Medicine: HRP258 Statistics in Medicine queses with answers

 Medicine: HRP258 Statistics in Medicine
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1. If scores on an exam are normally distributed with a mean of 80% and a standard deviation of 10%, what would a score of 90% be in Z-scores (e.g., how many standard deviations is this score above or below the mean)?

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Z=-1.5   

 Z=-1.0

Z=0

Z=1.0

Z=1.5  Answer

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2. Which of the following is a measure of variability?

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Median.

 Interquartile range.   Answer

Correlation coefficient.

 Mean.

Risk ratio.

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Question 3

(4 points possible)

3. A particular diagnostic test for disease X has a sensitivity of 90% and a specificity of 90%. What is the negative predictive value of the test?

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90%

10%

 1%

 18%

 It cannot be determined from the information given   Answer

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4. In a randomized trial of two drugs to treat depression (drug A and drug B), depressive symptoms decreased significantly in patients on drug A (p<.001) but not in patients on drug B (p>.05). It follows that:

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Drug A is superior to drug B.

 Drug A and drug B are equally effective.

 Drug B is superior to drug A.

None of the above   Answer

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Question 5

(4 points possible)

5. In a psychology experiment in which 100 volunteers were asked to read a paragraph about an engineer, 65 assumed that the engineer was male despite the fact that the paragraph did not specify gender (and avoided gendered pronouns such as “he” or “she”). If the null hypothesis here is that there is no gender bias, what is the two-sided p-value associated with this result? Use a normal approximation to solve this.

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P=.0001 

 p=.003

 p=.10

 p=.05 Answer

 p=.99

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1.   Question 6

(4 points possible)

6. Researchers reported that in a sample of U.S. women aged 50 to 54 who underwent mammography, 14.4% were recalled for further evaluation; however, in a similar sample of women undergoing mammography in the United Kingdom, only 7.6% were recalled for further evaluation.

What are the observed risk and odds ratios for the association between living in the United States (“exposed”) vs. the United Kingdom (“unexposed”) and being recalled for further evaluation following mammography (outcome)?

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RR=1.89; OR=2.05  Answer

 RR=2.05; OR=1.89

RR=0.40; OR=0.30

 RR=0.30; OR=0.40

RR=2.05; OR=3.0

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·         Question 7

·         (4 points possible)

·         Use the following table to answer questions 7 and 8:

·         This table displays results from a prospective cohort study evaluating meat intake and mortality. The table displays hazard ratios for mortality by quintile of red meat intake.

Table 2. Multivariate Analysis for Red, White, and Processed Meat Intake and Total and Cause-Specific Mortality in Men in the National Institutes of Health-AARP Diet and Health Studya
	Quintile
Mortality in Men (n=322263)	Q1	Q2	Q3	Q4	Q5	P Value for Trend
	Red Meat Intakeb
All mortality
Deaths	6437	7835	9366	10988	13350
Basic modelc	1 [Ref.]	1.07 (1.03-1.10)	1.17 (1.13-1.21)	1.27 (1.23-1.31)	1.48 (1.43-1.52)	<.001
Adjusted modeld	1 [Ref.]	1.06 (1.03-1.10)	1.14 (1.10-1.18)	1.21 (1.17-1.25)	1.31 (1.27-1.35)	<.001
Cancer mortality
Deaths	2136	2701	3309	3839	4448
Basic modelc	1 [Ref.]	1.10 (1.04-1.17)	1.23 (1.16-1.29)	1.31 (1.24-1.39)	1.44 (1.37-1.52)	<.001
Adjusted modeld	1 [Ref.]	1.05 (0.99-1.11)	1.13 (1.07-1.20)	1.18 (1.12-1.25)	1.22 (1.16-1.29)	<.001
CVD mortality
Deaths	1997	2304	2703	3256	3961
Basic modelc	1 [Ref.]	1.02 (0.96-1.08)	1.10 (1.04-1.17)	1.24 (1.17-1.31)	1.44 (1.37-1.52)	<.001
Adjusted modeld	1 [Ref.]	0.99 (0.96-1.09)	1.08 (1.02-1.15)	1.18 (1.12-1.26)	1.27 (1.20-1.35)	<.001
Mortality from injuries and sudden deaths
Deaths	184	216	228	280	343
Basic modelc	1 [Ref.]	1.02 (0.84-1.24)	0.97 (0.80-1.18)	1.09 (0.90-1.31)	1.24 (1.03-1.49)	.01
Adjusted modeld	1 [Ref.]	1.06 (0.86-1.29)	1.01 (0.83-1.24)	1.14 (0.94-1.39)	1.26 (1.04-1.54)	.008
All other deaths
Deaths	1268	1636	1971	2239	2962
Basic modelc	1 [Ref.]	1.13 (1.05-1.22)	1.25 (1.17-1.35)	1.33 (1.24-1.42)	1.68 (1.57-1.80)	<.001
Adjusted modeld	1 [Ref.]	1.17 (1.09-1.26)	1.28 (1.19-1.38)	1.34 (1.25-1.44)	1.58 (1.47-1.70)	<.001

·      

   ^a: Data are given as hazard ratio (95% confidence interval) unless otherwise specified.

·         ^b: Median red meat intake based on men and women (g/1000 kcal): Q1, 9.8; Q2, 21.4; Q3, 31.3; Q4, 42.8; and Q5, 62.5.

·         ^c: Basic model: age(continuous); race (non-Hispanic white, non-Hispanic black, Hispanic/Asian/Pacific Islander/American Indian/Alaskan native, or unknown); and total energy intake (continous).

·         ^d: Adjusted model: basic model plus education (<8 years or unknown, 8-11 years, 12 years [high school], some college, or college graduate); marital status (married: yes/no); family history of cancer (yes/no) (cancer mortality only); body mass index (18.5 to <25, 25 to <30, 30 to <35, ≥35 [calculated as weight in kilograms divided by height in meters squared]); 31-level; smoking history using smoking status (never, former, current), time since quitting for former smokers, and smoking dose; frequency of vigorous physical activity (never/rarely, 1-3 times/mo, 1-2 times/wk, 3-4 times/wk, ≥5 times/wk); alcohol intake (none, 0 to <5, 5 to <15, 15 to <30, ≥40 servings/1000 kcal, vitamin supplement user (≥1 supplement/mo); fruit consumption (0 to <0.7, 0.7 to <1.2, 1.2 to <1.7, 1.7 to <2.5, ≥2.5 servings/1000kcal); and vegetable consumption (0 to <1.3, 1.3 to <1.8, 1.8 to <2.2, 2.2 to <3.0, ≥3.0 serving/1000 kcal).

·         Reproduced with permission from: Sinha R, Cross AJ, Graubard BI, Leitzmann MF, Schatzkin A. Meat intake and mortality: a prospective study of over half a million people. Arch Intern Med 2009;169:562-71.

·         Question 7

·         7. What statistical method was used to calculate the Hazard Ratios given in the table?

·         Top of Form

·         Linear regression

·          Poisson regression

·          Cox regression

·         Logistic regression   

·          Multiple 2x2 tables  Answer

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arab

Question 8

(4 points possible)

Use the following table to answer questions 7 and 8:

This table displays results from a prospective cohort study evaluating meat intake and mortality. The table displays hazard ratios for mortality by quintile of red meat intake.

Table 2. Multivariate Analysis for Red, White, and Processed Meat Intake and Total and Cause-Specific Mortality in Men in the National Institutes of Health-AARP Diet and Health Studya
	Quintile
Mortality in Men (n=322263)	Q1	Q2	Q3	Q4	Q5	P Value for Trend
	Red Meat Intakeb
All mortality
Deaths	6437	7835	9366	10988	13350
Basic modelc	1 [Ref.]	1.07 (1.03-1.10)	1.17 (1.13-1.21)	1.27 (1.23-1.31)	1.48 (1.43-1.52)	<.001
Adjusted modeld	1 [Ref.]	1.06 (1.03-1.10)	1.14 (1.10-1.18)	1.21 (1.17-1.25)	1.31 (1.27-1.35)	<.001
Cancer mortality
Deaths	2136	2701	3309	3839	4448
Basic modelc	1 [Ref.]	1.10 (1.04-1.17)	1.23 (1.16-1.29)	1.31 (1.24-1.39)	1.44 (1.37-1.52)	<.001
Adjusted modeld	1 [Ref.]	1.05 (0.99-1.11)	1.13 (1.07-1.20)	1.18 (1.12-1.25)	1.22 (1.16-1.29)	<.001
CVD mortality
Deaths	1997	2304	2703	3256	3961
Basic modelc	1 [Ref.]	1.02 (0.96-1.08)	1.10 (1.04-1.17)	1.24 (1.17-1.31)	1.44 (1.37-1.52)	<.001
Adjusted modeld	1 [Ref.]	0.99 (0.96-1.09)	1.08 (1.02-1.15)	1.18 (1.12-1.26)	1.27 (1.20-1.35)	<.001
Mortality from injuries and sudden deaths
Deaths	184	216	228	280	343
Basic modelc	1 [Ref.]	1.02 (0.84-1.24)	0.97 (0.80-1.18)	1.09 (0.90-1.31)	1.24 (1.03-1.49)	.01
Adjusted modeld	1 [Ref.]	1.06 (0.86-1.29)	1.01 (0.83-1.24)	1.14 (0.94-1.39)	1.26 (1.04-1.54)	.008
All other deaths
Deaths	1268	1636	1971	2239	2962
Basic modelc	1 [Ref.]	1.13 (1.05-1.22)	1.25 (1.17-1.35)	1.33 (1.24-1.42)	1.68 (1.57-1.80)	<.001
Adjusted modeld	1 [Ref.]	1.17 (1.09-1.26)	1.28 (1.19-1.38)	1.34 (1.25-1.44)	1.58 (1.47-1.70)	<.001

^a: Data are given as hazard ratio (95% confidence interval) unless otherwise specified.

^b: Median red meat intake based on men and women (g/1000 kcal): Q1, 9.8; Q2, 21.4; Q3, 31.3; Q4, 42.8; and Q5, 62.5.

^c: Basic model: age(continuous); race (non-Hispanic white, non-Hispanic black, Hispanic/Asian/Pacific Islander/American Indian/Alaskan native, or unknown); and total energy intake (continous).

^d: Adjusted model: basic model plus education (<8 years or unknown, 8-11 years, 12 years [high school], some college, or college graduate); marital status (married: yes/no); family history of cancer (yes/no) (cancer mortality only); body mass index (18.5 to <25, 25 to <30, 30 to <35, ≥35 [calculated as weight in kilograms divided by height in meters squared]); 31-level; smoking history using smoking status (never, former, current), time since quitting for former smokers, and smoking dose; frequency of vigorous physical activity (never/rarely, 1-3 times/mo, 1-2 times/wk, 3-4 times/wk, ≥5 times/wk); alcohol intake (none, 0 to <5, 5 to <15, 15 to <30, ≥40 servings/1000 kcal, vitamin supplement user (≥1 supplement/mo); fruit consumption (0 to <0.7, 0.7 to <1.2, 1.2 to <1.7, 1.7 to <2.5, ≥2.5 servings/1000kcal); and vegetable consumption (0 to <1.3, 1.3 to <1.8, 1.8 to <2.2, 2.2 to <3.0, ≥3.0 serving/1000 kcal).

Reproduced with permission from: Sinha R, Cross AJ, Graubard BI, Leitzmann MF, Schatzkin A. Meat intake and mortality: a prospective study of over half a million people. Arch Intern Med 2009;169:562-71.

Question 8

8. The correct interpretation of the Hazard Ratio for all mortality of 1.48 for men with Quintile 5 red meat consumption is:

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The mortality rate is 48% higher for men in quintile 5 of red meat intake compared with men in quintile 1.   Answer

 The mortality rate is 148% higher for men in quintile 5 of red meat intake compared with men in quintile 1

 The mortality rate is 48% higher for men in quintile 5 of red meat intake compared with men in the lower four quintiles.

The odds of death is 48% higher for men in quintile 5 of red meat intake compared with men in quintile 1.

 The risk of death is 148% higher for men in quintile 5 of red meat intake compared with men in quintile 4.

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Question 9

(4 points possible)

Use the following table to answer questions 9-11:

Table 2 (from JNNP, 2009) displays the beta coefficients from separate linear regression models for each predictor. All models are adjusted for age (except the models for age).

Table 2. Determinants of cognitive test scores and vitamin D levels (25OHD): linear regression analysis. BDI=Beck Depression Index score; PASE=Physical Activity Scale for the Elderly.
	DSST score	25(OH)D (nmol/L)
Age (years)	-0.415 (-0.439, -0.391)	-0.0135 (-0.114, 0.087)
Age left education (years)	0.199 (0.165, 0.233)	-0.127 (-0.272, 0.017)
BDI score	-0.177 (-0.217, -0.136)	-0.815 (-0.985, -0.644)
BDI category:
Normal (0-10)	Reference	Reference
Mild-Borderline (11-20)	-1.743 (-2.427, -1.058)	-0.641 (-12.51, -6.774)
Moderate-Extreme (21+)	-4.050 (-5.405, -2.695)	-13.93 (-19.61, -8.249)
Body Mass Index (kg/m2)	-0.115 (-0.179, -0.050)	-0.811 (-1.081, -0.541)
PASE score tertiles:
Lower	Reference	Reference
Mid	1.836 (1.148, 2.523)	5.148 (2.244, 8.052)
Upper	1.381 (0.649, 2.113)	7.072 (4.514, 10.09)
Current Smoker
No	Reference	Reference
Yes	-2.502 (-3.158, -1.847)	-10.95 (-13.69, -8.207)
Alcohol (≥ 1day/week)
No	Reference	Reference
Yes	2.159 (1.630, 2.687)	8.521 (6.307, 10.74)

Question 9

9. What is the best interpretation of the Beta coefficient relating Body Mass Index (kg/m²) to 25(OH)D (vitamin D) levels [Beta coefficient (and 95% CI) = -0.811 (-1.081, -0.541)]?

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People who are overweight have an 81.1% decreased risk of developing vitamin D deficiency, after adjusting for age.   

 People who are overweight have, on average, a vitamin D level that is -0.811 nmol/L lower than people who are normal weight, after adjusting for age

. Every 1 kg/m2 increase in BMI is associated with an average 0.811 nmol/L increase in vitamin D levels, after adjusting for age.

 Every 1 kg/m2 increase in BMI is associated with an average 0.811 nmol/L decrease in vitamin D levels, after adjusting for age.

There is no relationship between BMI and vitamin D levels, as evidenced by the fact that the Beta coefficient is so close to 0.

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Question 10

(4 points possible)

Use the following table to answer questions 9-11:

Table 2 (from JNNP, 2009) displays the beta coefficients from separate linear regression models for each predictor. All models are adjusted for age (except the models for age).

Table 2. Determinants of cognitive test scores and vitamin D levels (25OHD): linear regression analysis. BDI=Beck Depression Index score; PASE=Physical Activity Scale for the Elderly.
	DSST score	25(OH)D (nmol/L)
Age (years)	-0.415 (-0.439, -0.391)	-0.0135 (-0.114, 0.087)
Age left education (years)	0.199 (0.165, 0.233)	-0.127 (-0.272, 0.017)
BDI score	-0.177 (-0.217, -0.136)	-0.815 (-0.985, -0.644)
BDI category:
Normal (0-10)	Reference	Reference
Mild-Borderline (11-20)	-1.743 (-2.427, -1.058)	-0.641 (-12.51, -6.774)
Moderate-Extreme (21+)	-4.050 (-5.405, -2.695)	-13.93 (-19.61, -8.249)
Body Mass Index (kg/m2)	-0.115 (-0.179, -0.050)	-0.811 (-1.081, -0.541)
PASE score tertiles:
Lower	Reference	Reference
Mid	1.836 (1.148, 2.523)	5.148 (2.244, 8.052)
Upper	1.381 (0.649, 2.113)	7.072 (4.514, 10.09)
Current Smoker
No	Reference	Reference
Yes	-2.502 (-3.158, -1.847)	-10.95 (-13.69, -8.207)
Alcohol (≥ 1day/week)
No	Reference	Reference
Yes	2.159 (1.630, 2.687)	8.521 (6.307, 10.74)

Question 10

10. Which of the following models was used to generate the Beta coefficient relating BMI (kg/m²) to vitamin D levels [Beta coefficient (and 95% CI) = -0.811 (-1.081, -0.541)]?

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Vitamin D = β_BMI *BMI

Vitamin D = intercept + β_age*age + β_BMI *BMI

BMI = intercept + β_age*age + β_vitaminD *vitamin D 

ln (odds(vitamin D deficiency)) = intercept + β_age*age + β_BMI *BMI  

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Question 11

(4 points possible)

Use the following table to answer questions 9-11:

Table 2 (from JNNP, 2009) displays the beta coefficients from separate linear regression models for each predictor. All models are adjusted for age (except the models for age).

Table 2. Determinants of cognitive test scores and vitamin D levels (25OHD): linear regression analysis. BDI=Beck Depression Index score; PASE=Physical Activity Scale for the Elderly.
	DSST score	25(OH)D (nmol/L)
Age (years)	-0.415 (-0.439, -0.391)	-0.0135 (-0.114, 0.087)
Age left education (years)	0.199 (0.165, 0.233)	-0.127 (-0.272, 0.017)
BDI score	-0.177 (-0.217, -0.136)	-0.815 (-0.985, -0.644)
BDI category:
Normal (0-10)	Reference	Reference
Mild-Borderline (11-20)	-1.743 (-2.427, -1.058)	-0.641 (-12.51, -6.774)
Moderate-Extreme (21+)	-4.050 (-5.405, -2.695)	-13.93 (-19.61, -8.249)
Body Mass Index (kg/m2)	-0.115 (-0.179, -0.050)	-0.811 (-1.081, -0.541)
PASE score tertiles:
Lower	Reference	Reference
Mid	1.836 (1.148, 2.523)	5.148 (2.244, 8.052)
Upper	1.381 (0.649, 2.113)	7.072 (4.514, 10.09)
Current Smoker
No	Reference	Reference
Yes	-2.502 (-3.158, -1.847)	-10.95 (-13.69, -8.207)
Alcohol (≥ 1day/week)
No	Reference	Reference
Yes	2.159 (1.630, 2.687)	8.521 (6.307, 10.74)

Question 11

11. Which of the following factors is associated with the greatest decrease in DSST score?

Smoking vs. not smoking.

Drinking vs. not drinking.

 A 10-year increase in age.

Being in the top category of BDI score (moderate to extreme depression, 21+) versus the lowest category (normal, 0-10)  

 A 20-unit increase in BDI score.

Question 12

(4 points possible)

Use the following figure to answer questions 12-14: Figure 3b (British J Derm 2008) comes from a randomized trial of DHA versus a placebo pill for treating eczema. The figure shows boxplots of the eczema severity score, SCORAD, at baseline and week 8 for each group. SCORAD score is not normally distributed and the sample size is small. 

Reproduced with permission from: Koch C, Dölle S, Metzger M, et al. Docosahexaenoic acid (DHA) supplementation in atopic eczema: a randomized, double-blind, controlled trial. Br J Dermatol 2008;158:786-792.

Question 12

12. What test should be used to determine whether DHA is a better treatment than control for treating eczema (SCORAD is not normally distributed)?

A t-test that compares A and B.   Answer

A Wilcoxon sign-rank test that compares B and D.

 Wilcoxon sign-rank tests comparing A with B and C with D.

 A Wilcoxon sum-rank test that compares the difference between A and B with the difference between C and D.

 A chi-square test that compares B and C.

Question 13

(4 points possible)

Use the following figure to answer questions 12-14: Figure 3b (British J Derm 2008) comes from a randomized trial of DHA versus a placebo pill for treating eczema. The figure shows boxplots of the eczema severity score, SCORAD, at baseline and week 8 for each group. SCORAD score is not normally distributed and the sample size is small.

Reproduced with permission from: Koch C, Dölle S, Metzger M, et al. Docosahexaenoic acid (DHA) supplementation in atopic eczema: a randomized, double-blind, controlled trial. Br J Dermatol 2008;158:786-792.

Question 13

13. Use the graph above to roughly estimate the standard deviation of SCORAD score in the DHA group at baseline and the control group at baseline.

DHA group = 50, control group=100  Answer

DHA group=100, control group=50

 DHA group = 6, control group=8

 DHA group = 8, control group=6   

Question 14

(4 points possible)

Use the following figure to answer questions 12-14: Figure 3b (British J Derm 2008) comes from a randomized trial of DHA versus a placebo pill for treating eczema. The figure shows boxplots of the eczema severity score, SCORAD, at baseline and week 8 for each group. SCORAD score is not normally distributed and the sample size is small.

Reproduced with permission from: Koch C, Dölle S, Metzger M, et al. Docosahexaenoic acid (DHA) supplementation in atopic eczema: a randomized, double-blind, controlled trial. Br J Dermatol 2008;158:786-792.

Question 14

14. What does the box in a box-plot represent?

Everyone who is not an outlier goes in the box.

The middle 50% of the data.

The mean +/- 1 standard deviation

The mean +/- 2 standard deviation  f

Question 15

(4 points possible)

15. You are performing a cohort study to look at risk factors for breast cancer in postmenopausal women. If the probability of developing breast cancer among smokers is .05 for the study duration, then if you sample 100 smokers, how many do you expect to develop the disease? Give the expected value +/- 1 standard deviation.

5±60  f

 10±6

 5±2

 10±5

 5±6

16. A study examining the relationship between fetal X-ray exposure and a particular type of childhood blood cancer found the following odds ratio (and 95% confidence interval) for the association: 2.44 (0.95 to 6.33). This result would likely be considered:

clinically significant, but statistically insignificant    Answer

neither clinically nor statistically significant

both clinically and statistically significant

clinically insignificant, but statistically significant

Question 17

(4 points possible)

17. Standard error:

Is always the standard deviation divided by the square root of n.  f

 Is a measure of the variability of a sample statistic.

Increases with bigger sample sizes.

 All of the above

Question 18

(4 points possible)

18. Which of the following would increase the width of a confidence interval?

Changing from a 99% to 95% confidence level. f

 Increasing the variability of the outcome.  Answer

Increasing the sample size.

Removing an outlier from the data

Question 19

(4 points possible)

Use Figure 3 to answer questions 19-20: The following figure is from a study of exercise endurance (duration on an exercise test) in a group of 13 children with cystic fibrosis before and after an intervention program (a 4-week summer camp). For example, subject 1 improved by 3 minutes. Figure 3. Exercise endurance (duration) before and after camp.

Reproduced with permission from: Blau H, et al. Effects of an Intensive 4-Week Summer Camp on Cystic Fibrosis: Pulmonary Function, Exercise Tolerance, and Nutrition. Chest. 2002;121:1117-1122.

Question 19

19. What is an appropriate statistical test for analyzing these data assuming that the change in exercise duration is normally distributed?

two-sample ttest

 paired ttest  Answer

 ANOVA

McNemar’s exact test     

 Fisher’s exact test

Question 20

(1 point possible)

Use Figure 3 to answer questions 19-20: The following figure is from a study of exercise endurance (duration on an exercise test) in a group of 13 children with cystic fibrosis before and after an intervention program (a 4-week summer camp). For example, subject 1 improved by 3 minutes. Figure 3. Exercise endurance (duration) before and after camp.

Reproduced with permission from: Blau H, et al. Effects of an Intensive 4-Week Summer Camp on Cystic Fibrosis: Pulmonary Function, Exercise Tolerance, and Nutrition. Chest. 2002;121:1117-1122.

Question 20

20. Suppose that the researchers had included a control group of 13 cystic fibrosis patients who did not attend the interventional camp. What would be an appropriate statistical test for comparing the improvements in the two groups (assuming that the change in exercise duration is normally distributed)?

two-sample ttest  Answer

 paired ttest

 chi-square test

 logistic regression

 Cox regression   

Question 21

(4 points possible)

21. A study was conducted to examine the peer review process. The investigators hypothesized that reviewers suggested by authors would give more favorable reviews than reviewers picked by journal editors. They obtained data on 40 manuscripts that had been reviewed by 1 author-suggested and 1 editor-suggested reviewer. They obtained the following results:

	Author-suggested reviewer
Editor-suggested reviewer	Favorable (accept/revise)	Unfavorable (reject)
Favorable (accept/revise)	10	1
Unfavorable (reject)	9	20

Calculate the exact two-sided p-value associated with this outcome (calculate the exact binomial probability).

.021   Answer

 .043

 less than .0001   f

 .51

 .01 

Question 22

(4 points possible)

22. In a survey of 1000 registered U.S. voters, 55% respond that they support health care reform. What is the 95% confidence interval for the true percentage of U.S. registered voters who support health care reform?

50%-60%

 55%-60%  

 51%-59%

 52%-58%  Answer

 49%-47%

Question 23

(4 points possible)

23. The following table comes from a hypothetical case-control study that compared adults with moderate-to-severe acne (cases) to adults without acne (controls). The researchers examined the relationship between smoking and acne. The prevalence of moderate-to-severe acne in the population of interest is 35%.

Table 1. Adjusted odds ratios from logistic regression for moderate-to-severe acne. Odds ratios are adjusted for age, BMI, and alcohol consumption.
Variable	Odds Ratio (95% CI)
Non-smoker (reference)	1.00
Ex-smoker	2.05 (1.02-4.23)
Current smoker	3.44 (0.98-6.46)
*p values <0.05.

How should we interpret the odds ratio of 3.44 for current smokers?

Current smokers have triple the risk of moderate-to-severe acne compared with non-smokers.

 Current smokers have triple the risk of moderate-to-severe acne compared with ex-smokers. Current smokers have no increase in the risk of moderate-to-severe acne, because the odds ratio is not statistically significant.

 None of the above.  Answer

 All of the above.   

Question 24

(4 points possible)

24. Assume that the probability of developing lung cancer in smokers is 15%; the probability of developing lung cancer in non-smokers is 1%; and the prevalence of smokers in the U.S. is 20%. If a person is diagnosed with lung cancer, what is the probability that he/she is a smoker?

38%

79%  Answer

 89%

95%

 50%    

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1.   Question 25

(4 points possible)

25. In a cross-sectional study of heart disease and gender in middle-aged men and women, 10% of men in the sample had prevalent heart disease compared with only 5% of women in the sample. After adjusting for age in multivariate logistic regression, the odds ratio for heart disease comparing males to females was 1.1 (95% confidence interval: 0.79—1.43). What conclusions can you draw?

Being male increases your risk of heart disease.     

 Age is a confounder of the relationship between gender and heart disease.

 The men in the study are younger than the women in the study.

 There is a statistically significant association between gender and heart disease.

 The study had insufficient power to detect an effect.

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·         Question 26

·         (4 points possible)

·         26. Researchers conducted a case-control study to identify risk factors for kidney cancer. They asked 50 cases and 50 controls about 100 different exposures and personal characteristics, and calculated the odds ratio for kidney cancer for each risk factor. They found statistically significant odds ratios with 2 factors: coffee intake (p=.03) and cell phone usage (p=.04). The authors should conclude that:

·         Coffee and cell-phone usage increase the risk of kidney cancer.  

·          The risks of coffee and cell-phone usage have been exaggerated due to the use of odds ratios.

·          These associations are likely chance findings.

·          The study had insufficient statistical power.

·         Coffee and cell-phone are statistically significant but not clinically significant risk factors for kidney cancer.