Combining the Results from Multiple SAS PROCS into a Publication Quality Table

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1 Combining the Results from Multiple SAS PROCS into a Publication Quality Table Robert Kabacoff, Management Research Group, Portland, ME ABSTRACT Data analysts are often faced with the daunting and repetitive task of extracting information from multiple SAS procedures and combining them into an attractive publication-ready table. This paper demonstrates simple methods of accomplishing this task using SAS MACRO, SAS ODS, PROC REPORT and PROC FORMAT. A SAS macro (%WILCOXON) is developed for examining difference between two groups on a set of ordinal or continuous outcomes. The user specifies a SAS data set, a binary grouping variable, and one or more outcome variables. The macro then generates a set of analyses using PROC NPAR1WAY and PROC MEANS, calculates an effect size for each group difference, and generates a compact, publication quality table. Optionally, the user can sort the results, add annotations, and generate publication quality graphs. INTRODUCTION Data exploration and statistical analyses are rarely the final step in a research project. The results have to be communicated to an audience (colleagues, students, journal reviewers) in an easily understandable format. SAS procedures provide comprehensive analytic capabilities, but their outputs are rarely publication ready. Consider a study of gender differences in management. Male and female department heads completed the Individual Directions Inventory (IDI, Management Research Group, 1998), a psychological instrument measuring 17 motivations or emotional drivers. The scales are reported in percentile rank scores normed against a large US business population. Since percentile rank scores are inherently ordinal, a reasonable approach would be to assess group differences using a nonparametric Wilcoxon test (equivalent to a Mann Whitney U test) (Siegel & Castellan, 1988). The results are displayed in figure 1. The first column lists the dependent variables, the second and third columns contain the group medians, the fourth column gives the two-tailed significance levels for the Wilcoxon tests, and the fifth column reports the effect sizes, expressed as odds. It is easy to scan this table and assess the group differences. For example, men tended to score higher than women on the motivator Winning (with medians 65 and 45 respectively), and the difference was statistically significant at p < From the effect size, we would estimate that a randomly selected male is almost twice (ES =1.85) as likely to score higher than a randomly selected woman on this variable. Conversely, women scored higher on Gaining Stature (p <.05) and were 1.4 times more likely to score higher than men. Effect sizes are very useful for ordering the magnitude of the group differences - especially when the dependent variables are not in the same metric. Fig 1. Table Produced by %WILCOXON Macro Table. Group Differences by Gender Medians Significance Variable Male Female p 1 ES 2 (N) Giving Receiving Belonging Expressing Gaining Stature Entertaining Creating Interpreting Excelling Enduring Structuring Maneuvering Winning Controlling Stability Independence Irreproachability Wilcoxon Test 2 Probability-based effect size expressed as odds 1

2 To analyze this data in SAS, we could use PROC NPAR1WAY to obtain Wilcoxon tests. The following code proc npar1way data=idi wilcoxon; class gender; var give--irrp; produces one page of output for each dependent variable, for a total of 17 pages. The results for the first variable (give) is provided in figure 2. The Wilcoxon 2-groups test is clearly not significant (p =.1583) for this variable at any reasonable level of alpha (e.g.,.05). Fig. 2.Output from PROC NPAR1WAY Wilcoxon Scores (Rank Sums) for Variable give Classified by Variable gender Sum of Expected Std Dev Mean gender N Scores Under H0 Under H0 Score Male Female Average scores were used for ties. Wilcoxon Two-Sample Test Statistic Normal Approximation Z One-Sided Pr > Z Two-Sided Pr > Z t Approximation One-Sided Pr > Z Two-Sided Pr > Z Additional results and 16 additional pages The medians for each group can be obtained using PROC MEANS: proc means data=idi median; class gender; var give--irrp; Fig. 3. Output from PROC MEANS The results are displayed in in figure 3. Unfortunately, the arrangement of the medians on the printout makes direct comparisons between groups a difficult (compare this with figure 1). In addition, most journals now require an estimate of effect size in addition to whether or not the difference is statistically significant. Neither printout provides such a measure. N Gender Obs Variable Label Median Male 156 give Giving recv Receiving blng Belonging expr Expressing gnst Gaining Stature more variables Female 107 give Giving recv Receiving blng Belonging expr Expressing gnst Gaining Stature more variables 2

3 The challenge for the analyst is how to take the 18 pages of SAS printout produced by PROC NPAR1WAY and PROC MEANS, calculate effect sizes and save the results as a publication-ready table. The remainder of this paper describes the typical steps involved in extracting information from SAS PROCs and assembling a publication quality table in RTF format. RTF files are easily incorporated into Microsoft Word or PowerPoint documents common file formats for presentations and publication submissions. A macro (%WILCOXON) is also provided to demonstrate the application of these techniques. It performs a nonparametric Wilcoxon two independent groups test on a set of dependent variables, calculates effect sizes, and saves the results in a publication ready RTF formatted table. The macro optionally sorts the results by effect size, annotates the results by replacing significance levels with stars, and generate a variety of publication quality graphs that are useful for visualizing group differences, These include an effect size plot, comparative box plots and comparative histograms with superimposed kernel density curves. Each is generated in a publication quality format and saved in the same RTF file. SAVING SAS PROC RESULTS IN A PUBLICATION READY TABLE A typical process for incorporating the results of multiple SAS PROCs into a publication quality table are given in the following figure: Save the results of the SAS PROCs into SAS datasets using PROC output options or ODS. Reformat the SAS datasets and compute any additional statistics. This is done through DATA step programming and may require PROC TRANSPOSE to change rows into columns, depending on the output in step 1. Combine the SAS datasets using either a DATA step or PROC SQL. The combined dataset should contain all the information needed for the final table. Open an RTF destination file via ODS RTF. Print the publication quality table using PROC PRINT or PROC REPORT. Close the RTF destination file. Each step is illustrated below, using the gender data discussed previously. Before starting, be sure that each dependent variable has a variable label. These labels will appear in the publication-ready table. 3

4 STEP 1. SAVE THE REULTS OF THE PROC NPAR1WAY AND PROC MEANS The output from PROC NPAR1WAY can be saved to a SAS dataset using the code proc npar1way data = idi noprint wilcoxon; class gender; var give--irrp; output out = T1(keep = _var_ p2_wil _wil_) wilcoxon; The contents of the dataset (_t1) are given in figure 4. The variable _WIL_ contains the Wilcoxon statistic, while P2_WIL contains the probability value for a two tailed test. Fig 4. Dataset T1 Obs _VAR WIL_ P2_WIL 1 give_pct recv_pct blng_pct expr_pct gnst_pct more observations The output from PROC MEANS can be saved to a SAS dataset using the code proc means data = idi noprint; class gender; var give--irrp; output out = T2 median=; The contents of the dataset (T2) are displayed in figure 5. The first observation contains the total sample medians, while the second and third observations contain the medians for males and females respectively. Fig 5. Dataset T2 g r b e g e c i e e s m w c s i i i e l x n n r n x n t a i o t n r g v c n p s t e t c d r n n n a d r e T F e v g r t r a g l r c v n t b p p n Y R _ O d P E p p p p p p p p p p p p p p p p p b e E Q c c c c c c c c c c c c c c c c c s r t t t t t t t t t t t t t t t t t Male Female Although the OUTPUT OUT= option was used in the code above, ODS OUTPUT could have been used as well. For example, the first set of code could have been re-written as ods listing close; ods output WilcoxonTest = T1; proc npar1way data = idi; class gender; var give--irrp; ods output close; ods listing; Datasets created by ODS OUTPUT and the OUTPUT or OUT option in a SAS PROC will usually differ in terms of variable names and data arrangement. An advantage of the ODS OUTPUT approach is that it can save results from PROCS that do not have output options. However, the format of datasets created with the OUTPUT OUT= option are often easer to work with. Both approaches can work, and the choice between them is often one of convenience or availability. The remainder of this example assumes that the OUTPUT OUT= approach is used. 4

5 STEP 2. REFORMAT THE DATA SETS AND CALCULATE EFFECT SIZES In order to build a table that looks like the one in figure 1, the medians in T2 need to be inverted and effect sizes calculated. Dataset T2 can be inverted using proc transpose data = T2 out = T2 name = _VAR_; id gender; The results are displayed in figure 6. Fig. 6. Dataset T2 (transposed) Obs _VAR LABEL_ Male Female 1 _TYPE_ _FREQ_ give_pct Giving recv_pct Receiving blng_pct Belonging expr_pct Expressing gnst_pct Gaining Stature more observations Effect Size A nonparametric measure of effect size that is appropriate for the Wilcoxon test is A = [#Y 1 > Y 2 ) +.5#(Y 1 =Y 2 )]/n 1 n 2 where # is the count function, Y is the dependent variable, n is the sample size, and the indexes refer to group 1 and group 2 (Ruscio, 2008). Effect size A estimates the probability that an individual in one group will score higher than an individual the other group, allowing for ties. It can be be re-expressed as the odds that an individual in one group will score higher than an individual in the other group by using the formula odds = A / 1 A. The formula for effect size A can be derived from the Mann Whitney U statistic and group sample sizes using A = n 1n 2 U n 1 n 2 PROC NPAR1WAY does not report the U statistic, but it is easily obtained from the Wilcoxon statistic using the formula U = W n s(n s + 1) 2 where n s is the smaller of the two sample sizes. The Wilcoxon statistics are contained in table t1. Calculating the Effect Size in SAS In order to calculate the effect sizes, it is convenient to save the sample sizes for the two groups in macro variables n1 and n2. This can be accomplished with the code proc freq data = idi noprint; tables gender / out = Nobs ; data _null_ ; set Nobs; if _N_ eq 1 then call symputx('n1', count); else if _N_ eq 2 then call symputx('n2', count); The effect sizes can then be calculated added to dataset T1 using data T1; set T1; ns = min(&n1, &n2); U = _WIL_ - (ns * (ns + 1)) / 2; A = (&n1 * &n2 - U) / (&n1 * &n2); odds = A / (1 - A); if A lt 0.5 then odds = 1 / odds; 5

6 Inverting the odds when they are less than 0.5 maintains the relationship that larger odds are associated with larger effects. The direction of the effect can be determined by examining the group medians. STEP 3. COMBINE THE DATASETS Dataset T1 containing the test statistics and effect sizes can be combined with dataset T2 containing the medians using PROC SQL and cleaned up using a DATA step. The results are saved as dataset T3 (figure 7). proc sql; create table T3 as select * from T2 left join T1 on T1._var_ = T2._var_; quit; data T3; set T3; if _var_ eq '_FREQ_' then _label_ = '(N)'; else if _var_ eq '_TYPE_' then delete; Fig. 7. Dataset T3 Obs _LABEL_ Male Female P2_WIL odds 1 (N) Giving Receiving Belonging Expressing Gaining Stature Entertaining Creating Interpreting Excelling additional observations Dataset T3 now has everything needed to create a publication quality table. STEP 4. OPEN AN RTF DESTINATION The most common format for submitting documents for publication is Microsoft Word. The ODS RTF statement will save results in rich text format that can be easily imported into Word documents as well as PowerPoint presentations. The code ods rtf file = 'myresults.rtf' startpage = never style = Journal bodytitle; ods escapechar = '~' ; will create an RTF document called myresults.rtf and save subsequent output to this file in Journal style (a simple style useful for publishing). The ~ character is specified as an ODS escape character that will be useful for embedding RTF code in the file. STEP 5. PRINT THE TABLE USING PROC REPORT While PROC PRINT can be used to print the dataset (T3 in this case), PROC REPORT provides greater control over the final formatted results. Getting the table to look just right is usually a matter of successive approximations. First, let s set up the structure of the table: proc report data = T3 nowindows; col (_label_) ('Medians' Male Female) ('Significance' P2_WIL odds); define _label_ / "Variable" ; define P2_WIL / "p"; define odds / "ES"; title "Table. Group Differences by Gender"; The resulting table is given in figure 8a. It looks good, but we want the table and title to be left justified, the column headings to be right justified (except for the first one), 2 digits after the decimal place for the p column and 3 digits after the decimal place for the ES column. We also want the font to be in Times New Roman instead of Arial. 6

7 Fig 8a. First Approximation Table. Group Differences by Gender Medians Significance Variable Male Female p ES (N) Giving Receiving Belonging Expressing Gaining Stature Entertaining Creating Interpreting Excelling Enduring Structuring Maneuvering Winning Controlling Stability Independence Irreproachability We can accomplish this by making the following additions to the code: proc report data = T3 nowindows style(report) = { just = left} style(header) = {font = ("times new roman", 10pt) just = right} style(column) = {font = ("times new roman", 10pt)}; col (_label_) ('Medians' Male Female) ('Significance' P2_WIL odds); define _label_ / "Variable" style(header) = {just = left} ; define P2_WIL / "p" format = 5.3; define odds / "ES" format = 4.2; title j = left "Table. Group Differences by Gender"; The resulting table is displayed in figure 8b. 7

8 Fig 8b. Second Approximation Table. Group Differences by Gender Medians Significance Variable Male Female p ES (N) Giving Receiving Belonging Expressing Gaining Stature Entertaining Creating Interpreting Excelling Enduring Structuring Maneuvering Winning Controlling Stability Independence Irreproachability The table is almost there. As a final step, we ll fix the font and style of the title and add foonotes. We can accomplish both tasks with the insertion of RTF style code: proc report data = T3 nowindows style(report) = { just = left} style(header) = {font = ("times new roman", 10pt) just = right} style(column) = {font = ("times new roman", 10pt)}; col (_label_) ('Medians' Male Female) ('Significance' P2_WIL odds); define _label_ / "Variable" style(header) = {just = left} ; define P2_WIL / "p~{super 1}" format = 4.2; define odds / "ES~{super 2}" format = 4.2; compute after _page_/left; line "~S = {font = ('times new roman', 8pt) just = left} ~{super 1}Wilcoxon Test"; line "~S = {font = ('times new roman', 8pt) just = left} ~{super 2}Probability-based effect size expressed as odds"; endcomp; title "~S = {font = ('times new roman', 11pt) just = left}" "Table. Group Differences by Gender"; 8

9 The final table is given in figure 8c which is what we started with in figure 1. Fig 8c. Final Table Table. Group Differences by Gender Medians Significance Variable Male Female p 1 ES 2 (N) Giving Receiving Belonging Expressing Gaining Stature Entertaining Creating Interpreting Excelling Enduring Structuring Maneuvering Winning Controlling Stability Independence Irreproachability Wilcoxon Test 2 Probability-based effect size expressed as odds As a final step, you may want to replace the probability values in column 4 with stars or asterisks. This can easily be accomplished with PROC FORMAT. See the %WILCOXON source code and figure 9 for an example. See Carpenter (2007) for an overview of basic PROC REPORT statements and Pass (2002) for an introduction to style elements. STEP 6. CLOSE THE RTF DESTINATION To complete the process, close the RTF destination. ods rtf close; 9

10 THE %WILCOXON MACRO Using the approaches described above, the %WILCOXON macro produces a Wilcoxon Mann Whitney test of group differences and outputs the results in an attractive format to an RTF file. The call is %WILCOXON(data=, var=, class=, optional parameters); where Parameter Values Description data dataset name SAS dataset containing the data to be analyzed. Default is _LAST_. var variable list List of numeric dependent variables. class variable name Dichotomous independent variable. file filename Output RTF file. Default is results.rtf. medianfmt format Print format for medians. order DATA, ES Sort results by dataset variable order (DATA) or by effect size (ES). Default is DATA stars YES, NO Replace table probabilities with stars. Default is NO. effectsplot YES, NONE Produce an effect size dot plot. Default is NONE. compareplot HIST, BOX, NONE Produce comparative histograms (HIST) or box plots (BOX). Default is NONE. alpha 0< number <1 Limit comparative plots to dependent variables with class differences that are significant at level alpha. Default = 1 (do not limit). orientation PORTRAIT, LANDSCAPE Page orientation. Default is PORTRAIT. height length Height of plots. Default is 3.5in. Specify blank to revert to SAS default. width length Width of plots. Default is 5in. Specify blank to revert to SAS default. For example, the call %WILCOXON(data = idi, class = gender, var = give--emph ); produced the table in figure 1. The call %WILCOXON(data = idi, class = gender, var = give--emph, order = ES, stars = YES, effectsplot = YES, compareplot = HIST); produces the table in figure 9, the effects dot plot in figure 10, and comparative histograms for each dependent variable. The comparative histogram for Winning is displayed in figure

11 Figure 8 shows the effect of the order=yes and stars=yes options. The variables have been sorted in descending order by effect size. The probability values have been replaced by stars and an annotation has been added to the footnotes. PROC FORMAT is used to replace the actual probability values with the asterisks. Fig 9. Table Produced by %WILCOXON Macro (with sorting and annotation options) Table. Group Differences by Gender Medians Significance Variable Male Female p 1 ES 2 Winning **** 1.85 Creating ** 1.54 Receiving * 1.43 Gaining Stature * 1.37 Maneuvering * 1.36 Expressing Giving Enduring Structuring Excelling Irreproachability Belonging Independence Entertaining Interpreting Stability Controlling (N) Wilcoxon Test * p <.05, ** p < 0.1, *** p <.001, **** p < Probability-based effect size expressed as odds The effects plot (figure 9) is useful for assessing the relative magnitudes of the group differences on the dependent variables. In this case it highlights the size of the Winning difference. An effect size (odds) of 1.0 implies no differences between the groups. The row order will match the table (ES order in this case). 11

12 Fig 10. Sorted Effect Size Plot The comparative histogram for Winning is given in figure 10. Fig 11. Comparative Histograms with Kernel Density Curves 12

13 The plot shows the histograms for the two groups with superimposed kernel density curves. The insets in each graph give the group sample size, dependent variable median and median absolute deviation. The later is a useful measure of spread when the outcome variable is ordinal. If the option compareplot=hist is replaced with compareplot=box, comparative box plots are produced. An example is given in figure 11. Fig 11. Comparative Box Plots The histograms and box plots are created with SAS Statistical Graphics procedures (PROC SGPANEL and PROC SGPLOT). Users can modify the appearance of these graphs through the Graph Template Language and PROC TEMPLATE (Kuhfeld, 2010). This allows you to create publication quality graphs that meet the requirements of the a given publisher or organization. CONCLUSIONS Publication quality tables can be created from almost any combination of SAS procedures by saving SAS PROC results to data sets, modifying and combining them, and printing them to an RTF destination. SAS ODS, PROC SQL, PROC FORMAT, and PROC REPORT give you all the tools you need. The current paper demonstrates a macro for nonparametric two group comparisons, but this macro is easily extended to multi-group parametric and nonparametric approaches. REFERENCES: Carpenter, A. (2007). PROC REPORT basics: Getting started with the primary statements. Paper presented at PNWSUG Retrieved from Kuhfeld, W. F. (2010). Statistical Graphics in SAS: An introduction to the Graph Template Language and the Statistical Graphics Procedures. NC: SAS Institute. 13

14 Management Research Group. (1998). Individual Directions Inventory: Technical Manual. Portland, ME: Author. Pass, R. (2002). PROC REPORT: Doin it in style! Paper presented at SUGI27. Retrieved from Ruscio, J. (2008). A probability-based measure of effect size: Robustness to base rates and other factors. Psychological Methods, 13, Siegel, S., & Castellan Jr., J. N. (1998). Nonparametric statistics for the behavioral sciences (2 nd ed.). NY: McGraw-Hill. ACKNOWLEDGMENTS SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. CONTACT INFORMATION Your comments and questions are valued and encouraged. Contact the author at: Robert Kabacoff, Ph.D. Management Research Group 14 York Street, Suite 301 Portland, ME rob.kabacoff@mrg.com The %WILCOXON macro can be downloaded from 14

15 Appendix. Source code of Macro %WILCOXON /* * Macro Name: WILCOXON Author: Robert Kabacoff Creation Date: June 2011 SAS Version: 9.2 Purpose: %WILCOXON produces an Wilcoxon-Mann-Whitney test of group differences and outputs the results in an attractive format to a RTF document. Given a set of ordinal dependent variables and a categorical variable representing two independent groups, WILCOXON generates a publication quality table summarizing results. The first column contains the dependent variable names, the second and third columns contain the group medians, the fourth column contains significance levels from statistical tests of group differences, and the fifth column contains a probability-based measure of effect size (A) expressed as odds. Optionally, significance levels can be replaced by stars and results can be sorted by effect size. An effect plot and comparative histograms or box plots for each dependent variable can also be generated. Output: Results as an RTF document * * Call: %WILCOXON (data =, var =, class =, file =, medianfmt =, order =, stars =, effectsplot =, compareplot =, alpha =, orientation =, height =, width = ); * * Required Parameters: data - Name of a SAS dataset var - List of numeric dependent variables class - Categorical independent variable * * Optional Parameters: General file - Output RTF file. Default is results.rtf. medianfmt - Print format for medians. order - Sort results by dataset variable order (DATA) or effect size (ES). Default is DATA. orientation - PORTRAIT or LANDSCAPE. Default is PORTRAIT For Table stars - Replace probabilities with stars. Default is NO. For Plots effectplot - Produce effect dot plot. Default is NONE. compareplot - Produce comparative histograms (HIST) or box plots (BOX). Default is NONE. 15

16 alpha - Limit comparative plots to dependent variables with class differences that are significant at alpha. Default = 1 (do not limit). height - Height of plots. Default is 3.5in. Specify blank to revert to SAS default. width - Width of plots. Default is 5in. Specify blank to revert to SAS default. * * Limitations: The combined length of all dependent variables names must be < 201. Listwise deletion of missing values is employed. At least one dependent variable MUST have a variable label. * */ %macro WILCOXON( data=_last_ /* dataset to be analyzed */, var = /* list of dependent variables */, class = /* grouping variable */, file = results.rtf /* RTF file to be created */, medianfmt = 4.0 /* print format for medians */, order = DATA /* order or results */, stars = NO /* replace actual p levels with stars */, effectsplot = NO /* generate effect size dot plot */, compareplot = NONE /* generate group comparison plots */, orientation = PORTRAIT /* page orientation */, alpha = 1 /* limit plots to variables that are significant */, height = 3.5in /* height of graphs */, width = 5in /* width of graphs */ ); /* set local macro variables */ %local error tbl probfmt footer classname n1 n2 connect dsid med1 med2 vlist nvar; /* check input parameters */ %let error = 0; %if %sysfunc(exist(&data)) = 0 %then %do; %put ERROR: Data set &data does not exist ; %let error = 1; %if %length(&var) = 0 %then %do; %put ERROR: You must specify a list of dependent variables (VAR=) ; %let error = 1; %if %length(&class) = 0 %then %do; %put ERROR: You must specify a classification variable (CLASS=) ; %let error = 1; %if &error = 1 %then %goto finish ; /* apply listwise deletion of missing values */ data _mydata; set &data; if nmiss(of &var) then delete; if &class eq '' then delete; 16

17 /* get class variable label for table title */ data _null_; set _mydata; if vlabel(&class) ne "" then call symputx('classname', vlabel(&class)); else call symputx('classname', &class); stop; /* get sample sizes */ proc freq data=_mydata noprint; tables &class/out=_n ; data _null_ ; set _n; if _N_ eq 1 then call symputx('n1', count); else if _N_ eq 2 then call symputx('n2', count); /* wilcoxon mann whitney U test */ proc npar1way data = _mydata noprint wilcoxon; class &class; var &var; output out = _t1(keep = _var_ p2_wil _wil_) wilcoxon; /* calculate effect sizes */ data _t1; set _t1; ns = min(&n1, &n2); U = _WIL_ - (ns * (ns + 1))/2; A = (&n1 * &n2 - U) / (&n1 * &n2); odds = A / (1 - A); if A lt 0.5 then odds = 1/odds; drop _WIL_ ns U A; /* get median profiles */ proc means data = _mydata n median noprint; class &class; var &var; output out = _t2 median=; proc transpose data=_t2 out=_t2 name=_var_; id &class; data _t2; set _t2; _order_ = _N_; /* combine medians and p values */ proc sql; create table _t3 as select * from _t2 left join _t1 on _t1._var_ = _t2._var_ order _order_; quit; 17

18 data _t3; set _t3; if _var_ eq '_FREQ_' then _label_='(n)'; if _var_ eq '_TYPE_' then delete; if _label_ eq "" then _label_ = _var_; _label_ = trim(_label_); drop _var order_; /* customize output */ /* sort results by effect size */ %if %upcase(%substr(&order, 1, 1)) ^= D %then %do; proc sort data = _t3 out = _t3; by descending odds; /* replace probability values with stars? */ %if %upcase(%substr(&stars, 1, 1)) ^= N %then %do; proc format; value stars low = '****' < = '***' 0.001<-0.01 = '**' 0.01<-.05 = '*' other = ' '; %let probfmt = stars.; %let footer = %str(* p <.05, ** p < 0.1, *** p <.001, **** p <.0001) ; %else %do; %let probfmt = 5.3 ; %let footer = ; /* save current options so they can be restored later */ %let number = %sysfunc(getoption(number)); %let date = %sysfunc(getoption(date)); %let orientation = %sysfunc(getoption(orientation)); /* set new options */ options nodate nonumber orientation=&orientation; /* set RTF output destination */ ods listing close; ods rtf file = "&file" startpage = never style = Journal bodytitle; ods escapechar = '~' noptitle; ods graphics on / noborder height = &height width = &width; 18

19 /* create table */ %let dsid = %sysfunc(open(_t3, i)); %let med1 = %sysfunc(varname(&dsid, 2)); %let med2 = %sysfunc(varname(&dsid, 3)); proc report data=_t3 nowd split='_' style(report) = {just=left} style(header) = {font = ("times new roman", 10pt) just = right} style(column) = {font = ("times new roman", 10pt)}; col (_label_) ('Medians' &med1 &med2) ('Significance' P2_WIL odds); define _label_ / "Variable" style(header) = {just = left}; define P2_WIL / "p~{super 1}" format=&probfmt; define odds / "ES~{super 2}" format=4.2; format &med1 &med2 &medianfmt; compute after _page_/left; line "~S={font=('times new roman', 8pt) just=left} ~{super 1}Wilcoxon Test"; %if %upcase(%substr(&stars,1,1)) = Y %then %do; line "~S = {font = ('times new roman', 8pt) just = left}&footer"; line "~S = {font = ('times new roman', 8pt) just = left} ~{super 2}Probability-based effect size expressed as odds"; endcomp; /* add table title */ title "~S = {font = ('times new roman', 11pt) just = left}" "Table. Group Differences by &classname"; /* plot effect sizes */ %if %upcase(%substr(&effectsplot, 1, 1)) ^= N %then %do; proc sgplot data = _t3; dot _label_/ response = odds; label _label_ = "" odds = "Effect sizes (odds)"; where odds ne.; yaxis discreteorder = data; title "Effect Size Plot"; /* create comparative plots */ /* limit plots to significant effects */ data _t1a; set _t1; if P2_WIL lt α /* order plots by decreasing effect sizes */ %if %upcase(%substr(&order, 1, 1)) = E %then %do; proc sort data = _t1a out = _t1a; by descending odds; /* obtain dependent variable list */ data _NULL_; 19

20 set _t1a end = eof; length _vlist_ $ 200; retain _vlist_ ' '; retain nvar 0; nvar + 1; _vlist_ = trim(_vlist_) ' ' trim(_var_); if eof then do; call symput('vlist', trim(_vlist_)); call symput('nvar', put(nvar, 4.0)); end; /* generate histograms */ %if %upcase(%substr(&compareplot, 1, 1)) = H %then %do; %do i = 1 %to &nvar; %let dv = %scan(&vlist, &i, ' '); proc univariate data=_mydata noprint; class &class; histogram &dv / nrows = 2 intertile = 1 kernel(noprint); inset N Median MAD / pos = nw; title; /* generate box plots */ %if %upcase(%substr(&compareplot, 1, 1)) = B %then %do; %do i = 1 %to &nvar; %let dv = %scan(&vlist, &i, ' '); proc sgplot data=_mydata; hbox &dv / category = &class spread; xaxis grid; title; /* clean up */ proc datasets library = WORK nolist; delete _t1 _n _t2 _t3 _mydata; /* reset destination and options */ ods graphics off; ods rtf close; ods listing; /* restore options */ options &number &date orientation = &orientation; %finish: %mend WILCOXON; 20