Statistical Methods in Medical Research 4ed

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804 Subject Index Growth curves 409, 432 standards 401 Haemolytic disease of newborn 468±471, 492, 493 Haemophilia 58, 59 Half-normal plot 255, 372 Harmonic analysis 453 Hazard function 578, 582, 583 cumulative/integrated 588 proportional 583±590 Health spending 9 H(ealy)R(ashbash)Y(ang) method 403 Heights, distribution 76, 77 Heterogeneity of means 208±215 (see also Analysis of variance, one-way) of proportions 227±231, 503, 504 of variances 214 (see also Heteroscedasticity) test for Poisson (see Poisson) Heteroscedasticity 190, 321 Hierarchical classification, x 2 tests for 513, 514 model 184, 419 Histogram 24 correction for unequal grouping 24 for relative frequency 24, 25 Historical prospective study 669, 685 HIV 4, 626, 658 Homoscedasticity 190 Hotelling's T 2 test paired one-sample test 474 two-sample test 437, 473 Hypergeometric distribution 137 Hyperparameter 534 Hyperprior distribution 534 Hypotensive drug trial 341±343, 350±352, 359±364 Hypothesis approximately true 89 null 87 separating directions 90 simplifying 89 Identifiability 690 Imputation 246, 657±659 (see also Missing data) Incidence 659, 672 of tumours 741 Incomplete block designs 263, 264 balanced 263 designs 261±265 Latin squares 263 Independence in random sampling 95, 105 in variance ratio test 213 of deviations 41 of events 51 of random variables 159, 161 Independent variable 338 (see also Explanatory variable) Indicator variable 349 (see also Dummy variable) Indirect assay 719 standardization 663±667, 686 Infant mortality 10, 187, 188 Inference (see Statistical inference) Influential points 368, 495 Information 566 Injury, functional outcome 499 Insecticides and houseflies 488, 513 Intent(ion) to treat 604, 607, 642, 709 Interaction 244, 361 definition in two-level factorial 253 in factorial analysis 247, 248 in generalized linear model 491 in multiple regression 361 in two-way analysis 244 incorrect negative sum of squares 246 qualitative 254 quantitative 254 significant, interpretation with 245, 254, 255 space-time 711, 713±715 sum of squares, calculation 244, 245 Intercept 338, 725 Interquartile range 38 Intraclass correlation 420, 640, 704±707 Inverse gamma distribution 173 matrix 339 Irradiation 310 Ischaemic heart disease (see Cardiovascular disease) Iterative generalized least squares 423 Iterative weighted least squares 490 Jackknife 304±306 after bootstrap 305 residual 367, 376, 395, 563 Joint probability 51 Kaplan±Meier method 575, 576 Kappa 700±704 weighted 701, 707
Kendall's S 278±282 Kendall's 290 Kernel smoothing 306, 389±391, 532 Kidney size 405, 406 Kolmogorov±Smirnov test 373 Lilliefors modification 373 Kruskal±Wallis test 287, 288 Kurtosis 406 Large samples 81, 86, 94, 163, 170 Latent roots 456±459, 477 Latin square 257±261 balanced for residual effect 634 randomization 258 replication 260, 261 Lattice designs 264 squares 264 Lead time 708, 709 Least significant difference 223 Least squares 190, 338, 339, 409 generalized 347 iterative generalized 423 iterative weighted 490 penalized 391±397 Length-biased sampling 708, 710 Leukaemia, clustering in 711, 713 Levels of factors 246, 247 Leverage 366, 495 Life expectation of 570 table 569±576, 740 abridged 569 adjustment for number at risk 573 cohort 569 current 569 in follow-up studies 571±574 Kaplan±Meier method 575, 576 modified 686 product-limit method (see Kaplan±Meier method) sampling variation in 574, 575 survivors 573 withdrawals 572 Likelihood 55±58, 85, 86, 165, 529, 615 function 85, 170 independent of stopping rule 615, 622 maximum 86, 162±164, 174, 191, 489, 490, 576 partial 585 principle 615 profile 415 ratio in contingency tables 525 Subject Index 805 in diagnosis 696 in discrimination 467, 479, 480, 696 Limiting dilution assay 730, 734±737 Lindley's paradox 172 Line diagram 9, 25 Linear contrast 223, 372 in biological assay 727 in two-level factorial 252, 253 discriminant function 464±467 predictor 486 regression 189±195, 337±356 Linearity transformations for 308 variance ratio test of 315, 316 Linearization 415±418 Lineweaver±Burk transformation 308, 416, 731 Link function 486 Liver disease 57, 58 LMS method 400±403 Location, measures of 31±33 Logarithm, common and natural 34, 126 Logarithmic scale 34 transformation 33±36, 43, 44, 308±311, 375, 499, 636, 707 as limiting power 308 for ratios 161 of dose in biological assay 718, 719 with zero values 309, 311 Logistic curve 413 regression 472, 484, 485, 488±496, 508, 521, 523, 524, 587, 681, 728, 730 conditional 496, 681 diagnostic methods 494 transformation 487, 488, 499, 635 and relative risk 488, 674, 680 in biological assay 718, 728, 730 in combination of 2 2 tables 518 linear model with (see Logistic regression) Logit (see Logistic transformation) Logit limits 127, 674 Log-linear model 499, 561, 657, 687 Log-normal distribution 309±311, 588 Logrank test 576±582, 681, 740 several groups 577 stratification in 581 LogXact 496 Longitudinal data 347, 419, 430±449 Loss function 173 Lung function (see Respiratory function) Lymphoma (see Cancer, lymphoma)
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To J.O. Irwin Mentor and friend
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# 1971, 1987, 1994, 2002 by Blackwe
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vi Contents 9.3 Factorial designs,
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Preface to the fourth edition In th
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Preface to the fourth edition xi Ho
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2 The scope of statistics other pat
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4 The scope of statistics groups is
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6 The scope of statistics pressure
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2 Describing data 2.1 Diagrams One
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10 Describing data 1- 4 weeks Under
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12 Describing data Table 2.1 Outcom
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14 Describing data Your height: ...
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16 Describing data be transferred f
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18 Describing data extracted from t
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20 Describing data many variables a
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22 Describing data cannot be less t
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24 Describing data Bufotenin (nmol/
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26 Describing data Frequency 20 18
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28 Describing data The number of as
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30 Describing data Frequency Measur
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32 Describing data may be abbreviat
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34 Describing data variables follow
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36 Describing data 107 01. The geom
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38 Describing data If the number of
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40 Describing data Therefore the me
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42 Describing data xi x will then n
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44 Describing data taking the antil
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46 Describing data against their ef
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48 Probability example, is sometime
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50 Probability It will appear in du
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52 Probability In general, pairs of
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54 Probability the five types be cl
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56 Probability converted to a ratio
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58 Probability Correct Equivocal In
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60 Probability Probability 1 . 0 0
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62 Probability Probability density
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64 Probability As a second example,
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66 Probability coefficient. In the
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68 Probability n r pr …1 p† n r
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70 Probability π = 0 Probability .
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72 Probability 0 } — T n Fig. 3.8
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74 Probability Probability 0 . 4 0
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76 Probability Table 3.4 Binomial a
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78 Probability where exp(z) is a co
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80 Probability approaches the shape
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82 Probability Probabililty density
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84 Analysing means and proportions
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148 Analysing variances Probability
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150 Analysing variances headings 0
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152 Analysing variances Method AB N
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154 Analysing variances Example 5.2
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156 Analysing variances Comparison
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158 Analysing variances With contin
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160 Analysing variances Let y ˆ x1
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162 Analysing variances p … log x
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164 Analysing variances as expected
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166 Bayesian methods This argument
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168 Bayesian methods nothing of the
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170 Bayesian methods in this way. F
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172 Bayesian methods In some situat
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174 Bayesian methods difference bet
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176 Bayesian methods the distributi
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178 Bayesian methods In the unpaire
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180 Bayesian methods The examples d
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182 Bayesian methods difference bet
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184 Bayesian methods variation may
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186 Bayesian methods The resulting
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188 Regression and correlation Infa
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190 Regression and correlation y x
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192 Regression and correlation on s
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194 Regression and correlation y ,
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196 Regression and correlation y (a
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198 Regression and correlation incr
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200 Regression and correlation From
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202 Regression and correlation y ,
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204 Regression and correlation Valu
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206 Regression and correlation disc
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8 Comparison of several groups 8.1
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210 Comparison of several groups P
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212 Comparison of several groups s
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214 Comparison of several groups to
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216 Comparison of several groups sh
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218 Comparison of several groups s
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220 Comparison of several groups su
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222 Comparison of several groups No
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224 Comparison of several groups yc
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226 Comparison of several groups Q
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228 Comparison of several groups te
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230 Comparison of several groups Ta
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232 Comparison of several groups Ta
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234 Comparison of several groups th
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9 Experimental design 9.1 General r
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238 Experimental design the estimat
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240 Experimental design Table 9.1 N
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242 Experimental design Similarly,
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244 Experimental design The sum of
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246 Experimental design If, in a tw
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248 Experimental design Table 9.5 S
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250 Experimental design interaction
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252 Experimental design difference
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254 Experimental design Table 9.7 C
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256 Experimental design interaction
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258 Experimental design litters and
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260 Experimental design randomizati
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262 Experimental design rows, colum
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264 Experimental design type of des
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266 Experimental design Main unit S
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268 Experimental design categories
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270 Experimental design Example 9.6
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10 Analysing non-normal data 10.1 D
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274 Analysing non-normal data The s
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276 Analysing non-normal data Estim
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278 Analysing non-normal data betwe
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Table 10.1 Some properties of three
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282 Analysing non-normal data This
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284 Analysing non-normal data High
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286 Analysing non-normal data which
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288 Analysing non-normal data Table
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290 Analysing non-normal data 1 It
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292 Analysing non-normal data It is
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294 Analysing non-normal data proba
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296 Analysing non-normal data Fract
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298 Analysing non-normal data h=…
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300 Analysing non-normal data This
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302 Analysing non-normal data Boots
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304 Analysing non-normal data more
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306 Analysing non-normal data Sampl
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308 Analysing non-normal data 3 squ
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310 Analysing non-normal data mG ˆ
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11 Modelling continuous data 11.1 A
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314 Modelling continuous data Examp
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316 Modelling continuous data n k i
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318 Modelling continuous data consi
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320 Modelling continuous data basis
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322 Modelling continuous data 11.4
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324 Modelling continuous data var
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326 Modelling continuous data Table
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328 Modelling continuous data Again
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330 Modelling continuous data SSq D
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332 Modelling continuous data Two g
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334 Modelling continuous data which
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336 Modelling continuous data the i
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338 Modelling continuous data predi
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340 Modelling continuous data equat
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344 Modelling continuous data Somet
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346 Modelling continuous data multi
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348 Modelling continuous data (i) D
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350 Modelling continuous data The r
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352 Modelling continuous data In th
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354 Modelling continuous data The c
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356 Modelling continuous data If th
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358 Modelling continuous data at th
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360 Modelling continuous data about
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362 Modelling continuous data y −
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364 Modelling continuous data y −
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366 Modelling continuous data outli
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370 Modelling continuous data Patie
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372 Modelling continuous data Norma
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374 Modelling continuous data Cumul
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12 Further regression models for a
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380 Further regression models Table
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382 Further regression models repre
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384 Further regression models where
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386 Further regression models Popul
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388 Further regression models and S
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390 Further regression models s(x)
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392 Further regression models SS ˆ
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394 Further regression models A…a
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396 Further regression models and t
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398 Further regression models that
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400 Further regression models Regre
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402 Further regression models M…T
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404 Further regression models a0
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406 Further regression models Maxim
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408 Further regression models using
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410 Further regression models suppo
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412 Further regression models Dose
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414 Further regression models Condu
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416 Further regression models the p
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418 Further regression models To be
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420 Further regression models 9.5.
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422 Further regression models in Ex
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424 Further regression models param
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426 Further regression models namel
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428 Further regression models All t
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430 Further regression models A not
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432 Further regression models Appro
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434 Further regression models form
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436 Further regression models times
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438 Further regression models block
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440 Further regression models This
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442 Further regression models Here
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444 Further regression models follo
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446 Further regression models missi
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448 Further regression models probl
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450 Further regression models perio
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452 Further regression models betwe
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454 Further regression models Spell
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456 Multivariate methods 13.2 Princ
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458 Multivariate methods High loadi
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Table 13.1 Correlation matrix of 20
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462 Multivariate methods eigenvalue
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464 Multivariate methods The place
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466 Multivariate methods allocation
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468 Multivariate methods would pred
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470 Multivariate methods and We fol
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472 Multivariate methods In the dis
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474 Multivariate methods Paired dat
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476 Multivariate methods Mean SE (m
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478 Multivariate methods x (3) x (5
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480 Multivariate methods Allocation
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482 Multivariate methods diagram in
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484 Multivariate methods explanator
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486 Modelling categorical data In t
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488 Modelling categorical data calc
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490 Modelling categorical data as t
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492 Modelling categorical data DF.
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494 Modelling categorical data in a
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496 Modelling categorical data if p
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498 Modelling categorical data When
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500 Modelling categorical data Tabl
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502 Modelling categorical data The
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504 Empirical methods for categoric
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16 Further Bayesian methods 16.1 Ba
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530 Further Bayesian methods Analyt
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532 Further Bayesian methods compli
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534 Further Bayesian methods One ap
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536 Further Bayesian methods result
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538 Further Bayesian methods (i) th
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540 Further Bayesian methods From (
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542 Further Bayesian methods A stra
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544 Further Bayesian methods the st
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546 Further Bayesian methods but ma
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548 Further Bayesian methods σ 30
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550 Further Bayesian methods practi
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552 Further Bayesian methods which
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554 Further Bayesian methods Densit
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556 Further Bayesian methods recurr
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558 Further Bayesian methods a ˆ m
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560 Further Bayesian methods values
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562 Further Bayesian methods distri
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564 Further Bayesian methods the se
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566 Further Bayesian methods probab
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17 Survival analysis 17.1 Introduct
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570 Survival analysis Table 17.1 Cu
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572 Survival analysis Table 17.2 Li
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574 Survival analysis otherwise the
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576 Survival analysis qtj ˆ dj=n 0
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578 Survival analysis quantities in
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580 Survival analysis Survival % 10
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582 Survival analysis logrank test
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584 Survival analysis the mean surv
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586 Survival analysis the death rat
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588 Survival analysis McGilchrist a
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590 Survival analysis The martingal
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592 Clinical trials trials. In drug
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594 Clinical trials first type of e
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596 Clinical trials . Proposed numb
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598 Clinical trials If the response
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600 Clinical trials methods of Baye
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602 Clinical trials represented by
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604 Clinical trials physicians find
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606 Clinical trials very minor proc
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608 Clinical trials each patient's
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610 Clinical trials Table 18.1 Resu
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612 Clinical trials solution is ava
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614 Clinical trials Administrative
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616 Clinical trials non-sequential
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618 Clinical trials Table 18.3 Maxi
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620 Clinical trials Standardized de
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622 Clinical trials A somewhat diff
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624 Clinical trials A trial showing
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626 Clinical trials Publication bia
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628 Clinical trials on different oc
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630 Clinical trials Table 18.5 Numb
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632 Clinical trials Treatment perio
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634 Clinical trials in cases where
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636 Clinical trials independent, an
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638 Clinical trials Sample size The
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640 Clinical trials and the restric
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642 Clinical trials original data.
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644 Clinical trials if the odds rat
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646 Clinical trials 3 2 1 0 -1 -2 -
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19 Statistical methods in epidemiol
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650 Statistical methods in epidemio
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Table 19.1 Death rate for two popul
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718 Laboratory assays such cases, t
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720 Laboratory assays and YT ˆ yT
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722 Laboratory assays significant a
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724 Laboratory assays The general p
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726 Laboratory assays This relation
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728 Laboratory assays Proportion po
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730 Laboratory assays P contribute
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732 Laboratory assays transformatio
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734 Laboratory assays Table 20.1 Es
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736 Laboratory assays P m iˆ1 ri l
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738 Laboratory assays synthesize hi
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740 Laboratory assays estimate of a
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Appendix tables 743
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2 0 0 02275 0 02222 0 02169 0 02118
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16 6 91 93 1 11 91 153 4 19 3 7 23
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16 0 128 0 258 0 392 0 535 0 690 0
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5 0 05 6 61 5 79 5 41 5 19 5 05 4 9
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Page 768 and 769: Table A7 Percentage points for the
Page 770 and 771: Table A9 Sample size for detecting
Page 772 and 773: Bailey N.T.J. (1975) The Mathematic
Page 774 and 775: Buyse M.E., Staquet M.J. and Sylves
Page 776 and 777: eceptor interactions. J. Ster. Bioc
Page 778 and 779: Etzioni R.D. and Weiss N.S. (1998)
Page 780 and 781: Goetghebeur E. and Lapp K. (1997) T
Page 782 and 783: sample data in randomized block and
Page 784 and 785: Lehmann E.L. (1975) Nonparametrics:
Page 786 and 787: Mehta C.R. (1994) The exact analysi
Page 788 and 789: Peto R., Pike M., Day N. et al. (19
Page 790 and 791: Scott J.E.S., Hunter E.W., Lee R.E.
Page 792 and 793: Thompson S.G. and Barber J.A. (2000
Page 794 and 795: Zhang H., Crowley J., Sox H.C. and
Page 796 and 797: 786 Author Index Brooks, S.P. 549,
Page 798 and 799: 788 Author Index Gart, J.J. 127, 67
Page 800 and 801: 790 Author Index Laurence, D.R. 519
Page 802 and 803: 792 Author Index RamoÂn, J.M. 536
Page 804 and 805: 794 Author Index Westley-Wise, V.J.
Page 806 and 807: 796 Subject Index Assays (cont.) ra
Page 808 and 809: 798 Subject Index Chronic obstructi
Page 810 and 811: 800 Subject Index Continuous variab
Page 812 and 813: 802 Subject Index Dot diagram 23, 2
Page 816 and 817: 806 Subject Index McNemar's test 12
Page 818 and 819: 808 Subject Index Normal (cont.) st
Page 820 and 821: 810 Subject Index Proportions Bayes
Page 822 and 823: 812 Subject Index Roughness penalty
Page 824 and 825: 814 Subject Index Standard (cont.)
Page 826: 816 Subject Index Tumour (cont.) in
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