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Statistical Procedures in SAS for Treatment Group Analysis

Learn how to interpret output and analyze treatment groups in a study on blood pressure drugs, focusing on side effects and statistical tests using SAS procedures. Program codes and outputs included.

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Statistical Procedures in SAS for Treatment Group Analysis

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  1. Lesson 10 - Topics • SAS Procedures for Standard Statistical Tests and Analyses • Programs 19 and 20 • LSB 9:4-7;12-13

  2. STATISTICAL PROCEDURES IN SAS

  3. STATISTICAL PROCEDURES IN SAS Important to understand the output

  4. Treatment Groups in TOMHS 1. Beta Blocker 2. Calcium Channel Blocker 3. Diuretic 4. Alpha Blocker 5. ACE Inhibitor 6. Placebo 1 - 5 are blood pressure drugs

  5. Side Effect Questions Have you been troubled in the past 3 months with any of the following? a. Fever b. Sweating ww. Feeling depressed 1. No, not troubled 2. Yes, mildly 3. Yes, moderately 4. Yes, severely Responses 2-4 indicate a positive response.

  6. * Program 19 DATA stat ; INFILE‘C:\SAS_Files\tomhsfull.data' LRECL = 300 ; INPUT @1 ptid $10. @25 group 1. @115 sbpbl 3. @123 sbp12 3. @76 ursod12 3. @278 se12_2 1. ; if se12_2 in(2,3,4) then tired12 = 1; else if se12_2 = 1then tired12 = 2; sbpchg = sbp12 - sbpbl; if group = 6then active = 2; else active = 1; if group IN(1,2,3,4,5) then drug = group;

  7. PROCFREQDATA=stat; TABLES active*tired/CHISQ RELRISK; TITLE'Chi-square Test Comparing Active vs Placebo Group for Tiredness'; RUN; CHISQ – displays Chi-square test RELRISK – displays odds ratio and relative risk Indepent Variable * dependent variable

  8. Table of active by tired12 active tired12 Frequency‚ Percent ‚ Row Pct ‚ Col Pct ‚ 1‚ 2‚ Total ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 1 ‚ 112 ‚ 522 ‚ 634 ‚ 13.04 ‚ 60.77 ‚ 73.81 ‚ 17.67 ‚ 82.33 ‚ ‚ 67.07 ‚ 75.43 ‚ ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 2 ‚ 55 ‚ 170 ‚ 225 ‚ 6.40 ‚ 19.79 ‚ 26.19 ‚ 24.44 ‚ 75.56 ‚ ‚ 32.93 ‚ 24.57 ‚ ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ Total 167 692 859 19.44 80.56 100.00 Frequency Missing = 43 Statistic DF Value Prob ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Chi-Square 1 4.8725 0.0273 Likelihood Ratio Chi-Square 1 4.6977 0.0302 Continuity Adj. Chi-Square 1 4.4493 0.0349 Mantel-Haenszel Chi-Square 1 4.8668 0.0274 Tests if two percentages are significantly different P-value

  9. Table of active by tired12 active tired12 Frequency‚ Percent ‚ Row Pct ‚ Col Pct ‚ 1‚ 2‚ Total ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 1 ‚ 112 ‚ 522 ‚ 634 ‚ 13.04 ‚ 60.77 ‚ 73.81 ‚ 17.67 ‚ 82.33 ‚ ‚ 67.07 ‚ 75.43 ‚ ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 2 ‚ 55 ‚ 170 ‚ 225 ‚ 6.40 ‚ 19.79 ‚ 26.19 ‚ 24.44 ‚ 75.56 ‚ ‚ 32.93 ‚ 24.57 ‚ ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ Total 167 692 859 19.44 80.56 100.00 Estimates of the Relative Risk (Row1/Row2) Type of Study Value 95% Confidence Limits ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Case-Control (Odds Ratio) 0.6632 0.4598 0.9565 Cohort (Col1 Risk) 0.7227 0.5437 0.9606 OR = Odds of tiredness (Active v Placebo) OR = (112/522)/(55/170) = 0.66 RR = Risk of tiredness (Active v Placebo) RR = 17.67/24.44 = 0.72

  10. PROCTTESTDATA=stat; VAR sbpchg; CLASS active; TITLE'T Test Comparing Active vs Placebo Group for Change in Blood Pressure'; RUN; Testing if mean SBP change is equal between 2 groups.

  11. Statistics Lower CL Upper CL Lower CL Variable active N Mean Mean Mean Std Dev Std Dev sbpchg 1 627 -19.44 -18.35 -17.26 13.19 13.92 sbpchg 2 221 -12.28 -10.38 -8.473 13.129 14.354 sbpchg Diff (1-2) -10.13 -7.972 -5.817 13.396 14.034 Statistics Upper CL Variable active Std Dev Std Err Minimum Maximum sbpchg 1 14.736 0.5559 -75 28 sbpchg 2 15.833 0.9656 -44 30 sbpchg Diff (1-2) 14.736 1.0979 T-Tests Variable Method Variances DF t Value Pr > |t| sbpchg Pooled Equal 846 -7.26 <.0001 sbpchg Satterthwaite Unequal 376 -7.16 <.0001 -7.26 = -7.972/1.0979

  12. Plot Generated from PROC TTEST

  13. PROCMEANSDATA=stat NMEANSTDERR T PRT ; CLASS group; VAR sbpchg; TITLE 'Paired T-Test, Are there significant changes in SBP within each group?'; RUN; Also used for a crossover design where each patient gets each treatment in random order

  14. Analysis Variable : sbpchg N group Obs N Mean Std Error t Value Pr > |t| ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ 1 132 126 -19.8095238 1.3655496 -14.51 <.0001 2 131 121 -17.2561983 1.0548020 -16.36 <.0001 3 136 124 -21.5967742 1.3047312 -16.55 <.0001 4 134 129 -15.8294574 1.2368065 -12.80 <.0001 5 135 127 -17.3228346 1.1691084 -14.82 <.0001 6 234 221 -10.3755656 0.9655718 -10.75 <.0001 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ T-value = Mean/Std Error

  15. * Compare 5 active drug groups; * For SBP change; PROCANOVADATA=stat; CLASS drug; * Treat as categories; MODEL sbpchg = drug; MEANS drug/BON ; TITLE'ANOVA Comparing 5 Active Treatment Groups for Change in SBP '; RUN;

  16. The ANOVA Procedure Class Level Information Class Levels Values drug 5 1 2 3 4 5 Number of observations 902 NOTE: Due to missing values, only 627 observations can be used in this analysis.

  17. Dependent Variable: sbpchg ANOVA TABLE Sum of Source DF Squares Mean Square F Value Pr > F Model 4 2673.8672 668.4668 3.51 0.0077 Error 622 118618.3370 190.7047 Corrected Total 626 121292.2041 3.51 = 668.5/190.7 Source DF Anova SS Mean Square F Value Pr > F drug 4 2673.867173 668.466793 3.51 0.0077

  18. Bonferroni (Dunn) t Tests for sbpchg Alpha 0.05 Critical Value of t 2.82 Minimum Significant Difference 4.92 Adjusts for 10 possible pairwise comparisons Required difference between any 2 groups to be significant.

  19. Comparisons significant at the 0.05 level are indicated by ***. Difference Simultaneous drug Between 95% Confidence Comparison Means Limits 4 - 2 1.427 -3.497 6.350 4 - 5 1.493 -3.370 6.356 4 - 1 3.980 -0.893 8.853 4 - 3 5.767 0.875 10.660 *** 2 - 4 -1.427 -6.350 3.497 2 - 5 0.067 -4.875 5.009 2 - 1 2.553 -2.398 7.505 2 - 3 4.341 -0.631 9.312 5 - 4 -1.493 -6.356 3.370 5 - 2 -0.067 -5.009 4.875 5 - 1 2.487 -2.405 7.378 5 - 3 4.274 -0.637 9.185 1 - 4 -3.980 -8.853 0.893 1 - 2 -2.553 -7.505 2.398 1 - 5 -2.487 -7.378 2.405 1 - 3 1.787 -3.134 6.708 3 - 4 -5.767 -10.660 -0.875 *** 3 - 2 -4.341 -9.312 0.631 3 - 5 -4.274 -9.185 0.637 3 - 1 -1.787 -6.708 3.134

  20. PROCGLMDATA=stat; * GLM (General Linear Model) CLASS drug; MODEL sbpchg = drug; ESTIMATE'BB vs Diuretic' drug 10 -100 ; ESTIMATE'CCB vs Diuretic' drug 01 -100 ; ESTIMATE'Alpha B vs Diuretic‘ drug 00 -110 ; ESTIMATE'ACE v Diuretic' drug 00 -101 ; MEANS drug; TITLE‘GLM Comparing 5 Active Treatment Groups for Change in SBP '; RUN; Compares drug 1 with drug 3

  21. The GLM Procedure Source DF Type III SS Mean Square F Value Pr > F drug 4 2673.867173 668.466793 3.51 0.0077 Output from estimate statements Standard Parameter Estimate Error t Value Pr > |t| BB vs Diuretic 1.78725038 1.74684597 1.02 0.3066 CCB vs Diuretic 4.34057585 1.76465673 2.46 0.0142 Alpha B vs Diuretic 5.76731683 1.73674192 3.32 0.0010 ACE v Diuretic 4.27393955 1.74343147 2.45 0.0145 Each group has higher BP than the diuretic group. ESTIMATE‘Avg 2-3 v 4' drug 0-.5 -.510 ;

  22. PLOT GENERATED FROM PROC GLM

  23. Distribution of Urinary Sodium Excretion

  24. PROCUNIVARIATEDATA = stat; VAR ursod12; HISTOGRAM ursod12 / NORMALMIDPOINTS=0 to 180 by 2; INSETN = 'N' (5.0) MEAN = 'Mean' (5.1) STD = 'Sdev' (5.1) MIN = 'Min' (5.1) MAX = 'Max' (5.1)/ POS=NW HEADER='Summary Statistics'; LABEL bmi = ‘Urinary Sodium'; TITLE‘Distribution of Urinary Sodium Excretion'; RUN;

  25. PROCNPAR1WAYDATA=stat WILCOXON ; CLASS drug; VAR ursod12; * Skewed distribution; TITLE'Non-parametric Test Comparing Groups in Urinary Sodium'; RUN; *The values for ursod12 are ordered from lowest to highest and given a value of 1 to N. Analyses is then done on these ranked values.

  26. Wilcoxon Scores (Rank Sums) for Variable ursod12 Classified by Variable drug Sum of Expected Std Dev Mean drug N Scores Under H0 Under H0 Score ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ 5 116 31422.50 34974.0 1682.82936 270.883621 3 118 38296.50 35577.0 1693.77858 324.546610 2 120 33286.50 36180.0 1704.53960 277.387500 4 126 40102.50 37989.0 1735.72813 318.273810 1 122 38395.00 36783.0 1715.11595 314.713115 Average scores were used for ties. Kruskal-Wallis Test Chi-Square 9.8522 DF 4 Pr > Chi-Square 0.0430 Of values 1-602 drug N Median ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ 1 122 38.5 2 120 36.0 3 118 40.5 4 126 39.5 5 116 36.0 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ

  27. * Program 20 * Chi-square tests from summary counts; DATA asthma; INFILE DATALINES; INPUT ses asthma count; DATALINES; 1 1 40 1 2 100 2 1 30 2 2 130 ; Asthma SES |YES | NO | ---------+--------+--------+ LOW | 40 | 100 | ---------+--------+--------+ HIGH | 30 | 130 | ---------+--------+--------+

  28. SAS LOG 1 DATA asthma; 2 INFILE DATALINES; 3 INPUT ses asthma count; 4 DATALINES; NOTE: The data set WORK.ASTHMA has 4 observations and 3 variables.

  29. PROCFREQDATA=asthma; TABLES ses*asthma/CHISQRELRISK ; WEIGHT COUNT; TITLE'Relationship between Asthma and SES'; RUN; ses asthma Frequency| Percent | Row Pct | Col Pct |1 |2 | Total ---------+--------+--------+ 1 | 40 | 100 | 140 | 13.33 | 33.33 | 46.67 | 28.57 | 71.43 | | 57.14 | 43.48 | ---------+--------+--------+ 2 | 30 | 130 | 160 | 10.00 | 43.33 | 53.33 | 18.75 | 81.25 | | 42.86 | 56.52 | ---------+--------+--------+ Total 70 230 300 23.33 76.67 100.00 Odds Ratio (Relative Odds) (40/100)/(30/130)= 1.73 Note: This is the odds ratio of having asthma (low v high SES)

  30. Statistics for Table of ses by asthma Statistic DF Value Prob ------------------------------------------------------ Chi-Square 1 4.0262 0.0448 Estimates of the Relative Risk (Row1/Row2) Type of Study Value 95% Confidence Limits -------------------------------------------------------------------- Case-Control 1.7333 1.0097 2.9756 Loosely speaking: There is a 73% increase chance of asthma if you are low SES (versus high SES).

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