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# Understanding Clinical Statistics - HEA00005M

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• Department: Health Sciences
• Credit value: 10 credits
• Credit level: M
• Academic year of delivery: 2022-23

## Module will run

Occurrence Teaching period
B Spring Term 2022-23

## Module aims

To equip students with the necessary skills and knowledge to allow interpretation and critical understanding of analysis of data. The module will focus on the interpretation and correctness of statistics in published healthcare research.

## Module learning outcomes

1. Students should understand the principles of the statistical methods described, particularly their appropriate use and their limitations.
2. Students should be able to read papers of the type published in the British Medical Journal, understanding the statistical methods employed, their rationale and interpretation, and comment on their appropriateness.

## Module content

Please note that this is a distance learning module with all materials delivered online with no formal face-to-face sessions. Tutors will be available for support throughout the module. You will be required to be available on the specified date for the assessment.

Session 1: Descriptive statistics: Type of data, frequency, distribution, histograms and other frequency graphs, symmetry and skewness, median and other quantiles, mean, range, inter-quantile ranges, variance and standard deviation

Session 2: Estimation, standard error and confidence intervals: Normal distribution, sampling variation and sampling distributions, standard error and confidence intervals

Session 3: Significance tests: Sign test as an example, principles of significance tests, hypotheses, types of error, presenting P values, multiple testing, one- and two-sided tests

Session 4: Comparing means: Large sample Normal methods, two sample t method, checking assumptions, Normal plot, deviations from assumptions, paired t methods, checking assumptions, deviations from assumptions, analysis of variance, checking assumptions, deviations from assumptions and comparison of means after anova.

Session 5: Transformations: Need for transformations, frequently used transformation, logarithms, logarithmic scales, interpreting transformed data in a single sample, choosing transformation when comparing samples and interpreting transformed data, transformations for paired data, data which cannot be transformed, are transformations a valid approach?

Session 6: Categorical data: Chi-squared and Fisher’s tests, Yates’ correction, chi-squared test for trend, relative risk, odds ratios and number needed to treat.

Session 7: Correlation and regression: Correlation coefficients, regression lines, simple and multiple regression, linear regression, logistic regression, interactions and minimum samples sizes for regression.

Session 8: Survival data: Time to event data and censoring, Kaplan Meier estimates and survival curves, logrank test and Cox regression.

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## Module feedback

Students are provided with collective exam feedback relating to their cohort, within the timescale specified in the programme handbook.