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226 Bibliography
O’Kelly, Michael, and Bohdana Ratitch. 2014. Clinical Trials with Missing
Data: A Guide for Practitioners. John Wiley & Sons.
Pearl, Judea. 2009. Causality. Cambridge University Press.
Pearl, Judea, Madelyn Glymour, and Nicholas Jewell. 2016. Causal Inference
in Statistics: A primer. John Wiley & Sons.
Pearl, Judea, and Dana Mackenzie. 2018. The Book of Why: The New Science
of Cause and Effect. Basic Books.
Prentice, Ross, John Kalbfleisch, Arthur Peterson, Nancy Flournoy, Vernon
Farewell, and Norman Breslow. 1978. “The analysis of failure times in
the presence of competing risks.” Biometrics 34 (4): 541–554.
Ratitch, Bohdana, and Michael O’Kelly. 2011. “Implementation of pattern-
mixture models using standard SAS/STAT procedures.” Proceedings of
Pharmaceutical Industry SAS User Group.
Robins, James. 1986. “A new approach to causal inference in mortality studies
with a sustained exposure period—application to control of the healthy
worker survivor effect.” Mathematical Modelling 7 (9-12): 1393–1512.
Rosenbaum, Paul, and Donald Rubin. 1983. “The central role of the propensity
score in observational studies for causal effects.” Biometrika 70 (1): 41–
55.
Royston, Patrick, and Mahesh Parmar. 2011. “The use of restricted mean
survival time to estimate the treatment effect in randomized clinical tri-
als when the proportional hazards assumption is in doubt.” Statistics in
Medicine 30 (19): 2409–2421.
Rubin, Donald. 1975. “Bayesian inference for causality: The importance of
randomization.” In The Proceedings of the social statistics section of the
American Statistical Association, 233:239. American Statistical Associa-
tion Alexandria, VA.
. 1976. “Inference and missing data.” Biometrika 63 (3): 581–592.
. 1980. “Discussion on “Randomization analysis of experimental data:
The Fisher randomization test” by Basu.” Journal of the American sta-
tistical association 75 (371): 591–593.
. 1996. “Multiple imputation after 18+ years.” Journal of the American
statistical Association 91 (434): 473–489.
. 2004. Multiple Imputation for Nonresponse in Surveys. John Wiley &
Sons.
Schneeweiss, Sebastian. 2006. “Sensitivity analysis and external adjustment
for unmeasured confounders in epidemiologic database studies of thera-
peutics.” Pharmacoepidemiology and Drug Safety 15 (5): 291–303.
Bibliography 227
Stein, Charles. 1956. “Efficient nonparametric testing and estimation.” In Pro-
ceedings of the Third Berkeley Symposium on Mathematical Statistics and
Probability, Volume 1: Contributions to the Theory of Statistics, 3: 187–
196. University of California Press.
Stitelman, Ori, Victor De Gruttola, and Mark van der Laan. 2012. “A general
implementation of TMLE for longitudinal data applied to causal inference
in survival analysis.” The International Journal of Biostatistics 8 (1).
Tchetgen Tchetgen, Eric, Andrew Ying, Yifan Cui, Xu Shi, and Wang Miao.
2020. “An introduction to proximal causal learning.” arXiv preprint
arXiv:2009.10982.
Thorpe, Kevin, Merrick Zwarenstein, Andrew Oxman, Shaun Treweek, Curt
Furberg, Douglas Altman, Sean Tunis, Eduardo Bergel, Ian Harvey,
David Magid, et al. 2009. “A pragmatic–explanatory continuum indicator
summary (PRECIS): a tool to help trial designers.” Journal of Clinical
Epidemiology 62 (5): 464–475.
Tsiatis, Anastasios. 2006. Semiparametric Theory and Missing Data. Springer.
Tsiatis, Anastasios, Marie Davidian, Shannon Holloway, and Eric Laber. 2020.
Dynamic Treatment Regimes: Statistical Methods for Precision Medicine.
Chapman & Hall/CRC.
van der Laan, Mark, Eric Polley, and Alan Hubbard. 2007. “Super learner.”
Statistical Applications in Genetics and Molecular Biology 6 (1).
van der Laan, Mark, and Sherri Rose. 2011. Targeted Learning: Causal Infer-
ence for Observational and Experimental Data. Springer Science & Busi-
ness Media.
van der Vaart, Aad. 2000. Asymptotic Statistics. Cambridge University Press.
van der Laan, Mark, and Daniel Rubin. 2006. “Targeted maximum likelihood
learning.” The International Journal of Biostatistics 2 (1).
VanderWeele, Tyler, and Peng Ding. 2017. “Sensitivity analysis in observa-
tional research: introducing the E-value.” Annals of Internal Medicine
167 (4): 268–274.
VanderWeele, Tyler, Peng Ding, and Maya Mathur. 2019. “Technical consid-
erations in the use of the E-value.” Journal of Causal Inference 7 (2):
20180007.
Zhang, Xiang, Douglas Faries, Hu Li, James Stamey, and Guido Imbens. 2018.
“Addressing unmeasured confounding in comparative observational re-
search.” Pharmacoepidemiology and Drug Safety 27 (4): 373–382.
228 Bibliography
Zhang, Xiang, James Stamey, and Maya Mathur. 2020. “Assessing the impact
of unmeasured confounders for credible and reliable real-world evidence.”
Pharmacoepidemiology and Drug Safety 29 (10): 1219–1227.
Index
AIPW, 120
AIPW-SL, 145
analysis dropout, 40
asymptotic, 15
linearity, 16, 122
normality, 15
variance, 120
average treatment effect, 103
among the treated, 113
ATC, 115
ATE, 103
ATT, 113
on the controls, 115
bias, 102
blinding, 19
block randomization, 33
bootstrap, 127
case-control, 100
causal estinand, 21
censoring, 72
central limit theorem, 12
central question, 1
chain, 13
claims data, 101
clever-covariate, 156
cohort, 100
collider, 14
complete randomization, 22
composite variable strategy, 59
confidence interval, 16
confounder, 102
confounding Bias, 101
consistency assumption, 4, 22, 182
consistent estimator, 15
copy reference, 186
counterfactual outcome, 2
covariates, 5
Cramer-Rao bound, 122
cross-sectional, 100
cross-validation, 144
CSR, 192
data collection, 194
decision rule, 94
direct effect, 20
directed acyclic graph (DAG), 13
double robustness, 139
double-blind, 20
doubly robust estimator, 120
duality, 9
dynamic treatment regime, 93
E-value, 183
efficient, 140
estimator, 140
influence function, 140
electronic health records, 101
empirical distribution, 8
endogenous variable, 51
endpoint, 3
estimand, 2, 21
estimating equation, 120
estimator, 24
evidence, 195
exchangeability assumption, 103, 183
exogenous variable, 51
external control, 101
externally controlled trial (ECT),
113
Fisher information, 122
Fisher’s approach, 22
fork, 14
229
230 Index
g-computation, 84
g-formula, 84
generalized linear model, 48
historical control, 101
hypothesis, 16
hypothetical strategy, 57
identifiability, 44
assumptions, 103
identification, 30, 45, 103
independence, 11
indirect effect, 20
individual treatment effect, 6
influence function, 122
initial estimator, 120
intent-to-treat (ITT), 8
intent-to-treat Effect, 41
intercurrent event (ICE), 48
invariance property, 125
IPW, 120
IPW-SL, 145
ITT effect, 41
jump to reference, 186
law of iterated expectations, 11
law of large numbers, 12
library, 143
likelihood, 122
log-likelihood, 122
longitudinal, 79
cohort, 109
study, 79
LTMLE, 167
M-estimation, 120
M-estimator, 120
machine learning, 143
MAR assumption, 43, 186
missing, 40
at random, 42
completely at random, 42
data, 40
not at random, 42
value, 43
missing data, 50
rate, 74
misspecification, 143
MLE, 120
MLE-SL, 145
monotonicity assumption, 65
Multiple imputation (MI), 186
multiple imputation (MI), 86
Neyman’s approach, 22
NHEFS study, 195
nonparametric, 143
NUC assumption, 103
observational studies, 100
open-label, 20
optimal estimator, 137
outcome, 1
outcome regression, 119
function, 142
parametric, 122, 143
submodel, 145
per-protocol effect, 45
personalized medicine, 94
placebo, 2
plug-in estimator, 127
population, 3
population-level summary, 7
positivity assumption, 43, 185
potentialoutcome,2,21
potential world, 3
PP effect, 46
pragmatic, 99
precision medicine, 94
principal ignorability assumption, 65
principal stratum strategy, 63
propensity score, 119
function, 142
PROTECT, 6
proximal, 109
proxy, 107
RAL estimator, 148
randomization, 19
Index 231
randomized controlled clinical trial
(RCT), 20
real world, 3
real-world data (RWD), 99
real-world evidence (RWE), 99
reference-based imputation, 186
registries, 101
regular and asymptotically linear
(RAL), 126
regularity, 121, 147
RMST, 7
roadmap, 191
robustness, 42
Rubin’s rule, 86
sample size calculation, 74
sandwich formula, 121
SAP, 192
score, 122
semiparametric, 143
sensitivity analysis, 180
shortcut, 157
simple randomization, 30
single-arm, 101
SMART design, 95
standard error, 29
standard of care, 2
standardization strategy, 31, 104
static treatment regime, 93
statistical estimand, 22
statistical inference, 29
stratification, 34
factor, 34
stratified randomization, 34
structural causal model (SCM), 51
study protocol, 40, 192
super learner, 143
super-efficiency, 125
super-population, 8
supplementary analysis, 181
survival rate, 7
SUTVA, 3
systematic bias, 19
targeted learning, 154
Taylor expansion, 121
time-dependent, 79
time-to-event, 7, 72
TMLE, 120, 155
treatment, 3
assignment, 19
dropout, 40
effect, 1
treatment policy strategy, 55
unmeasured confounder, 105
validity, 9
value, 167
weighting strategy, 31, 104
while on treatment strategy, 62
Z-statistic, 15