Unified Methods for Censored Longitudinal Data and CausalityDuring the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data on subjects or organisms over time. The goal of such studies can be formulated as estimation of a finite dimensional parameter of the population distribution corresponding to the observed time-dependent process. Such estimation problems arise in survival analysis, causal inference and regression analysis. This book provides a fundamental statistical framework for the analysis of complex longitudinal data. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures subject to informative censoring and treatment assignment in so called semiparametric models. Semiparametric models are particularly attractive since they allow the presence of large unmodeled nuisance parameters. These techniques include estimation of regression parameters in the familiar (multivariate) generalized linear regression and multiplicative intensity models. They go beyond standard statistical approaches by incorporating all the observed data to allow for informative censoring, to obtain maximal efficiency, and by developing estimators of causal effects. It can be used to teach masters and Ph.D. students in biostatistics and statistics and is suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data. |
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Contents
III | 8 |
IV | 16 |
V | 17 |
VI | 21 |
VII | 23 |
VIII | 27 |
IX | 40 |
XI | 45 |
LXXV | 205 |
LXXVI | 206 |
LXXVII | 208 |
LXXVIII | 211 |
LXXIX | 217 |
LXXX | 221 |
LXXXI | 224 |
LXXXII | 225 |
XII | 48 |
XIII | 55 |
XIV | 61 |
XV | 62 |
XVI | 64 |
XVII | 68 |
XVIII | 69 |
XIX | 77 |
XX | 79 |
XXI | 81 |
XXII | 93 |
XXIII | 94 |
XXIV | 97 |
XXV | 99 |
XXVI | 102 |
XXIX | 103 |
XXX | 105 |
XXXI | 107 |
XXXII | 111 |
XXXIII | 114 |
XXXV | 124 |
XXXVI | 125 |
XXXVII | 126 |
XXXVIII | 128 |
XXXIX | 131 |
XL | 135 |
XLI | 137 |
XLII | 139 |
XLIII | 141 |
XLIV | 142 |
XLV | 144 |
XLVI | 145 |
XLVII | 146 |
XLVIII | 150 |
XLIX | 151 |
L | 152 |
LI | 153 |
LII | 159 |
LIII | 166 |
LIV | 167 |
LV | 169 |
LVI | 170 |
LVII | 172 |
LVIII | 175 |
LIX | 176 |
LXI | 177 |
LXII | 179 |
LXIII | 181 |
LXIV | 183 |
LXV | 184 |
LXVI | 191 |
LXVII | 192 |
LXVIII | 195 |
LXIX | 196 |
LXX | 197 |
LXXI | 198 |
LXXII | 200 |
LXXIII | 201 |
LXXIV | 204 |
LXXXIII | 230 |
LXXXIV | 232 |
LXXXVII | 234 |
LXXXVIII | 235 |
LXXXIX | 236 |
XC | 239 |
XCI | 241 |
XCII | 243 |
XCIII | 244 |
XCIV | 245 |
XCV | 246 |
XCVI | 248 |
XCVII | 250 |
XCVIII | 251 |
XCIX | 252 |
C | 253 |
CI | 255 |
CII | 256 |
CIII | 257 |
CIV | 258 |
CV | 260 |
CVI | 262 |
CVII | 266 |
CIX | 268 |
CX | 270 |
CXI | 271 |
CXIII | 273 |
CXIV | 275 |
CXV | 276 |
CXVI | 282 |
CXIX | 286 |
CXX | 290 |
CXXI | 292 |
CXXII | 293 |
CXXIII | 294 |
CXXIV | 299 |
CXXV | 302 |
CXXVI | 311 |
CXXIX | 318 |
CXXX | 324 |
CXXXI | 326 |
CXXXII | 329 |
CXXXIII | 334 |
CXXXIV | 338 |
CXXXV | 343 |
CXXXVI | 347 |
CXXXVII | 353 |
CXXXVIII | 357 |
CXXXIX | 359 |
CXL | 360 |
CXLI | 362 |
CXLII | 366 |
CXLIII | 368 |
371 | |
388 | |
394 | |
CXLVII | |
Other editions - View all
Unified Methods for Censored Longitudinal Data and Causality Mark J. van der Laan,James M Robins No preview available - 2011 |
Unified Methods for Censored Longitudinal Data and Causality Mark J Van Der Laan,James M Robins No preview available - 2003 |
Common terms and phrases
addition apply assume assumption bivariate censoring Chapter choice components conditional Consider consistent continuous corresponding covariates data estimating functions defined denote depends derivative discrete distribution element equals example full data estimating full data model full data structure Fx,G given hazards hopt IC(Y includes independent influence curve initial intensity interest inverse known Laan Lemma mapping marginal mean measure mechanism method multivariate nonparametric Note nuisance parameter nuisance tangent space observed data observed data estimating obtain operator optimal orthogonal complement p(Fx parameter particular PFx,g points possible probability projection proposed proves random regression model regular represent representation result right-censored Robins robust estimator sample satisfying score selection shows simulation smooth specified Statistical Suppose TCAR Theorem treatment true u(Fx values variable variance weighted zero
Popular passages
Page 384 - Zhao, LP ( 1994). Estimation of regression coefficients when some regressors are not always observed.
Page 380 - Newey, WK, 1990. Semiparametric efficiency bounds. Journal of Applied Econometrics 5, 99-135.
Page 371 - Hepatitis C Virus Infection among Injection Drug Users in the United States', Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology 18 (Suppl.
Page 385 - A semiparametric proportional odds regression model for the analysis of current status data, Journal of the American Statistical Association, 91(N434), 713-721.
Page 387 - A Consistent Estimator for the Distribution of Quality Adjusted Survival Time.
Page 384 - The mathematical foundations of confounding in epidemiology, Computers and Mathematics with Applications 14, 869-916.
Page 379 - Lin, DY, Robins, JM, and Wei, LJ (1996), Comparing two failure time distributions in the presence of dependent censoring, Biometrika, 83, 381-393.