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Statistical Analysis of Clinical Data on a Pocket Calculator, Part 2

Part of the SpringerBriefs in Statistics Book Series

DOI: 10.1007/978-94-007-4704-3

ISBN: 978-94-007-4703-6

Publisher: Springer Netherlands - Springer Journals

Jun 28, 2012

1 - 10 of 20 Chapters

Available

Introduction

Cleophas, Ton J.; Zwinderman, Aeilko H.

Pages: 1 –2

[The first part of this title contained all of the statistical tests that are relevant to starting clinical investigators, and included statistical tests for continuous and binary data, as well as tests for power, sample size, multiple testing, variability, confounding, interaction, and...
Available

Basic Logarithm for a Better Understanding of Statistical Methods

Cleophas, Ton J.; Zwinderman, Aeilko H.

Pages: 3 –5

[Non-linear relationships in clinical research are often linear after logarithmic transformations. Also, logarithmic transformation normalizes skewed frequency distributions and is used for the analysis of likelihood ratios. Basic knowledge of logarithms is, therefore, convenient for a better...
Available

Missing Data Imputation

Cleophas, Ton J.; Zwinderman, Aeilko H.

Pages: 7 –10

[Missing data in clinical research data is often a real problem. As an example, a 35 patient data file of three variables consists of 3 × 35 = 105 values if the data are complete. With only five values missing (one value missing per patient) five patients will not have complete data, and are...
Available

Assessing Manipulated Data

Cleophas, Ton J.; Zwinderman, Aeilko H.

Pages: 11 –13

[Statistics is not good at detecting manipulated data, but it can assess the consistence of the data with randomness. An example is given of a cholesterol reducing trial. The results consisted of 96 risk ratios, and often a 0, 1 or 9 was observed as final digit of the odds ratios, while the...
Available

Propensity Scores and Propensity Score Matching for Assessing Multiple Confounders

Cleophas, Ton J.; Zwinderman, Aeilko H.

Pages: 15 –19

[Propensity score are ideal for assessing confounding, particularly, if multiple confounders are in a study. E.g., age and cardiovascular risk factors may not be similarly distributed in two treatment groups of a parallel-group study. Propensity score matching is used to make observational data...
Available

Markov Modeling for Predicting Outside the Range of Observations

Cleophas, Ton J.; Zwinderman, Aeilko H.

Pages: 21 –22

[Regression models are only valid within the range of the x-values observed in the data. Markov modeling goes one step further, and aims at predicting outside the range of x-values. Like with Cox regression it assumes an exponential-pattern in the data which may be a strong assumption for complex...
Available

Uncertainty in the Evaluation of Diagnostic Tests

Cleophas, Ton J.; Zwinderman, Aeilko H.

Pages: 23 –25

[Sensitivity and specificity are measures of diagnostic accuracy of qualitative diagnostic tests, and are obtained from data samples. Just like averages they are estimates and come with certain amounts of uncertainty. The standards for reporting diagnostic accuracy (STARDS) working party...
Available

Robust Tests for Imperfect Data

Cleophas, Ton J.; Zwinderman, Aeilko H.

Pages: 27 –30

[Robust tests are tests that can handle the inclusion of some outliers into a data file without largely changing the overall test results. Frailty score-improvements after physiotherapy of 33 patients are measured in a study (second column of underneath data).]
Available

Non-Linear Modeling on a Pocket Calculator

Cleophas, Ton J.; Zwinderman, Aeilko H.

Pages: 31 –33

[Non-linear relationships in clinical research are often linear after logarithmic transformations. Odds ratios, log likelihood ratios, Markov models and many regression models are models that make use of it. An example with real data is given. We have to add that logarithmic transformation is not...
Available

Fuzzy Modeling for Imprecise and Incomplete Data

Cleophas, Ton J.; Zwinderman, Aeilko H.

Pages: 35 –40

[Fuzzy modeling is a methodology that works with partial truths: it can answer questions to which the answers are “yes” and “no” at different times or partly “yes” and “no” at the same time. It can be used to match any type of data, particularly incomplete and imprecise data, and it is able to...

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