{"id":437627,"date":"2018-08-30T13:37:14","date_gmt":"2018-08-30T13:37:14","guid":{"rendered":"https:\/\/essaypaper.org\/?p=32294"},"modified":"2018-10-24T08:53:46","modified_gmt":"2018-10-24T08:53:46","slug":"statistical-methods-in-epidemiology-unit-no-401176","status":"publish","type":"post","link":"https:\/\/www.benedictsol.com\/blogs\/statistical-methods-in-epidemiology-unit-no-401176\/","title":{"rendered":"Statistical Methods in Epidemiology (unit no. 401176)"},"content":{"rendered":"<p>ASSIGNMENT 1<\/p>\n<p>Spring Semester, 2018<\/p>\n<p><strong>Due date: 10 September, 2018<\/strong><\/p>\n<p>Statistical Methods in Epidemiology (unit no. 401176)<\/p>\n<p><strong>Total marks is 100 which will be converted to 25. Every question carries 20 marks each. Please read the marking rubric towards the end of the document. No late submissions allowed without a valid reason (read the Learning Guide for instructions). Assignment cover sheet is also attached.<\/strong><\/p>\n<p><strong>Please answer all questions<\/strong><\/p>\n<p>Q1. Categorize the following variables as qualitative-nominal, qualitative-ordinal, quantitative-discrete or quantitative-continuous\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0(no explanation needed for your answer)<\/p>\n<ul>\n<li>Hospital discharge diagnosis<\/li>\n<li>Exact serum cholesterol measurements<\/li>\n<li>Exact age<\/li>\n<li>Age groups as 1=&lt;30,2=30-39,3=40-49,4=50+<\/li>\n<li>Causes of death<\/li>\n<li>Sites of a randomized trial<\/li>\n<li>Education levels coded as 1= high school not completed<\/li>\n<\/ul>\n<p>2= high school completed<\/p>\n<p>3 = some post-high school education<\/p>\n<ul>\n<li>Exact systolic blood pressure levels<\/li>\n<li>Being treated for hypertension with codes as 1=no,2=yes<\/li>\n<li>Pack years of cigarette smoking<\/li>\n<\/ul>\n<p><strong>Each question above has 2 marks.<\/strong><\/p>\n<p>Q2. The following stem-and-leaf plot was obtained from the values of BMI (body mass index) for a\u00a0\u00a0\u00a0\u00a0 random sample of 88 persons.<\/p>\n<p>Frequency\u00a0\u00a0\u00a0\u00a0\u00a0 Stem\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Leaf<\/p>\n<p>1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 19\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 7<\/p>\n<p>2\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 20\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 69<\/p>\n<p>7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 21\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 4788999<\/p>\n<p>7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 22\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 3666799<\/p>\n<p>9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 23\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 112355799<\/p>\n<p>17\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 24\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 01222222345555679<\/p>\n<p>18\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 25\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 002223344444577789<\/p>\n<p>9\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 26\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 002577799<\/p>\n<p>5\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 27\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 02689<\/p>\n<p>5\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 28\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 01289<\/p>\n<p>7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 29\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0001668<\/p>\n<p>1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 30\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2<\/p>\n<p>Stem width: 1.0<\/p>\n<p>Each leaf: 1 case<\/p>\n<p>[Hints on how to read the data from stem &amp; leaf plot:<\/p>\n<p>First, Frequency 1 with 19 (stem) and 7(leaf) means just one value, 19.7, frequency 2 with 20(stem) and 69(leaf) means two values, 20.6 and 20.9, and similarly for the remaining stem and leaf values]<\/p>\n<p><strong>Each question below has 4 marks.<\/strong><\/p>\n<ul>\n<li>What are the smallest and largest BMI values among these 88 persons?<\/li>\n<li>What percentage of BMI values exceed 25.0? [Hints: use results for a computed binary variable by SAS]<\/li>\n<li>Obtain the 1<sup>st<\/sup> quartile, median and 3<sup>rd<\/sup> quartile for BMI based on this sample, and sketch a stem and leaf plot and box and whisker plot for BMI.<\/li>\n<li>Interpret the histogram for BMI.<\/li>\n<li>Interpret the bar charts for mean BMI classified by males and females.<\/li>\n<\/ul>\n<p><strong>SAS codes are given below in order to answer all questions:<\/strong><\/p>\n<p>data a;<\/p>\n<p>input bmi;<\/p>\n<p>cards;<\/p>\n<p>19.7 0<\/p>\n<p>20.6 1<\/p>\n<p>20.9 0<\/p>\n<p>21.4 1<\/p>\n<p>21.7 0<\/p>\n<p>21.8 1<\/p>\n<p>21.8 0<\/p>\n<p>21.9 1<\/p>\n<p>21.9 0<\/p>\n<p>21.9 1<\/p>\n<p>22.3 0<\/p>\n<p>22.6 1<\/p>\n<p>22.6 0<\/p>\n<p>22.6 1<\/p>\n<p>22.7 0<\/p>\n<p>22.9 1<\/p>\n<p>22.9 0<\/p>\n<p>23.1 1<\/p>\n<p>23.1 0<\/p>\n<p>23.2 1<\/p>\n<p>23.3 0<\/p>\n<p>23.5 1<\/p>\n<p>23.5 0<\/p>\n<p>23.7 1<\/p>\n<p>23.9 0<\/p>\n<p>23.9 1<\/p>\n<p>24.0 0<\/p>\n<p>24.1 1<\/p>\n<p>24.2 0<\/p>\n<p>24.2 1<\/p>\n<p>24.2 0<\/p>\n<p>24.2 1<\/p>\n<p>24.2 0<\/p>\n<p>24.2 1<\/p>\n<p>24.3 0<\/p>\n<p>24.4 1<\/p>\n<p>24.5 0<\/p>\n<p>24.5 1<\/p>\n<p>24.5 0<\/p>\n<p>24.5 1<\/p>\n<p>24.6 0<\/p>\n<p>24.7 1<\/p>\n<p>24.9 0<\/p>\n<p>25.0 1<\/p>\n<p>25.0 0<\/p>\n<p>25.2 1<\/p>\n<p>25.2 0<\/p>\n<p>25.2 1<\/p>\n<p>25.3 0<\/p>\n<p>25.3 1<\/p>\n<p>25.4 0<\/p>\n<p>25.4 1<\/p>\n<p>25.4 0<\/p>\n<p>25.4 1<\/p>\n<p>25.4 0<\/p>\n<p>25.5 1<\/p>\n<p>25.7 0<\/p>\n<p>25.7 1<\/p>\n<p>25.7 0<\/p>\n<p>25.8 1<\/p>\n<p>25.9 0<\/p>\n<p>26.0 1<\/p>\n<p>26.0 0<\/p>\n<p>26.2 1<\/p>\n<p>26.5 0<\/p>\n<p>26.7 1<\/p>\n<p>26.7 0<\/p>\n<p>26.7 1<\/p>\n<p>26.9 0<\/p>\n<p>26.9 1<\/p>\n<p>27.0 0<\/p>\n<p>27.2 1<\/p>\n<p>27.6 0<\/p>\n<p>27.8 1<\/p>\n<p>27.9 0<\/p>\n<p>28.0 1<\/p>\n<p>28.1 0<\/p>\n<p>28.2 1<\/p>\n<p>28.8 0<\/p>\n<p>28.9 1<\/p>\n<p>29.0 0<\/p>\n<p>29.0 1<\/p>\n<p>29.0 0<\/p>\n<p>29.1 1<\/p>\n<p>29.6 0<\/p>\n<p>29.6 1<\/p>\n<p>29.8 0<\/p>\n<p>30.2 1<\/p>\n<p>;<\/p>\n<p>data a;<\/p>\n<p>set a;<\/p>\n<p>bmigt25=(BMI &gt;25);<\/p>\n<p>run;<\/p>\n<p>proc freq;<\/p>\n<p>tables bmigt25;<\/p>\n<p>run;<\/p>\n<p>proc sort data=a;<\/p>\n<p>by bmi;<\/p>\n<p>ods listing;<\/p>\n<p>ods graphics off;<\/p>\n<p>proc univariate data=a plot;<\/p>\n<p>var bmi;<\/p>\n<p>title &#8220;quartiles and mean bmi, side-by-side stem and leaf plot and boxplot for BMI&#8221;;<\/p>\n<p>run;<\/p>\n<p>proc univariate data=a plot;<\/p>\n<p>var bmi;<\/p>\n<p>histogram;<\/p>\n<p>title &#8220;histogram for a continuous variable bmi&#8221;;<\/p>\n<p>run;<\/p>\n<p>proc gchart data=a;<\/p>\n<p>vbar sex\/group=sex sumvar=bmi type=mean discrete;<\/p>\n<p>title &#8220;Vertical bar chart for mean BMI by sex,0=female, 1=male&#8221;;<\/p>\n<p>run;<\/p>\n<p>Q3. A variable can be a confounder, effect modifier, both or none of the two. There are statistical tests for detecting effect modification. But, there is no statistical test for detecting an operational confounder. For example, if a test for comparing unadjusted and adjusted odds ratios show no significant difference, but one is considerably larger than the other, then one would still adjust for the confounder. However, if a test for comparing unadjusted and adjusted odds ratios shows significant difference, but one is not considerably larger than the other, one would not have to adjust for the confounder.<\/p>\n<p>Let us consider a study for assessing the association between smoking &amp; lung cancer. Is sex a confounder or effect modifier (quantitative or qualitative)?\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0(10 marks)<\/p>\n<p>We have 4 different scenarios, such as:<\/p>\n<p><u>OR (Men)\u00a0\u00a0 OR (Women)\u00a0\u00a0\u00a0\u00a0 Crude OR\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Adj OR <\/u><\/p>\n<p>2.51\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2.15\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2.32\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2.35<\/p>\n<p>1.06\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.95\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1.01<\/p>\n<p>4.40\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 3.41\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 4.02\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2.63<\/p>\n<p>2.15\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.65\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1.42\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a01.29<\/p>\n<p>The following table presents unadjusted and age-adjusted coronary event rates and death subsequent to a coronary event, for men in north Glasgow, 1991. The exposure of interest is social deprivation. Is age a confounder in the relationship between social deprivation and coronary event rate and coronary death?<\/p>\n<p>(10 marks)<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Table for Coronary event rates and risk of death by deprivation group; north <\/strong><strong>Glasgow men in 1991:<\/strong><\/p>\n<p><strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Coronary \u00a0event rate<\/strong><\/p>\n<p>(per thousand)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <strong>Risk of coronary death<\/strong><\/p>\n<p>&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"128\"><strong>Deprivation \u00a0group<\/strong><\/td>\n<td width=\"70\"><strong>Unadjusted<\/strong><\/td>\n<td width=\"79\">Age adjusted<\/td>\n<td width=\"75\"><strong>Unadjusted<\/strong><\/td>\n<td width=\"79\"><strong>Age adjusted<\/strong><\/td>\n<\/tr>\n<tr>\n<td width=\"128\">I (most advantaged)<\/td>\n<td width=\"70\">2.95<\/td>\n<td width=\"79\">3.28<\/td>\n<td width=\"75\">0.57<\/td>\n<td width=\"79\">0.59<\/td>\n<\/tr>\n<tr>\n<td width=\"128\">II<\/td>\n<td width=\"70\">4.32<\/td>\n<td width=\"79\">4.20<\/td>\n<td width=\"75\">0.50<\/td>\n<td width=\"79\">0.50<\/td>\n<\/tr>\n<tr>\n<td width=\"128\">III<\/td>\n<td width=\"70\">6.15<\/td>\n<td width=\"79\">5.30<\/td>\n<td width=\"75\">0.51<\/td>\n<td width=\"79\">0.52<\/td>\n<\/tr>\n<tr>\n<td width=\"128\">IV (least advantaged)<\/td>\n<td width=\"70\">5.90<\/td>\n<td width=\"79\">5.75<\/td>\n<td width=\"75\">0.56<\/td>\n<td width=\"79\">0.56<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Total\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a04.83\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 4.88 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a00.53\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.54<\/p>\n<p>&nbsp;<\/p>\n<p>Q 4. The data below are modified from Jick, H. et al (Coffee and Myocardial Infarction, New England Journal of Medicine, Vol.289, No.2, pp.63-67, 1973). These authors used a case-control study to investigate the relationship between coffee consumption and myocardial infarction (MI). Cases were patients hospitalized on the basis of acute chest pain with an admission diagnosis of possible or definite MI. Controls were patients with various other diagnoses. To control for confounding, a multivariate risk score was derived for each patient, taken into account a patient\u2019s age, sex, history of MI, smoking status, admission to hospital, season admitted to hospital, history of antianginal drugs, history of digitalis use, presence of diabetes, and religion. The score was computed in such a way that patients with a high score were more at risk of an MI than patients with a low score. The distribution of all such computed scores was divided into quintiles, with patients in the first quintile representing 20% of subjects with lowest scores, and patients in the fifth quintile representing 20% of subjects with the highest scores. The table below shows the distribution of cases and controls among subjects drinking 0 cups of coffee a day and subjects drinking 6+ cups a day, separately within each quantile (the variables below are in the order of risk score, cups of coffee\/day, MI and frequency of cell count in the 2X2 table).\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (20 marks)<\/p>\n<p>Quintile 1 6 or more pres 12<\/p>\n<p>Quintile 1 6 or more abs\u00a0 670<\/p>\n<p>Quintile 1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pres 17<\/p>\n<p>Quintile 1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 abs\u00a0 1315<\/p>\n<p>Quintile 2 6 or more pres 5<\/p>\n<p>Quintile 2 6 or more abs\u00a0 261<\/p>\n<p>Quintile 2 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pres 12<\/p>\n<p>Quintile 2 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 abs\u00a0 395<\/p>\n<p>Quintile 3 6 or more pres 4<\/p>\n<p>Quintile 3 6 or more abs\u00a0 174<\/p>\n<p>Quintile 3 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pres 13<\/p>\n<p>Quintile 3 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 abs\u00a0 370<\/p>\n<p>Quintile 4 6 or more pres\u00a0 2<\/p>\n<p>Quintile 4 6 or more abs \u00a080<\/p>\n<p>Quintile 4 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pres 14<\/p>\n<p>Quintile 4 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 abs\u00a0 160<\/p>\n<p>Quintile 5 6 or more pres 1<\/p>\n<p>Quintile 5 6 or more abs\u00a0 38<\/p>\n<p>Quintile 5 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pres 14<\/p>\n<p>Quintile 5 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 abs\u00a0 117<\/p>\n<p>&nbsp;<\/p>\n<p>Using the aggregate or grouped data set given above, obtain the following<\/p>\n<ul>\n<li>Interpret Breslow and Day test for homogeneity of relative odds across the risk quintiles. Are the relative odds homogeneous across the risk quintiles ? (6 marks)<\/li>\n<li>If the relative odds are the same across the risk quintiles, interpret the Mantel-Haenszel test on whether the common relative odds differs significantly from 1. Is there a significant association between coffee drinking and myocardial infarction after adjusting for risk quintiles ? (7 marks)<\/li>\n<li>Interpret the Mantel-Haenszel and Woolf (logit) estimators of common relative odds and the corresponding 95% confidence intervals. \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0(7 marks)<\/li>\n<\/ul>\n<p><strong>SAS codes for all parts of the question are: <\/strong><\/p>\n<p>OPTIONS LINESIZE=80 PAGESIZE=60;<\/p>\n<p>data htbact;<\/p>\n<p>input score $ 1-10 coffee $ 12-20 mi $ 22-25 freq 27-29;<\/p>\n<p>cards;<\/p>\n<p>Quintile 1 6 or more pres 12<\/p>\n<p>Quintile 1 6 or more abs\u00a0 670<\/p>\n<p>Quintile 1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pres 17<\/p>\n<p>Quintile 1 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 abs\u00a0 1315<\/p>\n<p>Quintile 2 6 or more pres 5<\/p>\n<p>Quintile 2 6 or more abs\u00a0 261<\/p>\n<p>Quintile 2 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pres 12<\/p>\n<p>Quintile 2 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 abs\u00a0 395<\/p>\n<p>Quintile 3 6 or more pres 4<\/p>\n<p>Quintile 3 6 or more abs\u00a0 174<\/p>\n<p>Quintile 3 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pres 13<\/p>\n<p>Quintile 3 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 abs\u00a0 370<\/p>\n<p>Quintile 4 6 or more pres\u00a0 2<\/p>\n<p>Quintile 4 6 or more abs\u00a0 80<\/p>\n<p>Quintile 4 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pres 14<\/p>\n<p>Quintile 4 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 abs\u00a0 160<\/p>\n<p>Quintile 5 6 or more pres 1<\/p>\n<p>Quintile 5 6 or more abs\u00a0 38<\/p>\n<p>Quintile 5 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 pres 14<\/p>\n<p>Quintile 5 0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 abs\u00a0 117<\/p>\n<p>;<\/p>\n<p>run;<\/p>\n<p>proc freq order=data;<\/p>\n<p>weight freq;<\/p>\n<p>tables score*coffee*mi\/cmh;<\/p>\n<p>run;<\/p>\n<ol start=\"5\">\n<li>100 men with lung cancer and 100 men without lung cancer were asked if they had ever smoked; their answers are tabulated in the following table: (20 marks)<\/li>\n<\/ol>\n<table>\n<tbody>\n<tr>\n<td width=\"208\">&nbsp;<\/p>\n<p>Previous Smoking<\/td>\n<td width=\"208\">\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Lung cancer<\/p>\n<p>Present\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Absent<\/td>\n<td width=\"52\">Total<\/td>\n<\/tr>\n<tr>\n<td width=\"208\">Yes<\/p>\n<p>No<\/td>\n<td width=\"208\">38\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 22<\/p>\n<p>62\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 78<\/td>\n<td width=\"52\">60<\/p>\n<p>140<\/td>\n<\/tr>\n<tr>\n<td width=\"208\">Total<\/td>\n<td width=\"208\">100\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 100<\/td>\n<td width=\"52\">200<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<ul>\n<li>What type of epidemiological study design is this ? \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0(2 marks)<\/li>\n<li>Interpret an approximate 95% confidence interval (by Wald method) for the risk ratio of association between previous smoking and lung cancer. (6 marks)<\/li>\n<li>Interpret the Wald test for the risk ratio of association between previous smoking and lung cancer being equal 1. (6 marks)<\/li>\n<li>Interpret an approximate 95% confidence interval (by Wald method) for the relative odds of association between previous smoking and lung cancer. (6 marks)<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><strong>SAS codes for all parts of Q5 except for (i):<\/strong><\/p>\n<p>data lcancer;<\/p>\n<p>input smoking $ 1-10 lcancer $ 12-15 count 18-19;<\/p>\n<p>cards;<\/p>\n<p>Smoker\u00a0\u00a0\u00a0 \u00a0pres\u00a0 38<\/p>\n<p>Smoker\u00a0\u00a0\u00a0\u00a0 abs\u00a0\u00a0 22<\/p>\n<p>Non smoker pres\u00a0 62<\/p>\n<p>Non smoker abs\u00a0\u00a0 78<\/p>\n<p>;<\/p>\n<p>run;<\/p>\n<p>proc freq data=lcancer;<\/p>\n<p>tables smoking*lcancer \/relrisk(CL=(Wald) method=Wald equal) OR(CL=(Wald));<\/p>\n<p>weight count;<\/p>\n<p>title &#8216;Chi-Square Test of Association of smoking and CVD death&#8217;;<\/p>\n<p>run;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<h3>Marking rubric: Assessment 1<\/h3>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<table width=\"727\">\n<tbody>\n<tr>\n<td width=\"179\"><strong>Criteria<\/strong><\/td>\n<td width=\"170\"><strong>No marks<\/strong><\/td>\n<td width=\"189\"><strong>Part marks<\/strong><\/td>\n<td width=\"189\"><strong>Full marks<\/strong><\/td>\n<\/tr>\n<tr>\n<td width=\"179\">Where a single number or single word answer\u00a0 is required<\/td>\n<td width=\"170\">Answer is absent or incorrect<\/td>\n<td width=\"189\">SAS or R output screenshot or simply copied and pasted.<\/td>\n<td width=\"189\">Answer is written in Word and is<\/p>\n<p>correct.<\/td>\n<\/tr>\n<tr>\n<td width=\"179\">Where a graph is required<\/td>\n<td width=\"170\">Graph is absent or the wrong graph<\/td>\n<td width=\"189\">Correct graph, poorly labelled.<\/td>\n<td width=\"189\">Correct graph pasted into<\/p>\n<p>Word. Accurate and descriptive axes<\/p>\n<p>labels (within the constraints of<\/p>\n<p>SAS or R). Accurate and<\/p>\n<p>Descriptive title.<\/td>\n<\/tr>\n<tr>\n<td width=\"179\">Where a table is required<\/td>\n<td width=\"170\">Table is absent or the wrong table.<\/td>\n<td width=\"189\">SAS or R output or screenshot.\u00a0 Table is poorly labelled. Table contains transcription errors.<\/td>\n<td width=\"189\">Table is formatted in Word. An<\/p>\n<p>accurate and descriptive title is<\/p>\n<p>given. No transcription errors.<\/td>\n<\/tr>\n<tr>\n<td width=\"179\">Where \u2018show working\u2019 is requested.<\/p>\n<p>&nbsp;<\/td>\n<td width=\"170\">Working is absent, incoherent or irrelevant.<\/p>\n<p>&nbsp;<\/td>\n<td width=\"189\">SAS or R output or screenshots provided in lieu of working. Working contains errors or omissions.<\/td>\n<td width=\"189\">All calculations and derivations<\/p>\n<p>are fully documented and<\/p>\n<p>correct. No SAS or R outputs or<\/p>\n<p>screenshots. Working laid out in<\/p>\n<p>logical order.<\/td>\n<\/tr>\n<tr>\n<td width=\"179\">Where interpretation or explanation is requested<\/td>\n<td width=\"170\">No explanation is provided. Explanation is incorrect or incoherent.<\/td>\n<td width=\"189\">Explanation demonstrates general understanding but contains errors and\/or omissions. Explanation is general rather than specific to the question.<\/td>\n<td width=\"189\">Explanation is correct, complete<\/p>\n<p>and clear.<\/td>\n<\/tr>\n<tr>\n<td width=\"179\">Where you are required to perform a statistical test to answer a question<\/td>\n<td width=\"170\">No hypotheses to be tested are provided. Description of hypotheses is incorrect.<\/td>\n<td width=\"189\">Description of hypotheses to be tested is only partially correct.<\/td>\n<td width=\"189\">Hypotheses to be tested are<\/p>\n<p>correct, complete and clear.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<table width=\"607\">\n<tbody>\n<tr>\n<td width=\"64\"><strong>Criteria<\/strong><\/td>\n<td width=\"104\"><strong>Unsatisfactory<\/strong><\/td>\n<td width=\"95\"><strong>Pass<\/strong><\/td>\n<td width=\"104\"><strong>Credit<\/strong><\/td>\n<td width=\"104\"><strong>Distinction<\/strong><\/td>\n<td width=\"136\"><strong>High Distinction<\/strong><\/td>\n<\/tr>\n<tr>\n<td width=\"64\">\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Overall<\/td>\n<td width=\"104\">Few questions answered correctly. The majority of workings and explanations missing, incorrect or incoherent.<\/td>\n<td width=\"95\">The majority of questions answered correctly.\u00a0 Some working or explanation absent or containing serious errors or omissions.<\/td>\n<td width=\"104\">Most questions answered correctly. Required working and explanations provided with at worst a few serious errors or omissions.<\/td>\n<td width=\"104\">Most questions answered correctly. Required working and explanations provided with at worst a few serious errors or omissions.<\/td>\n<td width=\"136\">All questions answered correctly, completely and clearly. No omissions, errors or spurious information.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p><a name=\"_Toc454357352\"><\/a><a name=\"_Toc358790657\"><\/a><strong>\u00a0<\/strong><\/p>\n<p><strong>Assignment Cover Sheet<\/strong><\/p>\n<p>School of Medicine<\/p>\n<table width=\"597\">\n<tbody>\n<tr>\n<td width=\"190\"><strong>Student name:<\/strong><\/td>\n<td width=\"407\">&nbsp;<\/td>\n<\/tr>\n<tr>\n<td width=\"190\"><strong>Student number<\/strong>:<\/td>\n<td width=\"407\">&nbsp;<\/td>\n<\/tr>\n<tr>\n<td width=\"190\"><strong>Unit name and number:<\/strong><\/td>\n<td width=\"407\">401176 : Statistical Methods in Epidemiology<\/td>\n<\/tr>\n<tr>\n<td width=\"190\"><strong>Tutorial group:<\/strong><\/td>\n<td width=\"407\">&nbsp;<\/td>\n<\/tr>\n<tr>\n<td width=\"190\"><strong>Tutorial day and time:<\/strong><\/td>\n<td width=\"407\">&nbsp;<\/td>\n<\/tr>\n<tr>\n<td width=\"190\"><strong>Unit Coordinator:<\/strong><\/td>\n<td width=\"407\">Haider Mannan<\/td>\n<\/tr>\n<tr>\n<td width=\"190\"><strong>Title of assignment:<\/strong><\/td>\n<td width=\"407\">&nbsp;<\/td>\n<\/tr>\n<tr>\n<td width=\"190\"><strong>Length:<\/strong><\/td>\n<td width=\"407\">&nbsp;<\/td>\n<\/tr>\n<tr>\n<td width=\"190\"><strong>Date due:<\/strong><\/td>\n<td width=\"407\">&nbsp;<\/td>\n<\/tr>\n<tr>\n<td width=\"190\"><strong>Date submitted:<\/strong><\/td>\n<td width=\"407\">&nbsp;<\/td>\n<\/tr>\n<tr>\n<td width=\"190\"><strong>Campus enrolment:<\/strong><\/td>\n<td width=\"407\">&nbsp;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p>Declaration<strong>: <\/strong><\/p>\n<p>q I hold a copy of this assignment if the original is lost or damaged.<\/p>\n<p>q I hereby certify that no part of this assignment or product has been copied from any other student\u2019s work or from any other source except where due acknowledgement is made in the assignment.<\/p>\n<p>q I hereby certify that no part of this assignment or product has been submitted by me in another (previous or current) assessment, except where appropriately referenced, and with prior permission from the Lecturer\/Tutor\/ Unit Co-ordinator for this unit.<\/p>\n<p>q No part of the assignment\/product has been written\/produced for me by any other person except where collaboration has been authorised by the Lecturer\/Tutor\/Unit Co-ordinator concerned.<\/p>\n<p>q I am aware that this work will be reproduced and submitted to plagiarism detection software programs for the purpose of detecting possible plagiarism <strong><em>(which may retain a copy on its database for future plagiarism checking).<\/em><\/strong><\/p>\n<p>&nbsp;<\/p>\n<p>Signature:______________________________________<\/p>\n<p><strong>Note:\u00a0 An examiner or lecturer\/tutor has the right to not mark this assignment if the above declaration has not been signed.<\/strong><\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>ASSIGNMENT 1 Spring Semester, 2018 Due date: 10 September, 2018 Statistical Methods in Epidemiology (unit no. 401176) Total marks is 100 which will be converted to 25. Every question carries 20 marks each. Please read the marking rubric towards the <a href=\"https:\/\/www.benedictsol.com\/blogs\/statistical-methods-in-epidemiology-unit-no-401176\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15],"tags":[],"class_list":["post-437627","post","type-post","status-publish","format-standard","hentry","category-essay-paper-writing"],"_links":{"self":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts\/437627","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/comments?post=437627"}],"version-history":[{"count":0,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts\/437627\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/media?parent=437627"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/categories?post=437627"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/tags?post=437627"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}