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Medical statistics from scratch : an introduction for health professionals /
Medical Statistics from Scratch is the ideal learning partner for all medical students and health professionals needing accessible introduction, or a friendly refresher, to the fundamentals of medical statistics. This new fourth, edition been completely revised, the examples from current research up...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken NJ :
WileyBlackwell,
2020.
|
| Edición: | Fourth edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Machine generated contents note: 1. First things first
- the nature of data
- Variables and data
- Where are we going ...?
- The good, the bad, and the ugly
- types of variables
- Categorical data
- Nominal categorical data
- Ordinal categorical data
- Metric data
- Discrete metric data
- Continuous metric data
- How can I tell what type of variable I am dealing with?
- The baseline table
- 2. Describing data with tables
- Descriptive statistics. What can we do with raw data?
- Frequency tables
- nominal data
- The frequency distribution
- Relative frequency
- Frequency tables
- ordinal data
- Frequency tables
- metric data
- Frequency tables with discrete metric data
- Cumulative frequency
- Frequency tables with continuous metric data
- grouping the raw data
- Open-ended groups
- Cross-tabulation
- contingency tables
- Ranking data
- 3. Every picture tells a story
- describing data with charts
- Picture it!
- Charting nominal and ordinal data
- The pie chart
- The simple bar chart
- The clustered bar chart
- The stacked bar chart
- Charting discrete metric data
- Charting continuous metric data
- The histogram
- The box (and whisker) plot
- Charting cumulative data
- The cumulative frequency curve with discrete metric data
- The cumulative frequency curve with continuous metric data
- Charting time-based data
- the time series chart
- The scatterplot
- The bubbleplot
- 4. Describing data from its shape
- The shape of things to come
- Skewness and kurtosis as measures of shape
- Kurtosis
- Symmetric or mound-shaped distributions
- Normalness
- the Normal distribution
- Bimodal distributions
- Determining skew from a box plot
- 5. Measures of location
- Numbers R us
- Numbers, percentages, and proportions
- Preamble
- Numbers, percentages, and proportions
- Handling percentages
- for those of us who might need a reminder
- Summary measures of location
- The mode
- The median
- The mean
- Percentiles
- Calculating a percentile value
- What is the most appropriate measure of location?
- 6. Measures of spread
- Numbers R us
- (again)
- Preamble
- The range
- The interquartile range (IQR)
- Estimating the median and interquartile range from the cumulative frequency curve
- The boxplot (also known as the box and whisker plot)
- Standard deviation
- Standard deviation and the Normal distribution
- Testing for Normality
- Using SPSS
- Using Minitab
- Transforming data
- 7. Incidence, prevalence, and standardisation
- Preamble
- The incidence rate and the incidence rate ratio (IRR)
- The incidence rate ratio
- Prevalence
- A couple of difficulties with measuring incidence and prevalence
- Some other useful rates
- Crude mortality rate
- Case fatality rate
- Crude maternal mortality rate
- Crude birth rate
- Attack rate
- Age-specific mortality rate
- Standardisation
- the age-standardised mortality rate
- The direct method
- The standard population and the comparative mortality ratio (CMR)
- The indirect method
- The standardised mortality rate
- 8. Confounding
- like the poor, (nearly) always with us
- Preamble
- What is confounding?
- Confounding by indication
- Residual confounding
- Detecting confounding
- Dealing with confounding
- if confounding is such a problem, what can we do about it?
- Using restriction
- Using matching
- Frequency matching
- One-to-one matching
- Using stratification
- Using adjustment
- Using randomisation
- 9. Research design
- Part I: Observational study designs
- Preamble
- Hey ho! Hey ho! It's off to work we go
- Types of study
- Observational studies
- Case reports
- Case series studies
- Cross-sectional studies
- Descriptive cross-sectional studies
- Confounding in descriptive cross-sectional studies
- Analytic cross-sectional studies
- Confounding in analytic cross-sectional studies
- From here to eternity
- cohort studies
- Confounding in the cohort study design
- Back to the future
- case-control studies
- Confounding in the case-control study design
- Another example of a case-control study
- Comparing cohort and case-control designs
- Ecological studies
- The ecological fallacy
- 10. Research design
- Part II: getting stuck in
- experimental studies
- Clinical trials
- Randomisation and the randomised controlled trial (RCT)
- Block randomisation
- Stratification
- Blinding
- The crossover RCT
- Selection of participants for an RCT
- Intention to treat analysis (ITT)
- 11. Getting the participants for your study: ways of sampling
- From populations to samples
- statistical inference
- Collecting the data
- types of sample
- The simple random sample and its offspring
- The systematic random sample
- The stratified random sample
- The cluster sample
- Consecutive and convenience samples
- How many participants should we have? Sample size
- Inclusion and exclusion criteria
- Getting the data
- V Chance Would Be a Fine Thing
- 12. The idea of probability
- Preamble
- Calculating probability
- proportional frequency
- Two useful rules for simple probability
- Rule 1. The multiplication rule for independent events
- Rule 2. The addition rule for mutually exclusive events
- Conditional and Bayesian statistics
- Probability distributions
- Discrete versus continuous probability distributions
- The binomial probability distribution
- The Poisson probability distribution
- The Normal probability distribution
- 13. Risk and odds
- Absolute risk and the absolute risk reduction (ARR)
- The risk ratio
- The reduction in the risk ratio (or relative risk reduction (RRR))
- A general formula for the risk ratio
- Reference value
- Number needed to treat (NNT)
- 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 link between probability and odds
- The odds ratio
- Confounding with the odds ratio
- Approximating the risk ratio from the odds ratio
- 14. 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
- 15. Using confidence intervals to compare two population parameters
- What's the difference?
- Comparing two independent population means
- An example using birthweights
- Assessing the evidence using the confidence interval
- Comparing two paired population means
- Within-subject and between-subject variations
- 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
- 16. Confidence intervals for the ratio of two population parameters
- Getting a confidence interval for the ratio of two independent population means
- Confidence interval for a population risk ratio
- Confidence intervals for a population odds ratio
- Confidence intervals for hazard ratios
- 17.
- Testing hypotheses about the difference between two population parameters
- Answering the question
- The hypothesis
- The null 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
- Correction for multiple comparisons
- The Bonferroni correction for multiple testing
- Interpreting computer output for the Mann-Whitney test
- With Minitab
- With SPSS
- Two matched medians
- the Wilcoxon signed-ranks test
- Confidence intervals versus hypothesis testing
- What could possibly go wrong?
- Types of error
- The power of a test
- Maximising power
- calculating sample size
- Rule of thumb 1. Comparing the means of two independent populations (metric data)
- Rule of thumb 2. Comparing the proportions of two independent populations (binary data)
- 18. 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 for the equality of proportions
- Calculating the chi-squared statistic
- Using the chi-squared statistic
- 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
- 19. 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
- 20. Measuring the association between two variables
- Preamble
- plotting data
- Association
- The scatterplot
- The correlation coefficient
- Pearson's correlation coefficient
- Is the correlation coefficient statistically significant in the population?
- Spearman's rank correlation coefficient
- 21. Measuring agreement
- To agree or not agree: that is the question
- Cohen's kappa (x)
- Note continued: Some shortcomings of kappa
- Weighted kappa
- Measuring the agreement between two metric continuous variables, the Bland-Altmann plot
- 22. Straight line models: linear regression
- Health warning!
- Relationship and association
- A causal relationship
- explaining variation
- Refresher
- finding the equation of a straight line from a graph
- The linear regression model
- First, is the relationship linear?
- Estimating the regression parameters
- the method of ordinary least squares (OLS)
- Basic assumptions of the ordinary least squares procedure
- Back to the example
- is the relationship statistically significant?
- Using SPSS to regress birthweight on mother's weight
- Using Minitab
- Interpreting the regression coefficients
- Goodness-of-fit, R2
- Multiple linear regression
- Adjusted goodness-of-fit: R2
- Including nominal covariates 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
- Diagnostics
- checking the basic assumptions of the multiple linear regression model
- Analysis of variance
- 23. Curvy models: logistic regression
- A second health warning!
- The binary outcome variable
- Finding an appropriate model when the outcome variable is binary
- The logistic regression model
- Estimating the parameter values
- Interpreting the regression coefficients
- Have we got a significant result? statistical inference in the logistic regression model
- The Odds Ratio
- The multiple logistic regression model
- Building the model
- Goodness-of-fit
- 24. Counting models: Poisson regression
- Preamble
- Poisson regression
- The Poisson regression equation
- Estimating pi and 13, with the estimators b0 and b1
- Interpreting the estimated coefficients of a Poisson regression, b0 and b1
- Model building
- variable selection
- Goodness-of-fit
- Zero-inflated Poisson regression
- Negative binomial regression
- Zero-inflated negative binomial regression
- 25. Measuring survival
- Preamble
- Censored data
- A simple example of survival in a single group
- Calculating survival probabilities and the proportion surviving: the Kaplan-Meier table
- The Kaplan-Meier curve
- Determining median survival time
- Comparing survival with two groups
- The log-rank test
- An example of the log-rank test in practice
- The hazard ratio
- The proportional hazards (Cox's) regression model
- introduction
- The proportional hazards (Cox's) regression model
- the detail
- Checking the assumptions of the proportional hazards model
- An example of proportional hazards regression
- 26. Systematic review and meta-analysis
- Introduction
- Systematic review
- The forest plot
- Publication and other biases
- The funnel. plot
- Significance tests for bias
- Begg's and Egger's tests
- Combining the studies: meta-analysis
- The problem of heterogeneity
- the Q and I2 tests
- 27. Diagnostic testing
- Preamble
- The measures
- sensitivity and specificity
- The positive prediction and negative prediction values (PPV and NPV)
- The sensitivity-specificity trade-off
- Using the ROC curve to find the optimal sensitivity versus specificity trade-off
- 28. Missing data
- The missing data problem
- Types of missing data
- Missing completely at random (MCAR)
- Missing at Random (MAR)
- Missing not at random (MNAR)
- Consequences of missing data
- Dealing with missing data
- Do nothing
- the wing and prayer approach
- List-wise deletion
- Pair-wise deletion
- Imputation methods
- simple imputation
- Replacement by the Mean
- Last observation carried forward
- Regression-based imputation
- Multiple imputation
- Full Information Maximum Likelihood (FIML) and other methods.