SPSS Instructions for Introduction to Biostatistics

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SPSS Instructions for Introduction to
Biostatistics

• Larry Winner
• Department of Statistics
• University of Florida

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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

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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
  dont want multiple columns with the same variable

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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

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Example 1.3 - Grapefruit Juice Study
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
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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

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Example 1.3 - Grapefruit Juice Study

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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

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Example 1.4 - Smoking Status
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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

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Example 1.5 - Antibiotic Study
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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).

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Example 1.6 - Drug Approval Times
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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)

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Example 1.7 - Streptomycin Study
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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

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Example 1.8 - Theophylline Clearance
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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

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Example 1.8 - Theophylline Clearance
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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

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Example 1.10 - Alcohol Mortality
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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).

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Example 3.5 - Levocabastine in Renal Patients
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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

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Example 3.6 - Levocabastine in Renal Patients
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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)

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Example 3.7 - Cmax in SRCIRC Codeine
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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

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Example 3.8 - t1/2SS in SRCIRC Codeine
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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.

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Example 5.1 - Pamidronate Study
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Example 5.2 - Lip Cancer
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Fishers 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.

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Example 5.5 - Antiseptic Experiment
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McNemars 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.

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Example 5.6 - Report of Implant Leak
P-value
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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 COCHRANS 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.

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Example 5.7 Smoking/Death by Age
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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 gt3 in absolute value

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Example 5.8 - Marital Status Cancer
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Goodman Kruskals g / Kendalls tb

• 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 KENDALLS tb

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Examples 5.9,10 - Nicotine Patch/Exhaustion
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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

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Example 5.11 - Antibiotic Delivery
Note This statistic makes the adjustment for
ties. See Hollander and Wolfe (1973), p. 140.
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Cohens k

• 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).

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Example 5.12 - Siskel Ebert
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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)

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Examples 6.1,2 - HIV Clinical Trial
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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

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Example 6.2(a) - Thalidomide and HIV-1
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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

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Example 6.3 - Theophylline Clearance
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Example 6.3 - Theophylline Clearance
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Randomized Block Design - Friedmans 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

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Example 6.4 - Absorption of Valproate Depakote
Note This makes an adjustment for ties, see
Hollander and Wolfe (1973), p. 140.
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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

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Example 6.5 - Nortriptyline Clearance
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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

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Examples 7.1-7.6 - Gemfibrozil Clearance
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Examples 7.1-7.6 - Gemfibrozil Clearance
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Example 7.8 - TB/Thalidomide in HIV
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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

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Example 7.1 - Gemfibrozil Scatterplot
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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)

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Example 8.1 - Navelbine Toxicity
Omnibus test for all regression coefficients
(like F in linear regression)
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Example 8.2 - CHD, BP, Cholesterol
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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)

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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
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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

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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
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Examples 9.1-2 - Navelbine and Taxol in Mice
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
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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

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Example 9.3 - 6MP vs Placebo