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" Medical statistics from scratch : "
David Bowers.
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BL
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| Record Number
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840535
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| Main Entry
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Bowers, David,1938-
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| Title & Author
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Medical statistics from scratch : : an introduction for health professionals /\ David Bowers.
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| Edition Statement
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Fourth edition.
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| Publication Statement
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Hoboken NJ :: WileyBlackwell,, 2020.
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| Page. NO
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1 online resource (xx, 467 pages) :: illustrations
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| ISBN
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1119523885
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: 1119523923
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: 111952394X
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: 9781119523888
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: 9781119523925
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: 9781119523949
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9781119523888
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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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.
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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.
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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)
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| Abstract
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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.
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| Subject
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Medical statistics.
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Medicine-- Research-- Statistical methods.
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| Subject
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Medical statistics.
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| Subject
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MEDICAL-- Biostatistics.
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| Subject
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Medicine-- Research-- Statistical methods.
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| Subject
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Biometry.
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| Subject
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Statistics as Topic.
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| Dewey Classification
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610.72/7
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| LC Classification
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RA409
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| NLM classification
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WA 950
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