Ton J. Cleophas - SPSS for Starters and 2nd Levelers

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Part I
Continuous Outcome Data

Springer International Publishing Switzerland 2016

Ton J. Cleophas and Aeilko H. Zwinderman SPSS for Starters and 2nd Levelers 10.1007/978-3-319-20600-4_1

1. One-Sample Continuous Data (One-Sample T-Test, One-Sample Wilcoxon Signed Rank Test, 10 Patients)

Ton J. Cleophas 1, 2 and Aeilko H. Zwinderman 2, 3

(1)

Department Medicine, Albert Schweitzer Hospital, Dordrecht, The Netherlands

(2)

European College Pharmaceutical Medicine, Lyon, France

(3)

Department Biostatistics, Academic Medical Center, Amsterdam, The Netherlands

General Purpose

Because biological processes are full of variations, statistical tests give no certainties, only chances. Particularly, the chance that a prior hypothesis is true. What hypothesis? Often, a nullhypothesis, which means no difference in your data from a zero effect. A zero effect indicates that a factor, like an intervention or medical treatment does not have any effect. The one sample t-test is adequate for assessment.

Schematic Overview of Type of Data File

Primary Scientific Question

Is the mean outcome value significantly different from the value zero.

Data Example

The reduction of mean blood pressure after treatment is measured in a sample of patients. We wish to know whether the mean reduction is significantly larger than zero.

• Outcome
• outcome=decrease of mean blood pressure after treatment (mm Hg)

Analysis: One-Sample T-Test

The data file is in extras.springer.com, and is entitled chapter1onesamplecontinuous. Open it in SPSS . For analysis the module Compare Means is required. It consists of the following statistical models:

• Means,
• One-Sample T-Test ,
• Independent-Samples T-Test,
• Paired-Samples T-Test and
• One Way ANOVA

Command:

• Analyze....Compare Means ....One-Sample T-Test ....Test Variable(s): enter "mean blood pressure reduction"....click OK.

In the output sheets is the underneath table.

One-sample test

	Test value = 0
t	df	Sig. (2-tailed)	Mean difference	95 % confidence interval of the difference
Lower	Upper
VAR00001	2,429		,038	1,70000	,1165	3,2835

It shows that the t-value equals 2,429, which means that with 101=9 degrees of freedom a significant effect is obtained at p=0,038. The reduction of mean blood pressure has an average value of 1,7000 mm Hg, and this average reduction is significantly larger than a reduction of 0,00 mm Hg.

Alternative Analysis: One-Sample Wilcoxon Signed Rank Test

If the data do not follow a Gaussian distribution, this method will be required, but with Gaussian distributions it may be applied even so.

Command:

• Analyze....Nonparametric tests....One Sample Nonparametric Test s ....click Fields ....Test Fields: enter "mean blood pressure reduction"....click Settings....click Choose Tests....mark Customize Tests....mark Compare median to hypothesized ....Hypothesized median: type "0,00"....click Run.

The underneath table is in the output sheet. The median of the mean blood pressure reductions is significantly different from zero. The treatment is, obviously, successful. The p-value is very similar to that of the above one sample t-test .

Hypotheses test summary

Asymptotic significances are displayed. The significance level is ,05

Conclusion

The significant effects indicate that the nullhypothesis of no effect can be rejected. The treatment performs better than no treatment. It may be prudent to use non-parametric test s , if normality is doubtful or can not be proven like with small data as those in the current example.

Note

The theories of null hypotheses and frequency distributions are reviewed in Statistics applied to clinical studies 5th edition, Chaps. 1 and 2, entitled Hypotheses data stratification and The analysis of efficacy data, Springer Heidelberg Germany, 2012, from the same authors.

Springer International Publishing Switzerland 2016

Ton J. Cleophas and Aeilko H. Zwinderman SPSS for Starters and 2nd Levelers 10.1007/978-3-319-20600-4_2

2. Paired Continuous Data (Paired T-Test, Wilcoxon Signed Rank Test, 10 Patients)

Ton J. Cleophas 1, 2 and Aeilko H. Zwinderman 2, 3

(1)

Department Medicine, Albert Schweitzer Hospital, Dordrecht, The Netherlands

(2)

European College Pharmaceutical Medicine, Lyon, France

(3)

Department Biostatistics, Academic Medical Center, Amsterdam, The Netherlands

General Purpose

Studies where two outcomes in one patient are compared with one another are often called crossover studies, and the observations are called paired observations.

As paired observations are usually more similar than unpaired observations, special tests are required in order to adjust for a positive correlation between the paired observations.

Schematic Overview of Type of Data File

Primary Scientific Question

Is the first outcome significantly different from second one.

Data Example

The underneath study assesses whether some sleeping pill is more efficaceous than a placcebo. The hours of sleep is the outcome value.

Outcome 1	Outcome 2
6,1	5,2
7,0	7,9
8,2	3,9
7,6	4,7
6,5	5,3
8,4	5,4
6,9	4,2
6,7	6,1
7,4	3,8
5,8	6,3

Outcome=hours of sleep after treatment

Analysis: Paired T-Test

The data file is in extras.springer.com and is entitled chapter2pairedcontinuous. Open it in SPSS. We will start with a graph of the data.

Command:

• Graphs....Bars....mark Summary separate variables....Define....Bars Represent: enter "hours of sleep [outcomeone]"....enter "hours of sleep [outcometwo]"....click Options....mark Display error bars....mark Confidence Intervals....Level (%): enter 95,0....Continue....click OK.

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