Preface.Abbreviations.1 Basic ideas in clinical trial design.1.1 Historical perspective.1.2 Control groups.1.
3 Placebos and blinding.1.4 Randomisation.1.4.1 Unrestricted randomisation.1.4.
2 Block randomisation.1.4.3 Unequal randomisation.1.4.4 Stratified randomisation.1.
4.5 Central randomisation.1.4.6 Dynamic allocation and minimisation.1.4.7 Cluster randomisation.
1.5 Bias and precision.1.6 Between- and within-patient designs.1.7 Cross-over trials.1.8 Signal and noise.
1.8.1 Signal.1.8.2 Noise.1.8.
3 Signal-to-noise ratio.1.9 Confirmatory and exploratory trials.1.10 Superiority, equivalence and non-inferiority trials.1.11 Data types.1.
12 Choice of endpoint.1.12.1 Primary variables.1.12.2 Secondary variables.1.
12.3 Surrogate variables.1.12.4 Global assessment variables.1.12.5 Composite variables.
1.12.6 Categorisation.2 Sampling and inferential statistics.2.1 Sample and population.2.2 Sample statistics and population parameters.
2.2.1 Sample and population distribution.2.2.2 Median and mean.2.2.
3 Standard deviation.2.2.4 Notation.2.3 The normal distribution.2.4 Sampling and the standard error of the mean.
2.5 Standard errors more generally.2.5.1 The standard error for the difference between two means.2.5.2 Standard errors for proportions.
2.5.3 The general setting.3 Confidence intervals and p-values.3.1 Confidence intervals for a single mean.3.1.
1 The 95 per cent confidence interval.3.1.2 Changing the confidence coefficient.3.1.3 Changing the multiplying constant.3.
1.4 The role of the standard error.3.2 Confidence intervals for other parameters.3.2.1 Difference between two means.3.
2.2 Confidence intervals for proportions.3.2.3 General case.3.3 Hypothesis testing.3.
3.1 Interpreting the p-value.3.3.2 Calculating the p-value.3.3.3 A common process.
3.3.4 The language of statistical significance.3.3.5 One-tailed and two-tailed tests.4 Tests for simple treatment comparisons.4.
1 The unpaired t-test.4.2 The paired t-test.4.3 Interpreting the t-tests.4.4 The chi-square test for binary data.4.
4.1 Pearson chi-square.4.4.2 The link to a signal-to-noise ratio.4.5 Measures of treatment benefit.4.
5.1 Odds ratio (OR).4.5.2 Relative risk (RR).4.5.3 Relative risk reduction (RRR).
4.5.4 Number needed to treat (NNT).4.5.5 Confidence intervals.4.5.
6 Interpretation.4.6 Fisher's exact test.4.7 The chi-square test for categorical and ordinal data.4.7.1 Categorical data.
4.7.2 Ordered categorical (ordinal) data.4.7.3 Measures of treatment benefit for categorical and ordinal data.4.8 Extensions for multiple treatment groups.
4.8.1 Between-patient designs and continuous data.4.8.2 Within-patient designs and continuous data.4.8.
3 Binary, categorical and ordinal data.4.8.4 Dose ranging studies.4.8.5 Further discussion.5 Multi-centre trials.
5.1 Rationale for multi-centre trials.5.2 Comparing treatments for continuous data.5.3 Evaluating homogeneity of treatment effect.5.3.
1 Treatment-by-centre interactions.5.3.2 Quantitative and qualitative interactions.5.4 Methods for binary, categorical and ordinal data.5.5 Combining centres.
6 Adjusted analyses and analysis of covariance.6.1 Adjusting for baseline factors.6.2 Simple linear regression.'ˆ—6.3 Multiple regression.6.
4 Logistic regression.6.5 Analysis of covariance for continu.