| Description |
1 online resource (xx, 467 pages) : illustrations |
| Contents |
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 |
| Summary |
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 updated and new material added. --Book Jacket |
|
Table of Contents: First things first? : the nature of data Describing data with tables Describing data with charts Describing data from its shape Describing data with measures of location Describing data with measures of spread Confounding? : like poor(nearly) always with us Research design part I observational Research design part II experimental studies Getting the participants for your study. ways of sampling Chance would be a fine thing? : the idea of probability Risk and odds Estimating the value of a single population parameter? : the idea of confidence intervals Using confidence intervals to compare two population parameters Confidence intervals for the ratio of two population parameters Testing hypotheses about the difference between two population parameters The chi-squared test? : what, why, and how? Testing hypotheses about the ratio of two population parameters Measuring the association between two variables Measuring agreement Straight line models : linear regression Curvy models : logistic regression Measuring survival Systematic review and meta-analysis Diagnostic testing |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource, title from digital title page (viewed on July 2, 2020) |
| Subject |
Medical statistics.
|
|
Medicine -- Research -- Statistical methods
|
|
Biometry.
|
|
Statistics.
|
|
Biometry
|
|
Statistics as Topic
|
|
biometrics.
|
|
statistics.
|
|
MEDICAL -- Biostatistics.
|
|
Statistics
|
|
Biometry
|
|
Medical statistics
|
|
Medicine -- Research -- Statistical methods
|
| Form |
Electronic book
|
| LC no. |
2019015564 |
| ISBN |
9781119523925 |
|
1119523923 |
|
9781119523949 |
|
111952394X |
|
1119523885 |
|
9781119523888 |
|
