SPSS Instructions for Introduction to Biostatistics

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SPSS Instructions for Introduction to Biostatistics

  1. 1. SPSS Instructions for Introduction to Biostatistics Larry Winner Department of Statistics University of Florida
  2. 2. SPSS Windows • Data View – Used to display data – Columns represent variables – Rows represent individual units or groups of units that share common values of variables • Variable View – Used to display information on variables in dataset – TYPE: Allows for various styles of displaying – LABEL: Allows for longer description of variable name – VALUES: Allows for longer description of variable levels – MEASURE: Allows choice of measurement scale • Output View – Displays Results of analyses/graphs
  3. 3. Data Entry Tips I • For variables that are not identifiers (such as name, county, school, etc), use numeric values for levels and use the VALUES option in VARIABLE VIEW to give their levels. Some procedures require numeric labels for levels. SPSS will print the VALUES on output • For large datasets, use a spreadsheet such as EXCEL which is more flexible for data entry, and import the file into SPSS • Give descriptive LABEL to variable names in the VARIABLE VIEW • Keep in mind that Columns are Variables, you don’t want multiple columns with the same variable
  4. 4. Data Entry/Analysis Tips II • When re-analyzing previously published data, it is often possible to have only a few outcomes (especially with categorical data), with many individuals sharing the same outcomes (as in contingency tables) • For ease of data entry: – Create one line for each combination of factor levels – Create a new variable representing a COUNT of the number of individuals sharing this “outcome” • When analyzing data Click on: – DATA → WEIGHT CASES → WEIGHT CASES BY – Click on the variable representing COUNT – All subsequent analyses treat that outcome as if it occurred COUNT times
  5. 5. Example 1.3 - Grapefruit Juice Study crcl 38 66 74 99 80 64 80 120 To import an EXCEL file, click on: FILE → OPEN → DATA then change FILES OF TYPE to EXCEL (.xls) To import a TEXT or DATA file, click on: FILE → OPEN → DATA then change FILES OF TYPE to TEXT (.txt) or DATA (.dat) You will be prompted through a series of dialog boxes to import dataset
  6. 6. Descriptive Statistics-Numeric Data • After Importing your dataset, and providing names to variables, click on: • ANALYZE → DESCRIPTIVE STATISTICS→ DESCRIPTIVES • Choose any variables to be analyzed and place them in box on right • Options include: ( ) n S S n yy S y n y y n i i n i i n i i :MeanS.E. :Variance 1 :deviationStd. :Sum:Mean 21 2 1 1 − − = = ∑ ∑ ∑ = = =
  7. 7. Example 1.3 - Grapefruit Juice Study Descriptive Statistics 8 38 120 621 77.63 8.63 24.401 595.411 8 CRCL Valid N (listwise) Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Statistic N Minimum Maximum Sum Mean Std. Deviation Variance
  8. 8. Descriptive Statistics-General Data • After Importing your dataset, and providing names to variables, click on: • ANALYZE → DESCRIPTIVE STATISTICS→ FREQUENCIES • Choose any variables to be analyzed and place them in box on right • Options include (For Categorical Variables): – Frequency Tables – Pie Charts, Bar Charts • Options include (For Numeric Variables) – Frequency Tables (Useful for discrete data) – Measures of Central Tendency, Dispersion, Percentiles – Pie Charts, Histograms
  9. 9. Example 1.4 - Smoking Status SMKSTTS 1990 37.9 37.9 37.9 1063 20.3 20.3 58.2 609 11.6 11.6 69.8 1332 25.4 25.4 95.2 253 4.8 4.8 100.0 5247 100.0 100.0 Never Smoked Quit > 10 Years Ago Quit < 10 Years Ago Current Cigarette Smoker Other Tobacco User Total Valid Frequency Percent Valid Percent Cumulative Percent
  10. 10. Vertical Bar Charts and Pie Charts • After Importing your dataset, and providing names to variables, click on: • GRAPHS → BAR… → SIMPLE (Summaries for Groups of Cases) → DEFINE • Bars Represent N of Cases (or % of Cases) • Put the variable of interest as the CATEGORY AXIS • GRAPHS → PIE… (Summaries for Groups of Cases) → DEFINE • Slices Represent N of Cases (or % of Cases) • Put the variable of interest as the DEFINE SLICES BY
  11. 11. Example 1.5 - Antibiotic Study OUTCOME 54321 Count 80 60 40 20 0 5 4 3 2 1
  12. 12. Histograms • After Importing your dataset, and providing names to variables, click on: • GRAPHS → HISTOGRAM • Select Variable to be plotted • Click on DISPLAY NORMAL CURVE if you want a normal curve superimposed (see Chapter 3).
  13. 13. Example 1.6 - Drug Approval Times MONTHS 120.0 110.0 100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 30 20 10 0 Std. Dev = 20.97 Mean = 32.1 N = 175.00
  14. 14. Side-by-Side Bar Charts • After Importing your dataset, and providing names to variables, click on: • GRAPHS → BAR… → Clustered (Summaries for Groups of Cases) → DEFINE • Bars Represent N of Cases (or % of Cases) • CATEGORY AXIS: Variable that represents groups to be compared (independent variable) • DEFINE CLUSTERS BY: Variable that represents outcomes of interest (dependent variable)
  15. 15. Example 1.7 - Streptomycin Study TRT 21 Count 30 20 10 0 OUTCOME 1 2 3 4 5 6
  16. 16. Scatterplots • After Importing your dataset, and providing names to variables, click on: • GRAPHS → SCATTER → SIMPLE → DEFINE • For Y-AXIS, choose the Dependent (Response) Variable • For X-AXIS, choose the Independent (Explanatory) Variable
  17. 17. Example 1.8 - Theophylline Clearance DRUG 3.53.02.52.01.51.0.5 THCLRNCE 8 7 6 5 4 3 2 1 0
  18. 18. Scatterplots with 2 Independent Variables • After Importing your dataset, and providing names to variables, click on: • GRAPHS → SCATTER → SIMPLE → DEFINE • For Y-AXIS, choose the Dependent Variable • For X-AXIS, choose the Independent Variable with the most levels • For SET MARKERS BY, choose the Independent Variable with the fewest levels
  19. 19. Example 1.8 - Theophylline Clearance SUBJECT 1614121086420 THCLRNCE 8 7 6 5 4 3 2 1 0 DRUG Tagamet Pepcid Placebo
  20. 20. Contingency Tables for Conditional Probabilities • After Importing your dataset, and providing names to variables, click on: • ANALYZE → DESCRIPTIVE STATISTICS → CROSSTABS • For ROWS, select the variable you are conditioning on (Independent Variable) • For COLUMNS, select the variable you are finding the conditional probability of (Dependent Variable) • Click on CELLS • Click on ROW Percentages
  21. 21. Example 1.10 - Alcohol & Mortality WINE * DEATH Crosstabulation 10535 2155 12690 83.0% 17.0% 100.0% 521 74 595 87.6% 12.4% 100.0% 11056 2229 13285 83.2% 16.8% 100.0% Count % within WINE Count % within WINE Count % within WINE 0 1 WINE Total 0 1 DEATH Total
  22. 22. Independent Sample t-Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE → COMPARE MEANS → INDEPENDENT SAMPLES T-TEST • For TEST VARIABLE, Select the dependent (response) variable(s) • For GROUPING VARIABLE, Select the independent variable. Then define the names of the 2 levels to be compared (this can be used even when the full dataset has more than 2 levels for independent variable).
  23. 23. Example 3.5 - Levocabastine in Renal Patients Group Statistics 6 563.83 172.032 70.232 6 499.67 131.409 53.647 GROUP Non-Dialysis Hemodialysis AUC N Mean Std. Deviation Std. Error Mean Independent Samples Test .204 .661 .726 10 .484 64.17 88.377 -132.750 261.083 .726 9.353 .486 64.17 88.377 -134.613 262.946 Equal variances assumed Equal variances not assumed AUC F Sig. Levene's Test for Equality of Variances t df Sig. (2-tailed) Mean Difference Std. Error Difference Lower Upper 95% Confidence Interval of the Difference t-test for Equality of Means
  24. 24. Wilcoxon Rank-Sum/Mann-Whitney Tests • After Importing your dataset, and providing names to variables, click on: • ANALYZE → NONPARAMETRIC TESTS → 2 INDEPENDENT SAMPLES • For TEST VARIABLE, Select the dependent (response) variable(s) • For GROUPING VARIABLE, Select the independent variable. Then define the names of the 2 levels to be compared (this can be used even when the full dataset has more than 2 levels for independent variable). • Click on MANN-WHITNEY U
  25. 25. Example 3.6 - Levocabastine in Renal Patients Ranks 6 7.50 45.00 6 5.50 33.00 12 GROUP Non-Dialysis Hemodialysis Total AUC N Mean Rank Sum of Ranks Test Statisticsb 12.000 33.000 -.962 .336 .394 a Mann-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) Exact Sig. [2*(1-tailed Sig.)] AUC Not corrected for ties.a. Grouping Variable: GROUPb.
  26. 26. Paired t-test • After Importing your dataset, and providing names to variables, click on: • ANALYZE → COMPARE MEANS → PAIRED SAMPLES T-TEST • For PAIRED VARIABLES, Select the two dependent (response) variables (the analysis will be based on first variable minus second variable)
  27. 27. Example 3.7 - Cmax in SRC&IRC Codeine Paired Samples Statistics 217.838 13 79.7792 22.1268 138.815 13 59.3635 16.4645 SRC IRC Pair 1 Mean N Std. Deviation Std. Error Mean Paired Samples Correlations 13 .746 .003SRC & IRCPair 1 N Correlation Sig. Paired Samples Test 79.023 53.0959 14.7262 46.938 111.109 5.366 12 .000SRC - IRCPair 1 Mean Std. Deviation Std. Error Mean Lower Upper 95% Confidence Interval of the Difference Paired Differences t df Sig. (2-tailed)
  28. 28. Wilcoxon Signed-Rank Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE → NONPARAMETRIC TESTS → 2 RELATED SAMPLES • For PAIRED VARIABLES, Select the two dependent (response) variables (be careful in determining which order the differences are being obtained, it will be clear on output) • Click on WILCOXON Option
  29. 29. Example 3.8 - t1/2 SS in SRC&IRC Codeine Ranks 9a 6.89 62.00 3b 5.33 16.00 1c 13 Negative Ranks Positive Ranks Ties Total IRC - SRC N Mean Rank Sum of Ranks IRC < SRCa. IRC > SRCb. IRC = SRCc. Test Statisticsb -1.805a .071 Z Asymp. Sig. (2-tailed) IRC - SRC Based on positive ranks.a. Wilcoxon Signed Ranks Testb.
  30. 30. Relative Risks and Odds Ratios • After Importing your dataset, and providing names to variables, click on: • ANALYZE → DESCRIPTIVE STATISTICS → CROSSTABS • For ROWS, Select the Independent Variable • For COLUMNS, Select the Dependent Variable • Under STATISTICS, Click on RISK • Under CELLS, Click on OBSERVED and ROW PERCENTAGES • NOTE: You will want to code the data so that the outcome present (Success) category has the lower value (e.g. 1) and the outcome absent (Failure) category has the higher value (e.g. 2). Similar for Exposure present category (e.g. 1) and exposure absent (e.g. 2). Use Value Labels to keep output straight.
  31. 31. Example 5.1 - Pamidronate Study PAMIDREV * SKLEVREV Crosstabulation 47 149 196 24.0% 76.0% 100.0% 74 107 181 40.9% 59.1% 100.0% 121 256 377 32.1% 67.9% 100.0% Count % within PAMIDREV Count % within PAMIDREV Count % within PAMIDREV Pamidronate Placebo PAMIDREV Total Yes No SKLEVREV Total Risk Estimate .456 .293 .710 .587 .432 .795 1.286 1.113 1.486 377 Odds Ratio for PAMIDREV (Pamidronate / Placebo) For cohort SKLEVREV = Yes For cohort SKLEVREV = No N of Valid Cases Value Lower Upper 95% Confidence Interval
  32. 32. Example 5.2 - Lip Cancer PIPESREV * LIPCREV Crosstabulation 339 149 488 69.5% 30.5% 100.0% 198 351 549 36.1% 63.9% 100.0% 537 500 1037 51.8% 48.2% 100.0% Count % within PIPESREV Count % within PIPESREV Count % within PIPESREV Yes No PIPESREV Total Yes No LIPCREV Total Risk Estimate 4.033 3.111 5.229 1.926 1.698 2.185 .478 .412 .554 1037 Odds Ratio for PIPESREV (Yes / No) For cohort LIPCREV = Yes For cohort LIPCREV = No N of Valid Cases Value Lower Upper 95% Confidence Interval
  33. 33. Fisher’s Exact Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE → DESCRIPTIVE STATISTICS → CROSSTABS • For ROWS, Select the Independent Variable • For COLUMNS, Select the Dependent Variable • Under STATISTICS, Click on CHI-SQUARE • Under CELLS, Click on OBSERVED and ROW PERCENTAGES • NOTE: You will want to code the data so that the outcome present (Success) category has the lower value (e.g. 1) and the outcome absent (Failure) category has the higher value (e.g. 2). Similar for Exposure present category (e.g. 1) and exposure absent (e.g. 2). Use Value Labels to keep output straight.
  34. 34. Example 5.5 - Antiseptic Experiment TRTREV * DEATHREV Crosstabulation 6 34 40 15.0% 85.0% 100.0% 16 19 35 45.7% 54.3% 100.0% 22 53 75 29.3% 70.7% 100.0% Count % within TRTREV Count % within TRTREV Count % within TRTREV Antiseptic Control TRTREV Total Death No Death DEATHREV Total Chi-Square Tests 8.495b 1 .004 7.078 1 .008 8.687 1 .003 .005 .004 8.382 1 .004 75 Pearson Chi-Square Continuity Correctiona Likelihood Ratio Fisher's Exact Test Linear-by-Linear Association N of Valid Cases Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) Computed only for a 2x2 tablea. 0 cells (.0%) have expected count less than 5. The minimum expected count is 10.27. b.
  35. 35. McNemar’s Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE → DESCRIPTIVE STATISTICS → CROSSTABS • For ROWS, Select the outcome for condition/time 1 • For COLUMNS, Select the outcome for condition/time 2 • Under STATISTICS, Click on MCNEMAR • Under CELLS, Click on OBSERVED and TOTAL PERCENTAGES • NOTE: You will want to code the data so that the outcome present (Success) category has the lower value (e.g. 1) and the outcome absent (Failure) category has the higher value (e.g. 2). Similar for Exposure present category (e.g. 1) and exposure absent (e.g. 2). Use Value Labels to keep output straight.
  36. 36. Example 5.6 - Report of Implant Leak SELFREV * SURGREV Crosstabulation 69 28 97 41.8% 17.0% 58.8% 5 63 68 3.0% 38.2% 41.2% 74 91 165 44.8% 55.2% 100.0% Count % of Total Count % of Total Count % of Total Present Absent SELFREV Total Present Absent SURGREV Total Chi-Square Tests .000a 165 McNemar Test N of Valid Cases Value Exact Sig. (2-sided) Binomial distribution used.a. P-value
  37. 37. Cochran Mantel-Haenszel Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE → DESCRIPTIVE STATISTICS → CROSSTABS • For ROWS, Select the Independent Variable • For COLUMNS, Select the Dependent Variable • For LAYERS, Select the Strata Variable • Under STATISTICS, Click on COCHRAN’S AND MANTEL- HAENSZEL STATISTICS • NOTE: You will want to code the data so that the outcome present (Success) category has the lower value (e.g. 1) and the outcome absent (Failure) category has the higher value (e.g. 2). Similar for Exposure present category (e.g. 1) and exposure absent (e.g. 2). Use Value Labels to keep output straight.
  38. 38. Example 5.7 Smoking/Death by Age SMOKEREV * DEATHREV * AGE Crosstabulation Count 647 39990 40637 204 20132 20336 851 60122 60973 857 32894 33751 394 21671 22065 1251 54565 55816 855 20739 21594 488 19790 20278 1343 40529 41872 643 11197 11840 766 16499 17265 1409 27696 29105 Smoke No Smoke SMOKEREV Total Smoke No Smoke SMOKEREV Total Smoke No Smoke SMOKEREV Total Smoke No Smoke SMOKEREV Total AGE 50-54 55-59 60-64 65-69 Death No Death DEATHREV Total Mantel-Haenszel Common Odds Ratio Estimate 1.457 .377 .031 .000 1.372 1.548 .316 .437 Estimate ln(Estimate) Std. Error of ln(Estimate) Asymp. Sig. (2-sided) Lower Bound Upper Bound Common Odds Ratio Lower Bound Upper Bound ln(Common Odds Ratio) Asymp. 95% Confidence Interval The Mantel-Haenszel common odds ratio estimate is asymptotically normally distributed under the common odds ratio of 1.000 assumption. So is the natural log of the estimate.
  39. 39. Chi-Square Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE → DESCRIPTIVE STATISTICS → CROSSTABS • For ROWS, Select the Independent Variable • For COLUMNS, Select the Dependent Variable • Under STATISTICS, Click on CHI-SQUARE • Under CELLS, Click on OBSERVED, EXPECTED, ROW PERCENTAGES, and ADJUSTED STANDARDIZED RESIDUALS • NOTE: Large ADJUSTED STANDARDIZED RESIDUALS (in absolute value) show which cells are inconsistent with null hypothesis of independence. A common rule of thumb is seeing which if any cells have values >3 in absolute value
  40. 40. Example 5.8 - Marital Status & Cancer MARITAL * CANCREV Crosstabulation 29 47 76 38.1 37.9 76.0 38.2% 61.8% 100.0% -2.3 2.3 116 108 224 112.3 111.7 224.0 51.8% 48.2% 100.0% .7 -.7 67 56 123 61.6 61.4 123.0 54.5% 45.5% 100.0% 1.1 -1.1 5 5 10 5.0 5.0 10.0 50.0% 50.0% 100.0% .0 .0 217 216 433 217.0 216.0 433.0 50.1% 49.9% 100.0% Count Expected Count % within MARITAL Adjusted Residual Count Expected Count % within MARITAL Adjusted Residual Count Expected Count % within MARITAL Adjusted Residual Count Expected Count % within MARITAL Adjusted Residual Count Expected Count % within MARITAL Single Married Widowed Div/Sep MARITAL Total Cancer No Cancer CANCREV Total Chi-Square Tests 5.530a 3 .137 5.572 3 .134 3.631 1 .057 433 Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases Value df Asymp. Sig. (2-sided) 1 cells (12.5%) have expected count less than 5. The minimum expected count is 4.99. a.
  41. 41. Goodman & Kruskal’s γ / Kendall’s τb • After Importing your dataset, and providing names to variables, click on: • ANALYZE → DESCRIPTIVE STATISTICS → CROSSTABS • For ROWS, Select the Independent Variable • For COLUMNS, Select the Dependent Variable • Under STATISTICS, Click on GAMMA and KENDALL’S τb
  42. 42. Examples 5.9,10 - Nicotine Patch/Exhaustion DOSE * EXHSTN Crosstabulation Count 16 2 18 16 2 18 13 4 17 14 4 18 59 12 71 1 2 3 4 DOSE Total 1 2 EXHSTN Total Symmetric Measures .124 .104 1.166 .243 .269 .220 1.166 .243 71 Kendall's tau-b Gamma Ordinal by Ordinal N of Valid Cases Value Asymp. Std. Error a Approx. T b Approx. Sig. Not assuming the null hypothesis.a. Using the asymptotic standard error assuming the null hypothesis.b.
  43. 43. Kruskal-Wallis Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE → NONPARAMETRIC TESTS → k INDEPENDENT SAMPLES • For TEST VARIABLE, Select Dependent Variable • For GROUPING VARIABLE, Select Independent Variable, then define range of levels of variable (Minimum and Maximum) • Click on KRUSKAL-WALLIS H
  44. 44. Example 5.11 - Antibiotic Delivery Ranks 181 285.65 181 261.15 179 266.14 541 DELIVERY 1 2 3 Total OUTCOME N Mean Rank Test Statisticsa,b 2.755 2 .252 Chi-Square df Asymp. Sig. OUTCOME Kruskal Wallis Testa. Grouping Variable: DELIVERYb. Note: This statistic makes the adjustment for ties. See Hollander and Wolfe (1973), p. 140.
  45. 45. Cohen’s κ • After Importing your dataset, and providing names to variables, click on: • ANALYZE → DESCRIPTIVE STATISTICS → CROSSTABS • For ROWS, Select Rater 1 • For COLUMNS, Select Rater 2 • Under STATISTICS, Click on KAPPA • Under CELLS, Click on TOTAL Percentages to get the observed percentages in each cell (the first number under observed count in Table 5.17).
  46. 46. Example 5.12 - Siskel & Ebert SISKEL * EBERT Crosstabulation 24 8 13 45 15.0% 5.0% 8.1% 28.1% 8 13 11 32 5.0% 8.1% 6.9% 20.0% 10 9 64 83 6.3% 5.6% 40.0% 51.9% 42 30 88 160 26.3% 18.8% 55.0% 100.0% Count % of Total Count % of Total Count % of Total Count % of Total -1 0 1 SISKEL Total -1 0 1 EBERT Total Symmetric Measures .389 .060 6.731 .000 160 KappaMeasure of Agreement N of Valid Cases Value Asymp. Std. Error a Approx. T b Approx. Sig. Not assuming the null hypothesis.a. Using the asymptotic standard error assuming the null hypothesis.b.
  47. 47. 1-Factor ANOVA - Independent Samples (Parallel Groups) • After Importing your dataset, and providing names to variables, click on: • ANALYZE → COMPARE MEANS → ONE-WAY ANOVA • For DEPENDENT LIST, Click on the Dependent Variable • For FACTOR, Click on the Independent Variable • To obtain Pairwise Comparisons of Treatment Means: – Click on POST HOC – Then TUKEY and BONFERRONI (among many other choices)
  48. 48. Examples 6.1,2 - HIV Clinical Trial ANOVA CD4 7074.600 2 3537.300 8.901 .000 106108.0 267 397.408 113182.6 269 Between Groups Within Groups Total Sum of Squares df Mean Square F Sig. Multiple Comparisons Dependent Variable: CD4 7.10000* 2.971749 .046 .09601 14.10399 12.50000* 2.971749 .000 5.49601 19.50399 -7.10000* 2.971749 .046 -14.10399 -.09601 5.40000 2.971749 .166 -1.60399 12.40399 -12.50000* 2.971749 .000 -19.50399 -5.49601 -5.40000 2.971749 .166 -12.40399 1.60399 7.10000 2.971749 .053 -.05942 14.25942 12.50000* 2.971749 .000 5.34058 19.65942 -7.10000 2.971749 .053 -14.25942 .05942 5.40000 2.971749 .211 -1.75942 12.55942 -12.50000* 2.971749 .000 -19.65942 -5.34058 -5.40000 2.971749 .211 -12.55942 1.75942 (J) TRT SZ ZZ SZZ ZZ SZZ SZ SZ ZZ SZZ ZZ SZZ SZ (I) TRT SZZ SZ ZZ SZZ SZ ZZ Tukey HSD Bonferroni Mean Difference (I-J) Std. Error Sig. Lower Bound Upper Bound 95% Confidence Interval The mean difference is significant at the .05 level.*. CD4 90 -.30000 90 5.10000 90 12.20000 .166 1.000 TRT ZZ SZ SZZ Sig. Tukey HSDa N 1 2 Subset for alpha = .05 Means for groups in homogeneous subsets are displayed. Uses Harmonic Mean Sample Size = 90.000.a.
  49. 49. Kruskal-Wallis Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE → NONPARAMETRIC TESTS → k INDEPENDENT SAMPLES • For TEST VARIABLE, Select Dependent Variable • For GROUPING VARIABLE, Select Independent Variable, then define range of levels of variable (Minimum and Maximum) • Click on KRUSKAL-WALLIS H
  50. 50. Example 6.2(a) - Thalidomide and HIV-1 Ranks 8 24.44 8 21.63 8 6.56 8 13.38 32 TRT 1 2 3 4 Total WTGAIN N Mean Rank Test Statisticsa,b 18.070 3 .000 Chi-Square df Asymp. Sig. WTGAIN Kruskal Wallis Testa. Grouping Variable: TRTb.
  51. 51. Randomized Block Design - F-test • After Importing your dataset, and providing names to variables, click on: • ANALYZE → GENERAL LINEAR MODEL → UNIVARIATE • Assign the DEPENDENT VARIABLE • Assign the TREATMENT variable as a FIXED FACTOR • Assign the BLOCK variable as a RANDOM FACTOR • Click on MODEL, then CUSTOM, under BUILD TERMS choose MAIN EFFECTS, move both factors to MODEL list • Click on POST HOC and select the TREATMENT factor for POST HOC TESTS and BONFERRONI and TUKEY (among many choices) • For PLOTS, Select the BLOCK factor for HORIZONTAL AXIS and the TREATMENT factor for SEPARATE LINES, click ADD
  52. 52. Example 6.3 - Theophylline Clearance Tests of Between-Subjects Effects Dependent Variable: THEOPHCL 336.713 1 336.713 60.955 .000 71.811 13 5.524a 7.005 2 3.503 10.591 .000 8.599 26 .331b 71.811 13 5.524 16.703 .000 8.599 26 .331b Source Hypothesis Error Intercept Hypothesis Error DRUG Hypothesis Error SUBJECT Type III Sum of Squares df Mean Square F Sig. MS(SUBJECT)a. MS(Error)b. Multiple Comparisons Dependent Variable: THEOPHCL -.0800 .21736 .928 -.6201 .4601 .8236* .21736 .002 .2835 1.3637 .0800 .21736 .928 -.4601 .6201 .9036* .21736 .001 .3635 1.4437 -.8236* .21736 .002 -1.3637 -.2835 -.9036* .21736 .001 -1.4437 -.3635 -.0800 .21736 1.000 -.6362 .4762 .8236* .21736 .002 .2674 1.3798 .0800 .21736 1.000 -.4762 .6362 .9036* .21736 .001 .3474 1.4598 -.8236* .21736 .002 -1.3798 -.2674 -.9036* .21736 .001 -1.4598 -.3474 (J) DRUG Famotidine Placebo Cimetidine Placebo Cimetidine Famotidine Famotidine Placebo Cimetidine Placebo Cimetidine Famotidine (I) DRUG Cimetidine Famotidine Placebo Cimetidine Famotidine Placebo Tukey HSD Bonferroni Mean Difference (I-J) Std. Error Sig. Lower Bound Upper Bound 95% Confidence Interval Based on observed means. The mean difference is significant at the .05 level.*.
  53. 53. Example 6.3 - Theophylline Clearance THEOPHCL 14 2.2557 14 3.0793 14 3.1593 1.000 .928 DRUG Placebo Cimetidine Famotidine Sig. Tukey HSDa,b N 1 2 Subset Means for groups in homogeneous subsets are displayed. Based on Type III Sum of Squares The error term is Mean Square(Error) = .331. Uses Harmonic Mean Sample Size = 14.000.a. Alpha = .05.b. Estimated Marginal Means of THEOPHCL SUBJECT 1413121110987654321 EstimatedMarginalMeans 7 6 5 4 3 2 1 0 DRUG Cimetidine Famotidine Placebo
  54. 54. Randomized Block Design - Friedman’s test • After Importing your dataset, and providing names to variables, click on: • ANALYZE → NONPARAMETRIC TESTS → k RELATED SAMPLES • For TEST VARIABLES, select the variables representing the treatments (each line is a subject/block) • Click on FRIEDMAN
  55. 55. Example 6.4 - Absorption of Valproate Depakote Ranks 1.73 2.95 1.32 CAPFAST CAPNFAST ECFAST Mean Rank Test Statisticsa 11 17.550 2 .000 N Chi-Square df Asymp. Sig. Friedman Testa. Note: This makes an adjustment for ties, see Hollander and Wolfe (1973), p. 140.
  56. 56. 2-Way ANOVA • After Importing your dataset, and providing names to variables, click on: • ANALYZE → GENERAL LINEAR MODEL → UNIVARIATE • Assign the DEPENDENT VARIABLE • Assign the FACTOR A variable as a FIXED FACTOR • Assign the FACTOR B variable as a FIXED FACTOR • Click on MODEL, then CUSTOM, select FULL FACTORIAL • Click on POST HOC and select the both factors for POST HOC TESTS and BONFERRONI and TUKEY (among many choices) • For PLOTS, Select FACTOR B for HORIZONTAL AXIS and FACTOR A for SEPARATE LINES, click ADD
  57. 57. Example 6.5 - Nortriptyline Clearance Tests of Between-Subjects Effects Dependent Variable: CLRNCE 23.164a 3 7.721 1.444 .267 1131.008 1 1131.008 211.532 .000 .450 1 .450 .084 .775 20.402 1 20.402 3.816 .068 2.312 1 2.312 .432 .520 85.548 16 5.347 1239.720 20 108.712 19 Source Corrected Model Intercept ETHNIC GENDER ETHNIC * GENDER Error Total Corrected Total Type III Sum of Squares df Mean Square F Sig. R Squared = .213 (Adjusted R Squared = .066)a. Estimated Marginal Means of CLRNCE ETHNIC 21 EstimatedMarginalMeans 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 GENDER 1 2
  58. 58. Linear Regression • After Importing your dataset, and providing names to variables, click on: • ANALYZE → REGRESSION → LINEAR • Select the DEPENDENT VARIABLE • Select the INDEPENDENT VARAIABLE(S) • Click on STATISTICS, then ESTIMATES, CONFIDENCE INTERVALS, MODEL FIT • For histogram of residuals, click on PLOTS, and HISTOGRAM under STANDARDIZED RESIDUAL PLOTS
  59. 59. Examples 7.1-7.6 - Gemfibrozil Clearance Coefficientsa 460.828 54.338 8.481 .000 345.010 576.646 -3.215 1.181 -.575 -2.723 .016 -5.732 -.698 (Constant) CLCR Model 1 B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Lower Bound Upper Bound 95% Confidence Interval for B Dependent Variable: CLGMa. Regression Standardized Residual 1.501.00.500.00-.50-1.00-1.50 Histogram Dependent Variable: CLGM Frequency 6 5 4 3 2 1 0 Std. Dev = .97 Mean = 0.00 N = 17.00
  60. 60. Examples 7.1-7.6 - Gemfibrozil Clearance ANOVAb 107168.2 1 107168.158 7.413 .016a 216865.8 15 14457.723 324034.0 16 Regression Residual Total Model 1 Sum of Squares df Mean Square F Sig. Predictors: (Constant), CLCRa. Dependent Variable: CLGMb. Model Summaryb .575a .331 .286 120.240 Model 1 R R Square Adjusted R Square Std. Error of the Estimate Predictors: (Constant), CLCRa. Dependent Variable: CLGMb.
  61. 61. Example 7.8 - TB/Thalidomide in HIV Coefficientsa 2.662 .635 4.190 .000 .597 .116 .692 5.161 .000 -.330 .258 -.167 -1.279 .211 -.571 .262 -.289 -2.179 .038 (Constant) LN_X DRUG TB Model 1 B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: LN_Ya. ANOVAb 16.698 3 5.566 10.696 .000a 14.571 28 .520 31.270 31 Regression Residual Total Model 1 Sum of Squares df Mean Square F Sig. Predictors: (Constant), TB, DRUG, LN_Xa. Dependent Variable: LN_Yb.
  62. 62. Useful Regression Plots • Scatterplot with Fitted (Least Squares) Line – GRAPHS → INTERACTIVE → SCATTERPLOT – Select DEPENDENT VARIABLE for UP/DOWN AXIS – Select INDEPENDENT VARIABLE for RIGHT/LEFT AXIS – Click on FIT Tab, then REGRESSION for METHOD – NOTE: Be certain both variables are SCALE in VARIABLE VIEW under MEASURE • Partial Regression Plots (Multiple Regression) to observe association of each Independent Variable with Y, controlling for all others – Fit REGRESSION model with all Independent Variables – Click PLOTS, then PRODUCE ALL PARTIAL PLOTS
  63. 63. Example 7.1 - Gemfibrozil Scatterplot Linear Regression 20 40 60 clcr 200 300 400 500 600 clgm                 clgm = 460.83 + -3.22 * clcr R-Square = 0.33
  64. 64. Logistic Regression • After Importing your dataset, and providing names to variables, click on: • ANALYZE → REGRESSION → BINARY LOGISTIC • Select the DEPENDENT VARIABLE • Select the INDEPENDENT VARAIABLE(S) as COVARIATES • For a 95% CI for the odds ratio, click on OPTIONS, then CI for exp(B) • Declare any CATEGORICAL COVARIATES (Independent variables whose levels are categorical, not numeric)
  65. 65. Example 8.1 - Navelbine Toxicity Variables in the Equation .488 .052 88.238 1 .000 1.628 1.471 1.803 -6.381 .690 85.498 1 .000 .002 DOSE Constant Step 1 a B S.E. Wald df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B) Variable(s) entered on step 1: DOSE.a. Omnibus Tests of Model Coefficients 210.310 1 .000 210.310 1 .000 210.310 1 .000 Step Block Model Step 1 Chi-square df Sig. Omnibus test for all regression coefficients (like F in linear
  66. 66. Example 8.2 - CHD, BP, Cholesterol Variables in the Equation 6.394 1.475 18.792 1 .000 598.277 33.218 10775.391 3.454 .838 17.008 1 .000 31.631 6.126 163.319 -24.020 3.699 42.158 1 .000 .000 LOG10SC LOG10BP Constant Step 1 a B S.E. Wald df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B) Variable(s) entered on step 1: LOG10SC, LOG10BP.a. Omnibus Tests of Model Coefficients 42.566 2 .000 42.566 2 .000 42.566 2 .000 Step Block Model Step 1 Chi-square df Sig.
  67. 67. Nonlinear Regression • After Importing your dataset, and providing names to variables, click on: • ANALYZE → REGRESSION → NONLINEAR • Select the DEPENDENT VARIABLE • Define the MODEL EXPRESSION as a function of the INDEPENDENT VARIABLE(s) and unknown PARAMETERS • Define the PARAMETERS and give them STARTING VALUES (this may take several attempts)
  68. 68. Example 8.3 - MK-639 in AIDS Patients Nonlinear Regression Summary Statistics Dependent Variable RNACHNG Source DF Sum of Squares Mean Square Regression 3 24.97099 8.32366 Residual 2 .02783 .01391 Uncorrected Total 5 24.99881 (Corrected Total) 4 10.83973 R squared = 1 - Residual SS / Corrected SS = .99743 Asymptotic 95 % Asymptotic Confidence Interval Parameter Estimate Std. Error Lower Upper A 3.521788512 .121466117 2.999161991 4.044415032 B 35.598069675 7.532265897 3.189345253 68.006794097 C 18374.392967 82.899219276 18017.706415 18731.079519 Parameters,,:Model 60 ≡= + = − CBAAUCx Cx Ax y hBB B
  69. 69. Survival Analysis -Kaplan-Meier Estimates and Log-Rank Test • After Importing your dataset, and providing names to variables, click on: • ANALYZE → SURVIVAL → KAPLAN-MEIER • Select the variable representing the survival TIME of individual • Select the variable representing the STATUS of individual (whether or not event has occured). NOTE: If the variable is an indicator that the observation was CENSORED, then a value of 0 for that variable will mean the event has occured. • Select the variable representing the FACTOR containing the groups to be compared • Click on COMPARE FACTOR, select LOG-RANK, and POOL ACROSS STRATA
  70. 70. Examples 9.1-2 - Navelbine and Taxol in Mice Survival Analysis for TIME Factor REGIMEN = 1 Time Status Cumulative Standard Cumulative Number Survival Error Events Remaining 6 0 .9796 .0202 1 48 8 0 .9592 .0283 2 47 22 0 .9388 .0342 3 46 32 0 4 45 32 0 .8980 .0432 5 44 35 0 .8776 .0468 6 43 41 0 .8571 .0500 7 42 46 0 .8367 .0528 8 41 54 0 .8163 .0553 9 40 Factor REGIMEN = 2 Time Status Cumulative Standard Cumulative Number Survival Error Events Remaining 8 0 .9333 .0644 1 14 10 0 .8667 .0878 2 13 27 0 .8000 .1033 3 12 31 0 .7333 .1142 4 11 34 0 .6667 .1217 5 10 35 0 .6000 .1265 6 9 39 0 .5333 .1288 7 8 47 0 .4667 .1288 8 7 57 0 .4000 .1265 9 6
  71. 71. Examples 9.1-2 - Navelbine and Taxol in Mice Survival Functions TIME 706050403020100 CumSurvival 1.1 1.0 .9 .8 .7 .6 .5 .4 .3 REGIMEN 2 2-censored 1 1-censored Test Statistics for Equality of Survival Distributions for REGIMEN Statistic df Significance Log Rank 10.93 1 .0009 This is the square of the Z-statistic in text, and is a chi-square statistic
  72. 72. Relative Risk Regression (Cox Model) • After Importing your dataset, and providing names to variables, click on: • ANALYZE → SURVIVAL → COX REGRESSION • Select the variable representing the survival TIME of individual • Select the variable representing the STATUS of individual (whether or not event has occured). NOTE: If the variable is an indicator that the observation was CENSORED, then a value of 0 for that variable will mean the event has occured. • Select the variable(s) representing the COVARIATES (Independent Variables in Model) • Identify any CATEGORICAL COVARIATES including Dummy/Indicator variables • K-M PLOTS can be obtained, with separate SURVIVAL curves by categories
  73. 73. Example 9.3 - 6MP vs Placebo Variables in the Equation -1.509 .410 13.578 1 .000 .221 .099 .493TRT B SE Wald df Sig. Exp(B) Lower Upper 95.0% CI for Exp(B) Survival Function for patterns 1 - 2 REMSTIME 3020100-10 CumSurvival 1.2 1.0 .8 .6 .4 .2 0.0 TRT Placebo 6MP

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