Statistics at square one / / edited by Michael J. Campbell

• Pubblicazione: Hoboken, New Jersey ; ; Chichester : , : Wiley Blackwell, , [2021]; ©2021
• Edizione: Twelfth edition.
• Descrizione fisica: 1 online resource (303 pages)
• Disciplina: 610.727
• Soggetto topico: Medical statistics
• Soggetto genere / forma: Electronic books.
• Persona (resp. second.): CampbellMichael J. <1950->
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Contents -- Preface -- About the companion website -- Chapter 1 Understanding basic numbers -- When is a number large? -- Ratios -- Using ratios to adjust for other variables -- Proportions, percentages and odds -- Percentage difference and percentage change: importance of baseline -- Rounding proportions and percentages -- Probabilities and risks -- Prevalence and incidence rate -- Trusting numbers -- Conclusions -- Further reading -- Exercises -- References -- Chapter 2 Data display and summary -- Types of data -- Stem-and-leaf plots and dot plots -- Box-whisker plots -- Median -- Measures of variation -- Frequency tables and histograms -- Bar charts -- Further reading -- Common questions -- What is the distinction between a histogram and a bar chart? -- What are poor methods of displaying data? -- Displaying data in papers -- Exercises -- References -- Chapter 3 Summary statistics for quantitative data -- Mean -- Variance and standard deviation -- Standard deviation from ungrouped data -- Standard deviation from grouped data -- Normal distribution -- Skewness -- Between-subjects and within-subjects standard deviation -- Common questions -- When should I quote the mean and when should I quote the median to describe my data? -- When should I use a standard deviation to summarise variability? -- How can I tell if data are skewed from a table? -- When should I use the mode? -- Formula appreciation -- Reading and Displaying Summary Statistics -- Exercises -- References -- Chapter 4 Summary statistics for binary data -- Summarising one binary variable -- Summarising the relationship between two binary variables -- Relative Risks versus Odds Ratios -- Odds ratios and cross-sectional studies -- Odds ratios and case-control studies -- Example of a case-control study. ; Estimating relative risk from case-control studies -- Common questions -- When should I quote an odds ratio and when should I quote a relative risk? -- How does one choose the numerator and denominator for a relative risk? -- How should one quote relative risks? -- Should one ever quote a number needed to treat? -- Reading and displaying summary statistics -- Exercises -- References -- Chapter 5 Diagnostic and screening tests -- Diagnostic and screening tests -- Examples -- Example 1: Test for COVID-19 -- Example 2: Test for generalised anxiety disorder -- Sensitivity and Specificity -- Positive predictive value in relation to prevalence -- Likelihood ratio -- Receiver operating characteristics curves -- Further discussion on diagnostic and screening tests -- Limitations of the conventional diagnostic testing paradigm -- Reading and reporting diagnostic/screening tests -- Exercises -- References -- Chapter 6 Populations and samples -- Populations -- Samples -- Unbiasedness and precision -- Problems of bias in non-randomised samples (especially Big Data) -- Randomisation -- Variation between samples -- Standard error of the mean -- Example of standard error -- Standard error of a proportion or a percentage -- Problems with non-random samples -- Common questions -- What is an acceptable response rate from a survey? -- Given measurements on a sample, what is the difference between a standard deviation and a standard error? -- When should I use a standard deviation to describe data and when should I use a standard error? -- Important points -- Reading and reporting populations and samples -- Exercises -- References -- Chapter 7 Statements of probability and confidence intervals -- Reference ranges -- Confidence intervals -- Large sample standard error of difference between means -- Large sample confidence interval for the difference in two means. ; Standard error of difference between percentages or proportions -- Confidence interval for a difference in proportions or percentages -- Confidence interval for an odds ratio -- Confidence interval for a relative risk -- Confidence Intervals for other estimates -- Common Questions -- What is the difference between a reference range and a confidence interval? -- If I repeated a study with the same sample size, would the new results fall in the confidence interval 95% of the time? -- Reading and reporting confidence intervals -- Formula appreciation -- Exercises -- References -- Chapter 8 P values, power, type I and type II errors -- Null hypothesis and type I error -- Testing for differences of two means -- Testing for a difference in two proportions -- P value -- P values, confidence intervals and clinically important results -- Alternative hypothesis and type II error -- Other types of statistical inference -- Issues with P values -- One-sided and two-sided tests -- Tests for superiority, tests for non-inferiority and tests for equivalence -- Links with diagnostic tests -- Common questions -- Why is the P value not the probability that the null hypothesis is true? -- Why is 5% usually used as the level at which results are deemed 'significant'? -- Reading and reporting significance tests -- Exercises -- References -- Chapter 9 Tests for differences between two groups of a quantitative outcome with small samples -- Student's t test -- Confidence interval for the mean from a small sample -- Difference of sample mean from population mean (one-sample t test) -- Difference between means of two samples -- Unequal standard deviations -- Difference between means of paired samples (paired t test) -- Non-parametric or distribution-free tests -- Tests for differences in unpaired samples of non-Normally distributed data (Mann-Whitney U test). ; Tests for differences in paired samples of non-Normally distributed data (Wilcoxon test) -- Computer-intensive methods -- Permutation tests: unpaired tests -- Permutation tests: paired tests -- The bootstrap -- Discussion -- Reading and reporting t tests and non-parametric tests -- Common questions -- Should I test my data for Normality before using the t test? -- Should I test for equality of the standard deviations before using the usual t test? -- Why should I use a paired test if my data are paired? What happens if I don't? -- Do non-parametric tests compare medians? -- How is the Mann-Whitney U test related to the t test? -- How is the Mann-Whitney U test related to the area under the receiver operating characteristics curve of Chapter 5? -- References -- Chapter 10 Tests for association in binary and categorical data -- General chi-squared test -- 2 × 2 tables -- Small numbers: Yates' correction, Fisher's Exact Test and the permutation test -- 2 test for trend -- Comparison of an observed and a theoretical distribution -- Tests for paired binary data -- Examples of a paired comparison -- Extensions of the 2 test -- Common questions -- There are a number of tests of association for a 2 × 2 table. Which should I choose? -- I have matched data, but the matching criteria were very weak. Should I use McNemar's test? -- Do chi-squared tests apply to large contingency tables? -- Is the chi-squared test a non-parametric test? -- Formula appreciation -- Reading and reporting chi-squared tests -- Exercises -- References -- Chapter 11 Correlation and regression -- The correlation coefficient -- Looking at data: scatter diagrams -- Calculation of the correlation coefficient -- Significance test for a correlation coefficient -- Spearman rank correlation -- The regression equation -- Simple checks of the model -- Using regression in t tests. ; More advanced methods -- Common questions -- If two variables are correlated, are they causally related? -- How do I test the assumptions underlying linear regression? -- When should I use correlation and when should I use regression? -- Which are the important assumptions for linear regression? -- Formula appreciation -- Reading and reporting correlation and regression -- Exercises -- References -- Chapter 12 Survival analysis -- Why survival analysis is different -- Kaplan-Meier survival curve -- Example of calculation of survival curve -- The log rank test -- Further methods -- Common questions -- Do I need to test for a constant relative risk before doing the log rank test? -- If I don't have any censored observations, do I need to use survival analysis? -- How does the hazard calculated under the log rank compare with the usual estimate of risk? -- Reading and reporting survival analysis -- Exercises -- References -- Chapter 13 Modelling data -- Basics -- Models -- Model fitting and analysis: exploratory and confirmatory analyses -- Bayesian methods -- Models generally -- X1 binary and X2 binary -- X1 continuous and X2 continuous -- X1 binary and X2 continuous -- Multiple linear regression -- Example linear regression -- Paper critique -- Logistic regression -- Logistic regression instead of a chi-squared test -- Example of logistic regression from the literature -- Paper critique -- Survival analysis -- Proportional hazards models -- Proportional hazards model instead of log rank -- Example of proportional hazards model -- Paper critique -- Other things to consider in modelling -- References -- Chapter 14 Study design and choosing a statistical test -- Design -- Sample size -- Choice of test -- Reading and reporting on the design of a study -- Further reading -- Exercises -- References -- Chapter 15 Use of computer software. ; Chapter 2: Data display and summary.
• Titolo autorizzato: Statistics at square one
• ISBN: 9781119402350; 1-119-40234-4; 1-119-40235-2; 1-119-40142-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910555112703321
• Lo trovi qui: Univ. Federico II