{"product_id":"medical-statistics-from-scratch-isbn-9781119523888","title":"Medical Statistics from Scratch","description":"\u003cp\u003eCorrectly understanding and using medical statistics is a key skill for all medical students and health professionals.\u003cbr\u003e\u003cbr\u003eIn an informal and friendly style, \u003ci\u003eMedical Statistics from Scratch\u003c\/i\u003e provides a practical foundation for everyone whose first interest is probably not medical statistics. Keeping the level of mathematics to a minimum, it clearly illustrates statistical concepts and practice with numerous real-world examples and cases drawn from current medical literature.\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMedical Statistics from Scratch\u003c\/i\u003e is an ideal learning partner for all medical students and health professionals needing an accessible introduction, or a friendly refresher, to the fundamentals of medical statistics.\u003c\/p\u003e \u003cp\u003ePreface to the 4th Edition xix\u003c\/p\u003e \u003cp\u003ePreface to the 3rd Edition xxi\u003c\/p\u003e \u003cp\u003ePreface to the 2nd Edition xxiii\u003c\/p\u003e \u003cp\u003ePreface to the 1st Edition xxv\u003c\/p\u003e \u003cp\u003eIntroduction xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI Some Fundamental Stuff 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 First things first – the nature of data 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eVariables and data 3\u003c\/p\u003e \u003cp\u003eWhere are we going …? 5\u003c\/p\u003e \u003cp\u003eThe good, the bad, and the ugly – types of variables 5\u003c\/p\u003e \u003cp\u003eCategorical data 6\u003c\/p\u003e \u003cp\u003eNominal categorical data 6\u003c\/p\u003e \u003cp\u003eOrdinal categorical data 7\u003c\/p\u003e \u003cp\u003eMetric data 8\u003c\/p\u003e \u003cp\u003eDiscrete metric data 8\u003c\/p\u003e \u003cp\u003eContinuous metric data 9\u003c\/p\u003e \u003cp\u003eHow can I tell what type of variable I am dealing with? 10\u003c\/p\u003e \u003cp\u003eThe baseline table 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII Descriptive Statistics 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Describing data with tables 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDescriptive statistics. What can we do with raw data? 18\u003c\/p\u003e \u003cp\u003eFrequency tables – nominal data 18\u003c\/p\u003e \u003cp\u003eThe frequency distribution 19\u003c\/p\u003e \u003cp\u003eRelative frequency 20\u003c\/p\u003e \u003cp\u003eFrequency tables – ordinal data 20\u003c\/p\u003e \u003cp\u003eFrequency tables – metric data 22\u003c\/p\u003e \u003cp\u003eFrequency tables with discrete metric data 22\u003c\/p\u003e \u003cp\u003eCumulative frequency 24\u003c\/p\u003e \u003cp\u003eFrequency tables with continuous metric data – grouping the raw data 25\u003c\/p\u003e \u003cp\u003eOpen‐ended groups 27\u003c\/p\u003e \u003cp\u003eCross‐tabulation – contingency tables 28\u003c\/p\u003e \u003cp\u003eRanking data 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Every picture tells a story – describing data with charts 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePicture it! 32\u003c\/p\u003e \u003cp\u003eCharting nominal and ordinal data 32\u003c\/p\u003e \u003cp\u003eThe pie chart 32\u003c\/p\u003e \u003cp\u003eThe simple bar chart 34\u003c\/p\u003e \u003cp\u003eThe clustered bar chart 35\u003c\/p\u003e \u003cp\u003eThe stacked bar chart 37\u003c\/p\u003e \u003cp\u003eCharting discrete metric data 39\u003c\/p\u003e \u003cp\u003eCharting continuous metric data 39\u003c\/p\u003e \u003cp\u003eThe histogram 39\u003c\/p\u003e \u003cp\u003eThe box (and whisker) plot 42\u003c\/p\u003e \u003cp\u003eCharting cumulative data 44\u003c\/p\u003e \u003cp\u003eThe cumulative frequency curve with discrete metric data 44\u003c\/p\u003e \u003cp\u003eThe cumulative frequency curve with continuous metric data 44\u003c\/p\u003e \u003cp\u003eCharting time‐based data – the time series chart 47\u003c\/p\u003e \u003cp\u003eThe scatterplot 48\u003c\/p\u003e \u003cp\u003eThe bubbleplot 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Describing data from its shape 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe shape of things to come 51\u003c\/p\u003e \u003cp\u003eSkewness and kurtosis as measures of shape 52\u003c\/p\u003e \u003cp\u003eKurtosis 55\u003c\/p\u003e \u003cp\u003eSymmetric or mound‐shaped distributions 56\u003c\/p\u003e \u003cp\u003eNormalness – the Normal distribution 56\u003c\/p\u003e \u003cp\u003eBimodal distributions 58\u003c\/p\u003e \u003cp\u003eDetermining skew from a box plot 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Measures of location – Numbers R us 62\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNumbers, percentages, and proportions 62\u003c\/p\u003e \u003cp\u003ePreamble 63\u003c\/p\u003e \u003cp\u003eN umbers, percentages, and proportions 64\u003c\/p\u003e \u003cp\u003eHandling percentages – for those of us who might need a reminder 65\u003c\/p\u003e \u003cp\u003eSummary measures of location 67\u003c\/p\u003e \u003cp\u003eThe mode 68\u003c\/p\u003e \u003cp\u003eThe median 69\u003c\/p\u003e \u003cp\u003eThe mean 70\u003c\/p\u003e \u003cp\u003ePercentiles 71\u003c\/p\u003e \u003cp\u003eCalculating a percentile value 72\u003c\/p\u003e \u003cp\u003eWhat is the most appropriate measure of location? 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Measures of spread – Numbers R us – (again) 75\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePreamble 76\u003c\/p\u003e \u003cp\u003eThe range 76\u003c\/p\u003e \u003cp\u003eThe interquartile range (IQR) 76\u003c\/p\u003e \u003cp\u003eEstimating the median and interquartile range from the cumulative frequency curve 77\u003c\/p\u003e \u003cp\u003eThe boxplot (also known as the box and whisker plot) 79\u003c\/p\u003e \u003cp\u003eStandard deviation 82\u003c\/p\u003e \u003cp\u003eStandard deviation and the Normal distribution 84\u003c\/p\u003e \u003cp\u003eTesting for Normality 86\u003c\/p\u003e \u003cp\u003eUsing SPSS 86\u003c\/p\u003e \u003cp\u003eUsing Minitab 87\u003c\/p\u003e \u003cp\u003eTransforming data 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Incidence, prevalence, and standardisation 92\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePreamble 93\u003c\/p\u003e \u003cp\u003eThe incidence rate and the incidence rate ratio (IRR) 93\u003c\/p\u003e \u003cp\u003eThe incidence rate ratio 94\u003c\/p\u003e \u003cp\u003ePrevalence 94\u003c\/p\u003e \u003cp\u003eA couple of difficulties with measuring incidence and prevalence 97\u003c\/p\u003e \u003cp\u003eSome other useful rates 97\u003c\/p\u003e \u003cp\u003eCrude mortality rate 97\u003c\/p\u003e \u003cp\u003eCase fatality rate 98\u003c\/p\u003e \u003cp\u003eCrude maternal mortality rate 99\u003c\/p\u003e \u003cp\u003eCrude birth rate 99\u003c\/p\u003e \u003cp\u003eAttack rate 99\u003c\/p\u003e \u003cp\u003eAge‐specific mortality rate 99\u003c\/p\u003e \u003cp\u003eStandardisation – the age‐standardised mortality rate 101\u003c\/p\u003e \u003cp\u003eThe direct method 102\u003c\/p\u003e \u003cp\u003eThe standard population and the comparative mortality ratio (CMR) 103\u003c\/p\u003e \u003cp\u003eThe indirect method 106\u003c\/p\u003e \u003cp\u003eThe standardised mortality rate 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII The Confounding Problem 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Confounding – like the poor, (nearly) always with us 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePreamble 114\u003c\/p\u003e \u003cp\u003eWhat is confounding? 114\u003c\/p\u003e \u003cp\u003eConfounding by indication 117\u003c\/p\u003e \u003cp\u003eResidual confounding 119\u003c\/p\u003e \u003cp\u003eDetecting confounding 119\u003c\/p\u003e \u003cp\u003eDealing with confounding – if confounding is such a problem, what can we do about it? 120\u003c\/p\u003e \u003cp\u003eUsing restriction 120\u003c\/p\u003e \u003cp\u003eUsing matching 121\u003c\/p\u003e \u003cp\u003eFrequency matching 121\u003c\/p\u003e \u003cp\u003eOne‐to‐one matching 121\u003c\/p\u003e \u003cp\u003eUsing stratification 122\u003c\/p\u003e \u003cp\u003eUsing adjustment 122\u003c\/p\u003e \u003cp\u003eUsing randomisation 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV Design and Data 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Research design – Part I: Observational study designs 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePreamble 128\u003c\/p\u003e \u003cp\u003eHey ho! Hey ho! it’s off to work we go 129\u003c\/p\u003e \u003cp\u003eTypes of study 129\u003c\/p\u003e \u003cp\u003eObservational studies 130\u003c\/p\u003e \u003cp\u003eCase reports 130\u003c\/p\u003e \u003cp\u003eCase series studies 131\u003c\/p\u003e \u003cp\u003eCross‐sectional studies 131\u003c\/p\u003e \u003cp\u003eDescriptive cross‐sectional studies 132\u003c\/p\u003e \u003cp\u003eConfounding in descriptive cross‐sectional studies 132\u003c\/p\u003e \u003cp\u003eAnalytic cross‐sectional studies 133\u003c\/p\u003e \u003cp\u003eConfounding in analytic cross‐sectional studies 134\u003c\/p\u003e \u003cp\u003eFrom here to eternity – cohort studies 135\u003c\/p\u003e \u003cp\u003eConfounding in the cohort study design 139\u003c\/p\u003e \u003cp\u003eBack to the future – case–control studies 139\u003c\/p\u003e \u003cp\u003eConfounding in the case–control study design 141\u003c\/p\u003e \u003cp\u003eAnother example of a case–control study 142\u003c\/p\u003e \u003cp\u003eComparing cohort and case–control designs 143\u003c\/p\u003e \u003cp\u003eEcological studies 144\u003c\/p\u003e \u003cp\u003eThe ecological fallacy 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Research design – Part II: getting stuck in – experimental studies 146\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eClinical trials 147\u003c\/p\u003e \u003cp\u003eRandomisation and the randomised controlled trial (RCT) 148\u003c\/p\u003e \u003cp\u003eBlock randomisation 149\u003c\/p\u003e \u003cp\u003eStratification 149\u003c\/p\u003e \u003cp\u003eBlinding 149\u003c\/p\u003e \u003cp\u003eThe crossover RCT 150\u003c\/p\u003e \u003cp\u003eSelection of participants for an RCT 153\u003c\/p\u003e \u003cp\u003eIntention to treat analysis (ITT) 154\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Getting the participants for your study: ways of sampling 156\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFrom populations to samples – statistical inference 157\u003c\/p\u003e \u003cp\u003eCollecting the data – types of sample 158\u003c\/p\u003e \u003cp\u003eThe simple random sample and its offspring 159\u003c\/p\u003e \u003cp\u003eThe systematic random sample 159\u003c\/p\u003e \u003cp\u003eThe stratified random sample 160\u003c\/p\u003e \u003cp\u003eThe cluster sample 160\u003c\/p\u003e \u003cp\u003eConsecutive and convenience samples 161\u003c\/p\u003e \u003cp\u003eHow many participants should we have? Sample size 162\u003c\/p\u003e \u003cp\u003eInclusion and exclusion criteria 162\u003c\/p\u003e \u003cp\u003eGetting the data 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003eV Chance Would Be a Fine Thing 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 The idea of probability 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePreamble 167\u003c\/p\u003e \u003cp\u003eCalculating probability – proportional frequency 168\u003c\/p\u003e \u003cp\u003eTwo useful rules for simple probability 169\u003c\/p\u003e \u003cp\u003eRule 1. The multiplication rule for independent events 169\u003c\/p\u003e \u003cp\u003eRule 2. The addition rule for mutually exclusive events 170\u003c\/p\u003e \u003cp\u003eConditional and Bayesian statistics 171\u003c\/p\u003e \u003cp\u003eProbability distributions 171\u003c\/p\u003e \u003cp\u003eDiscrete versus continuous probability distributions 172\u003c\/p\u003e \u003cp\u003eThe binomial probability distribution 172\u003c\/p\u003e \u003cp\u003eThe Poisson probability distribution 173\u003c\/p\u003e \u003cp\u003eThe Normal probability distribution 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Risk and odds 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbsolute risk and the absolute risk reduction (ARR) 176\u003c\/p\u003e \u003cp\u003eThe risk ratio 178\u003c\/p\u003e \u003cp\u003eThe reduction in the risk ratio (or relative risk reduction (RRR)) 178\u003c\/p\u003e \u003cp\u003eA general formula for the risk ratio 179\u003c\/p\u003e \u003cp\u003eReference value 179\u003c\/p\u003e \u003cp\u003eN umber needed to treat (NNT) 180\u003c\/p\u003e \u003cp\u003eWhat happens if the initial risk is small? 181\u003c\/p\u003e \u003cp\u003eConfounding with the risk ratio 182\u003c\/p\u003e \u003cp\u003eOdds 183\u003c\/p\u003e \u003cp\u003eWhy you can’t calculate risk in a case–control study 185\u003c\/p\u003e \u003cp\u003eThe link between probability and odds 186\u003c\/p\u003e \u003cp\u003eThe odds ratio 186\u003c\/p\u003e \u003cp\u003eConfounding with the odds ratio 189\u003c\/p\u003e \u003cp\u003eApproximating the risk ratio from the odds ratio 189\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVI The Informed Guess – An Introduction to Confidence Intervals 191\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Estimating the value of a \u003ci\u003esingle \u003c\/i\u003epopulation parameter – the idea of confidence intervals 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConfidence interval estimation for a population mean 194\u003c\/p\u003e \u003cp\u003eThe standard error of the mean 195\u003c\/p\u003e \u003cp\u003eHow we use the standard error of the mean to calculate a confidence interval for a population mean 197\u003c\/p\u003e \u003cp\u003eConfidence interval for a population proportion 200\u003c\/p\u003e \u003cp\u003eEstimating a confidence interval for the median of a single population 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Using confidence intervals to compare two population parameters 206\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat’s the difference? 207\u003c\/p\u003e \u003cp\u003eComparing two \u003ci\u003eindependent \u003c\/i\u003epopulation means 207\u003c\/p\u003e \u003cp\u003eAn example using birthweights 208\u003c\/p\u003e \u003cp\u003eAssessing the evidence using the confidence interval 211\u003c\/p\u003e \u003cp\u003eComparing two \u003ci\u003epaired \u003c\/i\u003epopulation means 215\u003c\/p\u003e \u003cp\u003eWithin‐subject and between‐subject variations 215\u003c\/p\u003e \u003cp\u003eComparing two \u003ci\u003eindependent \u003c\/i\u003epopulation proportions 217\u003c\/p\u003e \u003cp\u003eComparing two \u003ci\u003eindependent \u003c\/i\u003epopulation medians – the Mann–Whitney rank sums method 219\u003c\/p\u003e \u003cp\u003eComparing two \u003ci\u003ematched \u003c\/i\u003epopulation medians – the Wilcoxon signed‐ranks method 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Confidence intervals for the \u003ci\u003eratio \u003c\/i\u003eof two population parameters 224\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting a confidence interval for the \u003ci\u003eratio \u003c\/i\u003eof two independent population means 225\u003c\/p\u003e \u003cp\u003eConfidence interval for a population risk ratio 226\u003c\/p\u003e \u003cp\u003eConfidence intervals for a population odds ratio 229\u003c\/p\u003e \u003cp\u003eConfidence intervals for hazard ratios 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVII Putting it to the Test 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Testing hypotheses about the \u003ci\u003edifference \u003c\/i\u003ebetween two population parameters 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAnswering the question 238\u003c\/p\u003e \u003cp\u003eThe hypothesis 238\u003c\/p\u003e \u003cp\u003eThe null hypothesis 239\u003c\/p\u003e \u003cp\u003eThe hypothesis testing process 240\u003c\/p\u003e \u003cp\u003eThe p‐value and the decision rule 241\u003c\/p\u003e \u003cp\u003eA brief summary of a few of the commonest tests 242\u003c\/p\u003e \u003cp\u003eUsing the \u003ci\u003ep\u003c\/i\u003e‐value to compare the means of two independent populations 244\u003c\/p\u003e \u003cp\u003eInterpreting computer hypothesis test results for the difference in two independent population means – the two‐sample \u003ci\u003et \u003c\/i\u003etest 245\u003c\/p\u003e \u003cp\u003eOutput from Minitab – two‐sample \u003ci\u003et \u003c\/i\u003etest of difference in mean birthweights of babies born to white mothers and to non‐white mothers 245\u003c\/p\u003e \u003cp\u003eOutput from SPSS_: two‐sample \u003ci\u003et \u003c\/i\u003etest of difference in mean birthweights of babies born to white mothers and to non‐white mothers 246\u003c\/p\u003e \u003cp\u003eComparing the means of two paired populations – the matched‐pairs \u003ci\u003et \u003c\/i\u003etest 248\u003c\/p\u003e \u003cp\u003eUsing \u003ci\u003ep\u003c\/i\u003e‐values to compare the medians of two independent populations: the Mann–Whitney rank‐sums test 248\u003c\/p\u003e \u003cp\u003eHow the Mann–Whitney test works 249\u003c\/p\u003e \u003cp\u003eCorrection for multiple comparisons 250\u003c\/p\u003e \u003cp\u003eThe Bonferroni correction for multiple testing 250\u003c\/p\u003e \u003cp\u003eInterpreting computer output for the Mann–Whitney test 252\u003c\/p\u003e \u003cp\u003eWith Minitab 252\u003c\/p\u003e \u003cp\u003eWith SPSS 252\u003c\/p\u003e \u003cp\u003eTwo matched medians – the Wilcoxon signed‐ranks test 254\u003c\/p\u003e \u003cp\u003eConfidence intervals versus hypothesis testing 254\u003c\/p\u003e \u003cp\u003eWhat could possibly go wrong? 255\u003c\/p\u003e \u003cp\u003eTypes of error 256\u003c\/p\u003e \u003cp\u003eThe power of a test 257\u003c\/p\u003e \u003cp\u003eMaximising power – calculating sample size 258\u003c\/p\u003e \u003cp\u003eRule of thumb 1. Comparing the means of two independent populations (metric data) 258\u003c\/p\u003e \u003cp\u003eRule of thumb 2. Comparing the proportions of two independent populations (binary data) 259\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 The Chi‐squared (χ\u003csup\u003e\u003ci\u003e2\u003c\/i\u003e\u003c\/sup\u003e) test – what, why, and how? 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOf all the tests in all the world – you had to walk into my hypothesis testing procedure 262\u003c\/p\u003e \u003cp\u003eUsing chi‐squared to test for related‐ness or for the equality of proportions 262\u003c\/p\u003e \u003cp\u003eCalculating the chi‐squared statistic 265\u003c\/p\u003e \u003cp\u003eUsing the chi-squared statistic 267\u003c\/p\u003e \u003cp\u003eYate’s correction (continuity correction) 268\u003c\/p\u003e \u003cp\u003eFisher’s exact test 268\u003c\/p\u003e \u003cp\u003eThe chi‐squared test with Minitab 269\u003c\/p\u003e \u003cp\u003eThe chi‐squared test with SPSS 270\u003c\/p\u003e \u003cp\u003eThe chi‐squared test for trend 272\u003c\/p\u003e \u003cp\u003eSPSS output for chi‐squared trend test 274\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Testing hypotheses about the \u003ci\u003eratio \u003c\/i\u003eof two population parameters 276\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePreamble 276\u003c\/p\u003e \u003cp\u003eThe chi‐squared test with the risk ratio 277\u003c\/p\u003e \u003cp\u003eThe chi‐squared test with odds ratios 279\u003c\/p\u003e \u003cp\u003eThe chi‐squared test with hazard ratios 281\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVIII Becoming Acquainted 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Measuring the association between two variables 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePreamble – plotting data 286\u003c\/p\u003e \u003cp\u003eAssociation 287\u003c\/p\u003e \u003cp\u003eThe scatterplot 287\u003c\/p\u003e \u003cp\u003eThe correlation coefficient 290\u003c\/p\u003e \u003cp\u003ePearson’s correlation coefficient 290\u003c\/p\u003e \u003cp\u003eIs the correlation coefficient statistically significant in the population? 292\u003c\/p\u003e \u003cp\u003eSpearman’s rank correlation coefficient 294\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Measuring agreement 298\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTo agree or not agree: that is the question 298\u003c\/p\u003e \u003cp\u003eCohen’s kappa (\u003ci\u003eκ\u003c\/i\u003e) 300\u003c\/p\u003e \u003cp\u003eSome shortcomings of kappa 303\u003c\/p\u003e \u003cp\u003eWeighted kappa 303\u003c\/p\u003e \u003cp\u003eMeasuring the agreement between two metric continuous variables, the Bland–Altmann plot 303\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIX Getting into a Relationship 307\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Straight line models: linear regression 309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHealth warning! 310\u003c\/p\u003e \u003cp\u003eRelationship and association 310\u003c\/p\u003e \u003cp\u003eA causal relationship – explaining variation 312\u003c\/p\u003e \u003cp\u003eRefresher – finding the equation of a straight line from a graph 313\u003c\/p\u003e \u003cp\u003eThe linear regression model 314\u003c\/p\u003e \u003cp\u003eFirst, is the relationship linear? 315\u003c\/p\u003e \u003cp\u003eEstimating the regression parameters – the method of ordinary least squares (OLS) 316\u003c\/p\u003e \u003cp\u003eBasic assumptions of the ordinary least squares procedure 317\u003c\/p\u003e \u003cp\u003eBack to the example – is the relationship statistically significant? 318\u003c\/p\u003e \u003cp\u003eUsing SPSS to regress birthweight on mother’s weight 318\u003c\/p\u003e \u003cp\u003eUsing Minitab 319\u003c\/p\u003e \u003cp\u003eInterpreting the regression coefficients 320\u003c\/p\u003e \u003cp\u003eGoodness‐of‐fit, \u003ci\u003eR\u003csup\u003e2\u003c\/sup\u003e \u003c\/i\u003e320\u003c\/p\u003e \u003cp\u003eMultiple linear regression 322\u003c\/p\u003e \u003cp\u003eAdjusted goodness‐of‐fit: \u003ci\u003eR̄\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003cb\u003e\u003csup\u003e \u003c\/sup\u003e\u003c\/b\u003e324\u003c\/p\u003e \u003cp\u003eIncluding nominal covariates in the regression model: design variables and coding 326\u003c\/p\u003e \u003cp\u003eBuilding your model. Which variables to include? 327\u003c\/p\u003e \u003cp\u003eAutomated variable selection methods 328\u003c\/p\u003e \u003cp\u003eManual variable selection methods 329\u003c\/p\u003e \u003cp\u003eAdjustment and confounding 330\u003c\/p\u003e \u003cp\u003eDiagnostics – checking the basic assumptions of the multiple linear regression model 332\u003c\/p\u003e \u003cp\u003eAnalysis of variance 333\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Curvy models: logistic regression 334\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA second health warning! 335\u003c\/p\u003e \u003cp\u003eThe binary outcome variable 335\u003c\/p\u003e \u003cp\u003eFinding an appropriate model when the outcome variable is binary 335\u003c\/p\u003e \u003cp\u003eThe logistic regression model 337\u003c\/p\u003e \u003cp\u003eEstimating the parameter values 338\u003c\/p\u003e \u003cp\u003eInterpreting the regression coefficients 338\u003c\/p\u003e \u003cp\u003eHave we got a significant result? statistical inference in the logistic regression model 340\u003c\/p\u003e \u003cp\u003eThe Odds Ratio 341\u003c\/p\u003e \u003cp\u003eThe multiple logistic regression model 343\u003c\/p\u003e \u003cp\u003eBuilding the model 344\u003c\/p\u003e \u003cp\u003eGoodness‐of‐fit 346\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Counting models: Poisson regression 349\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePreamble 350\u003c\/p\u003e \u003cp\u003ePoisson regression 350\u003c\/p\u003e \u003cp\u003eThe Poisson regression equation 351\u003c\/p\u003e \u003cp\u003eEstimating β\u003csub\u003e1\u003c\/sub\u003e and β\u003csub\u003e2\u003c\/sub\u003e with the estimators \u003ci\u003eb\u003c\/i\u003e\u003csub\u003e0\u003c\/sub\u003e and \u003ci\u003eb\u003c\/i\u003e\u003csub\u003e1\u003c\/sub\u003e 352\u003c\/p\u003e \u003cp\u003eInterpreting the estimated coefficients of a Poisson regression, \u003ci\u003eb\u003c\/i\u003e\u003csub\u003e0\u003c\/sub\u003e and \u003ci\u003eb\u003c\/i\u003e\u003csub\u003e1\u003c\/sub\u003e 352\u003c\/p\u003e \u003cp\u003eModel building – variable selection 355\u003c\/p\u003e \u003cp\u003eGoodness‐of‐fit 357\u003c\/p\u003e \u003cp\u003eZero‐inflated Poisson regression 358\u003c\/p\u003e \u003cp\u003eNegative binomial regression 359\u003c\/p\u003e \u003cp\u003eZero‐inflated negative binomial regression 361\u003c\/p\u003e \u003cp\u003e\u003cb\u003eX Four More Chapters 363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Measuring survival 365\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePreamble 366\u003c\/p\u003e \u003cp\u003eCensored data 366\u003c\/p\u003e \u003cp\u003eA simple example of survival in a single group 366\u003c\/p\u003e \u003cp\u003eCalculating survival probabilities and the proportion surviving: the Kaplan–Meier table 368\u003c\/p\u003e \u003cp\u003eThe Kaplan–Meier curve 369\u003c\/p\u003e \u003cp\u003eDetermining median survival time 369\u003c\/p\u003e \u003cp\u003eComparing survival with two groups 370\u003c\/p\u003e \u003cp\u003eThe log‐rank test 371\u003c\/p\u003e \u003cp\u003eAn example of the log‐rank test in practice 372\u003c\/p\u003e \u003cp\u003eThe hazard ratio 372\u003c\/p\u003e \u003cp\u003eThe proportional hazards (Cox’s) regression model – introduction 373\u003c\/p\u003e \u003cp\u003eThe proportional hazards (Cox’s) regression model – the detail 376\u003c\/p\u003e \u003cp\u003eChecking the assumptions of the proportional hazards model 377\u003c\/p\u003e \u003cp\u003eAn example of proportional hazards regression 377\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Systematic review and meta‐analysis 380\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 381\u003c\/p\u003e \u003cp\u003eSystematic review 381\u003c\/p\u003e \u003cp\u003eThe forest plot 383\u003c\/p\u003e \u003cp\u003ePublication and other biases 384\u003c\/p\u003e \u003cp\u003eThe funnel plot 386\u003c\/p\u003e \u003cp\u003eSignificance tests for bias – Begg’s and Egger’s tests 387\u003c\/p\u003e \u003cp\u003eCombining the studies: meta‐analysis 389\u003c\/p\u003e \u003cp\u003eThe problem of heterogeneity – the Q and I\u003csup\u003e2\u003c\/sup\u003e tests 389\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Diagnostic testing 393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePreamble 393\u003c\/p\u003e \u003cp\u003eThe measures – sensitivity and specificity 394\u003c\/p\u003e \u003cp\u003eThe positive prediction and negative prediction values (PPV and NPV) 395\u003c\/p\u003e \u003cp\u003eThe sensitivity–specificity trade‐off 396\u003c\/p\u003e \u003cp\u003eUsing the ROC curve to find the optimal sensitivity versus specificity trade‐off 397\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Missing data 400\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe missing data problem 400\u003c\/p\u003e \u003cp\u003eTypes of missing data 403\u003c\/p\u003e \u003cp\u003eMissing completely at random (MCAR) 403\u003c\/p\u003e \u003cp\u003eMissing at Random (MAR) 403\u003c\/p\u003e \u003cp\u003eMissing not at random (MNAR) 404\u003c\/p\u003e \u003cp\u003eConsequences of missing data 405\u003c\/p\u003e \u003cp\u003eDealing with missing data 405\u003c\/p\u003e \u003cp\u003eDo nothing – the wing and prayer approach 406\u003c\/p\u003e \u003cp\u003eList‐wise deletion 406\u003c\/p\u003e \u003cp\u003ePair‐wise deletion 407\u003c\/p\u003e \u003cp\u003eImputation methods – simple imputation 408\u003c\/p\u003e \u003cp\u003eReplacement by the Mean 408\u003c\/p\u003e \u003cp\u003eLast observation carried forward 409\u003c\/p\u003e \u003cp\u003eRegression‐based imputation 410\u003c\/p\u003e \u003cp\u003eMultiple imputation 411\u003c\/p\u003e \u003cp\u003eFull Information Maximum Likelihood (FIML) and other methods 412\u003c\/p\u003e \u003cp\u003eAppendix: Table of random numbers 414\u003c\/p\u003e \u003cp\u003eReferences 415\u003c\/p\u003e \u003cp\u003eSolutions to Exercises 424\u003c\/p\u003e \u003cp\u003eIndex 457\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDAVID BOWERS,\u003c\/b\u003e Leeds Institute of Health Sciences, School of Medicine, University of Leeds, Leeds, UK.\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eFOURTH EDITION\u003c\/i\u003e \u003cb\u003eMedical Statistics \u003ci\u003efrom\u003c\/i\u003e Scratch\u003c\/b\u003e An Introduction for Health Professionals \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMedical Statistics from Scratch\u003c\/i\u003e is the ideal learning partner for all medical students and health professionals needing an accessible introduction, or a friendly refresher, to the fundamentals of medical statistics. This new fourth edition has been completely revised, the examples from current research updated and new material added. \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePraise for previous editions\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\"I love this book. It lays out the problem of how to approach statistics in a digestible, understandable, and rather complete way. The book actually follows my biostatistics class very nicely even though the class is using a different and more difficult text. I wish I was in class with the writer of this book. He is really a great teacher. This is now one of my favorite books, and I carry it with me all the time.\" \u003c\/p\u003e\u003cp\u003e\"After years of trying and failing, this is the only book on medical statistics that I have managed to read and understand. I would certainly recommend this to anyone, especially medical professionals who need to have a good grasp of statistics in order to take up postgraduate exams or to understand peer-reviewed publications. I especially found the exercise quite useful. I only wish I had come across this book earlier.\" \u003c\/p\u003e\u003cp\u003e\"I though this was an outstanding book. It is organised in a way that logically walks you through the rationale behind picking the appropriate statistical tool for your type of data. It is comprehensive in covering the different situations you'll encounter whether you're designing your own study or reading someone else's. The mathematics are presented in an easy-to-understand format striking just the right balance of providing the important concepts without getting bogged down in minute details. It utilises practical examples and references from the medical literature that you'll be comfortable applying day one to that journal lying on your desk. Whether you're starting out as a student or have been in practice for years and want a refresher, this text should be on your shelf.\" \u003c\/p\u003e\u003cp\u003e\"This book will help the average healthcare worker understand the essentials of statistics to prepare for a board or be involved in medical research. It is a great vantage point to understand the concept and go from there.\" \u003c\/p\u003e\u003cp\u003e\"Starts with very basic information and lays the information out clearly in a logical sequence that builds up at an easy pace. Plenty of practice exercises to help cement the concepts taught.\" \u003c\/p\u003e\u003cp\u003e\"\u003ci\u003eMedical Statistics from Scratch\u003c\/i\u003e is an excellent introduction which I frequently recommend to students and colleagues with little or no knowledge of statistics!\" \u003c\/p\u003e\u003cp\u003e\"My work involves much analysis and evaluation of medical studies. This book helps me, a \"non-scientist\" make certain that my lack of statistical training does not lead me astray. I found this very helpful.\" \u003c\/p\u003e\u003cp\u003e\"I used this book while I was doing a medical statistic module for my degree. I was new to statistics and found this book a very good introduction for a complete beginner. The language is very simple, chatty, and easy to understand. There are worked example and questions and answers. It covers the basics of statistics first like standard deviations, averages etc. and then progresses onto the medical statistics such as log-rank test, survival curves etc.\" \u003c\/p\u003e\u003cp\u003e\"I've been wanting to improve my ability to critically read articles from the medical literature and have found your book to be the perfect tool for that purpose. It's easy to read, understandable, and concise. What has been most valuable to me is how well you explain the concepts and rationale behind a method rather than just the mechanics of the method itself. Thank you for a job well done.\"\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989602517221,"sku":"NP9781119523888","price":52.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119523888.jpg?v=1761784768","url":"https:\/\/k12savings.com\/es\/products\/medical-statistics-from-scratch-isbn-9781119523888","provider":"K12savings","version":"1.0","type":"link"}