Biostatistics and Epidemiology Using Stata: A Course Manual Gregory J Stoddard University of Utah School of Medicine Available on two websites: http://medicine.utah.edu/ccts/sdbc/stoddard_textbook.php http://www.ctspedia.org/do/view/CTSpedia/CourseMaterials see next page for citation suggestion Copyright © 2007, 2008, 2009, 2010, 2011, 2012, 2013 by Gregory J Stoddard All rights reserved The copyright owner grants permission for anyone to freely reproduced this bookfor academic and research purposes _ Source: Stoddard GJ Biostatistics and Epidemiology Using Stata: A Course Manual Salt Lake City, UT: University of Utah School of Medicine Title & Copyright Page (Accessed July 8, 2013, at http://medicine.utah.edu/ccts/sdbc/stoddard_textbook.php) Title & Copyright Page (revision Jul 2013) p Preface and Suggestions for Use and Author Contact Information Intended Audience The material is written for biomedical researchers who are not statisticians, and requires no mathematic skill beyond high school algebra Statisticians, however, will greatly benefit from it, because it contains a great number of solutions to common problems encountered in statistical consulting situations This course manual was designed to teach biostatistics and epidemiology to research fellows, faculty, and graduate students in biological fields The aim is to teach students to compute their own statistics for their research problems, rather than turn the analysis over to a biostatistician Teaching Style Teaching with PowerPoint is not effective In that format, not enough detail is provided, and it does not permit the student to go back months later to see how to solve a real research problem Teaching applied statistics with the traditional textbook formula approach is also not effective Statistics students fail to learn how to go beyond the formulas to apply statistics to real-word problems Nonstatisticians are not interested in the formulas, and they also fail to learn how to apply statistics to real-word problems Real-word problems are not as simple as textbook problems, almost always being something a bit messier than what is described in introductory statistics textbooks For statistics majors, the formula approach is very valuable, however, so this book should be used only as a supplement for that type of training This course manual intentionally contains less detail than a traditional textbook, but with much more detail than a PowerPoint presentation It is designed to be projected on the screen, with the instructor touching on the main points, rather than reading it as a lecture The students can see what is contained in the chapters, so they know where to refer back to when they have to apply that method to their own research problem later Having the students bring laptops to class with Stata installed allows them to the examples during the lecture, if they find this interesting and helpful _ Source: Stoddard GJ Biostatistics and Epidemiology Using Stata: A Course Manual Salt Lake City, UT: University of Utah School of Medicine Preface Page (Accessed November 5, 2011, at http://www.ccts.utah.edu/biostats/ ?pageId=5385) preface & suggestions for use (revision Nov 2011) p Consulting Help Sheets For the professional biostatistician, or the instructor who uses in this manual in the classroom, the material is useful for answering technical questions The chapter that contains the solution can be emailed to the client or student, with a note on what page the solution to their problem is presented The book is a compendium of solutions to common research problems, so is basically a book of “help sheets.” Statistical Sections for Grants and Manuscripts In many places, suggested wording for protocols and manuscripts is given These are suggested wordings are interchangeable, by writing in future tense for a protocol and past tense for a manuscript Feel free to cut-and-paste these suggestions into your grants and manuscripts, and then tailor the suggestion to match your study Suggested Citation It is perfectly appropriate to cite a web-based textbook, such as this one, and detailed instructions on found on the “title & copyright page.” However, please not feel you need to cite this course manual Wherever something worth citing is mentioned, I attempted to provide a citation to another published work You are welcome to cite those, instead, as published works have the status of being more authoritative About the Author Gregory J Stoddard is a professional biostatistician and Co-Director of the Study Design and Biostatistics Center (SDBC), www.ccts.utah.edu/biostats The SDBC is the biostatistics and epidemiology consulting group of the University of Utah Center for Clinical and Translational Science (CCTS) The CCTS is funded by a National Instititute of Health (NIH) Clinical and Translational Science Award (CTSA) His University home is in the Division of Epidemiology, Department of Internal Medicine, Univeristy of Utah School of Medicine He is also an Adjunct Assistant Professor in the Department of Orthopaedics, University of Utah School of Medicine He can be contacted at: Division of Epidemiology University of Utah School of Medicine 295 Chipeta Way, Room 1N455 Salt Lake City UT 84132 Phone: 801-213-3774 Fax: 801-581-3623 Email: greg.stoddard@hsc.utah.edu preface & suggestions for use (revision Nov 2011) p Biostatistics and Epidemiology Using Stata: A Course Manual Table of Contents > Section Stata: Data Management, Graphics, and Programming 1-1 Installing Stata and recovering Stata windows installing Stata 3 1-2 adding an icon to the desktop (PC Windows) run Stata to finish setup updating Stata after setup recoving Windows: load factory settings Getting data into Stata and some other basics 1 opening a Stata formatted data file: 1) clicking on file icon showing full dictory path in Windows Explorer showing file extensions in Windows Explorer opening a Stata formatted data file: 2) File icon on menu bar opening a Stata formatted data file: 3) change directory (cd) command directory list (dir) command read in Stata data file (use) command scrolling in Stata’s Results window general syntax, or structure, of Stata commands Stata help facility, help command Stata Manuals books on Stata setting file attributes in Windows (turn of Read Only) using do-files suggested do-file structure 10 increasing memory size of Stata’s workspace: set memory (set mem) command 10 importing Excel file into Stata 11 reading in a *.csv or *.txt formatted file: insheet command 11 saving a Stata formatted data file: save command 12 saving a Stata formatted data file compatable with Stata version or 9: saveold command _ Source: Stoddard GJ Biostatistics and Epidemiology Using Stata: A Course Manual [unpublished manuscript] University of Utah School of Medicine, 2010 table of contents (revision 16 May 2010) p 1-3 Cleaning data 2 3 5 6 7 9 1-4 listing data: list command block comment: /* … */ deleting variables: drop command inline comment “//” tabulation of values of variables using frequency table: tabulate (tab) command examining smallest and largest values: summarize, detail (sum) command replacing value of variable: replace command assignment “=” and logical equals “==” recoding values of a variable with generate (gen) and replace commands keeping a command from crashing the do-file: capture command, e.g., capture drop the “0 observations” result: attempting to arithmetic on a string variable describing variables: decribe command variable storage types missing value for string variable: the null string "" converting a string variable to numeric, destring command recoding values of a variable: recode command converting to all upper or lower case: upper and lower string functions renaming the variable name: rename command Merging files adding a file to bottom of file in memory: append command adding a file in rightmost columns of file in memory, one-to-one merge without matching on some variable: merge command merging files while matching on some variable such as a subject ID, match merge: merge command checking how well the matching worked: Stata’s _match variable (values to 3) non-overwrite feature of merge command (the default) non-overwrite of missing values feature of merge command (the default) updating file in memory with another file by replacing missing values only: update option updating file in memory with another file by replacing both missing and nonmissing values, update with replace: replace option checking how well the update with replaced worked: Stata’s _match variable (values to 5) 1-5 Labeling variables and values 3 adding label to a variable: label variable command adding labels to the values of a variable: label define and label values commands listing value labels: label list command suspending value labels in data browser and outputs, nolabel option removing variables labels removing value labels: label drop command table of contents (revision 16 May 2010) p removing value labels: capture label drop command displaying values and value labels 1-6 Basic graphics 1 3 3 4 5 using graphs from Stata version 7: graph7 and version commands redisplaying a graph: graph display command scatterplot: graph twoway scatter command appreviated scatterplot commands: twoway scatter and scatter commands side by side graph: by option linear regression line graph: lfit command overlaying graphs: “||” operator overlaying linear regression line on scatterplot using || operator overlaying graphs using binding notation: ( ) ( ) overlaying linear regression line on scatterplot using ( ) approach generating a variable with rounding: round function generating a mean across data rows for subgroups: by specification with egen command with mean function listing variables: list command extending a command across several lines in do-file editor: #delimit command line graph: line command requirement to sort on x variable before plotting a line graph: sort command table of descriptive statistics for a two variable crossclassification: table command smooth line graph using fractional polynomial fit: fpfit command fractional polynomial fit with covariates: fracpoly command adding title to graph: title command adding subtitle to graph: subtitle command adding axis titles to graph: ytitle and xtitle commands adding footnote to graph: note command adding more tick marks and labels to axes: ylabel and xlabel commands better labels for legend: legend command 10 list of choices for line graph line widths: graph query linewidthstyle command 10 changing connect line width of line graph: clwidth option 11 list of choices for graph scheme: graph query, schemes command 11 changing default graph scheme for current session or permanently: set scheme command 11 chaning graph scheme just for current graph: scheme option 12 basic black-and-white scheme for manuscripts: scheme(s1mono) option 13 eliminating border around graph: plotregion(style(none)) option 14 adding text to graph: text option 15 placement options for positioning text: placement option 16 adding space between x-axis title and x-axis tick labels: height(5) option in xtitle 17 changing color of connect line of line graph: clcolor option 17 turning off legend: legend(off) option 19 reading in graph data by putting data in do-file: input and end commands 19 adding error bars to graph: rcap command 20 overlaying errors bars on scatterplot to get symbol with error bars: table of contents (revision 16 May 2010) p twoway (rcap…) (scatter…) commands 21 adding white space to left and right side of graph: xlabel command 21 change tick mark labels to more descriptive labels: xlabel command 22 drop tick marks from graph while retaining labels: noticks option 23 adding horizontal or vertical reference lines: yline and xline options 24 list of choices for colors: help colorstyle command 24 list of choices for symbols: help symbolstyle command 24 changing marker symbol for scatterplot: msymbol option 24 changing color to marker symbol border line and inside fill: mlcolor and mfcolor options 1-7 1-8 1-9 1-10 1-11 Looping, collapsing, and reshaping Operators, ifs, dates, and times More graphics: popular scientific graphs Programming Stata Compilation of frequently used variable generation and modifying commands (a chapter for quick look up) 1-12 Stata results into Excel & Word 1-13 Homework problems Section Biostatistics 2-1 Describing variables, levels of measurement, and vhoice of descriptive statistics Describing a variable (distribution): with tables: frequency tables with graphs: histogram, boxplots with descriptive statistics: mean, standard deviation, etc Levels of measurement (nominal, ordinal, categorical, continuous ) How to decide what descriptive statistic to use to describe a variable in the “Table Patient Characteristics” table of an article 2-2 Logic of significance tests What a probability distribution is Logic of a significance test (same logic as a laboratory reference range) Chance, randomness, sampling variability Statistical regularity (the basis of statistical theory) Strong Law of Large Numbers (formal statement of statistical regularity) Deriving the form of statistical test (significance test) intuitively Sampling distribution p value 2-3 Choice of significance test table of contents (revision 16 May 2010) p 2-4 Comparison of two independent groups Role of p values in a Table Patients Characteristics table Confounding variables chi-square test Fisher’s exact test Asymptotic vs exact tests (parametric vs nonparametric tests) Minimum expected frequency rule for choosing between chi-square test and Fisher’s exact test Barnard’s unconditional exact test Fisher-Freeman-Halton test Wilcoxon-Mann-Whitney test Fisher-Pitman Permutation Test for Independent Samples Central Limit Theorem Levene’s test for equality of variances t test (both equal and unequal variances) Shapiro-Wilks test for normality Reporting styles Outliers Prespecification of analysis 2-5 Basics of power analysis definition of power power increases as sample size increases decision errors of significance tests [ Type I error (alpha), Type II error (beta) ] Type II error and sample size paragraph in journal article conclusions of equivalence power of a significance test effect of one- or two-sided comparison on power effect of choice of alpha on power effect of choice of minimum detectable effect size on power effect of size of assumed standard deviation (SD) on power – coming up with a SD estimate effect of sample size on power sample size and power calculations for an interval scaled outcome variable what to if you don’t know anything (no effect size or standard deviation estimates): the standard deviation units approach, Cohen’s d sample size calculation when a multiple comparison adjustment is planned overfitting switching the dependent and independent variables sample size based on precision (desired width of confidence interval) excessive power (sample size very large) two group comparison of interval scale outcome sample size paragraph in study protocol table of contents (revision 16 May 2010) p 2-6 More on levels of measurement sums of ordinal scales produce interval sacles dichotomous scales are actually interval scales can statistical tests that require interval scales be used with ordinal scales ( the ordinal-interval controversy in statistics) 2-7 Comparison of two paired groups 2-8 Multiplicity and the Comparison of 3+ Groups multiplicity multiple comparison problem p value based multiple comparison procedures: family-wise error rate (Bonferroni, Holm, Sidak, Holm-Sidak, Hochberg, Finner, Hommel, Tukey-Ciminera-Heyse) p value based multiple comparison procedures: false discovery rate (Benjamini-Hochberg procedure) alternative approaches to using multiple-comparison procedures simultaneous comparison of 3+ groups (includes one-way analysis of variance) sample size when multiple comparisons are planned 2-9 Correlation 2-10 Linear regression how linear regression controls for covariates 2-11 Logistic regression and dummy variables linear regression estimates risk difference (difference between proportions), but is criticized because it can estimate predicted probabilities outside of the 0-1 range logistic regression is designed to constrain the predicted probability between and definition of an odds ratio assessing linearity of effect dummy variables (indicator variables) 2-12 Survival analysis: Kaplan-Meier graphs, Log-rank Test, and Cox regression life tables Kaplan-Meier survival probabilies & Kaplan-Meier curves log-rank test Cox regression assessing goodness of fit with c-statistic (ROC area) interpreting the c-statistic testing proportional hazards assumption of Cox regression table of contents (revision 16 May 2010) p 2-13 2-14 2-15 2-16 2-17 2-18 2-19 Confidence intervals versus p values and trends toward significance Pearson correlation coefficient with clustered data Equivalence and noninferiority tests Validity and reliability Methods comparison studies One sample tests Homework problems Section Epidemiology 3-1 3-2 3-3 3-4 3-5 3-6 3-7 3-8 3-9 3-10 3-11 3-12 3-13 3-14 3-15 Introduction to epidemiologic thinking Sufficient/component cause theory of disease Hill’s causal criteria Logic and errors Effect measures Study designs Randomization using Excel Bias and confounding Random error and statistics Crude analysis Stratified analysis Standardization Sensitivity (bias) analysis Case-cohort study design Homework problems Section Power Analysis Chapter 4-1 Sample Size Determination and Power Analysis for Specific Applications two independent group comparison of means (independent groups t test) linear regression: comparing two groups adjusted for covariates two independent groups comparison of dichotomous outcome variable (chi-square test, Fisher’s exact test) two indendpent groups comparison of a nominal outcome variable (chi-square test and Fisher-Freeman-Halton 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