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A.S. Whittemore 395variate values. A risk model then uses these baseline covariates to assign eachsubject a risk of developing the outcome of interest during a specified subsequenttime period. For example, the Breast Cancer Family Registry (BCFR),a consortium of six institutions in the United States, Canada and Australia,has been monitoring the vital statuses and cancer occurrences of registry participantsfor more than ten years (John et al., 2004). The New York siteof the BCFR has used the baseline covariates of some 1900 female registryparticipants to assign each of them a ten-year probability of breast cancerdevelopment according to one of several risk models (Quante et al., 2012).These assigned risks are then compared to actual outcomes during follow-up.Subjects who die before outcome occurrence are classified as negative for theoutcome, so those with life-threatening co-morbidities may have low outcomerisk because they are likely to die before outcome development.Using cohort data to estimate outcome probabilities presents statisticalchallenges. For example, some subjects may not be followed for the full riskperiod; instead they are last observed alive and outcome-free after only a fractionof the period. An analysis that excludes these subjects may yield biasedestimates. Instead, censored time-to-failure analysis is needed, and the analysismust accommodate the competing risk of death (Kalbfleisch and Lawless,1998; Kalbfleisch and Prentice, 2002; Putter et al., 2007). Another challengearises when evaluating risk models that include biomarkers obtained fromblood collected at cohort entry. Budgetary constraints may prohibit costlybiomarker assessment for the entire cohort, and cost-efficient sampling designsare needed, such as a nested case-control design (Ernster, 1994), a casecohortdesign (Prentice, 1986), or a two-stage sampling design (Whittemoreand Halpern, 2013).Model calibration is often assessed by grouping subjects into quantiles ofassigned risk, and comparing estimated outcome probability to mean assignedrisk within each quantile. Results are plotted on a graph called an attributiondiagram (AD) (Hsu and Murphy, 1986). For example, the top two panelsof Figure 35.2 show ADs for subjects from the NY-BCFR cohort who havebeen grouped in quartiles of breast cancer risk as assigned by two breastcancer risk models, the BCRAT model and the International Breast CancerIntervention Study (IBIS) model (Tyrer et al., 2004). The null hypothesis ofequality between quantile-specific mean outcome probabilities and mean assignedrisks is commonly tested by classifying subjects into risk groups andapplying a chi-squared goodness-of-fit statistic. This approach has limitations:1) the quantile grouping is arbitrary and varies across cohorts sampled fromthe same population; 2) the averaging of risks over subjects in a quantile canobscure subsets of subjects with poor model fit; 3) when confidence intervalsfor estimated outcome probabilities exclude the diagonal line it is difficult totrouble-shoot the risk model; 4) assuming a chi-squared asymptotic distributionfor the goodness-of-fit statistic ignores the heterogeneity of risks withinquantiles.
396 Good health: Statistical challengesFIGURE 35.2Grouped and individualized goodness-of-fit of BCRAT- and IBIS-assignedbreast cancer risks in NY BCFR data.Some of these limitations are addressed by an alternative approach thatuses nearest-neighbor (NN) methods to estimate an outcome probability foreach set of subjects with a given assigned risk (Akritas, 1994; Heagerty et al.,2000). The NN estimate of actual risk for a subject with assigned risk r isbased on the set of all individuals with risks r ′ such that |G(r) − G(r ′ )|
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ContentsPrefacexviiContributorsI Th
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viii15 We live in exciting times 15
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x24.6 Open areas for research . . .
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xii36 Buried treasures 405Michael A
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xiv45.3 Multi-phase inference . . .
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PrefaceStatistics is the science of
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PrefacexixPart V comprises seven ar
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xxiiNancy FlournoyUniversity of Mis
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Part IThe history of COPSS
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4 A brief history of COPSSOnchiota
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6 A brief history of COPSSThe items
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8 A brief history of COPSSTABLE 1.1
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10 A brief history of COPSSwomen of
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12 A brief history of COPSSTABLE 1.
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16 A brief history of COPSS1.4.3 Ge
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20 A brief history of COPSSFor more
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2Reminiscences of the Columbia Univ
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I. Olkin 25Bechhofer, Allan Birnbau
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I. Olkin 27Milton Sobel was the tea
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3AcareerinstatisticsHerman Chernoff
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H. Chernoff 31given three-day basic
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H. Chernoff 33Savage tried to defen
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H. Chernoff 35In working on an arti
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H. Chernoff 37measure the loss as t
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H. Chernoff 39ReferencesAlbert, A.E
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4“. . . how wonderful the field o
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D.R. Brillinger 43He went on to be
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D.R. Brillinger 45Tukey wrote many
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D.R. Brillinger 4718. Personal comm
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50 Unorthodox journey to statistics
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6Statistics before and after my COP
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P.J. Bickel 616.3.1 Imperial Colleg
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P.J. Bickel 63make. This was first
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P.J. Bickel 65We eventually develop
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P.J. Bickel 67school interest, biol
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P.J. Bickel 69ReferencesBean, D., B
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P.J. Bickel 71Meinshausen, N., Bick
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74 Accidental biostatistics profess
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84 Passion for statisticsme is its
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86 Passion for statisticsTheir Biom
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88 Passion for statisticsthe first
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90 Passion for statisticsIn my expe
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92 Passion for statistics8.5 Job an
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94 Passion for statisticsperiod. An
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96 Passion for statisticsNeyman, J.
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98 Reflections on a statistical car
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100 Reflections on a statistical ca
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110 Science mixes it up with statis
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11Lessons from a twisted career pat
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J.S. Rosenthal 119My math professor
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J.S. Rosenthal 121contrary, it null
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J.S. Rosenthal 123of statistical re
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J.S. Rosenthal 125be open to whatev
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J.S. Rosenthal 12711.4 Final though
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130 Promoting equityand fellowship
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132 Promoting equityevaluate such e
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134 Promoting equityfor equity. But
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136 Promoting equityAssistant Secre
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Part IIIPerspectives on the fieldan
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142 Statistics in service to the na
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154 Where are the majors? ArtBachel
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156 Where are the majors?References
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158 Exciting timesCritically, Efron
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160 Exciting timesIn general I find
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162 Exciting timesI entered the Uni
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164 Exciting times15.3.3 Joining th
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166 Exciting timesvariability of a
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168 Exciting timesHabemma, J.D.F.,
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16The bright future of applied stat
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R.A. Irizarry 173FIGURE 16.1Illustr
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R.A. Irizarry 17516.4 The bright fu
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17The road travelled: From statisti
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N. Chatterjee 179the Kaplan-Meyer e
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N. Chatterjee 18117.4 Genome-wide a
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N. Chatterjee 183by any means. I ju
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N. Chatterjee 185right information
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N. Chatterjee 187The risk of cancer
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190 Journey into genetics and genom
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19Reflections on women in statistic
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M.E. Thompson 205Pearson in England
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M.E. Thompson 207TABLE 19.1The 14 C
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M.E. Thompson 209time series, the s
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M.E. Thompson 211the founding Chair
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M.E. Thompson 21319.7 The current s
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M.E. Thompson 215Yarmie, A. (2003).
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218 “The whole women thing”seem
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220 “The whole women thing”of t
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222 “The whole women thing”on a
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224 “The whole women thing”20.5
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226 “The whole women thing”Ifin
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228 “The whole women thing”Lond
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230 Reflections on diversityas a st
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232 Reflections on diversityall of
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234 Reflections on diversityforce m
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22Why does statistics have two theo
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D.A.S. Fraser 23922.2 65 years and
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D.A.S. Fraser 241function is taken
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D.A.S. Fraser 24322.4 Inference for
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D.A.S. Fraser 245that record the ef
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D.A.S. Fraser 247variable that give
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D.A.S. Fraser 249simple scalar case
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D.A.S. Fraser 251DiCiccio, T.J. and
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23Conditioning is the issueJames O.
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J.O. Berger 255Pedagogical example:
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J.O. Berger 257The SRP does not say
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J.O. Berger 25923.5 Conditional fre
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J.O. Berger 261conditional error pr
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J.O. Berger 263others). If x is the
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J.O. Berger 265How does a frequenti
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24Statistical inference from aDemps
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A.P. Dempster 269tion is simply une
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A.P. Dempster 271As the world of st
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A.P. Dempster 273when the X i are a
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A.P. Dempster 27524.5 Nonparametric
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A.P. Dempster 277can easily happen
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A.P. Dempster 279ReferencesBorel,
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25Nonparametric BayesDavid B. Dunso
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D.B. Dunson 283troduced, providing
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D.B. Dunson 285els in spatial stati
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D.B. Dunson 287methods as also prov
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D.B. Dunson 289ing background, and
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D.B. Dunson 291Murray, J.S., Dunson
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294 Choosing default methodsimagine
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296 Choosing default methods(a) mat
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298 Choosing default methodspartial
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300 Choosing default methodsdirecti
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27Serial correlation and Durbin-Wat
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T.W. Anderson 305The characteristic
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T.W. Anderson 307Consider the seria
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28Anon-asymptoticwalkinprobabilitya
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P. Massart 311(at least for discret
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P. Massart 31328.2.2 Non-asymptopia
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P. Massart 315where P = sµ. In oth
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P. Massart 317Hence by Talagrand’
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P. Massart 319ReferencesAkaike, H.
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P. Massart 321Talagrand, M. (1995).
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324 The present’s futuredata. In
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326 The present’s futureTABLE 29.
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328 The present’s future124356981
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330 The present’s futuremethod, t
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332 The present’s futureFIGURE 29
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334 The present’s futureIchino, M
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336 Lessons in biostatistics30.2 It
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338 Lessons in biostatisticsprognos
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340 Lessons in biostatisticstime fo
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342 Lessons in biostatisticsmeaning
Page 365 and 366: 344 Lessons in biostatistics75% to
Page 367 and 368: 346 Lessons in biostatisticshistolo
Page 370 and 371: 31AvignetteofdiscoveryNancy Flourno
Page 372 and 373: N. Flournoy 351FIGURE 31.1Incidence
Page 374 and 375: N. Flournoy 353FIGURE 31.3Mean resp
Page 376 and 377: N. Flournoy 355FIGURE 31.4Kaplan-Me
Page 378 and 379: N. Flournoy 357As the study proceed
Page 380 and 381: 32Statistics and public health rese
Page 382 and 383: R.L. Prentice 36132.2 Public health
Page 384 and 385: R.L. Prentice 363from urinary excre
Page 386 and 387: R.L. Prentice 36532.5 Clinical tria
Page 388: R.L. Prentice 367Kaplan, E.L. and M
Page 391 and 392: 370 Statistics in a new erative eff
Page 393 and 394: 372 Statistics in a new eraular com
Page 395 and 396: 374 Statistics in a new erajoint de
Page 397 and 398: 376 Statistics in a new eraof the n
Page 399 and 400: 378 Statistics in a new eraLai, T.L
Page 402 and 403: 34Meta-analyses: Heterogeneity can
Page 404 and 405: N.M. Laird 383into the sum of a “
Page 406 and 407: N.M. Laird 385are “approximately
Page 408 and 409: N.M. Laird 387included in the major
Page 410: N.M. Laird 389Yusuf, S., Peto, R.,
Page 413 and 414: 392 Good health: Statistical challe
Page 415: 394 Good health: Statistical challe
Page 427 and 428: 406 Buried treasuresmeasurement tec
Page 429 and 430: 408 Buried treasuresIn a most rewar
Page 431 and 432: 410 Buried treasureslems different
Page 434 and 435: 37Survey sampling: Past controversi
Page 436 and 437: R.J. Little 41537.2 Probability or
Page 438 and 439: R.J. Little 417for the “design-ba
Page 440 and 441: R.J. Little 419estimate of variance
Page 442 and 443: R.J. Little 421Example 2 continued
Page 444 and 445: R.J. Little 42337.3.4 The design-mo
Page 446 and 447: R.J. Little 42537.5 ConclusionsI am
Page 448 and 449: R.J. Little 427Isaki, C.T. and Full
Page 450 and 451: 38Environmental informatics: Uncert
Page 452 and 453: N. Cressie 431and conquer” strate
Page 454 and 455: N. Cressie 433In the last 20 years,
Page 456 and 457: N. Cressie 435Once the satellite ha
Page 458 and 459: N. Cressie 437FIGURE 38.1Locations
Page 460 and 461: N. Cressie 439regional and seasonal
Page 462 and 463: N. Cressie 441ciated with the spati
Page 464 and 465: N. Cressie 443is the set of all pix
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N. Cressie 445challenge is to devel
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N. Cressie 447Gourdji, S.M., Muelle
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N. Cressie 449Wikle, C.K., Milliff,
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452 Statistical geneticsTABLE 39.1T
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454 Statistical geneticsStewart, 19
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456 Statistical geneticslocidata
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458 Statistical geneticssufficientl
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460 Statistical geneticsBrown, M.D.
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462 Statistical geneticsLange, K. a
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464 Statistical geneticsThompson, E
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466 Targeted learningods for nonpar
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468 Targeted learningdenote the obs
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470 Targeted learningSuch an estima
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472 Targeted learningliterature pro
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474 Targeted learninglihood estimat
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476 Targeted learning40.6 Some spec
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478 Targeted learningmemory challen
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480 Targeted learningvan der Laan,
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482 Ah-ha momentwe have just seen.
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484 Ah-ha momentthe data is unchang
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486 Ah-ha momentis well defined and
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488 Ah-ha momentit was something of
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490 Ah-ha momentdefines a Euclidean
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492 Ah-ha momentparticipate in the
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494 Ah-ha momentLee, Y., Wahba, G.,
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42In praise of sparsity and convexi
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R.J. Tibshirani 499TABLE 42.1Asampl
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R.J. Tibshirani 501CancerEpithelial
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R.J. Tibshirani 503that the error v
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R.J. Tibshirani 505Tibshirani, R.J.
Page 529 and 530:
508 Features of Big Dataproblems en
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510 Features of Big Datalearning st
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512 Features of Big Data1210(a)p =
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514 Features of Big Data25002000150
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516 Features of Big DataAs an illus
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518 Features of Big Datafor some gi
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520 Features of Big Data43.7.3 Prop
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522 Features of Big DataBühlmann,
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44Rise of the machinesLarry A. Wass
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L.A. Wasserman 527if an idea requir
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L.A. Wasserman 529●●●● ●
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L.A. Wasserman 531●●●●●
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L.A. Wasserman 533FIGURE 44.4Simula
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L.A. Wasserman 53544.7 Education an
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45Atrioofinferenceproblemsthatcould
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X.-L. Meng 539it is directly rooted
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X.-L. Meng 541exactly, but this doe
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X.-L. Meng 543That is, when we do n
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X.-L. Meng 545(a) For what classes
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X.-L. Meng 547subsequent analysts c
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X.-L. Meng 549can prove only that (
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X.-L. Meng 551In general, “What t
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X.-L. Meng 553vironmental progress
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X.-L. Meng 555which will be compare
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X.-L. Meng 557(a) Given partial kno
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X.-L. Meng 559AcknowledgementsThe m
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X.-L. Meng 561Knuth, D. (1997). The
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Part VAdvice for the nextgeneration
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566 Inspiration, aspiration, ambiti
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568 Inspiration, aspiration, ambiti
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47Personal reflections on the COPSS
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R.J. Carroll 573them is, by definit
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R.J. Carroll 575(Carroll, 2003). In
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R.J. Carroll 57747.8 After the Pres
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R.J. Carroll 579Carroll, R.J., Spie
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582 Publishing without perishingthe
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584 Publishing without perishingbeg
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586 Publishing without perishingme,
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588 Publishing without perishingalr
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590 Publishing without perishingpor
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49Converting rejections into positi
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D.B. Rubin 595This pair of submissi
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D.B. Rubin 597There are several les
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D.B. Rubin 599very sound article, w
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D.B. Rubin 60149.8 ConclusionIhaveb
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D.B. Rubin 603Rubin, D.B. (1972). A
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606 Importance of mentorstwice as t
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608 Importance of mentorsexperiment
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610 Importance of mentorsposition a
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612 Importance of mentors50.7 The t
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51Never ask for or give advice, mak
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T. Speed 617overly generous. Perhap
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T. Speed 619and for finding enjoyme
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622 Thirteen rules3. Waste a lot of
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