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SPSS Instructions for Introduction to

Biostatistics

- Larry Winner
- Department of Statistics
- University of Florida

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

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

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

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

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

Example 1.3 - Grapefruit Juice Study

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

Example 1.4 - Smoking Status

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

Example 1.5 - Antibiotic Study

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).

Example 1.6 - Drug Approval Times

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)

Example 1.7 - Streptomycin Study

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

Example 1.8 - Theophylline Clearance

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

Example 1.8 - Theophylline Clearance

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

Example 1.10 - Alcohol Mortality

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).

Example 3.5 - Levocabastine in Renal Patients

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

Example 3.6 - Levocabastine in Renal Patients

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)

Example 3.7 - Cmax in SRCIRC Codeine

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

Example 3.8 - t1/2SS in SRCIRC Codeine

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.

Example 5.1 - Pamidronate Study

Example 5.2 - Lip Cancer

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.

Example 5.5 - Antiseptic Experiment

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.

Example 5.6 - Report of Implant Leak

P-value

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.

Example 5.7 Smoking/Death by Age

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

Example 5.8 - Marital Status Cancer

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

Examples 5.9,10 - Nicotine Patch/Exhaustion

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

Example 5.11 - Antibiotic Delivery

Note This statistic makes the adjustment for

ties. See Hollander and Wolfe (1973), p. 140.

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).

Example 5.12 - Siskel Ebert

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)

Examples 6.1,2 - HIV Clinical Trial

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

Example 6.2(a) - Thalidomide and HIV-1

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

Example 6.3 - Theophylline Clearance

Example 6.3 - Theophylline Clearance

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

Example 6.4 - Absorption of Valproate Depakote

Note This makes an adjustment for ties, see

Hollander and Wolfe (1973), p. 140.

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

Example 6.5 - Nortriptyline Clearance

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

Examples 7.1-7.6 - Gemfibrozil Clearance

Examples 7.1-7.6 - Gemfibrozil Clearance

Example 7.8 - TB/Thalidomide in HIV

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

Example 7.1 - Gemfibrozil Scatterplot

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)

Example 8.1 - Navelbine Toxicity

Omnibus test for all regression coefficients

(like F in linear regression)

Example 8.2 - CHD, BP, Cholesterol

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)

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

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

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

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

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

Example 9.3 - 6MP vs Placebo

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