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1. A study is being planned measuring the amount of time clinicians spend navigating an Electronic Medical Record, in particular, measuring the difference in time between MDs and their administrators. They hope to gather 100 physicians and 100 administrators separately, and they think that a difference of 10 seconds between the groups would be significant. We will assume that the times are reasonably normally distributed, and that the standard deviation for times spent navigating the system is 40 seconds. (a) Calculate the power of this study. (b) What would be a sufficient sample size? Assume a power of 80%. (c) If the sample size is fixed, what would you change about the study to increase the power? Be specific. 2. A study is being conducted to determine if there is an effect of gender on the rate of breech birth amongst infants. They expect half the babies to be male, and hope to be able to gather up to 1600 patients. They have no firm data on the rate of breech births, but they believe that the rate is around 10%, and they believe that a difference of 5% would be a meaningful clinical difference. (a) Calculate the power of this study. (b) What would be a sufficient sample size? Assume a power of 80%. (c) When the study is completed they found that the breech rate was actually 45% and 50% for males and females respectively. Why would this be a problem? 3. The following table produced an OR of 0.3822 and a chi-square statistic of 9.15. Interpret the OR and its significance. Cancer No Cancer Total Treatment Yes 15 185 200 No 35 165 200 TOTAL 50 350 400 4. For the following table, calculate RD and NNT (and interpret them). The chi-square statistic is 4.31, is the effect of drug A significant? Cancer No Cancer Total Drug A 28 72 100 B 42 58 100 TOTAL 70 130 200 Paper Questions 5. The Short 2015 paper presented RR as a measure of the effect of their intervention. What other metrics could be used to summarize the effect? Provide an evaluation of their paper using two other effect measures. R Questions Use only the first encounter for each subject in the Framingham Dataset for the R questions. 6. Calculate a 90% confidence interval for the DIABP variable in the Framingham dataset. 7. Test the relationship between smoking (CURSMOKE) and angina (ANGINA) using the Framingham data (a) State the null and alternative hypothesis (b) Present the test statistic (c) Provide a p-value and draw your conclusions (d) Present the RR and 95% confidence interval for the risk of having ANGINA for smokers compared to nonsmokers 8. Test the ordinal relationship between smoking and BMI category (a) State the null and alternative hypotheses (b) Provide the p-value and draw your conclusions

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# 7
tbl <- table(CURSMOKE=d$CURSMOKE,ANGINA=d$ANGINA) # CURSMOKE will be rows, ANGINA will be columns
tbl
#             ANGINA
# CURSMOKE    0    1
#       0   1862   391
#       1   1847   334

# (b) and (c)
chisq.test(tbl, correct=F)
# Pearson's Chi-squared test
#
# data: tbl
# X-squared = 3.3738, df = 1, p-value = 0.06624

# (d)

install.packages("fmsb")
library(fmsb)
riskratio(Y=391,X=334,,m2=2253,m1=2181)
# Disease Nondisease Total
# Exposed       334       1847 2181
# Nonexposed    391       1862 2253
#
# Risk ratio estimate and its significance probability
#
# data: 334 391 2181 2253
# p-value = 0.06627
# 95 percent confidence interval:
#   0.7720333 1.0085895
# sample estimates:
#   [1] 0.8824198...

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