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Medical Statistics : A Guide to SPSS, Data Analysis and Critical Appraisal.

By: Contributor(s): Material type: TextTextPublisher: Newark : John Wiley & Sons, Incorporated, 2014Copyright date: ©2020Edition: 2nd edDescription: 1 online resource (411 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781118589915
Subject(s): Genre/Form: Additional physical formats: Print version:: Medical StatisticsDDC classification:
  • 610.285/555
LOC classification:
  • R853.S7 -- .B378 2014eb
Online resources:
Contents:
Cover -- Title Page -- Copyright -- Contents -- Introduction -- Acknowledgements -- About the companion website -- Chapter 1 Creating an SPSS data file and preparing to analyse the data -- 1.1 Creating an SPSS data file -- 1.1.1 Variable View screen -- 1.1.2 Saving the SPSS file -- 1.1.3 Data View screen -- 1.2 Opening data from Excel in SPSS -- 1.3 Categorical and continuous variables -- 1.4 Classifying variables for analyses -- 1.5 Hypothesis testing and P values -- 1.6 Choosing the correct statistical test -- 1.7 Sample size requirements -- 1.8 Study handbook and data analysis plan -- 1.9 Documentation -- 1.10 Checking the data -- 1.11 Avoiding and replacing missing values -- 1.12 SPSS data management capabilities -- 1.12.1 Using subsets of variables -- 1.12.2 Recoding variables and using syntax -- 1.12.3 Dialog recall -- 1.12.4 Displaying names or labels -- 1.13 Managing SPSS output -- 1.13.1 Formatting SPSS output -- 1.13.2 Exporting output and data from SPSS -- 1.14 SPSS help commands -- 1.15 Golden rules for reporting numbers -- 1.16 Notes for critical appraisal -- References -- Chapter 2 Descriptive statistics -- 2.1 Parametric and non-parametric statistics -- 2.2 Normal distribution -- 2.3 Skewed distributions -- 2.4 Checking for normality -- 2.4.1 Using the standard deviation to check for normality -- 2.4.2 Skewness -- 2.4.3 Kurtosis -- 2.4.4 Critical values -- 2.4.5 Extreme values -- 2.4.6 Outliers -- 2.4.7 Statistical tests of normality -- 2.4.8 Histograms and plots -- 2.4.9 Kolmogorov-Smirnov test -- 2.4.10 Deciding whether a variable is normally distributed -- 2.5 Transforming skewed variables -- 2.5.1 Back transformation -- 2.6 Data analysis pathway -- 2.7 Reporting descriptive statistics -- 2.8 Checking for normality in published results -- 2.9 Notes for critical appraisal -- References.
Chapter 3 Comparing two independent samples -- 3.1 Comparing the means of two independent samples -- 3.1.1 Assumptions of a two-sample t-test -- 3.2 One- and two-sided tests of significance -- 3.3 Effect sizes -- 3.3.1 Cohen's d -- 3.3.2 Hedges's g -- 3.3.3 Glass's Δ (delta) -- 3.4 Study design -- 3.5 Influence of sample size -- 3.6 Two-sample t-test -- 3.7 Confidence intervals -- 3.7.1 Interpreting the overlap of 95% confidence intervals -- 3.8 Reporting the results from two-sample t-tests -- 3.8.1 Reporting results in a graph -- 3.8.2 Drawing a figure in SigmaPlot -- 3.9 Rank-based non-parametric tests -- 3.9.1 Mann-Whitney U test -- 3.10 Notes for critical appraisal -- References -- Chapter 4 Paired and one-sample t-tests -- 4.1 Paired t-tests -- 4.1.1 Data sheet layout -- 4.1.2 Assumptions for a paired t-test -- 4.1.3 Testing the assumptions of a paired t-test -- 4.1.4 Interpretation of the results -- 4.1.5 Calculating the effect size -- 4.2 Non-parametric test for paired data -- 4.3 Standardizing for differences in baseline measurements -- 4.4 Single-sample t-test -- 4.5 Testing for a between-group difference -- 4.5.1 Plotting the results -- 4.6 Notes for critical appraisal -- References -- Chapter 5 Analysis of variance -- 5.1 Building ANOVA and ANCOVA models -- 5.2 ANOVA models -- 5.2.1 Assumptions for ANOVA models -- 5.2.2 Within- and between-group variance -- 5.3 One-way analysis of variance -- 5.3.1 Sample size for a one-way ANOVA -- 5.4 Effect size for ANOVA -- 5.5 Post-hoc tests for ANOVA -- 5.5.1 Fisher's least significant difference (LSD) post-hoc test -- 5.5.2 Bonferroni post-hoc test -- 5.5.3 Duncan post-hoc test -- 5.6 Testing for a trend -- 5.7 Reporting the results of a one-way ANOVA -- 5.8 Factorial ANOVA models -- 5.8.1 Fixed factors, random factors and interactions -- 5.9 An example of a three-way ANOVA.
5.9.1 Reporting the results of a three-way ANOVA -- 5.10 Analysis of covariance (ANCOVA) -- 5.10.1 Effect size for ANCOVA -- 5.11 Testing the model assumptions of ANOVA/ANCOVA -- 5.11.1 Homogeneity of variance -- 5.11.2 Interactions -- 5.11.3 Lack of fit -- 5.11.4 Testing residuals: Unbiased and normality -- 5.11.5 Identifying multivariate outliers: leverage and discrepancy -- 5.12 Reporting the results of an ANCOVA -- 5.13 Notes for critical appraisal -- References -- Chapter 6 Analyses of longitudinal data -- 6.1 Study design -- 6.2 Sample size and power -- 6.3 Covariates -- 6.4 Assumptions of repeated measures ANOVA and mixed models -- 6.5 Repeated measures analysis of variance -- 6.5.1 Assumptions of sphericity and homogeneity -- 6.5.2 Multivariate test -- 6.5.3 Univariate test -- 6.5.4 Missing values -- 6.5.5 Data layout -- 6.5.6 Group comparisons -- 6.5.7 Advantages and disadvantages of repeated measures ANOVA -- 6.6 Linear mixed models -- 6.6.1 Covariance structures -- 6.6.2 Advantages and disadvantages of mixed models -- 6.6.3 Data layout -- 6.6.4 Obtaining a plot -- 6.6.5 Building a mixed model -- 6.6.6 Reporting the results of a linear mixed model -- 6.6.7 Comparison of results: Repeated measures ANOVA and mixed model -- 6.7 Notes for critical appraisal -- References -- Chapter 7 Correlation and regression -- 7.1 Correlation coefficients -- 7.1.1 Types of correlation coefficients -- 7.1.2 Obtaining correlations in SPSS -- 7.1.3 Effect size for correlations -- 7.1.4 Influence of the range of the variable -- 7.1.5 Reporting correlation coefficients -- 7.2 Regression models -- 7.2.1 Relationship between regression and ANCOVA -- 7.2.2 The regression equation -- 7.2.3 Assumptions for regression -- 7.2.4 R value and effect size -- 7.2.5 Sample size required -- 7.2.6 Generalizability of regression -- 7.2.7 Plotting a regression line.
7.2.8 Reporting a simple linear regression -- 7.3 Multiple linear regression -- 7.3.1 Building a multiple regression model -- 7.3.2 Methods of multivariate modelling -- 7.3.3 Sample size considerations -- 7.3.4 Multicollinearity -- 7.3.5 Multiple linear regression: Testing for group differences -- 7.3.6 Plotting a regression line with one categorical explanatory variables -- 7.3.7 Regression models with two explanatory categorical variables -- 7.3.8 Plotting regression lines with two explanatory categorical variables -- 7.3.9 Including multi-level categorical variables -- 7.3.10 Dummy variables -- 7.3.11 Multiple linear regression with two continuous variables and two categorical variables -- 7.4 Interactions -- 7.4.1 Identifying interactions -- 7.4.2 Including interactions in the model -- 7.5 Residuals -- 7.6 Outliers and remote points -- 7.7 Validating the model -- 7.8 Reporting a multiple linear regression -- 7.9 Non-linear regression -- 7.10 Centering -- 7.11 Notes for critical appraisal -- References -- Chapter 8 Rates and proportions -- 8.1 Summarizing categorical variables -- 8.2 Describing baseline characteristics -- 8.3 Incidence and prevalence -- 8.4 Chi-square tests -- 8.4.1 Assumptions -- 8.4.2 Which chi-square test and P value to report? -- 8.4.3 Calculating chi-square values -- 8.4.4 Sample size requirements -- 8.4.5 Confidence intervals -- 8.4.6 Creating a figure using SigmaPlot -- 8.5 2x3 Chi-square tables -- 8.6 Cells with small numbers -- 8.7 Exact chi square test -- 8.8 Number of cells that can be tested -- 8.9 Reporting chi-square tests and proportions -- 8.9.1 Differences in percentages -- 8.10 Large contingency tables -- 8.11 Categorizing continuous variables -- 8.12 Chi-square trend test for ordered variables -- 8.12.1 Reporting the results -- 8.13 Number needed to treat (NNT) -- 8.13.1 Calculating NNT.
8.13.2 How to report NNT -- 8.14 Paired categorical variables: McNemar's chi-square test -- 8.14.1 Calculating the change in proportion -- 8.14.2 Reporting the results of paired data -- 8.15 Notes for critical appraisal -- References -- Chapter 9 Risk statistics -- 9.1 Risk statistics -- 9.2 Study design -- 9.3 Odds ratio -- 9.3.1 Assumptions -- 9.3.2 Calculating odds ratio -- 9.3.3 Coding -- 9.3.4 Interpreting the odds ratio -- 9.3.5 Reporting odds ratios -- 9.4 Protective odds ratios -- 9.4.1 Changing the direction of risk statistics -- 9.5 Adjusted odds ratios -- 9.5.1 Binary logistic regression -- 9.5.2 Assumptions of logistic regression -- 9.5.3 Study design and sample size -- 9.5.4 Model building -- 9.5.5 Assessing the model and predictors -- 9.5.6 Interpretation of confounding effects -- 9.5.7 Reporting adjusted odds ratios -- 9.5.8 Plotting the results in a figure -- 9.6 Relative risk -- 9.6.1 Assumptions -- 9.6.2 Calculating relative risk -- 9.6.3 Interpreting the relative risk -- 9.6.4 Requesting relative risk statistics using SPSS -- 9.7 Number needed to be exposed for one additional person to be harmed (NNEH) -- 9.8 Notes for critical appraisal -- References -- Chapter 10 Tests of reliability and agreement -- 10.1 Reliability and agreement -- 10.1.1 Reliability -- 10.1.2 Agreement -- 10.1.3 Study design -- 10.2 Kappa statistic -- 10.2.1 Sample size -- 10.2.2 Reporting kappa results -- 10.3 Reliability of continuous measurements -- 10.4 Intra-class correlation -- 10.4.1 Different types of intra-class correlation -- 10.4.2 Intra-class correlation notation -- 10.4.3 Reporting the results of ICC -- 10.5 Measures of agreement -- 10.5.1 Limits of agreement -- 10.5.2 Differences-vs-means plot -- 10.6 Notes for critical appraisal -- References -- Chapter 11 Diagnostic statistics -- 11.1 Coding for diagnostic statistics.
11.2 Positive and negative predictive values.
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Cover -- Title Page -- Copyright -- Contents -- Introduction -- Acknowledgements -- About the companion website -- Chapter 1 Creating an SPSS data file and preparing to analyse the data -- 1.1 Creating an SPSS data file -- 1.1.1 Variable View screen -- 1.1.2 Saving the SPSS file -- 1.1.3 Data View screen -- 1.2 Opening data from Excel in SPSS -- 1.3 Categorical and continuous variables -- 1.4 Classifying variables for analyses -- 1.5 Hypothesis testing and P values -- 1.6 Choosing the correct statistical test -- 1.7 Sample size requirements -- 1.8 Study handbook and data analysis plan -- 1.9 Documentation -- 1.10 Checking the data -- 1.11 Avoiding and replacing missing values -- 1.12 SPSS data management capabilities -- 1.12.1 Using subsets of variables -- 1.12.2 Recoding variables and using syntax -- 1.12.3 Dialog recall -- 1.12.4 Displaying names or labels -- 1.13 Managing SPSS output -- 1.13.1 Formatting SPSS output -- 1.13.2 Exporting output and data from SPSS -- 1.14 SPSS help commands -- 1.15 Golden rules for reporting numbers -- 1.16 Notes for critical appraisal -- References -- Chapter 2 Descriptive statistics -- 2.1 Parametric and non-parametric statistics -- 2.2 Normal distribution -- 2.3 Skewed distributions -- 2.4 Checking for normality -- 2.4.1 Using the standard deviation to check for normality -- 2.4.2 Skewness -- 2.4.3 Kurtosis -- 2.4.4 Critical values -- 2.4.5 Extreme values -- 2.4.6 Outliers -- 2.4.7 Statistical tests of normality -- 2.4.8 Histograms and plots -- 2.4.9 Kolmogorov-Smirnov test -- 2.4.10 Deciding whether a variable is normally distributed -- 2.5 Transforming skewed variables -- 2.5.1 Back transformation -- 2.6 Data analysis pathway -- 2.7 Reporting descriptive statistics -- 2.8 Checking for normality in published results -- 2.9 Notes for critical appraisal -- References.

Chapter 3 Comparing two independent samples -- 3.1 Comparing the means of two independent samples -- 3.1.1 Assumptions of a two-sample t-test -- 3.2 One- and two-sided tests of significance -- 3.3 Effect sizes -- 3.3.1 Cohen's d -- 3.3.2 Hedges's g -- 3.3.3 Glass's Δ (delta) -- 3.4 Study design -- 3.5 Influence of sample size -- 3.6 Two-sample t-test -- 3.7 Confidence intervals -- 3.7.1 Interpreting the overlap of 95% confidence intervals -- 3.8 Reporting the results from two-sample t-tests -- 3.8.1 Reporting results in a graph -- 3.8.2 Drawing a figure in SigmaPlot -- 3.9 Rank-based non-parametric tests -- 3.9.1 Mann-Whitney U test -- 3.10 Notes for critical appraisal -- References -- Chapter 4 Paired and one-sample t-tests -- 4.1 Paired t-tests -- 4.1.1 Data sheet layout -- 4.1.2 Assumptions for a paired t-test -- 4.1.3 Testing the assumptions of a paired t-test -- 4.1.4 Interpretation of the results -- 4.1.5 Calculating the effect size -- 4.2 Non-parametric test for paired data -- 4.3 Standardizing for differences in baseline measurements -- 4.4 Single-sample t-test -- 4.5 Testing for a between-group difference -- 4.5.1 Plotting the results -- 4.6 Notes for critical appraisal -- References -- Chapter 5 Analysis of variance -- 5.1 Building ANOVA and ANCOVA models -- 5.2 ANOVA models -- 5.2.1 Assumptions for ANOVA models -- 5.2.2 Within- and between-group variance -- 5.3 One-way analysis of variance -- 5.3.1 Sample size for a one-way ANOVA -- 5.4 Effect size for ANOVA -- 5.5 Post-hoc tests for ANOVA -- 5.5.1 Fisher's least significant difference (LSD) post-hoc test -- 5.5.2 Bonferroni post-hoc test -- 5.5.3 Duncan post-hoc test -- 5.6 Testing for a trend -- 5.7 Reporting the results of a one-way ANOVA -- 5.8 Factorial ANOVA models -- 5.8.1 Fixed factors, random factors and interactions -- 5.9 An example of a three-way ANOVA.

5.9.1 Reporting the results of a three-way ANOVA -- 5.10 Analysis of covariance (ANCOVA) -- 5.10.1 Effect size for ANCOVA -- 5.11 Testing the model assumptions of ANOVA/ANCOVA -- 5.11.1 Homogeneity of variance -- 5.11.2 Interactions -- 5.11.3 Lack of fit -- 5.11.4 Testing residuals: Unbiased and normality -- 5.11.5 Identifying multivariate outliers: leverage and discrepancy -- 5.12 Reporting the results of an ANCOVA -- 5.13 Notes for critical appraisal -- References -- Chapter 6 Analyses of longitudinal data -- 6.1 Study design -- 6.2 Sample size and power -- 6.3 Covariates -- 6.4 Assumptions of repeated measures ANOVA and mixed models -- 6.5 Repeated measures analysis of variance -- 6.5.1 Assumptions of sphericity and homogeneity -- 6.5.2 Multivariate test -- 6.5.3 Univariate test -- 6.5.4 Missing values -- 6.5.5 Data layout -- 6.5.6 Group comparisons -- 6.5.7 Advantages and disadvantages of repeated measures ANOVA -- 6.6 Linear mixed models -- 6.6.1 Covariance structures -- 6.6.2 Advantages and disadvantages of mixed models -- 6.6.3 Data layout -- 6.6.4 Obtaining a plot -- 6.6.5 Building a mixed model -- 6.6.6 Reporting the results of a linear mixed model -- 6.6.7 Comparison of results: Repeated measures ANOVA and mixed model -- 6.7 Notes for critical appraisal -- References -- Chapter 7 Correlation and regression -- 7.1 Correlation coefficients -- 7.1.1 Types of correlation coefficients -- 7.1.2 Obtaining correlations in SPSS -- 7.1.3 Effect size for correlations -- 7.1.4 Influence of the range of the variable -- 7.1.5 Reporting correlation coefficients -- 7.2 Regression models -- 7.2.1 Relationship between regression and ANCOVA -- 7.2.2 The regression equation -- 7.2.3 Assumptions for regression -- 7.2.4 R value and effect size -- 7.2.5 Sample size required -- 7.2.6 Generalizability of regression -- 7.2.7 Plotting a regression line.

7.2.8 Reporting a simple linear regression -- 7.3 Multiple linear regression -- 7.3.1 Building a multiple regression model -- 7.3.2 Methods of multivariate modelling -- 7.3.3 Sample size considerations -- 7.3.4 Multicollinearity -- 7.3.5 Multiple linear regression: Testing for group differences -- 7.3.6 Plotting a regression line with one categorical explanatory variables -- 7.3.7 Regression models with two explanatory categorical variables -- 7.3.8 Plotting regression lines with two explanatory categorical variables -- 7.3.9 Including multi-level categorical variables -- 7.3.10 Dummy variables -- 7.3.11 Multiple linear regression with two continuous variables and two categorical variables -- 7.4 Interactions -- 7.4.1 Identifying interactions -- 7.4.2 Including interactions in the model -- 7.5 Residuals -- 7.6 Outliers and remote points -- 7.7 Validating the model -- 7.8 Reporting a multiple linear regression -- 7.9 Non-linear regression -- 7.10 Centering -- 7.11 Notes for critical appraisal -- References -- Chapter 8 Rates and proportions -- 8.1 Summarizing categorical variables -- 8.2 Describing baseline characteristics -- 8.3 Incidence and prevalence -- 8.4 Chi-square tests -- 8.4.1 Assumptions -- 8.4.2 Which chi-square test and P value to report? -- 8.4.3 Calculating chi-square values -- 8.4.4 Sample size requirements -- 8.4.5 Confidence intervals -- 8.4.6 Creating a figure using SigmaPlot -- 8.5 2x3 Chi-square tables -- 8.6 Cells with small numbers -- 8.7 Exact chi square test -- 8.8 Number of cells that can be tested -- 8.9 Reporting chi-square tests and proportions -- 8.9.1 Differences in percentages -- 8.10 Large contingency tables -- 8.11 Categorizing continuous variables -- 8.12 Chi-square trend test for ordered variables -- 8.12.1 Reporting the results -- 8.13 Number needed to treat (NNT) -- 8.13.1 Calculating NNT.

8.13.2 How to report NNT -- 8.14 Paired categorical variables: McNemar's chi-square test -- 8.14.1 Calculating the change in proportion -- 8.14.2 Reporting the results of paired data -- 8.15 Notes for critical appraisal -- References -- Chapter 9 Risk statistics -- 9.1 Risk statistics -- 9.2 Study design -- 9.3 Odds ratio -- 9.3.1 Assumptions -- 9.3.2 Calculating odds ratio -- 9.3.3 Coding -- 9.3.4 Interpreting the odds ratio -- 9.3.5 Reporting odds ratios -- 9.4 Protective odds ratios -- 9.4.1 Changing the direction of risk statistics -- 9.5 Adjusted odds ratios -- 9.5.1 Binary logistic regression -- 9.5.2 Assumptions of logistic regression -- 9.5.3 Study design and sample size -- 9.5.4 Model building -- 9.5.5 Assessing the model and predictors -- 9.5.6 Interpretation of confounding effects -- 9.5.7 Reporting adjusted odds ratios -- 9.5.8 Plotting the results in a figure -- 9.6 Relative risk -- 9.6.1 Assumptions -- 9.6.2 Calculating relative risk -- 9.6.3 Interpreting the relative risk -- 9.6.4 Requesting relative risk statistics using SPSS -- 9.7 Number needed to be exposed for one additional person to be harmed (NNEH) -- 9.8 Notes for critical appraisal -- References -- Chapter 10 Tests of reliability and agreement -- 10.1 Reliability and agreement -- 10.1.1 Reliability -- 10.1.2 Agreement -- 10.1.3 Study design -- 10.2 Kappa statistic -- 10.2.1 Sample size -- 10.2.2 Reporting kappa results -- 10.3 Reliability of continuous measurements -- 10.4 Intra-class correlation -- 10.4.1 Different types of intra-class correlation -- 10.4.2 Intra-class correlation notation -- 10.4.3 Reporting the results of ICC -- 10.5 Measures of agreement -- 10.5.1 Limits of agreement -- 10.5.2 Differences-vs-means plot -- 10.6 Notes for critical appraisal -- References -- Chapter 11 Diagnostic statistics -- 11.1 Coding for diagnostic statistics.

11.2 Positive and negative predictive values.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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