@Book{Cameron+Trivedi:2013,
  author = {A. Colin Cameron and Pravin K. Trivedi},
  title = {Regression Analysis of Count Data},
  year = {2013},
  edition = {2nd},
  publisher = {Cambridge University Press},
  address = {Cambridge},
}

@Book{Chambers+Hastie:1992,
  editor = {John M. Chambers and Trevor J. Hastie},
  title = {Statistical Models in \proglang{S}},
  publisher = {Chapman \& Hall},
  year = {1992},
  address = {London},
}

@Manual{Jackman:2015,
  title = {\pkg{pscl}: Classes and Methods for \proglang{R} Developed in the Political Science Computational Laboratory, Stanford University},
  author = {Simon Jackman},
  year = {2015},
  note = {\proglang{R} package version 1.4.9},
  url = {https://CRAN.R-project.org/package=pscl},
}

@Article{Mullahy:1986,
  author = {John Mullahy},
  title = {Specification and Testing of Some Modified Count Data Models},
  year = {1986},
  journal = {Journal of Econometrics},
  volume = {33},
  number = {3},
  pages = {341--365},
  doi = {10.1016/0304-4076(86)90002-3},
}

@Book{McCullagh+Nelder:1989,
  author = {Peter McCullagh and John A. Nelder},
  title = {Generalized Linear Models},
  edition = {2nd},
  year = {1989},
  publisher = {Chapman \& Hall},
  address = {London},
  doi = {10.1007/978-1-4899-3242-6},
}

@Manual{R,
  title = {\proglang{R}: {A} Language and Environment for Statistical Computing},
  author = {{\proglang{R} Core Team}},
  organization = {\proglang{R} Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2017},
  url = {https://www.R-project.org/},
}

@Article{Stasinopoulos+Rigby:2007,
  author = {D. Mikis Stasinopoulos and Robert A. Rigby},
  title = {Generalized Additive Models for Location Scale and Shape ({GAMLSS}) in \proglang{R}},
  journal = {Journal of Statistical Software},
  year = {2007},
  volume = {23},
  number = {7},
  pages = {1--46},
  doi = {10.18637/jss.v023.i07},
}

@Book{Venables+Ripley:2002,
  author = {William N. Venables and Brian D. Ripley},
  title = {Modern Applied Statistics with \proglang{S}},
  edition = {4th},
  year = {2002},
  pages = {495},
  publisher = {Springer-Verlag},
  address = {New York},
  doi = {10.1007/978-0-387-21706-2},
}

@Book{Wood:2006,
  author = {Simon N. Wood},
  title = {Generalized Additive Models: An Introduction with \proglang{R}},
  year = {2006},
  publisher = {Chapman \& Hall/CRC},
  address = {Boca Raton},
}

@Article{Yee:2009,
  author = {Thomas W. Yee},
  title = {The \pkg{VGAM} Package for Categorical Data Analysis},
  journal = {Journal of Statistical Software},
  year = {2010},
  volume = {32},
  number = {10},
  pages = {1--34},
  doi = {10.18637/jss.v032.i10},
}

@Article{Zeileis+Kleiber+Jackman:2008,
  author = {Achim Zeileis and Christian Kleiber and Simon Jackman},
  title = {Regression Models for Count Data in \proglang{R}},
  journal = {Journal of Statistical Software},
  year = {2008},
  volume = {27},
  number = {8},
  pages = {1--25},
  doi = {10.18637/jss.v027.i08},
}

@Article{rosenbaum1983,
  title={The Central Role of the Propensity Score in Observational Studies for Causal Effects},
  author={Rosenbaum, Paul R and Rubin, Donald B},
  journal={Biometrika},
  volume={70},
  number={1},
  pages={41--55},
  year={1983},
  publisher={Oxford University Press}
}

@Article{rosenbaum1985,
  title     = "Constructing a Control Group Using Multivariate Matched Sampling
              Methods That Incorporate the Propensity Score",
  author    = "Rosenbaum, Paul R and Rubin, Donald B",
  journal   = {The American Statistician},
  volume    = {39},
  number    = {1},
  pages     = {33--38},
  year      = {1985},
  publisher = {Taylor \& Francis Group}
}

@Article{stang2010,
  title       = "Advancing the Science for Active Surveillance: Rationale and
                 Design for the Observational Medical Outcomes Partnership",
  author      = {Stang, Paul E and Ryan, Patrick B and Racoosin, Judith A and
                 Overhage, J Marc and Hartzema, Abraham G and Reich, Christian
                 and Welebob, Emily and Scarnecchia, Thomas and Woodcock, Janet},
  journal={Annals of Internal Medicine},
  volume={153},
  number={9},
  pages={600--606},
  year={2010},
  publisher={American College of Physicians}
}

@Article{ryan2012,
  title       = "Empirical Assessment of Methods for Risk Identification in
                 Healthcare Data: Results from the Experiments of the
                 Observational Medical Outcomes Partnership",
  author      = "Ryan, Patrick B and Madigan, David and Stang, Paul E and
                 Overhage, J Marc and Racoosin, Judith A and Hartzema, Abraham
                 G",
  affiliation = "Johnson \& Johnson Pharmaceutical Research and Development
                 LLC, Titusville, NJ 08560, USA. ryan@omop.org",
  journal={Statistics in Medicine},
  volume={31},
  number={30},
  pages={4401--4415},
  year={2012},
  publisher={John Wiley \& Sons}
}

@Article{iacus2012,
  title    = {Causal Inference Without Balance Checking: Coarsened Exact Matching},
  author   = "Iacus, Stefano M and King, Gary and Porro, Giuseppe",
  journal={Political analysis},
  volume={20},
  number={1},
  pages={1--24},
  year={2012},
  publisher={Oxford University Press}
}

@Article{cox1972,
  title     = "Regression Models and {Life-Tables}",
  author    = "Cox, D R",
  publisher = "John Wiley \& Sons",
  journal={Journal of the Royal Statistical Society},
  volume={34},
  pages={187--220},
  year={1972}
}

@Article{gray1988,
  title     = "A Class of {K-Sample} Tests for Comparing the Cumulative
               Incidence of a Competing Risk",
  author    = "Gray, Robert J",
  abstract  = "In this paper, for right censored competing risks data, a class
               of tests developed for comparing the cumulative incidence of a
               particular type of failure among different groups. The tests are
               based on comparing weighted averages of the hazards of the
               subdistribution for the failure type of interest. Asymptotic
               results are derived by expressing the statistics in terms of
               counting processes and using martingale central limit theory. It
               is proposed that weight functions very similar to those for the
               Gp tests from ordinary survival analysis be used. Simulation
               results indicate that the asymptotic distributions provide
               adequate approximations in moderate sized samples.",
  journal   = "The Annals of Statistics",
  publisher = "Institute of Mathematical Statistics",
  volume    =  16,
  number    =  3,
  pages     = "1141--1154",
  month     =  "1~" # sep,
  year      =  1988
}

@Article{fine1999,
  title     = "A Proportional Hazards Model for the Subdistribution of a
               Competing Risk",
  author    = "Fine, Jason P and Gray, Robert J",
  abstract  = "With explanatory covariates, the standard analysis for competing
               risks data involves modeling the cause-specific hazard functions
               via a proportional hazards assumption. Unfortunately, the
               cause-specific hazard function does not have a direct
               interpretation in terms of survival probabilities for the
               particular failure type. In recent years many clinicians have
               begun using the cumulative incidence function, the marginal
               failure probabilities for a particular cause, which is
               intuitively appealing and more easily explained to the
               nonstatistician. The cumulative incidence is especially relevant
               in cost-effectiveness analyses in which the survival
               probabilities are needed to determine treatment utility.
               Previously, authors have considered methods for combining
               estimates of the cause-specific hazard functions under the
               proportional hazards formulation. However, these methods do not
               allow the analyst to directly assess the effect of a covariate
               on the marginal probability function. In this article we propose
               a novel semiparametric proportional hazards model for the
               subdistribution. Using the partial likelihood principle and
               weighting techniques, we derive estimation and inference
               procedures for the finite-dimensional regression parameter under
               a variety of censoring scenarios. We give a uniformly consistent
               estimator for the predicted cumulative incidence for an
               individual with certain covariates; confidence intervals and
               bands can be obtained analytically or with an easy-to-implement
               simulation technique. To contrast the two approaches, we analyze
               a dataset from a breast cancer clinical trial under both models.",
  journal   = "Journal of the American Statistical Association",
  publisher = "Taylor \& Francis, Ltd. on behalf of the American Statistical
               Association",
  volume    =  94,
  number    =  446,
  pages     = "496--509",
  month     =  "1~" # jun,
  year      =  1999
}

@Article{gayat2012,
  title       = "Propensity Score Applied to Survival Data Analysis Through
                 Proportional Hazards Models: A Monte Carlo Study",
  author      = "Gayat, Etienne and Resche-Rigon, Matthieu and Mary, Jean-Yves
                 and Porcher, Rapha{\"e}l",
  affiliation = "Clinical Epidemiology and Biostatistics, Inserm U717, Paris
                 France; Universit{\'e} Paris Diderot, Paris, France.",
  abstract    = "Propensity score methods are increasingly used in medical
                 literature to estimate treatment effect using data from
                 observational studies. Despite many papers on propensity score
                 analysis, few have focused on the analysis of survival data.
                 Even within the framework of the popular proportional hazard
                 model, the choice among marginal, stratified or adjusted
                 models remains unclear. A Monte Carlo simulation study was
                 used to compare the performance of several survival models to
                 estimate both marginal and conditional treatment effects. The
                 impact of accounting or not for pairing when analysing
                 propensity-score-matched survival data was assessed. In
                 addition, the influence of unmeasured confounders was
                 investigated. After matching on the propensity score, both
                 marginal and conditional treatment effects could be reliably
                 estimated. Ignoring the paired structure of the data led to an
                 increased test size due to an overestimated variance of the
                 treatment effect. Among the various survival models
                 considered, stratified models systematically showed poorer
                 performance. Omitting a covariate in the propensity score
                 model led to a biased estimation of treatment effect, but
                 replacement of the unmeasured confounder by a correlated one
                 allowed a marked decrease in this bias. Our study showed that
                 propensity scores applied to survival data can lead to
                 unbiased estimation of both marginal and conditional treatment
                 effect, when marginal and adjusted Cox models are used. In all
                 cases, it is necessary to account for pairing when analysing
                 propensity-score-matched data, using a robust estimator of the
                 variance.",
  journal     = "Pharmaceutical Statistics",
  volume      =  11,
  number      =  3,
  pages       = "222--229",
  month       =  may,
  year        =  2012,
  keywords    = "bias; propensity score; simulation; survival; treatment effect"
}

@Article{austin2014_1,
  title       = "The Use of Propensity Score Methods with Survival or
                 Time-to-Event Outcomes: Reporting Measures of Effect Similar
                 to Those Used in Randomized Experiments",
  author      = "Austin, Peter C",
  affiliation = "Institute for Clinical Evaluative Sciences, Toronto, Canada;
                 Institute of Health Management, Policy and Evaluation,
                 University of Toronto, Toronto, Canada; Dalla Lana School of
                 Public Health, University of Toronto, Toronto, Canada.",
  abstract    = "Propensity score methods are increasingly being used to
                 estimate causal treatment effects in observational studies. In
                 medical and epidemiological studies, outcomes are frequently
                 time-to-event in nature. Propensity-score methods are often
                 applied incorrectly when estimating the effect of treatment on
                 time-to-event outcomes. This article describes how two
                 different propensity score methods (matching and inverse
                 probability of treatment weighting) can be used to estimate
                 the measures of effect that are frequently reported in
                 randomized controlled trials: (i) marginal survival curves,
                 which describe survival in the population if all subjects were
                 treated or if all subjects were untreated; and (ii) marginal
                 hazard ratios. The use of these propensity score methods
                 allows one to replicate the measures of effect that are
                 commonly reported in randomized controlled trials with
                 time-to-event outcomes: both absolute and relative reductions
                 in the probability of an event occurring can be determined. We
                 also provide guidance on variable selection for the propensity
                 score model, highlight methods for assessing the balance of
                 baseline covariates between treated and untreated subjects,
                 and describe the implementation of a sensitivity analysis to
                 assess the effect of unmeasured confounding variables on the
                 estimated treatment effect when outcomes are time-to-event in
                 nature. The methods in the paper are illustrated by estimating
                 the effect of discharge statin prescribing on the risk of
                 death in a sample of patients hospitalized with acute
                 myocardial infarction. In this tutorial article, we describe
                 and illustrate all the steps necessary to conduct a
                 comprehensive analysis of the effect of treatment on
                 time-to-event outcomes.",
  journal     = "Statistics in Medicine",
  volume      =  33,
  number      =  7,
  pages       = "1242--1258",
  month       =  "30~" # mar,
  year        =  2014,
  keywords    = "confounding; event history analysis; inverse probability of
                 treatment weighting; marginal effects; observational study;
                 propensity score; propensity score matching; survival analysis"
}

@Article{austin2014_2,
  title       = "The Performance of Different Propensity Score Methods for
                 Estimating Absolute Effects of Treatments on Survival
                 Outcomes: A Simulation Study",
  author      = "Austin, Peter C and Schuster, Tibor",
  affiliation = "Institute for Clinical Evaluative Sciences, Toronto, Ontario,
                 Canada.",
  abstract    = "Observational studies are increasingly being used to estimate
                 the effect of treatments, interventions and exposures on
                 outcomes that can occur over time. Historically, the hazard
                 ratio, which is a relative measure of effect, has been
                 reported. However, medical decision making is best informed
                 when both relative and absolute measures of effect are
                 reported. When outcomes are time-to-event in nature, the
                 effect of treatment can also be quantified as the change in
                 mean or median survival time due to treatment and the absolute
                 reduction in the probability of the occurrence of an event
                 within a specified duration of follow-up. We describe how
                 three different propensity score methods, propensity score
                 matching, stratification on the propensity score and inverse
                 probability of treatment weighting using the propensity score,
                 can be used to estimate absolute measures of treatment effect
                 on survival outcomes. These methods are all based on
                 estimating marginal survival functions under treatment and
                 lack of treatment. We then conducted an extensive series of
                 Monte Carlo simulations to compare the relative performance of
                 these methods for estimating the absolute effects of treatment
                 on survival outcomes. We found that stratification on the
                 propensity score resulted in the greatest bias. Caliper
                 matching on the propensity score and a method based on earlier
                 work by Cole and Hern{\'a}n tended to have the best
                 performance for estimating absolute effects of treatment on
                 survival outcomes. When the prevalence of treatment was less
                 extreme, then inverse probability of treatment weighting-based
                 methods tended to perform better than matching-based methods.",
  journal     = "Statistical Methods in Medical Research",
  volume      = {25},
  number      = {5},
  pages       = {2214--2237},
  year        = {2016},
  publisher   = {SAGE Publications Sage UK: London, England}
}

@MISC{obenchain2005,
  title        = {Unsupervised and Supervised Propensity Scoring in "\proglang{R}"},
  booktitle    = "The Comprehensive {R} Archive Network",
  author       = "Obenchain, Robert L",
  month        =  feb,
  year         =  2005,
  howpublished = "\url{https://cran.r-project.org/src/contrib/Archive/USPS/}",
  note         = "Accessed: 2016-6-8"
}

@INCOLLECTION{obenchain2010,
  title     = "The Local Control Approach Using {JMP}",
  booktitle = "Analysis of Observational Health Care Data Using \proglang{SAS}",
  author    = "Obenchain, Robert L",
  editor    = "Faries, Douglas and Leon, Andrew C and Haro, Josep Maria and
               Obenchain, Robert L",
  abstract  = "The local control approach to adjustment for treatment selection
               bias and confounding in observational studies is illustrated
               here using JMP because local control is best implemented and
               applied in highly visual ways. The local control approach is
               also unique because it hierarchically clusters patients in
               baseline covariate x-space; applies simple nested analysis of
               variance (ANOVA) models (treatment within cluster); and ends up
               being highly flexible, non-parametric, and robust. Although the
               local control approach is classical rather than Bayesian, its
               primary output is a full distribution of local treatment
               differences (LTDs) that contains all potential information
               relevant to patient differential responses to treatment. All
               concepts are illustrated using freely distributable data on
               10,000 patients that were generated to be like those from a
               published cardiovascular registry containing 996 patients.",
  publisher = "SAS Institute",
  pages     = "151--194",
  year      =  2010,
  address   = "Cary, NC"
}

@Article{obenchain2013,
  title    = "Advancing Statistical Thinking in Observational Health Care
              Research",
  author   = "Obenchain, R L and Young, S S",
  abstract = "Observational medical studies are becoming more prominent due to
              interest in using data from current medical practice to help
              guide treatment selection. We start by reviewing why current
              analysis strategies for observational data tend to produce
              findings that fail to replicate. We then argue that there is a
              real need for simple and objective statistical strategies that
              yield findings more likely to be dependable. Our proposed way
              forward is to focus on the empirical distribution of local
              treatment differences, LTDs, which reveal heterogeneity in
              effect-sizes. The LTD is the difference in mean outcomes for
              treated and control patients within a cluster of patients
              relatively well matched on their observed pretreatment
              characteristics. Because we focus on only one question (comparing
              two alternative treatment choices for a given disease or
              condition) and clustering is nonparametric, our proposed approach
              is more simple and objective than commonly used statistical
              analysis strategies. By studying graphical displays of
              information from an LTD distribution, a doctor and patient not
              only can see a full picture of current treatment outcomes but
              also can make a truly well-informed, individualized treatment
              choice.",
  journal  = "Journal of Statistical Theory and Practice",
  volume   =  7,
  number   =  2,
  pages    = "456--506",
  year     =  2013
}

@Article{lopiano2014,
  title     = "Fair Treatment Comparisons in Observational Research",
  author    = "Lopiano, Kenneth K and Obenchain, Robert L and Young, S Stanley",
  abstract  = "The proliferation of electronic health records, driven by
               advances in technology and legislative measures, is stimulating
               interest in the analysis of passively collected administrative
               and clinical data. Observational data present exciting
               challenges and opportunities to researchers interested in
               comparing the effectiveness of different treatment regimes and,
               as personalized medicine requires, estimating how effectiveness
               varies among subgroups. In this study, we provide new motivation
               for the local control approach to the analysis of large
               observational datasets in which patients are first clustered in
               pretreatment covariate space and treatment comparisons are made
               within subgroups of similar patients. The motivation for such an
               analysis is that the resulting local treatment effect estimates
               make inherently fair comparisons even when treatment cohorts
               suffer variation in balance (treatment choice fraction) across
               pretreatment covariate space. We use an example of Simpson's
               paradox to show that estimates of the overall average treatment
               effect, which marginalize over covariate space, can be
               misleading. Thus, we provide an alternative definition that uses
               a single, shared marginal distribution to define overall
               treatment comparisons that are inherently fair given the
               observed covariates. However, we also argue that overall
               treatment comparisons should no longer be the focus of
               comparative effectiveness research; the possibility that
               treatment effectiveness does vary across patient subpopulations
               must not be left unexplored. In the spirit of the now ubiquitous
               concept of personalized medicine, estimating heterogeneous
               treatment effects in clinically relevant subgroups will allow
               for, within the limits of the available data, fair treatment
               comparisons that are more relevant to individual patients.",
  journal   = "Statistical Analysis and Data Mining",
  publisher = "John Wiley \& Sons",
  volume    =  7,
  number    =  5,
  pages     = "376--384",
  year      =  2014,
  keywords  = "observational data, fair conditional comparisons, common
               support, re-marginalization, local control, Simpson's Paradox"
}

@Article{faries2013,
  title       = "Local Control for Identifying Subgroups of Interest in
                 Observational Research: Persistence of Treatment for Major
                 Depressive Disorder",
  author      = "Faries, Douglas E and Chen, Yi and Lipkovich, Ilya and Zagar,
                 Anthony and Liu, Xianchen and Obenchain, Robert L",
  affiliation = "Eli Lilly and Company, Indianapolis, IN, USA.",
  abstract    = "Caregivers are regularly faced with decisions between
                 competing treatments. Large observational health care
                 databases provide a golden opportunity for research on
                 heterogeneity in patient response to guide caregiver
                 decisions, due to their sample size, diverse populations, and
                 real-world setting. Local control is a promising tool for
                 using observational data to detect patient subgroups with
                 differential response on one treatment relative to another.
                 While standard data mining approaches find subgroups with
                 optimal responses for a particular population, detecting
                 subgroups that reveal treatment differences while also
                 adjusting for confounding in observational data is
                 challenging. Local control utilizes unsupervised clustering to
                 form non-parametric patient-level counterfactual treatment
                 differences and displays them as an observed distribution of
                 effect-size estimates. Classification and regression trees
                 (CART) then find the factors that drive the greatest outcome
                 differentiation between treatments. In this manuscript, we
                 demonstrate the use of this two-step strategy using local
                 control plus CART to identify depression patients most (least)
                 likely to benefit from treatment with duloxetine relative to
                 extended-release venlafaxine. Prior medication costs and age
                 were found to be factors most associated with differential
                 outcome, with prior medication costs remaining as an important
                 factor after sensitivity analyses using a second dataset.
                 Copyright \copyright{} 2013 John Wiley \& Sons, Ltd.",
  journal     = "International Journal of Methods in Psychiatric Research",
  publisher   = {John Wiley \& Sons}, 
  volume      =  22,
  number      =  3,
  pages       = "185--194",
  month       =  "18~" # aug,
  year        =  2013
}

@UNPUBLISHED{young2015,
  title    = "Bias and Response Heterogeneity in an Air Quality Data Set",
  author   = "Young, S Stanley and Obenchain, Robert L and Lambert, Christophe",
  abstract = "It is well-known that claims coming from observational studies
              often fail to replicate when rigorously re-tested. The technical
              problems include multiple testing, multiple modeling and bias.
              Any or all of these problems can give rise to claims that will
              fail to replicate. There is a need for statistical methods that
              are easily applied, are easy to understand, and are likely to
              give reliable results. In particular, simple ways for reducing
              the influence of bias are essential. In this paper, the Local
              Control method developed by Robert Obenchain is explicated using
              a small air quality/longevity data set first analyzed in the New
              England Journal of Medicine. The benefits of our paper are
              twofold. First, we describe a reliable strategy for analysis of
              observational data. Second and importantly, the global claim that
              longevity increases with improvements in air quality made in the
              NEJM paper needs to be modified. There is subgroup heterogeneity
              in the effect of air quality on longevity (one size does not fit
              all), and this heterogeneity is largely explained by factors
              other than air quality.",
  Eprint = {arXiv:1504.00975},
  year     =  2015
}

@INCOLLECTION{young2016,
  title     = "A Problem of Bias and Response Heterogeneity",
  booktitle = "Standing with giants: A collection of public health essays in
               memoriam to Dr. Elizabeth M. Whelan",
  author    = "Young, S Stanley and Obenchain, Robert L and Lambert, Christophe
               G",
  editor    = "Alan Moghissi, A and Ross, Gilbert",
  publisher = "American Council on Science and Health",
  pages     = "153--169",
  month     =  "17~" # aug,
  year      =  2016,
  address   = "New York, NY"
}

@INCOLLECTION{gosset1911,
  title     = "Appendix to Mercer and Hall's Paper on 'The Experimental Error
               of Field Trials'. Journal of Agricultural Science 4, 128-131.",
  booktitle = {"Student's" Collected Papers},
  editor    = "Pearson, E and Wishart, J",
  journal   = "Journal of Agricultural Science",
  pages     = "49--52",
  author    = "Student",
  nameaddon = "Gosset, WS",
  year      =  1911,
  publisher = {Biometrika Office, University College, London}
}

@Article{fisher1926,
  title    = "The Arrangement of Field Experiments",
  author   = "Fisher, Ronald Aylmer",
  abstract = "Reproduced with permission of H.M.S.O.",
  journal  = "Journal of the Ministry of Agriculture of Great Britain",
  volume   =  33,
  pages    = "503--513",
  year     =  1926
}

@Article{addelman1969,
  title     = "The Generalized Randomized Block Design",
  author    = "Addelman, Sidney",
  journal   = "The American Statistician",
  publisher = "Taylor \& Francis, Ltd. on behalf of the American Statistical
               Association",
  volume    =  23,
  number    =  4,
  pages     = "35--36",
  month     =  "1~" # oct,
  year      =  1969
}

@Manual{maechler,
    title  = {\pkg{cluster}: Cluster Analysis Basics and Extensions},
    author = {Martin Maechler and Peter Rousseeuw and Anja Struyf and
              Mia Hubert and Kurt Hornik},
    year   = {2017},
    note   = {\proglang{R}~package version 2.0.6 --- For new features, see the
              'Changelog' file (in the package source)},
}

@Article{kereiakes2000,
  title       = "Abciximab Provides Cost-Effective Survival Advantage in
                 High-Volume Interventional Practice",
  author      = "Kereiakes, D J and Obenchain, R L and Barber, B L and Smith, A
                 and McDonald, M and Broderick, T M and Runyon, J P and
                 Shimshak, T M and Schneider, J F and Hattemer, C R and Roth, E
                 M and Whang, D D and Cocks, D and Abbottsmith, C W",
  affiliation = "Carl and Edyth Lindner Center for Research and Education,
                 Cincinnati, Ohio, USA.",
  abstract    = "BACKGROUND: Placebo-controlled randomized trials of platelet
                 glycoprotein (GP) IIb/IIIa blockade during percutaneous
                 coronary intervention have demonstrated efficacy of these
                 agents for reducing the risk of periprocedural ischemic
                 events. However, cost-effectiveness of this adjunctive
                 pharmacotherapy has been scrutinized. Extrapolation of
                 cost-efficacy observations from clinical trials to ``real
                 world'' interventional practice is problematic. METHODS:
                 Consecutive percutaneous coronary interventions (n = 1472)
                 performed by Ohio Heart Health Center operators at The Christ
                 Hospital, Cincinnati, Ohio, in 1997 were analyzed for
                 procedural and long-term (6-month) outcomes and charges.
                 Observations on cost and efficacy (survival) were adjusted for
                 nonrandomized abciximab allocation by means of ``propensity
                 scoring'' methods. RESULTS: Abciximab therapy was associated
                 with a survival advantage to 6 months after percutaneous
                 coronary intervention. The average reduction in mortality rate
                 at 6 months was 3.4\% (unadjusted) and 4.9\% when adjusted for
                 nonrandomization. The average charge increment to 6 months was
                 $1512 (unadjusted) and $950 when adjusted for
                 nonrandomization. Patients deriving the greatest reduction in
                 mortality rates also had a reduction in total cardiovascular
                 charges to 6 months. Distinguishing demographics of this
                 population included multivessel coronary intervention,
                 coronary stent deployment, intervention within 1 week of
                 myocardial infarction, and lower left ventricular ejection
                 fraction. The average cost per life-year gained in this study
                 was $2875 for all patients (unadjusted) and $1243 when
                 adjusted for nonrandomization. CONCLUSIONS: Abciximab provides
                 a cost-effective survival advantage in high-volume
                 interventional practice that compares favorably with currently
                 accepted standards. Clinical and procedural demographics
                 associated with increased cost-effectiveness included
                 multivessel coronary intervention, stent deployment, recent
                 (<1 week) myocardial infarction, and impaired left ventricular
                 function.",
  journal     = "American Heart Journal",
  volume      =  140,
  number      =  4,
  pages       = "603--610",
  month       =  oct,
  year        =  2000
}

@Article{iacus2011,
  title     = "Multivariate Matching Methods That Are Monotonic Imbalance
               Bounding",
  author    = "Iacus, Stefano M and King, Gary and Porro, Giuseppe",
  abstract  = "We introduce a new ``Monotonic Imbalance Bounding'' (MIB) class
               of matching methods for causal inference with a surprisingly
               large number of attractive statistical properties. MIB
               generalizes and extends in several new directions the only
               existing class, ``Equal Percent Bias Reducing'' (EPBR), which is
               designed to satisfy weaker properties and only in expectation.
               We also offer strategies to obtain specific members of the MIB
               class, and analyze in more detail a member of this class, called
               Coarsened Exact Matching, whose properties we analyze from this
               new perspective. We offer a variety of analytical results and
               numerical simulations that demonstrate how members of the MIB
               class can dramatically improve inferences relative to EPBR-based
               matching methods.",
  journal   = "Journal of the American Statistical Association",
  publisher = "amstat.tandfonline.com",
  volume    =  106,
  number    =  493,
  pages     = "345--361",
  year      =  2011
}

@Book{dasgupta2008,
  title={The Hardness of K-means Clustering},
  author={Dasgupta, S.},
  series={Technical report (University of California, San Diego. Department of Computer Science and Engineering)},
  url={https://books.google.com/books?id=riJuAQAACAAJ},
  year={2008},
  publisher={Department of Computer Science and Engineering, University of California, San Diego}
}

@INPROCEEDINGS{fischer2003,
  title      = "Fast {Smallest-Enclosing-Ball} Computation in High Dimensions",
  booktitle  = "Algorithms - {ESA} 2003",
  author     = "Fischer, Kaspar and G{\"a}rtner, Bernd and Kutz, Martin",
  abstract   = "We develop a simple combinatorial algorithm for computing the
                smallest enclosing ball of a set of points in high dimensional
                Euclidean space. The resulting code is in most cases faster
                (sometimes significantly) than recent dedicated methods that
                only deliver approximate results, and it beats off-the-shelf
                solutions, based e.g. on quadratic programming solvers. The
                algorithm resembles the simplex algorithm for linear
                programming; it comes with a Bland-type rule to avoid cycling
                in presence of degeneracies and it typically requires very few
                iterations. We provide a fast and robust floating-point
                implementation whose efficiency is based on a new dynamic data
                structure for maintaining intermediate solutions.The code can
                efficiently handle point sets in dimensions up to 2,000, and it
                solves instances of dimension 10,000 within hours. In low
                dimensions, the algorithm can keep up with the fastest
                computational geometry codes that are available.",
  publisher  = "Springer-Verlag",
  pages      = "630--641",
  month      =  "16~" # sep,
  year       =  2003,
  language   = "en",
  conference = "European Symposium on Algorithms"
}

@book{box2005,
  title={Statistics for Experimenters: Design, Innovation, and Discovery},
  author={Box, G.E.P. and Hunter, J.S. and Hunter, W.G.},
  isbn={9780471718130},
  lccn={2004063780},
  series={Wiley series in probability and statistics},
  address={New York},
  year={2005},
  publisher={John Wiley \& Sons}
}

@Article{choudhury2002,
  title       = "Non-Parametric Confidence Interval Estimation for Competing
                 Risks Analysis: Application to Contraceptive Data",
  author      = "Choudhury, Jahar B",
  affiliation = "Department of Mathematics and Statistics, University of
                 Western Australia, Nedlands, WA 6907, Australia.
                 jahar@maths.uwa.edu.au",
  abstract    = "Non-parametric maximum likelihood estimation of the cause
                 specific failure probability, and of its standard error, in
                 the presence of competing risks is discussed with reference to
                 some contraceptive use dynamics data from Bangladesh. The
                 cause specific incidence function provides an intuitively
                 appealing summary curve for failure rates and probabilities,
                 such as probabilities of discontinuation of different kinds of
                 contraception, based on right-censored data of the particular
                 event. Dinse and Larson's formula can be utilized to calculate
                 the standard error of the cause specific probability for the
                 failure type of interest, and to construct pointwise
                 confidence intervals. The accuracy of these intervals, as well
                 as those based on the log(-log) transformation and the arcsine
                 transformation, are compared by simulations. We find that
                 Dinse and Larson's formula, used in conjuction with a
                 log(-log) transform, yields reliable standard error estimates
                 and accurate coverage in samples of small and large size, and
                 can be recommended for use in this situation.",
  journal     = "Statistics in Medicine",
  volume      =  21,
  number      =  8,
  pages       = "1129--1144",
  month       =  "30~" # apr,
  year        =  2002
}

@Article{pepemori,
  title={Kaplan-Meier, Marginal or Conditional Probability Curves in Summarizing Competing Risks Failure Time Data?},
  author={Pepe, Margaret Sullivan and Mori, Motomi},
  journal={Statistics in Medicine},
  volume={12},
  number={8},
  pages={737--751},
  year={1993},
  publisher={John Wiley \& Sons}
}

@book{pintilie2006,
  title={Competing Risks: A Practical Perspective},
  author={Pintilie, Melania},
  volume={58},
  year={2006},
  publisher={John Wiley \& Sons}
}

@Article{framingham,
  title    = "Epidemiological Approaches to Heart Disease: The Framingham Study",
  author   = "Dawber, T R and Meadors, G F and Moore, Jr, F E",
  journal  = "American Journal of Public Health and the Nation's Health",
  volume   =  41,
  number   =  3,
  pages    = "279--281",
  month    =  mar,
  year     =  1951,
  keywords = "HEART DISEASE",
  language = "en"
}

@Article{seltzer1974,
  title    = "Effect of Smoking on Blood Pressure",
  author   = "Seltzer, C C",
  journal  = "American Heart Journal",
  volume={87},
  number={5},
  pages={558--564},
  year={1974},
  publisher={Elsevier}
}

@Article{virdis2010,
  title       = "Cigarette Smoking and Hypertension",
  author      = "Virdis, A and Giannarelli, C and Neves, M Fritsch and Taddei,
                 S and Ghiadoni, L",
  affiliation = "Department of Internal Medicine, University of Pisa, Via Roma,
                 6756100 Pisa, Italy. a.virdis@med.unipi.it",
  abstract    = "Cigarette smoking is a powerful cardiovascular risk factor and
                 smoking cessation is the single most effective lifestyle
                 measure for the prevention of a large number of cardiovascular
                 diseases. Impairment of endothelial function, arterial
                 stiffness, inflammation, lipid modification as well as an
                 alteration of antithrombotic and prothrombotic factors are
                 smoking-related major determinants of initiation, and
                 acceleration of the atherothrombotic process, leading to
                 cardiovascular events. Cigarette smoking acutely exerts an
                 hypertensive effect, mainly through the stimulation of the
                 sympathetic nervous system. As concern the impact of chronic
                 smoking on blood pressure, available data do not put clearly
                 in evidence a direct causal relationship between these two
                 cardiovascular risk factors, a concept supported by the
                 evidence that no lower blood pressure values have been
                 observed after chronic smoking cessation. Nevertheless,
                 smoking, affecting arterial stiffness and wave reflection
                 might have greater detrimental effect on central blood
                 pressure, which is more closely related to target organ damage
                 than brachial blood pressure. Hypertensive smokers are more
                 likely to develop severe forms of hypertension, including
                 malignant and renovascular hypertension, an effect likely due
                 to an accelerated atherosclerosis.",
  journal     = "Current Pharmaceutical Design",
  volume      =  16,
  number      =  23,
  pages       = "2518--2525",
  year        =  2010,
  language    = "en"
}

@Article{bou-hamad2011,
  title     = "A Review of Survival Trees",
  author    = "Bou-Hamad, Imad and Larocque, Denis and Ben-Ameur, Hatem",
  abstract  = "Project Euclid - mathematics and statistics online",
  journal   = "Statistics Surveys",
  publisher = "The author, under a Creative Commons Attribution License",
  volume    =  5,
  pages     = "44--71",
  year      =  2011,
  keywords  = "Survival trees; CART; time--varying covariate; right--censored
               data; discrete--time; ensemble methods; time--varying effect;
               bagging; survival forest",
  language  = "en"
}

@Article{kaplanmeier,
  title={Nonparametric Estimation from Incomplete Observations},
  author={Kaplan, Edward L and Meier, Paul},
  journal={Journal of the American Statistical Association},
  volume={53},
  number={282},
  pages={457--481},
  year={1958},
  publisher={Taylor \& Francis}
}


@Manual{xtable,
  title = {\pkg{xtable}: Export Tables to LaTeX or HTML},
  author = {David B. Dahl},
  year = {2016},
  note = {\proglang{R}~package version 1.8-2},
  url = {https://CRAN.R-project.org/package=xtable},
}

@Manual{TeachingDemos,
    title = {\pkg{TeachingDemos}: Demonstrations for Teaching and Learning},
    author = {Greg Snow},
    year = {2016},
    note = {\proglang{R}~package version 2.10},
    url = {https://CRAN.R-project.org/package=TeachingDemos},
}

@Manual{gplots,
    title = {\pkg{gplots}: Various \proglang{R}~Programming Tools for Plotting Data},
    author = {Gregory R. Warnes and Ben Bolker and Lodewijk Bonebakker and Robert Gentleman and Wolfgang Huber Andy Liaw and Thomas Lumley and Martin Maechler and Arni Magnusson and Steffen Moeller and Marc Schwartz and Bill Venables},
    year = {2016},
    note = {\proglang{R}~package version 3.0.1},
    url = {https://CRAN.R-project.org/package=gplots},
}

@Article{dendextend,
  author = {Tal Galili},
  title = {\pkg{dendextend}: An \proglang{R}~package for Visualizing, Adjusting, and Comparing Trees of Hierarchical Clustering},
  journal = {Bioinformatics},
  year = {2015},
  doi = {10.1093/bioinformatics/btv428},
  url = {https://academic.oup.com/bioinformatics/article/31/22/3718/240978/dendextend-an-R-package-for-visualizing-adjusting},
  eprint = {https://academic.oup.com/bioinformatics/article-pdf/31/22/3718/17122682/btv428.pdf},
}

@Manual{data.table,
  title = {\pkg{data.table}: Extension of `data.frame`},
  author = {Matt Dowle and Arun Srinivasan},
  year = {2017},
  note = {\proglang{R}~package version 1.10.4-3},
  url = {https://CRAN.R-project.org/package=data.table},
}

@Manual{colorspace,
  title = {\pkg{colorspace}: Color Space Manipulation},
  author = {Ross Ihaka and Paul Murrell and Kurt Hornik and Jason C. Fisher and Achim Zeileis},
  year = {2016},
  note = {\proglang{R}~package version 1.3-2},
  url = {https://CRAN.R-project.org/package=colorspace},
}

@Manual{RColorBrewer,
  title = {\pkg{RColorBrewer}: ColorBrewer Palettes},
  author = {Erich Neuwirth},
  year = {2014},
  note = {\proglang{R}~package version 1.1-2},
  url = {https://CRAN.R-project.org/package=RColorBrewer},
}

@Manual{gridExtra,
  title = {\pkg{gridExtra}: Miscellaneous Functions for "Grid" Graphics},
  author = {Baptiste Auguie},
  year = {2017},
  note = {\proglang{R}~package version 2.3},
  url = {https://CRAN.R-project.org/package=gridExtra},
}

@Book{ggplot2,
  author = {Hadley Wickham},
  title = {\pkg{ggplot2}: Elegant Graphics for Data Analysis},
  publisher = {Springer-Verlag New York},
  year = {2009},
  isbn = {978-0-387-98140-6},
  url = {http://ggplot2.org},
}

@Manual{rpart,
  title = {\pkg{rpart}: Recursive Partitioning and Regression Trees},
  author = {Terry Therneau and Beth Atkinson},
  year = {2018},
  note = {\proglang{R}~package version 4.1-12},
  url = {https://CRAN.R-project.org/package=rpart},
}

@Manual{rpart.plot,
  title = {\pkg{rpart.plot}: Plot '\pkg{rpart}' Models: An Enhanced Version of '\code{plot.rpart}'},
  author = {Stephen Milborrow},
  year = {2017},
  note = {\proglang{R}~package version 2.1.2},
  url = {https://CRAN.R-project.org/package=rpart.plot},
}

