- Page 2 and 3: To J.O. Irwin Mentor and friend
- Page 4 and 5: # 1971, 1987, 1994, 2002 by Blackwe
- Page 6 and 7: vi Contents 9.3 Factorial designs,
- Page 10 and 11: x Preface to the fourth edition . T
- Page 12 and 13: 1 The scope of statistics In one se
- Page 14 and 15: The scope of statistics 3 and quest
- Page 16 and 17: the acquired immune deficiency synd
- Page 18 and 19: The scope of statistics 7 Studies 1
- Page 20 and 21: 8 . 1% 6 . 1% 8 . 6% 11 . 2% Austra
- Page 22 and 23: y a careful choice of origin. A sud
- Page 24 and 25: ous form and the precise definition
- Page 26 and 27: has been created. The second method
- Page 28 and 29: mistaken for a real observation. Th
- Page 30 and 31: of this quantity will be valid to,
- Page 32 and 33: Table 2.2 Result of sputum examinat
- Page 34 and 35: 2.3 Summarizing numerical data 23 f
- Page 36 and 37: Frequency 12 000 11 000 10 000 9000
- Page 38 and 39: Cumulative relative frequency 100 7
- Page 40 and 41: 2 . 5 2 1 . 5 1 0 . 5 < 35 35-46 >
- Page 42 and 43: 2.4 Means and other measures of loc
- Page 44 and 45: From a purely descriptive point of
- Page 46 and 47: Figures 2.12 and 2.13 illustrate th
- Page 48 and 49: 2.6 Measures of variation 37 assay,
- Page 50 and 51: maximum values. Elaborations of thi
- Page 52 and 53: The modification of the divisor fro
- Page 54 and 55: 2.6 Measures of variation 43 P …x
- Page 56 and 57: can be carried out routinely, once
- Page 58 and 59:
3 Probability 3.1 The meaning of pr
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3.1 The meaning of probability 49 r
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Probability of A or B or both = (Pr
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The probability that there will be
- Page 66 and 67:
Consider a simple example in which
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of symptoms is potentially dangerou
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equals 1 0 9 ˆ 0 1, and l yj2, the
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Probability 1 . 0 0 . 5 0 0 1 2 3 4
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3.5 Expectation There is some diffi
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s 2 ˆ E…x m† 2 ˆ…1 ˆ 0 5:
- Page 78 and 79:
n ˆ 1: …3:11† 0 This is clearl
- Page 80 and 81:
The square in the multiplying facto
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3.7 The Poisson distribution 3.7 Th
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situation in which particles are ra
- Page 86 and 87:
Example 3.7 As an example, Table 3.
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Frequency 12 000 10 000 8000 6000 4
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Table 3.5 Some probabilities associ
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Table 3.7 Examples of the approxima
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4 Analysing means and proportions 4
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patternless way. We are putting for
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Significance tests 4.1 Statistical
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4.1 Statistical inference: tests an
- Page 102 and 103:
4.1 Statistical inference: tests an
- Page 104 and 105:
very large population. We may think
- Page 106 and 107:
Table 4.1 Distribution of means of
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would be less than 1 96 or greater
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Scale of variable, x µ x - + x -
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Probability density 0 . 5 0 . 4 0 .
- Page 114 and 115:
eceiving different diets. These are
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for the reduction in anxiety was fr
- Page 118 and 119:
s 2 1 ˆ P …1† 2 …x x1† , n
- Page 120 and 121:
The 95% confidence limits for m1 m2
- Page 122 and 123:
Are these results consistent with t
- Page 124 and 125:
From (4.1), p…1 var…x† ˆ n p
- Page 126 and 127:
Example 4.6 In a clinical trial to
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Newcombe (1998a), in an extensive s
- Page 130 and 131:
Therefore, a result significant at
- Page 132 and 133:
4.5 Comparison of two proportions A
- Page 134 and 135:
z ˆ 1 96, the lower confidence lim
- Page 136 and 137:
more acceptable properties. A more
- Page 138 and 139:
R ˆ r1=n1 r2=n2 and approximately
- Page 140 and 141:
4.5 Comparison of two proportions 1
- Page 142 and 143:
the 0 01 point of the x2 …1† di
- Page 144 and 145:
Both the standard error test and th
- Page 146 and 147:
Table 4.7 Data on malocclusion of t
- Page 148 and 149:
and causes no difficulty if the pre
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2 Given difference to be significan
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4.6 Sample-size determination 141 T
- Page 154 and 155:
may easily be determined using (4.2
- Page 156 and 157:
Unequal-sized groups It is usually
- Page 158 and 159:
5 Analysing variances, counts and o
- Page 160 and 161:
The result (5.2) enables us to find
- Page 162 and 163:
To use Table A4 we denote by s2 1 t
- Page 164 and 165:
The reason here is similar to that
- Page 166 and 167:
Example 5.2, continued With x ˆ 33
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Consider now an estimation problem.
- Page 170 and 171:
Many situations involve functions o
- Page 172 and 173:
g ˆ t2 f , a s2 v22 x 2 2 …5:16
- Page 174 and 175:
^p ˆ p, the `hat' symbol indicatin
- Page 176 and 177:
6 Bayesian methods 6.1 Subjective a
- Page 178 and 179:
6.1 Subjective and objective probab
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distributed. The methods developed
- Page 182 and 183:
hypothesis tested was that the popu
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unknown, and this is taken into acc
- Page 186 and 187:
6.3 Bayesian inference for proporti
- Page 188 and 189:
Example 6.2 In the clinical trial d
- Page 190 and 191:
dispersed reversed J-shape. With th
- Page 192 and 193:
1 r 2 0 ˆ s2 =n s2 0 ‡ s2 =n , w
- Page 194 and 195:
This approach has been used in conn
- Page 196 and 197:
ith mean, xi, is distributed as N
- Page 198 and 199:
7 Regression and correlation 7.1 As
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We must be careful to distinguish b
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y y i Y i x y i - Y i Fig. 7.3 A li
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Table 7.1 Birth weights of 32 babie
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The regression line of x on y may b
- Page 208 and 209:
This provides a useful interpretati
- Page 210 and 211:
and var…a† ˆs 2 1 0 n ‡ x 2
- Page 212 and 213:
y Fig. 7.6 A three-dimensional repr
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and the limits var…y0† ˆvar…
- Page 216 and 217:
7.5 Regression to the mean 205 anot
- Page 218 and 219:
7.5 Regression to the mean 207 true
- Page 220 and 221:
there is none, or because a conside
- Page 222 and 223:
P …yij yi† i, j 2 ˆ S1 3 Betwe
- Page 224 and 225:
null hypothesis these two mean squa
- Page 226 and 227:
Table 8.1 One-way analysis of varia
- Page 228 and 229:
If there is a variable xi associate
- Page 230 and 231:
µ µ i Distribution of µ i var(µ
- Page 232 and 233:
We have SSq DF MSq VR Between anima
- Page 234 and 235:
A further difficulty is that (8.22)
- Page 236 and 237:
Separate significance tests are now
- Page 238 and 239:
8.5 Comparison of several proportio
- Page 240 and 241:
The contribution to X 2 from these
- Page 242 and 243:
where P ˆ 57=288 ˆ 0 1979. This g
- Page 244 and 245:
8.7 Comparison of several variances
- Page 246 and 247:
Example 8.5 The following data were
- Page 248 and 249:
9.1 General remarks 237 another vac
- Page 250 and 251:
combination of r periods of storage
- Page 252 and 253:
Between columns : P …y:j y† 2
- Page 254 and 255:
Table 9.3 Clotting times (min) of p
- Page 256 and 257:
Table 9.4 Clotting time (min) of pl
- Page 258 and 259:
and adrenaline concentrations. Ther
- Page 260 and 261:
SAB ˆ… P T i, j 2 ij: =nK T 2 =N
- Page 262 and 263:
Example 9.3 Table 9.6* shows the re
- Page 264 and 265:
The two-factor interaction between
- Page 266 and 267:
2 In a multifactor experiment many
- Page 268 and 269:
9.4 Latin squares Suppose we wish t
- Page 270 and 271:
Table 9.8 Notation for Latin square
- Page 272 and 273:
Table 9.9 Measurements of area of b
- Page 274 and 275:
Incomplete block designs 9.5 Other
- Page 276 and 277:
studied and where attention very of
- Page 278 and 279:
andom selection of such families li
- Page 280 and 281:
significant; there is therefore no
- Page 282 and 283:
SSq DF MSq Between aliquots 19 898
- Page 284 and 285:
4 A measurement to be analysed may
- Page 286 and 287:
more extreme than that observed. Ta
- Page 288 and 289:
8 7 3 3 2 1 ‡1 ‡1 ‡1 ‡8 8 8
- Page 290 and 291:
and S ˆ UXY UYX , as already given
- Page 292 and 293:
Table 10.2 Percentage change in are
- Page 294 and 295:
UYX. The number of values to be exc
- Page 296 and 297:
Now, these scores can be regarded a
- Page 298 and 299:
In some studies treatments may be c
- Page 300 and 301:
than 0 86 and may be infinitely hig
- Page 302 and 303:
An alternative, and earlier method
- Page 304 and 305:
10.6Permutation and Monte Carlo tes
- Page 306 and 307:
Table 10.5 Some of the permutations
- Page 308 and 309:
sample of permutations. If the null
- Page 310 and 311:
The bootstrapÐbasic ideas and stan
- Page 312 and 313:
Standard errors for regression coef
- Page 314 and 315:
espectively. For a 100…1 2a†% i
- Page 316 and 317:
use for the estimation of standard
- Page 318 and 319:
scale, although the extent to which
- Page 320 and 321:
standard methods, it may have a far
- Page 322 and 323:
that the logarithm of the MPD does
- Page 324 and 325:
y y - y i Y i } } Yi - y - y } yi -
- Page 326 and 327:
y (a) y y x (b) (c) Fig. 11.2 Devia
- Page 328 and 329:
Table 11.3 Radiographic assessments
- Page 330 and 331:
Suppose, however, that the purpose
- Page 332 and 333:
11.3 Straight lines through the ori
- Page 334 and 335:
E…y† ˆa ‡ bx), or whether, a
- Page 336 and 337:
Equations (11.21) and (11.22) can e
- Page 338 and 339:
Vital capacity (l) 6 5 4 3 20 30 40
- Page 340 and 341:
Table 11.5 Analysis of variance for
- Page 342 and 343:
11.5 Analysis of covariance 11.5 An
- Page 344 and 345:
t ˆ d=SE…d† …11:35† has n1
- Page 346 and 347:
This analysis is based on the assum
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the calculations. Nowadays this is
- Page 350 and 351:
y ˆ b0 ‡ b1x1 ‡ b2x2 ‡ ...
- Page 352 and 353:
If a particular bj is not significa
- Page 354 and 355:
The 53 patients are divided into th
- Page 356 and 357:
follow the general multiple regress
- Page 358 and 359:
y T y y T Hy ˆ y T …I H†y …1
- Page 360 and 361:
in group 1 and 0 for all observatio
- Page 362 and 363:
These values suggest the possibilit
- Page 364 and 365:
ing zi and xj variables are also in
- Page 366 and 367:
11.8 Multiple regression in the ana
- Page 368 and 369:
analysis, the nature of the possibl
- Page 370 and 371:
Collinearity is a feature of the ex
- Page 372 and 373:
The SSq for (iii) is obtained by su
- Page 374 and 375:
y − Y (min) y − Y (min) 40 36 3
- Page 376 and 377:
influence on the fitted regression
- Page 378 and 379:
ti ˆ ei s… i† … p 1 hi† gi
- Page 380 and 381:
egression coefficients when the ith
- Page 382 and 383:
case, it is important to recognize
- Page 384 and 385:
to avoid values smaller than 2, by
- Page 386 and 387:
Both the Shapiro±Wilk and Lilliefo
- Page 388 and 389:
11.10 More on data transformation 3
- Page 390 and 391:
y y (a) Linear p = 1 (c) Cubic p =
- Page 392 and 393:
Population (millions) 60 50 40 30 2
- Page 394 and 395:
The SSq due to the linear and quadr
- Page 396 and 397:
There is only one conventional poly
- Page 398 and 399:
polynomials can be used for general
- Page 400 and 401:
Although appealingly simple, the ru
- Page 402 and 403:
Conduction velocity 30 25 20 15 10
- Page 404 and 405:
SSp ˆ Pn ‰yi s…xi†Š 2 ‡ a
- Page 406 and 407:
In this context it is useful to ext
- Page 408 and 409:
The present discussion has been ent
- Page 410 and 411:
12.3 Reference ranges 399 using the
- Page 412 and 413:
mi and si have been found, they are
- Page 414 and 415:
method achieves this by using maxim
- Page 416 and 417:
ather than a single measure of cent
- Page 418 and 419:
to be adequately accommodated by th
- Page 420 and 421:
As pointed out in §12.1, polynomia
- Page 422 and 423:
12.4 Non-linear regression 411 can
- Page 424 and 425:
12.4 Non-linear regression 413 f
- Page 426 and 427:
The sensitivity of the estimate of
- Page 428 and 429:
will not have good properties. Esse
- Page 430 and 431:
another. The distributions of diffe
- Page 432 and 433:
parameter m now measures the mean n
- Page 434 and 435:
12.5 Multilevel models 423 s2 F ‡
- Page 436 and 437:
potentially useful, especially for
- Page 438 and 439:
The estimate of s2 P is considerabl
- Page 440 and 441:
Non-linear models can be accommodat
- Page 442 and 443:
12.6 Longitudinal data 431 traditio
- Page 444 and 445:
12.6 Longitudinal data 433 Use of t
- Page 446 and 447:
esponses on an individual to a suit
- Page 448 and 449:
measures is sufficiently flexible t
- Page 450 and 451:
12.6 Longitudinal data 439 constant
- Page 452 and 453:
This discussion is rather more math
- Page 454 and 455:
H where H ˆ P N iˆ1 DT i 1 PN D i
- Page 456 and 457:
12.6 Longitudinal data 445 has been
- Page 458 and 459:
drop-out. In this it is supposed th
- Page 460 and 461:
A second and more important conside
- Page 462 and 463:
concerned with the relative amplitu
- Page 464 and 465:
of serial independence will not be
- Page 466 and 467:
13 Multivariate methods 13.1 Genera
- Page 468 and 469:
With each eigenvalue there is an as
- Page 470 and 471:
of (13.2) and the component correla
- Page 472 and 473:
Table 13.1 (cont.) Variable 10 11 1
- Page 474 and 475:
were perceived that were similar to
- Page 476 and 477:
D 2 …zA zB† ˆ 2 …13:5† var
- Page 478 and 479:
Another way of assessing the effect
- Page 480 and 481:
Table 13.3 Concentration of haemogl
- Page 482 and 483:
Bilirubin (mg per 100 ml), y 7 6 5
- Page 484 and 485:
The above considerations are in ter
- Page 486 and 487:
Table 13.4 Efficacy and safety meas
- Page 488 and 489:
according to their values of this f
- Page 490 and 491:
programs print the correlations bet
- Page 492 and 493:
Then g will take the value lj in ca
- Page 494 and 495:
depends on the structure in the dat
- Page 496 and 497:
14 Modelling categorical data 14.1
- Page 498 and 499:
Since the linear predictor of (14.2
- Page 500 and 501:
Fitting a model Two approaches are
- Page 502 and 503:
Table 14.1 A2 4 factorial set of pr
- Page 504 and 505:
where and b 0 0 Y ˆ b 0 0 ‡ b0 1
- Page 506 and 507:
14.2 Logistic regression 495 Altern
- Page 508 and 509:
The cumulative logits model Denote
- Page 510 and 511:
Example 14.2 Bishop (2000) followed
- Page 512 and 513:
giving fitted expectations of exp(0
- Page 514 and 515:
15 Empirical methods for categorica
- Page 516 and 517:
Frequency Group Variable x Positive
- Page 518 and 519:
where NT b ˆ N P nix2 i … P 2 ,
- Page 520 and 521:
the exponential, which gives a redu
- Page 522 and 523:
Sxy ˆ 252 …123 126†=66 ˆ 17 1
- Page 524 and 525:
Hierarchical classification In §15
- Page 526 and 527:
x 2 will not add exactly to the tot
- Page 528 and 529:
example, the data are stratified by
- Page 530 and 531:
p SE…d† ˆ var…d† , and, on
- Page 532 and 533:
X 2 MH ˆ…4 072†2 =3 808 ˆ 4 3
- Page 534 and 535:
Table 15.8 Combination of trends in
- Page 536 and 537:
In the above the probabilities of t
- Page 538 and 539:
Example 15.10 In Example 15.7 (Tabl
- Page 540 and 541:
16.2 Prior and posterior distributi
- Page 542 and 543:
of interval estimator is the highes
- Page 544 and 545:
Prior distributions A Bayesian anal
- Page 546 and 547:
16.2 Prior and posterior distributi
- Page 548 and 549:
16.2 Prior and posterior distributi
- Page 550 and 551:
to the likelihood, is a multivariat
- Page 552 and 553:
16.3 The Bayesian linear model 541
- Page 554 and 555:
(16.7) and (16.8). The density of t
- Page 556 and 557:
too small to be practical. Unfortun
- Page 558 and 559:
Table 16.2 Estimated summary quanti
- Page 560 and 561:
Markov chain Monte Carlo methods, o
- Page 562 and 563:
The second problem centres on the c
- Page 564 and 565:
p…yja, b, s 2 †/s n Q exp 2s2 ,
- Page 566 and 567:
16.4 Markov chain Monte Carlo metho
- Page 568 and 569:
close to the stationary distributio
- Page 570 and 571:
An important idea, although not one
- Page 572 and 573:
implementing these ideas for the li
- Page 574 and 575:
yi Yi, where yi is the ith data poi
- Page 576 and 577:
where g…u† ˆpa…u†=p…u†
- Page 578 and 579:
16.5 Model assessment and model cho
- Page 580 and 581:
of the population of which it is a
- Page 582 and 583:
Both forms of life-table are useful
- Page 584 and 585:
(5) The adjusted number at risk dur
- Page 586 and 587:
Application of (17.7) can lead to i
- Page 588 and 589:
E…djA† ˆn 0 jA dj=n 0 j , var
- Page 590 and 591:
Table 17.3 Survival of patients wit
- Page 592 and 593:
Thus it is demonstrated that the di
- Page 594 and 595:
17.8 Regression and proportional-ha
- Page 596 and 597:
Cox's proportional-hazards model Si
- Page 598 and 599:
elation to logistic regression and
- Page 600 and 601:
17.9 Diagnostic methods 589 is ofte
- Page 602 and 603:
18 Clinical trials 18.1 Introductio
- Page 604 and 605:
18.2 Phase I and Phase II trials 59
- Page 606 and 607:
consequences of selecting or reject
- Page 608 and 609:
commonly found in medical practice,
- Page 610 and 611:
18.3 Planning a Phase III trial 599
- Page 612 and 613:
Balance 18.4 Treatment assignment 6
- Page 614 and 615:
ment will exceed, perhaps very cons
- Page 616 and 617:
The principle of masking the identi
- Page 618 and 619:
might, for example, be thought that
- Page 620 and 621:
group could be assigned to a patien
- Page 622 and 623:
18.6 Protocol departures 611 absolu
- Page 624 and 625:
assumptions that may not be easily
- Page 626 and 627:
Sequential analysis 18.7 Data monit
- Page 628 and 629:
To control the Type I error probabi
- Page 630 and 631:
Example 18.2 18.7 Data monitoring 6
- Page 632 and 633:
Haybittle (1971) and Peto et al. (1
- Page 634 and 635:
ealization of the effect of repeate
- Page 636 and 637:
trial results should be reported in
- Page 638 and 639:
sion and survival will inevitably i
- Page 640 and 641:
patients to settle down and for the
- Page 642 and 643:
d* ˆ 2 172 s 2 ˆ 11 005 p SE…d*
- Page 644 and 645:
18.9 Special designs 633 If there i
- Page 646 and 647:
than two periods, the possibility m
- Page 648 and 649:
quite acceptable as being within th
- Page 650 and 651:
a recurrence of their hernia within
- Page 652 and 653:
18.10 Meta-analysis Glass (1976) de
- Page 654 and 655:
1993; Proskin, 1993). There is thus
- Page 656 and 657:
Study start year, code and name Dea
- Page 658 and 659:
18.10 Meta-analysis 647 the combine
- Page 660 and 661:
19.2 The planning of surveys 19.2 T
- Page 662 and 663:
where the first number specifies th
- Page 664 and 665:
Apart from the increased precision
- Page 666 and 667:
example discussed above we take a s
- Page 668 and 669:
heterogeneity between animals in pr
- Page 670 and 671:
important. Indeed, in a complete en
- Page 672 and 673:
alone may explain a difference in c
- Page 674 and 675:
asymmetric (§5.2). The standardize
- Page 676 and 677:
want to compare one SMR with anothe
- Page 678 and 679:
comparison of two groups, A and B,
- Page 680 and 681:
may relate to workers employed 30 y
- Page 682 and 683:
Table 19.4 Standardized annual deat
- Page 684 and 685:
P1 P3 P2 P4 ˆ P1P4 …ˆ c, say†
- Page 686 and 687:
Mantel±Haenszel method is more rob
- Page 688 and 689:
R ˆ r : …19:26† s This can be
- Page 690 and 691:
Table 19.5 Combination of relative
- Page 692 and 693:
(19.25). For further details of the
- Page 694 and 695:
An alternative expression may be de
- Page 696 and 697:
Therefore approximate 95% confidenc
- Page 698 and 699:
Table 19.6 Subject-years at risk (
- Page 700 and 701:
then ln m ij ˆ ln nij ‡ b 0j ‡
- Page 702 and 703:
one born in 1937 and recorded in 20
- Page 704 and 705:
The reliability of diagnostic tests
- Page 706 and 707:
The proportion of true positives am
- Page 708 and 709:
distribution of x. Nevertheless, th
- Page 710 and 711:
or median, values in the main data
- Page 712 and 713:
Then Io ˆ 152 ˆ 0 905 168 Ie ˆ k
- Page 714 and 715:
An alternative set of weights, whic
- Page 716 and 717:
19.11 Intraclass correlation 705 Su
- Page 718 and 719:
The agreement between the two rater
- Page 720 and 721:
2 We shall be interested in the mea
- Page 722 and 723:
If a screening programme is availab
- Page 724 and 725:
If the geographical distribution of
- Page 726 and 727:
329 cases of Creutzfeld±Jakob dise
- Page 728 and 729:
20 Laboratory assays 20.1 Biologica
- Page 730 and 731:
Probability density (a) T S Ratio
- Page 732 and 733:
where P …Sxx† is the pooled Wit
- Page 734 and 735:
labelled antigen so that the count
- Page 736 and 737:
where b T ˆ rb S. Equations (20.11
- Page 738 and 739:
The SSq for `intersection' shows wh
- Page 740 and 741:
where Vd ˆ var(d ), Cdb ˆ cov(d,
- Page 742 and 743:
Michaelis±Menten assays In the pre
- Page 744 and 745:
(1989) propose an approach which tr
- Page 746 and 747:
the means to detect the presence or
- Page 748 and 749:
generally provides a close approxim
- Page 750 and 751:
attempt to model the processes that
- Page 752:
20.6 Tumour incidence studies 741 t
- Page 755 and 756:
z 1 2 P Table A1 Areas in tail of t
- Page 757 and 758:
V Degrees of freedom, n χ 2 ν, P
- Page 759 and 760:
−t υ, P Degrees of freedom, n To
- Page 761 and 762:
DF for denominator, n2 F P,v1,v2 P
- Page 763 and 764:
Table A4 (continued) DF for denomin
- Page 765 and 766:
Q p, α α Number of groups, p Tabl
- Page 767 and 768:
756 Appendix tables Table A6 Percen
- Page 769 and 770:
758 Appendix tables Table A8 Sample
- Page 771 and 772:
References Abdelbasit K.M. and Plac
- Page 773 and 774:
762 References Besag J. (1974) Spat
- Page 775 and 776:
764 References Cohen J. (1968) Weig
- Page 777 and 778:
766 References Does R.J.M.M., Strij
- Page 779 and 780:
768 References the Community Interv
- Page 781 and 782:
770 References Haybittle J.L. (1971
- Page 783 and 784:
772 References wellbeing and future
- Page 785 and 786:
774 References erythemal responses
- Page 787 and 788:
776 References Newcombe R.G. (1998a
- Page 789 and 790:
778 References Roberts G.O. (1996)
- Page 791 and 792:
780 References Spiegelhalter D.J.,
- Page 793 and 794:
782 References Whitehead J. (1997)
- Page 795 and 796:
Author Index Abdalla, M. 613 Abdelb
- Page 797 and 798:
DeGruttola, V. 627 Dellaportas, P.
- Page 799 and 800:
Hoenich, N.A. 634 Hogan, A. 710 Hog
- Page 801 and 802:
Mock, P.A. 578 Moeschberger, M.L. 5
- Page 803 and 804:
Spiegal, N. 426 Spiegelhalter, D.J.
- Page 805 and 806:
Subject Index Note: Page nubers in
- Page 807 and 808:
pressures distribution of 30, 76, 7
- Page 809 and 810:
of several variances 233, 234 of tw
- Page 811 and 812:
Deletion of variables 343, 344 auto
- Page 813 and 814:
with proportions 490 with two-level
- Page 815 and 816:
Kendall's S 278±282 Kendall's 290
- Page 817 and 818:
generalized linear 309, 355, 377, 3
- Page 819 and 820:
heterogeneity (dispersion) test 234
- Page 821 and 822:
(see also Collinearity, Residual) e
- Page 823 and 824:
Shrinkage 169, 179±181, 204, 425,
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product-limit (Kaplan±Meier) metho