ASPIRE Workshop 5: Analysis Supplementary Slides Katie Derington, PharmD Outcomes Research Fellow in Ambulatory Care Kaiser Permanente Colorado & University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences

Statistical Tests Options for Statistical Tests (if you don’t have access to a biostatistician/analyst) Many basic statistical tests can be performed in Excel Learn to use some of the open source or free software “R” is very common and robust: https://www.r-project.org/ “PSPP”, open source similar to SPSS: https://www.gnu.org/software/pspp/get.html If you are affiliated with a University, you may have free access to some statistical software For example, SAS University Edition (SAS Studio), JMP Using Excel is probably best for quick calculations as the options for statistical tests are limited. However, if your analysis is relatively straightforward, it could be used. Keep in mind that if you are seeking to publish your work, use of an official statistical analysis software is probably preferred. For R, you may be able to change the user interface (such to use drop down menus instead of programming), can consider installing some packages to change your interface. Such as JGR and Deducer. SAS studio runs SAS through your web browser and you may be able to access it for free depending on contracts in place at your University.

Data Analysis Toolpak To activate: file  options  add-ins Go to manage Excel add-ins and select the analysis toolpak to activate The data analysis toolpak offers more options for tests than are available – take a look to see what is available. We will not go through how to use all of the statistical tests here. If you aren’t sure how to use one – suggest using the help feature and/or searching for help online. One nice thing about the analysis toolpak is it can be a faster way to do multiple tests at once. For example, using the “descriptive statistics” option on a continuous variable can give you a broad overview of the data including mean, standard error, median, mode, standard deviation, variance, kurtosis, skewness, minimum, maximum, sum, count. If activated, you can select the analysis toolpak options under “data” by selecting data analysis

Statistical tests in Excel: T-test Task: compare age in the COPD versus non-COPD patients If using the basic t-test (not the analysis toolpak), suggest setting up desired data in two columns in a separate sheet. If keep the values in the same column of the original spreadsheet (‘cohort’), this can lead to inaccuracies in the calculation. (I believe this is because excel will calculate the variance of the sample using the whole column). T-test: tests for equality of the population means that underlie each sample Note that Excel can be used for some basic statistical tests successfully, but there are some challenges you could face: Missing values are handled inconsistently at times. The only way for Excel to recognize missing data is using a blank cell. You may need to reorganize your data into different arrangements depending on what analyses you are doing. Many analyses can only be done on one column at a time, which can make it inconvenient Variances Equal variances: assumes that the two data sets come from distributions with the same variances (homescedastic). Use to determine whether two samples are likely to have come from distributions with equal population means. Unequal variances: assumes unequal variances (heteroscedastic). Can use to determine whether the two samples are likely to have come from distributions with equal population means. better to assume this if not sure, as it is more strict If variances are not known, use the Z-test for two sample means. Ftest Returns the two-tailed probability that the variances in the two groups are not significantly different. Use to determine if there are different variances.

Statistical tests in Excel: T-test Task: compare age in the COPD versus non-COPD patients Go to formulas  insert function  select “ttest”

Statistical tests in Excel: T-test Task: compare age in the COPD versus non-COPD patients Array1: range of data for COPD patients Array2: range of data for Non-COPD patients Tails: 2 means two-tailed test; use 1 if want a one-tailed test Type: select 1 for paired data, 2 if equal variance, and 3 for unequal variance

Statistical tests in Excel: T-test Task: compare age in the COPD versus non-COPD patients P-value

Statistical tests in Excel: T-test Task: compare age in the COPD versus non-COPD patients Other option: t-test using analysis toolpak Data  data analysis  select desired t-test

Statistical tests in Excel: T-test Task: compare age in the COPD versus non-COPD patients Other option: t-test using analysis toolpak Select range of age data for COPD and non-COPD. Option to change alpha level. One nice thing about doing it this way is that you don’t have to enter the ages into new columns. You can calculate with the ages in the same column, but you need to make sure the data is sorted by group first.

Statistical tests in Excel: T-test Task: compare age in the COPD versus non-COPD patients Other option: t-test using analysis toolpak As you can see, it provides more data than using the t-test without the analysis toolpak. P-value for two-sided t-test

Statistical tests in Excel: Chi-Square test Task: compare the number of males and females in each group Step 1: create a 2 x 2 contingency table with observed values Create using a pivot table or by hand. Need to count number of males and females in each group, and enter into the table. Also need to add up the total in each row, column, and overall total.

Statistical tests in Excel: Chi-Square test Task: compare the number of males and females in each group Step 2: create a 2 x 2 contingency table with expected values Copy expected table, including totals. Then delete observed values.

Statistical tests in Excel: Chi-Square test Task: compare the number of males and females in each group Step 2: create a 2 x 2 contingency table with expected values Fill in expected values: formula = ((row total x column total)/grand total) A = (155*86/223) C = (86*68/223) B = (155*137/223) D = (137*68/223)

Statistical tests in Excel: Chi-Square test Task: compare the number of males and females in each group Step 2: create a 2 x 2 contingency table with expected values

Statistical tests in Excel: Chi-Square test Task: compare the number of males and females in each group Step 3: execute chi-square test Go to formulas  insert function  select “chitest”

Statistical tests in Excel: Chi-Square test Task: compare the number of males and females in each group Step 3: execute chi-square test Observed values from first table Expected values from second table

Statistical tests in Excel: Chi-Square test Task: compare the number of males and females in each group