Cover -- Title Page -- Copyight -- Contents -- Preface to the 3rd Edition -- Preface to the 2nd Edition -- Preface to the 1st Edition -- Introduction -- Part I Some Fundamental Stuff -- Chapter 1 First things first - the nature of data -- Variables and data -- The good, the bad, and the ugly - types of variables -- Categorical data -- Metric data -- Part II Descriptive Statistics -- Chapter 2 Describing data with tables -- Descriptive statistics. What can we do with raw data? -- Frequency tables - nominal data -- Frequency tables - ordinal data -- Frequency tables - metric data -- Cumulative frequency -- Cross-tabulation - contingency tables -- Ranking data -- Chapter 3 Every picture tells a story - describing data with charts -- Picture it! -- Charting nominal and ordinal data -- Charting discrete metric data -- Charting continuous metric data -- Charting cumulative data -- Charting time-based data - the time series chart -- Chapter 4 Describing data from its shape -- The shape of things to come -- Negative skew -- Positive skew -- Symmetric or mound-shaped distributions -- Normal-ness - the Normal distribution -- Bimodal distributions -- Determining skew from a box plot -- Chapter 5 Numbers R us -- Preamble -- Numbers, percentages, and proportions -- Summary measures of location -- Chapter 6 Measures of spread -- Preamble -- The range -- The interquartile range (IQR) -- The boxplot (also known as the box and whisker plot) -- Standard deviation -- Standard deviation and the Normal distribution -- Transforming data -- Part III The Confounding Problem -- Chapter 7 Confounding - like the poor, (nearly) always with us -- Preamble -- What is confounding? -- Detecting confounding -- If confounding is such a problem, what can we do about it? -- Part IV Design and Data -- Chapter 8 Research design - Part I: Observational study designs.

Preamble -- Hey ho! Hey ho! It's off to work we go -- Types of study -- Observational studies -- From here to eternity - cohort studies -- Back to the future - case - control studies -- Another example of a case - control study -- Comparing cohort and case - control designs -- Ecological studies -- The ecological fallacy -- Chapter 9 Research design - Part II: Getting stuck in - experimental studies -- Clinical trials -- Randomisation -- Blinding -- The cross-over randomised controlled trial -- Selection of participants -- Intention-to-treat -- Chapter 10 Getting the participants for your study: ways of sampling -- From populations to samples - statistical inference -- Collecting the data-types of sample -- How many participants should we have? Sample size -- Inclusion and exclusion criteria -- Getting the data -- Part V Chance Would be a Fine Thing -- Chapter 11 The idea of probability -- Preamble -- Calculating probability - proportional frequency -- Two useful rules for simple probability -- Conditional and Bayesian statistics -- Probability distributions -- Chapter 12 Risk and odds -- Absolute risk and the absolute risk reduction -- The risk ratio -- Reference value -- What happens if the initial risk is small? -- Confounding with the risk ratio -- Odds -- Why you can't calculate risk in a case - control study -- The odds ratio -- Confounding with the odds ratio -- Part VI The Informed Guess - An Introduction to Confidence Intervals -- Chapter 13 Estimating the value of a single population parameter - the idea of confidence intervals -- Confidence interval estimation for a population mean -- The standard error of the mean -- How we use the standard error of the mean to calculate a confidence interval for a population mean -- Confidence interval for a population proportion.

Estimating a confidence interval for the median of a single population -- Chapter 14 Using confidence intervals to compare two population parameters -- What's the difference? -- Comparing two independent population means -- Comparing two paired population means -- Comparing two independent population proportions -- Comparing two independent population medians - the Mann-Whitney rank sums method -- Comparing two matched population medians - the Wilcoxon signed-ranks method -- Chapter 15 Confidence intervals for the ratio of two population parameters -- Using confidence intervals for ratios -- Confidence interval for a population risk ratio -- Confidence intervals for a population odds ratio -- Confidence intervals for hazard ratios -- Part VII Putting it to the Test -- Chapter 16 Testing hypotheses about the difference between two population parameters -- Answering the question -- The hypothesis -- The hypothesis testing process -- The p-value and the decision rule -- A brief summary of a few of the commonest tests -- Using the p-value to compare the means of two independent populations -- Interpreting computer hypothesis test results for the difference in two independent population means - the two-sample t test -- Output from Minitab - two-sample t test of difference in mean birthweights of babies born to White mothers and to non-White mothers -- Output from SPSS: two-sample t test of difference in mean birthweights of babies born to White mothers and to non-White mothers -- Comparing the means of two paired populations - the matched-pairs t test -- Using p-values to compare the medians of two independent populations: the Mann-Whitney rank-sums test -- How the Mann-Whitney test works -- Interpreting computer output for the Mann-Whitney test -- What could possibly go wrong? -- Types of error -- The power of a test.

Maximising power - calculating sample size -- Chapter 17 The chi-squared (x2) test - what, why, and how? -- Of all the tests in all the world - you had to walk into my hypothesis testing procedure -- Using chi-squared to test for related-ness or the equality of proportions -- Applying the chi-squared test -- Yate's correction (continuity correction) -- Fisher's exact test -- The chi-squared test with Minitab -- The chi-squared test with SPSS -- The chi-squared test for trend -- SPSS output for chi-squared trend test -- Chapter 18 Testing hypotheses about the ratio of two population parameters -- Preamble -- The chi-squared test with the risk ratio -- The chi-squared test with odds ratios -- The chi-squared test with hazard ratios -- Part VIII Becoming Acquainted -- Chapter 19 Measuring the association between two variables -- Preamble - plotting data -- Association -- The scatterplot -- The correlation coefficient -- Pearson's correlation coefficient -- An example from practice -- Spearman's rank correlation coefficient -- Chapter 20 Measuring agreement -- To agree or not agree: that is the question -- Cohen's kappa (k) -- Weighted kappa -- Measuring the agreement between two metric continuous variables -- Part IX Getting into a Relationship -- Chapter 21 Straight line models: linear regression -- Health warning! -- Relationship and association -- A causal relationship - explaining variation -- The linear regression model -- Basic assumptions of the ordinary least squares procedure -- Back to the example-is the relationship statistically significant? -- Goodness-of-fit, R2 -- Multiple linear regression -- Adjusted goodness-of-fit: R2 -- Including nominal independent variables in the regression model: design variables and coding -- Building your model. Which variables to include?.

Automated variable selection methods -- Manual variable selection methods -- Adjustment and confounding -- An example from practice -- Diagnostics - checking the basic assumptions of the multiple linear regression model -- Analysis of variance -- Chapter 22 Curvy models: Logistic regression -- A second health warning! -- The logistic regression model -- Estimating the parameter values -- The odds ratio -- Interpreting the regression coefficient -- Statistical inference in the logistic regression model -- The multiple logistic regression model -- Building the model -- Goodness-of-fit -- Poisson regression (just to say 'Hello') -- Part X Three More Chapters -- Chapter 23 Measuring survival -- Preamble -- Censored data -- Calculating survival probabilities and the proportion surviving: the Kaplan-Meier table -- The Kaplan-Meier curve -- Comparing survival with two groups -- The log-rank test -- The hazard ratio -- The proportional hazards (Cox's) regression model -- Checking the proportional hazards assumption -- Chapter 24 Systematic review and meta-analysis -- Introduction -- Systematic review -- The forest plot -- Publication and other biases -- The funnel plot -- Combining the studies: meta-analysis -- The problem of heterogeneity -- Chapter 25 Diagnostic testing -- Preamble -- The measures -- The sensitivity versus specificity trade-off: the ROC curve -- Appendix: Table of random numbers -- References -- Solutions to Exercises -- Index -- EULA.