A Set of SAS Macros for Producing Statistical Reports Greg Grandits, M.S. Ken Svendsen, M.S....

A Set of SAS Macros for Producing Statistical Reports

Greg Grandits, M.S.

Ken Svendsen, M.S.

Division of Biostatistics

University of Minnesota

®

Example Statistical Report

Result:

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Number (%) of Selected CVD Events by Treatment GroupAnd Hazard Ratio from Cox Regression Analyses

Experimental Standard Cox Regression Summary

Endpoint N % N % HR L95% U95% P-Val

Primary CVD MI (Fatal/NF) Stroke (Fatal/NF) CVD Death

Any CVD Hospitalization Revascularization TIA Angina Heart Failure Hypertension Renal Failure

364 4.5 365 4.4 133 1.6 166 2.0 133 1.6 118 1.4 152 1.9 143 1.7

793 9.7 775 9.3 163 2.0 166 2.0 89 1.1 105 1.3 202 2.5 190 2.3 126 1.5 100 1.2 22 0.3 18 0.2 27 0.3 34 0.4

1.02 0.88 1.18 0.771 0.82 0.65 1.03 0.089 1.15 0.90 1.48 0.265 1.09 0.87 1.37 0.471

1.05 0.95 1.16 0.307 1.01 0.82 1.26 0.913 0.87 0.66 1.15 0.330 1.09 0.89 1.33 0.389 1.30 1.00 1.69 0.051 1.26 0.67 2.34 0.474 0.81 0.49 1.35 0.426

Goals for Developing Report Macros

• Place text anywhere onto page

• Macros take care of tedious formatting

• Have code that is easy to write and modify

• Place several types of statistics from multiple procedures onto a single page

- N, means, SDs, etc.

- p-values, RRs, regression coefficients, etc.

SAS Tools Needed for Macros

• Data step

• Put statement

• Ability to output statistics from PROCs to SAS datasets

• ODS

Step 1: Declare Column Widths%report;%colset (25 7 7 2X 7 7 4X 7 7 7 2X 7);

Result:

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123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890

COL 1

WIDTH = 25

COL 2

7

COL 3

7 7 72X 4X7

COL 4

7

COL 5 COL 6 COL 7

7

COL 8

7

COL 9

2X

Step 2: Move Text To Page%move (‘Number (%) of Selected CVD Events by Treatment Group’: ‘And Hazard Ratio from Cox Regression Analyses’, col=1-9, line=3L2);

Result:

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123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890


Step 2: Move Text To Page%move (‘Experimental’ : ’Standard’: ’Cox Regression Summary’, col = 2-3 4-5 6-9, line = 9, u=y);

Result:

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Step 2: Move Text To Page%move (‘Endpoint’, col=1, center=n, line=11, u=y);%move (‘N’:’%’, col=2.5, line=11, u=y);%move (‘HR’:’L95%’:’U95%’:’P-Val’, col=6.9, line=11, u=y);

Result:

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Step 2: Move Text To Page%move (‘Primary CVD’:’MI’:’Stroke’:’CVD Death’:’Any CVD Hospitalization”: ‘Revascularization’:’TIA’:’Angina’:’Heart Failure’:’Hypertension’: ‘Renal Failure’, col=1, center=n, line=14L4 19L7) ;

Result:

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Step 3: Move Statistics To Pageset out1;%nmove (sum1-sum22, col = 2 4, line = 14L4 19L7, fmt=5.0);%nmove (m1-m22, col = 3 5, fmt=5.1, scaler=100);

Result:

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364 4.5 365 4.4 133 1.6 166 2.0 133 1.6 118 1.4 152 1.9 143 1.7

793 9.7 775 9.3 163 2.0 166 2.0 89 1.1 105 1.3 202 2.5 190 2.3 126 1.5 100 1.2 22 0.3 18 0.2 27 0.3 34 0.4

Step 3: Move Cox Regression Statisticsset out2;%nmove (r1-r11, L1-L11, u1-u11, col=6.8, fmt=5.2);%nmove (p1-p11, col=9, fmt=6.3);

Result:

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364 4.5 365 4.4 133 1.6 166 2.0 133 1.6 118 1.4 152 1.9 143 1.7

793 9.7 775 9.3 163 2.0 166 2.0 89 1.1 105 1.3 202 2.5 190 2.3 126 1.5 100 1.2 22 0.3 18 0.2 27 0.3 34 0.4

1.02 0.88 1.18 0.771 0.82 0.65 1.03 0.089 1.15 0.90 1.48 0.265 1.09 0.87 1.37 0.471

1.05 0.95 1.16 0.307 1.01 0.82 1.26 0.913 0.87 0.66 1.15 0.330 1.09 0.89 1.33 0.389 1.30 1.00 1.69 0.051 1.26 0.67 2.34 0.474 0.81 0.49 1.35 0.426

Obtaining the Summary Statistics

• Computes summary statistics for each variable in var by each level in group

• Stores statistics in one observation dataset out1 (M1-M22 contains means)

%let evlist = primary mi str cvddth allcvd corevas tia angina chf acchyp renalf ;

%breakdn (data=temp, class= group 2, var=&evlist, out=out1)

Obtaining P-values from PROG PHREG

• Performs PHREG for each variable in dlist with independent variables in ilist

• Factor indicates which independent variable(s) statistics are requested

• Out names the one observation SAS dataset which contains p-values

• P-values are named P1-P11

%phregp (data=temp, dlist=&evlist, strata=country, tlist=&tmlist, ilist=group, factor=group, out=out2)

Complete Program for Example - Assume Data is Ready to Go

%breakdn (data=temp, class= group 2, var=&evlist, out=out1);

%phregp (data=lifetab, dlist = &evlist, strata=country, tlist= &tmlist, ilist=group, factor=group, out = out2);

%report%colset (25 7 7 2X 7 7 4X 7 7 7 2X 7)

%move (‘Number (%) of Selected CVD Events by Treatment Group’: ‘And Hazard Ratio from Cox Regression Analyses’, col=1-9, line=3L2) %move (‘Experimental’:’Standard’:’Cox Regression Summary’, col=2-3 4-5 6-9, line=9, u=y)

%move (‘Endpoint’, col=1, center=2, line=11, u=y)%move (‘N’: ‘%’, col=2.5, line=11, u=y)%move (‘HR’:’L95%’:’U95%’:’P-Val’, col=6.9, line=11, u=y)

%move (‘Primary’:’MI’:’Stroke’:’CVD Death’:’Any CVD Hospitalization’:’Revascularization’:‘TIA’: ‘Angina’:’Heart Failure’:’Hypertension’:’Renal Failure’, col=1, center=n, line=12L4 19L7)

set out1;%nmove (sum1-sum22, col=2 4 , line=14L4 19L7, fmt=5.0)%nmove (m1-m22, col=3 5 , fmt=5.1, scaler=100)

set out2;%nmove (r1-r11 L1-L11 u1-u11, col=6.8, line=14L4 19L7, fmt=5.2)%nmove (p1-p11, col=9, fmt=6.3)

Behind the Scenes

%move (‘Number (%) of Selected CVD Events by Treatment Group’: ‘And Hazard Ratio from Cox Regression Analyses’, col=1-9, line=3L2) ;

Code Generated:

PUT #3 @ 19 ‘Number (%) of Selected CVD Events by Treatment Group’;

PUT #4 @ 23 ‘And Hazard Ratio from Cox Regression Analyses’;

Behind the Scenes

%breakdn (data=temp, class=group 2, var=&evlist, out=out1);

Code Generated:

PROC MEANS DATA=temp;CLASS group;VAR primary mi str cvddth allcvd corevas tia angina chf acchyp renalf;

OUTPUT OUT=out1 N=n1-n11 MEAN=m1-m11 STD=s1-s11 SUM=sum1-sum11 MAX=max1-max11 MIN=min1-min11;

*** Further data step to get into one observation dataset ***

22 N’s; n1-n22

Behind the Scenes

%phregp (data=temp, dlist=&evlist, strata=country, tlist=&tmlist, ilist=group, factor=group, out=out2) ;

Code Generated:

ODS output ParamterEstimates (match_all = _est_ persist=proc) = _est_;PROC PHREG DATA=temp; MODEL tprimary*primary(0) = group;

PROC PHREG DATA=temp; MODEL tmi*mi(0) = group;

. . . more phregs

*** More data steps to get desired one observation dataset ***

Statistic Generating Macros

breakdn summary N, mean, summary statistics by

sdev, etc. level of class variable(s)

freqdis summary counts, %s distribution of variable

cum. counts, by level of other variable cum %s

glmp glm p-values, statistics from ANOVA

LS means

MACRO PROCSTATISTICS

OUTPUT DESCRIPTION OF MACRO

Statistic Generating Macros (cont’d.)

regp reg p-values, coef. statistics from linear

SE, t-stat regression

phregp phreg p-values, coef. statistics from

HRs, 95% CI proportional hazards model

MACRO PROCSTATISTICS

OUTPUT DESCRIPTION OF MACRO

Others: logistp, chisqp, mixedp (All make 1-record datasets)

Report Macros

1. %report Starts new report

2. %colset Defines columns and column widths

3. %move Moves text strings to report page

4. %nmove Moves numeric values from SAS dataset to report page

Summary - Order of a Report

• 1 or more statistic generating macros

• %report

• %colset (define column widths)

• %move statements (for text placement)

• set statement (bring in data with statistics)

• %nmove statements (for statistics placement)

[more set/nmove statements]

Making HTML and WORD Tables

With minor changes an HTML table can be created which can be imported into Word.

Additional features:

- Font parameters: type, size, italics, bold, etc.

- Table parameters: indenting, cell spacing, etc.

- Combine and format multiple statistics in a single column

• 45.5 ± 7.3

• 364 (4.5%)

• 0.82 (0.65 – 1.03)

Number and Percent of Selected Cardiovascular Events by Treatment Group

And Hazard Ratio (Experimental/Standard) from Cox Regression Analyses

Number of Patients

With Event

Cox Regression Analyses

Experimental/Standard

Endpoint Experimental Standard HR (95% CI) P-value

Primary CVD 364 (4.5%) 365 (4.4%) 1.02 (0.88 - 1.18) 0.771

MI (Fatal/NF) 133 (1.6%) 166 (2.0%) 0.82 (0.65 - 1.03) 0.089

Stroke (Fatal/NF) 133 (1.6%) 118 (1.4%) 1.15 (0.90 - 1.48) 0.265

CVD Death 152 (1.9%) 143 (1.7%) 1.09 (0.87 - 1.37) 0.471

Any CVD Hospitalization 793 (9.7%) 775 (9.3%) 1.05 (0.95 - 1.16) 0.307

Revascularization 163 (2.0%) 166 (2.0%) 1.01 (0.82 - 1.26) 0.913

TIA 89 (1.1%) 105 (1.3%) 0.87 (0.66 - 1.15) 0.330

Angina 202 (2.5%) 190 (2.3%) 1.09 (0.89 - 1.33) 0.389

Heart Failure 126 (1.5%) 100 (1.2%) 1.30 (1.00 - 1.69) 0.051

Hypertension 22 (0.3%) 18 (0.2%) 1.26 (0.67 - 2.34) 0.474

Renal Failure 27 (0.3%) 34 (0.4%) 0.81 (0.49 - 1.35) 0.426

%move (‘Number of Patients<br>With Event’, col=2-3, line=9, fontweight=bold)

%move (‘Endpoint’, col=1, center=n, line=11, fontstyle=italic fontweight=bold)

Example Calls

* This moves the HR and 95% CI to a single column;

%nmove (r1-r11 L1-L11, u1-u11, line=14L4 19L7, col=4, combine=y, fmt=5.2 5.2 5.2, fchar=3)

Advantages of Using Report Macros

• Customized tables, including statistics from multiple procedures

• Great for routine reports where only the data is changing (e.g. DSMB report)

• No transcription of numbers

• Templates can be developed

• Journal style tables

Cautionary Note

Tables look great but the numbers are wrong !

With freedom comes responsibility – check your program.

How to Get Report Macros

Code and Documentation

http://www.biostat.umn.edu/~greg-g

E-mail (correspondence)

[email protected]

Spotlight on

• Places text onto the report page

• Use

%move (‘string1’: ‘string2’: …, line=, col=, center=, under=, pat=, mfirst=)

• ‘string1’: ‘string2’: … text to be placed on report page

• line which lines?

• col which columns?

• center center text?

• under underline text?

• pat line skipping patterns

• mfirst columns or lines to advance first

MOVE

MOVE Parameter: Text Strings

%move (‘string1’: ‘string2’, line=, col=, center=, under=, pat=, mfirst=)

• Text strings are enclosed in quotes separated by colons

Example:

‘Men’: ‘Women’: ‘Total’

MOVE Parameters: line and pat

%move ( ‘string1’: ‘string2’, line=, col=, center=, under=, pat=, mfirst=)

• Line numbers can be given in two ways

line = 12 21 23

line = 12L3 (same as 12 13 14)

• Pat can be used to skip lines

line = 12L3

pat = 1st/1 (will double space)

This is the same as line = 12 14 16

MOVE Parameter: col

%move ( ‘string1’: ‘string2’, line=, col=, center=, under=, pat= , mfirst=)

• Columns can be specified in three ways

col = 3 4 8

col = 2.8 (same as 2 3 4 5 6 7 8)

col = 2-3 (text moved to the combined column 2 and 3)

MOVE Parameters: center and under


• center=Y for centered text (default)

center=N for left justified text

• under=Y for underlined text

under=N text not underlined (default)

MOVE Parameter: mfirst


• Mfirst=L for lines advancing first (default)

Example: %move (‘1’: ‘2’: ‘3’: ‘4’, col=1 2, line=12, mfirst=L)

Output: 1 32 4

• Mfirst=C for columns advancing first

Example: %move (‘1’: ‘2’: ‘3’: ‘4’, col=12, line=1 2, mfirst=c)

Output: 1 23 4

Spotlight on

• Places numeric values of variables to the report page

• Use

%nmove (var1 var2 …, line=, col=, pat=, mfirst=, fmt=, scaler=)

• var1 var2 … variables to be placed on report page

fmt what format?

scaler Number each variable is multiplied by prior to being placed on report page

• Parameters line, col, pat, and mfirst are the same as in %move

NMOVE

NMOVE Parameter: Variable Lists

%nmove ( var1 var2 …, line=, col=, pat=, mfirst=, fmt=, scaler=)

• Variable names can be given in two ways

age chol dbp (move values of these variables to report page)

M1-M15 (move values of variable M1 through M15 to report page – 15 variables)

NMOVE Parameter: fmt


• Formats are specified as number1.number2

number1 width of field

number2 number of decimals used

NMOVE Parameter: scaler


• Each variable is multiplied by the value specified before placed on report page

• Used for converting fractions to percentages, or vice versa

Example: scaler=100 (multiplies values by 100)

A Set of SAS Macros for Producing Statistical Reports Greg Grandits, M.S. Ken Svendsen, M.S....

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