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

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Springer Science & Business Media, Jun 28, 2012 - Medical - 78 pages

The first part of this title contained all statistical tests relevant to starting clinical investigations, and included tests for continuous and binary data, power, sample size, multiple testing, variability, confounding, interaction, and reliability. The current part 2 of this title reviews methods for handling missing data, manipulated data, multiple confounders, predictions beyond observation, uncertainty of diagnostic tests, and the problems of outliers. Also robust tests, non-linear modeling , goodness of fit testing, Bhatacharya models, item response modeling, superiority testing, variability testing, binary partitioning for CART (classification and regression tree) methods, meta-analysis, and simple tests for incident analysis and unexpected observations at the workplace and reviewed.

Each test method is reported together with (1) a data example from practice, (2) all steps to be taken using a scientific pocket calculator, and (3) the main results and their interpretation. Although several of the described methods can also be carried out with the help of statistical software, the latter procedure will be considerably slower.

Both part 1 and 2 of this title consist of a minimum of text and this will enhance the process of mastering the methods. Yet the authors recommend that for a better understanding of the test procedures the books be used together with the same authors' textbook "Statistics Applied to Clinical Studies" 5th edition edited 2012, by Springer Dordrecht Netherlands. More complex data files like data files with multiple treatment modalities or multiple predictor variables can not be analyzed with a pocket calculator. We recommend that the small books "SPSS for starters", Part 1 and 2 (Springer, Dordrecht, 2010, and 2012) from the same authors be used as a complementary help for the readers' benefit.


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1 Introduction
2 Basic LogarithmLogarithm for a Better Understanding of Statistical MethodsBetter Understanding of Statistical Methods
3 Missing Data Imputation
4 Assessing Manipulated Data
5 Propensity Scores and Propensity Score MatchingMatching for Assessing Multiple ConfoundersAssessing Multiple Confounders
6 Markov ModelingMarkov Modeling for Predicting Outside the Range of Observations
7 Uncertainty in the Evaluation of Diagnostic Tests
8 Robust Tests for Imperfect Data
13 Item Response Modeling Instead of Classical Linear Analysis of Questionnaires
14 Superiority Testing Instead of Null Hypothesis Testing
15 Variability Analysis With the Bartletts Test
16 Binary Partitioning for CART Classification and Regression Tree Methods
17 MetaAnalysis of Continuous Data
18 MetaAnalysis of Binary Data
19 Physicians Daily Life and the Scientific Method
20 Incident Analysis and the Scientific Method

9 NonLinear Modeling on a Pocket Calculator
10 Fuzzy Modeling for Imprecise and Incomplete Dataincomplete data
11 Goodness of Fit Tests for Normal and Cumulatively Normal Datacumulatively normal data
12 Bhattacharya Modeling for Unmasking Hidden Gaussian Curves
Final Remarks

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