Seminar #21 Karen Jakubowski

  2. Finding 95% Confidence Intervals: • An approximate 95% confidence interval for unknown population proportion p is based on sample proportion p-hat from a random sample of size n = sample proportion +/- 2 standard deviations

  3. Article #1: The White Coat Syndrome • Some people exhibit a psychophysiological response to seeing doctors or other medical professionals • This has been termed the “White Coat Syndrome” or “White Coat Hypertension” • Patients suffer from hypertension only when in the presence of a person in a “white coat.”

  4. Study design: • Study involved 419 patients who exhibited hypertension while at their doctor’s office. • They were provided with portable blood pressure-measuring devices that measured their levels outside of the office.

  5. Results: • 26% (109) of the patients suffered from hypertension only while visiting their physician.

  6. What are some possible reasons that individuals exhibit the “White Coat Syndrome?” • Fear of bad news • Knowledge of experiences of friends/family • Embarrassment (ie: have not been taking medication regularly or following past directions)

  7. Finding 95% Confidence Interval • .26 +/- 2SqRoot (.26(1-.26)) / (419) = (.217, .303)

  8. Article #2: Nine Percent of U.S. Children Age 8 to 15 Meet Criteria For Having ADHD • 8.7% of U.S. children age 8 to 15 meet diagnostic criteria for ADHD • fewer than half receive treatment • ADHD is characterized by hyperactivity, impulsive behavior, and an inability to pay attention to tasks.

  9. Study Design: • Study involved 3,082 children • sample population designed to represent the entire population of 8 to 15-year-olds in the U.S. • Parents/caregivers provided information about their child’s ADHD symptoms and medical history, as well as sociodemographic details via phone interview.

  10. Study Design: • How could this study design have been flawed? • Some parents may not have been entirely truthful about their child’s ADHD symptoms or medical history. • Bias from parents’ inaccurate memories about when their child first displayed symptoms of ADHD or the severity of the symptoms.

  11. Finding 95% Confidence Interval • 8.7% (268) of the 3,082 children studied fulfilled criteria for ADHD. • .087 +/- 2SqRt ((.087(1-.087)) / 3082 = (.077, .097)

  12. Finding 95% Confidence Interval • 47.9% of the children who met ADHD criteria (268 children) had been previously diagnosed with the condition. • Before we calculate the interval, do you think that the children meeting ADHD criteria who had been diagnosed were in a minority?

  13. Finding 95% Confidence Interval • .479 +/- 2SqRt ( (.479 (1-.479)) / 268 ) = (0.42, 0.54) Since the values in our interval surround .5 , we cannot be certain that the children who were diagnosed with ADHD were in a minority.

  14. Article #3: Surgeons With Video Game Skill Appear To Perform Better In Simulated Surgery Skills Course • Study involved 33 surgeons: 12 attending physicians and 21 residents • Asked about their video game-playing habits, then assessed on their performance at the Rosser Top Gun Laparoscopic Skills and Suturing Program • A 1.5 day course that scores surgeons on time and errors during simulated surgery skills.

  15. Results: • Surgeons who had played video games in the past for more than 3 hours/week made 37% fewer errors, were 27% faster, and scored 42% better overall than surgeons who never played video games. • Current video game players made 32% fewer errors, were 24% faster and scored 26% better overall than non-players. • Surgeons in the top 1/3 of gaming skill made 47% fewer errors, performed 39% faster, and scored 41% better overall than those in the bottom 1/3.

  16. Does anyone notice a problem …? • From the information provided, we cannot find 95% confidence intervals! • The data was summarized in quantitative terms, but we were not given any mean values, just percentages comparing how much higher the mean for one group is compared to another. • Therefore we cannot set up a confidence interval around a proportion in this example.

  17. Article #4: U.S. College Students’ Exposure to Tobacco Promotions: Prevalence and Association With Tobacco Use • This study assessed college students’ exposure to the tobacco industry marketing strategy of sponsoring social events at bars, nightclubs, and college campuses.

  18. Study design: • Data came from the 2001 Harvard College Alcohol Study - • a random sample of 10,904 students enrolled in 119 “nationally representative” 4-year colleges and universities.

  19. Study design: • Questionnaires were mailed to 21,055 students in February 2001. • 3 mailings were sent within 3 weeks: the questionnaire, a reminder, and a second questionnaire. • Responses were anonymous, and cash prizes were awarded to encourage responses.

  20. What types of questions should the questionnaire have included?

  21. Study design: • The questionnaire assessed students’: • demographics (ie: age, sex, race, GPA) • tobacco, alcohol, and marijuana use • Tobacco use was defined as having (in the past 30 days): • smoked a cigarette, cigar, pipe, or bidi (a small hand-rolled often flavored cigarette made in India) • used smokeless tobacco

  22. Study design: • What aspects of this study reduced bias? • Using a large sample • Using a sample that was representative of the larger college student population • Anonymous questionnaire

  23. Study design: • What aspects of this study could have caused bias? • Non-response bias • Not answering the questions truthfully • Wording of the questions

  24. Results: • 52% (5,670 students) responded to the questionnaire • Do you think that 52% a good response rate? • Does it provide enough information to allow accurate inferences to be made about the larger population of college students?

  25. Results: • The effect of exposure of tobacco promotions differed by the age at which students first began smoking • Out of the 78% (8482) of students who did not smoke regularly before 19 years of age (approximately the age most students enter college) the current smoking rate was • 23.7% for students who had attended a promotional event • 11.8% for students who had not attended an event

  26. Finding 95% Confidence Interval • For the 23.7% of students who had not smoked before age 19, but were current smokers and had attended a promotional event: .237 +/- 2SqRt ( (.237(1-.237)) / 8482) = (.227, .246) • For the 11.8% of students who had not smoked before age 19, but were current smokers and had neverattended a promotional event: .118 +/- 2SqRt (.118(1-.118)) / 8482 = (.111, .125)

  27. What does it mean? • Since the confidence intervals for the two separate groups do not overlap, the data suggests that one population proportion is higher than the other.

  28. Results: • For the 22% (2334) of students who smoked regularly before 19 years of age, there was no significant difference between the percentage of students who had or had not attended a tobacco promotional event. • 77.5% vs 72.2%, respectively

  29. Conclusion: • Tobacco promotional events may encourage previously non-smoking college students to begin smoking, or current smokers to continue smoking.

  30. The End!

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