# References

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*J Clin Epi*,*61*, 1009–1017."in general, a bootstrap
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backward variable elimination for identifying the true regression model.
In most settings, both methods performed poorly at correctly identifying
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*J Clin Epi*,*63*, 1145–1155.ROC areas
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0.620-0.651 for recursive partitioning;repeated data simulation showed
large variation in tree structure

Barnes, S. A., Lindborg, S. R., & Seaman, J. W. (2006). Multiple
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*Stat Med*,*25*, 233–245.bad performance of
LOCF including high bias and poor confidence interval
coverage;simulation setup;longitudinal data;serial
data;RCT;dropout;assumed missing at random (MAR);approximate Bayesian
bootstrap;Bayesian least squares;missing data;nice background
summary;new completion score method based on fitting a Poisson model for
the number of completed clinic visits and using donors and approximate
Bayesian bootstrap

Barzi, F., & Woodward, M. (2004). Imputations of missing values in
practice: Results from imputations of serum cholesterol in
28 cohort studies.

*Am J Epi*,*160*, 34–45.excellent review article for multiple imputation;list
of variables to include in imputation model;"Imputation models should
ideally include all covariates that are related to the missing data
mechanism, have distributions that differ between the respondents and
nonrespondents, are associated with cholesterol, and will be included in
the analyses of the final complete data sets";detailed comparison of
results (cholesterol effect and confidence limits) for various
imputation methods

Belcher, H. (1992). The concept of residual confounding in regression
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*Stat Med*,*11*, 1747–1758.
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*Biometrika*,*69*, 343–349.
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*BMC Med Res Methodol*,*12*(1), 21+. https://doi.org/10.1186/1471-2288-12-21terrific graphical examples; nice display of outcome
heterogeneity within quantile groups of PSA

Berhane, K., Hauptmann, M., & Langholz, B. (2008). Using tensor
product splines in modeling exposure–time–response relationships:
Application to the Colorado Plateau Uranium
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*Stat Med*,*27*, 5484–5496.discusses taking product of all univariate spline
basis functions

Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted
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*International Journal of Epidemiology*,*46*(1), 348–355. https://doi.org/10.1093/ije/dyw098
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*Stat Med*,*10*, 1703–1710. https://doi.org/10.1002/sim.4780101108
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*Stat Med*,*12*, 1325–1338.
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*Stat Med*,*35*(17), 3007–3020. https://doi.org/10.1002/sim.6926
Booth, J. G., & Sarkar, S. (1998). Monte Carlo
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*Am Statistician*,*52*, 354–357.number of resamples required
to estimate variances, quantiles; 800 resamples may be required to
guarantee with 0.95 confidence that the relative error of a variance
estimate is 0.1;Efron’s original suggestions for as low as 25 resamples
were based on comparing stability of bootstrap estimates to sampling
error, but small relative effects can significantly change
P-values;number of bootstrap resamples

Bordley, R. (2007). Statistical decisionmaking without math.

*Chance*,*20*(3), 39–44.
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*Classification and Regression Trees*. Wadsworth and Brooks/Cole.
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*Biometrics*,*40*, 1049–1062.
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*Biometrics*,*64*, 250–261."statistics such as the AUC are not especially
relevant to someone who must make a decision about a particular x_c. ...
ROC curves lack or obscure several quantities that are necessary for
evaluating the operational effectiveness of diagnostic tests. ... ROC
curves were first used to check how radio <i>receivers</i> (like radar receivers) operated
over a range of frequencies. ... This is not how most ROC curves are
used now, particularly in medicine. The receiver of a diagnostic
measurement ... wants to make a decision based on some x_c, and is not
especially interested in how well he would have done had he used some
different cutoff."; in the discussion David Hand states "when
integrating to yield the overall AUC measure, it is necessary to decide
what weight to give each value in the integration. The AUC implicitly
does this using a weighting derived empirically from the data. This is
nonsensical. The relative importance of misclassifying a case as a
noncase, compared to the reverse, cannot come from the data itself. It
must come externally, from considerations of the severity one attaches
to the different kinds of misclassifications."; see Lin, Kvam, Lu Stat
in Med 28:798-813;2009

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in defining cutoff points of continuous prognostic factors:
Example of tumor thickness in primary cutaneous melanoma.

*J Clin Epi*,*50*, 1201–1210.choice of cut point depends on marginal distribution
of predictor

Buuren, Stef. (2012).

*Flexible imputation of missing data*. Chapman & Hall/CRC. https://doi.org/10.1201/b11826
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*Bulletin Cancer, Paris*,*67*, 477–488.
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*JAMA*,*261*, 2077–2086.
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*J Stat Software*,*76*(1), 1–32. https://doi.org/10.18637/jss.v076.i01
Carpenter, J. R., & Smuk, M. (2021). Missing data: A
statistical framework for practice.

*Biometrical Journal*,*63*(5), 915–947. https://doi.org/10.1002/bimj.202000196
Carpenter, J., & Bithell, J. (2000). Bootstrap confidence intervals:
When, which, what? A practical guide for medical
statisticians.

*Stat Med*,*19*, 1141–1164.unconditional nonparametric bootstrap becomes more
equivalent to conditional bootstrap based on regression residuals when
full models are fitted

Centers for Disease Control and Prevention CDC. National Center for
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*Statistical Models in S*. Wadsworth and Brooks/Cole.
Chan, K. W., & Meng, X.-L. (2022). Multiple improvements of multiple
imputation likelihood ratio tests.

*Statistica Sinica*,*32*, 1489–1514. https://doi.org/10.5705/ss.202019.0314
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*Stat Sci*,*6*, 240–268.
Chatfield, C. (1995). Model uncertainty, data mining and statistical
inference (with discussion).

*J Roy Stat Soc A*,*158*, 419–466.bias by selecting model because it fits
the data well; bias in standard errors;P. 420: ... need for a better
balance in the literature and in statistical teaching between
techniques and problem solving strategies. P. 421: It is “well
known” to be “logically unsound and practically
misleading” (Zhang, 1992) to make inferences as if a model is
known to be true when it has, in fact, been selected from the same data
to be used for estimation purposes. However, although statisticians may
admit this privately (Breiman (1992) calls it a “quiet
scandal”), they (we) continue to ignore the difficulties because
it is not clear what else could or should be done. P. 421: Estimation
errors for regression coefficients are usually smaller than errors from
failing to take into account model specification. P. 422: Statisticians
must stop pretending that model uncertainty does not exist and begin to
find ways of coping with it. P. 426: It is indeed strange that we often
admit model uncertainty by searching for a best model but then ignore
this uncertainty by making inferences and predictions as if certain that
the best fitting model is actually true. P. 427: The analyst needs to
assess the model selection process and not just the best fitting model.
P. 432: The use of subset selection methods is well known to introduce
alarming biases. P. 433: ... the AIC can be highly biased in data-driven
model selection situations. P. 434: Prediction intervals will generally
be too narrow. In the discussion, Jamal R. M. Ameen states that a model
should be (a) satisfactory in performance relative to the stated
objective, (b) logically sound, (c) representative, (d) questionable and
subject to on-line interrogation, (e) able to accommodate external or
expert information and (f) able to convey information.

Chatterjee, S., & Hadi, A. S. (2012).

*Regression Analysis by Example*(Fifth). Wiley.
Chavent, M., Kuentz-Simonet, V., Liquet, B., & Saracco, J. (2012).
ClustOfVar: An R package for the clustering of
variables.

*J Stat Software*,*50*(13), 1–16.
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*Comp Stat Data Analysis*,*1986*, 185–204.
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*J Am Stat Assoc*,*74*, 829–836.
Collett, D. (2002).

*Modelling Binary Data*(Second). Chapman and Hall.
Collins, G. S., Ogundimu, E. O., & Altman, D. G. (2016). Sample size
considerations for the external validation of a multivariable prognostic
model: A resampling study.

*Stat Med*,*35*(2), 214–226. https://doi.org/10.1002/sim.6787
Collins, G. S., Ogundimu, E. O., Cook, J. A., Manach, Y. L., &
Altman, D. G. (2016). Quantifying the impact of different approaches for
handling continuous predictors on the performance of a prognostic model.

*Stat Med*,*35*(23), 4124–4135. https://doi.org/10.1002/sim.6986used rms package hazard regression method (hare) for
survival model calibration

Cook, E. F., & Goldman, L. (1988). Asymmetric stratification:
An outline for an efficient method for controlling
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*Am J Epi*,*127*, 626–639.
Cook, N. R. (2007). Use and misues of the receiver operating
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*Circ*,*115*, 928–935.example of large change in predicted risk
in cardiovascular disease with tiny change in ROC area;possible limits
to c index when calibration is perfect;importance of calibration
accuracy and changes in predicted risk when new variables are
added

Copas, J. B. (1983). Regression, prediction and shrinkage (with
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*J Roy Stat Soc B*,*45*, 311–354.
Copas, J. B. (1987). Cross-validation shrinkage of regression
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*J Roy Stat Soc B*,*49*, 175–183.
Cox, C., Chu, H., Schneider, M. F., & Muñoz, A. (2007). Parametric
survival analysis and taxonomy of hazard functions for the generalized
gamma distribution.

*Stat Med*,*26*, 4352–4374.nice tutoria;GG includes bathtub-shape hazard
function; failed to reference Herndon

Cox, D. R. (1972). Regression models and life-tables (with discussion).

*J Roy Stat Soc B*,*34*, 187–220.
Crawford, S. L., Tennstedt, S. L., & McKinlay, J. B. (1995). A
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data.

*J Clin Epi*,*48*, 209–219.
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*Stat Med*,*8*, 1351–1362.
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in the presence of multiple indicators: The Framingham
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*Stat Med*,*14*, 1757–1770.
Davis, C. E., Hyde, J. E., Bangdiwala, S. I., & Nelson, J. J.
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*Modern Statistical Methods in Chronic Disease Epidemiology*(pp. 140–147). Wiley.
Davis, C. S. (2002).

*Statistical Methods for the Analysis of Repeated Measurements*. Springer.
Derksen, S., & Keselman, H. J. (1992). Backward, forward and
stepwise automated subset selection algorithms: Frequency
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regression modeling.

*Proceedings of the Eleventh Annual SAS Users Group International Conference*, 646–651.
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*Analysis of Longitudinal Data*(second). Oxford University Press.
Donders, van der Heijden, G. J. M. G., Stijnen, T., & Moons, K. G.
M. (2006). Review: A gentle introduction to imputation of
missing values.

*J Clin Epi*,*59*, 1087–1091.simple demonstration of failure of the add new
category method (indicator variable)

Donohue, M. C., Langford, O., Insel, P. S., van Dyck, C. H., Petersen,
R. C., Craft, S., Sethuraman, G., Raman, R., Aisen, P. S., &
Initiative, F. the A. D. N. (n.d.). Natural cubic splines for the
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*Pharmaceutical Statistics*,*n/a*(n/a). https://doi.org/10.1002/pst.2285
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retransformation method.

*J Am Stat Assoc*,*78*, 605–610.
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*Stat Med*, n/a. https://doi.org/10.1002/sim.7331
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cubic splines.

*Stat Med*,*8*, 551–561.
Efron, B. (1983). Estimating the error rate of a prediction rule:
Improvement on cross-validation.

*J Am Stat Assoc*,*78*, 316–331.suggested need at least 200
models to get an average that is adequate, i.e., 20 repeats of 10-fold
cv

Efron, Bradley, & Narasimhan, B. (2020). The Automatic
Construction of Bootstrap Confidence Intervals.

*Journal of Computational and Graphical Statistics*,*0*(0), 1–12. https://doi.org/10.1080/10618600.2020.1714633
Efron, Bradley, & Tibshirani, R. (1993).

*An Introduction to the Bootstrap*. Chapman and Hall.
Efron, Bradley, & Tibshirani, R. (1997). Improvements on
cross-validation: The .632+ bootstrap method.

*J Am Stat Assoc*,*92*, 548–560.
Erler, N. S., Rizopoulos, D., Rosmalen, J., Jaddoe, V. W. V., Franco, O.
H., & Lesaffre, E. M. E. H. (2016). Dealing with missing covariates
in epidemiologic studies: A comparison between multiple imputation and a
full Bayesian approach.

*Stat Med*,*35*(17), 2955–2974. https://doi.org/10.1002/sim.6944
Fan, J., & Levine, R. A. (2007). To amnio or not to amnio:
That is the decision for Bayes.

*Chance*,*20*(3), 26–32.
Faraggi, D., & Simon, R. (1996). A simulation study of
cross-validation for selecting an optimal cutpoint in univariate
survival analysis.

*Stat Med*,*15*, 2203–2213.bias in point estimate of effect from selecting
cutpoints based on P-value; loss of information from dichotomizing
continuous predictors

Faraway, J. J. (1992). The cost of data analysis.

*J Comp Graph Stat*,*1*, 213–229.
Fedorov, V., Mannino, F., & Zhang, R. (2009). Consequences of
dichotomization.

*Pharm Stat*,*8*, 50–61. https://doi.org/10.1002/pst.331optimal cutpoint depends on unknown parameters;should
only entertain dichotomization when "estimating a value of the
cumulative distribution and when the assumed model is very different
from the true model";nice graphics

Fienberg, S. E. (2007).

*The Analysis of Cross-Classified Categorical Data*(Second). Springer.
Filzmoser, P., Fritz, H., & Kalcher, K. (2012).

*pcaPP: Robust PCA by Projection Pursuit*. http://CRAN.R-project.org/package=pcaPP
Freedman, D., Navidi, W., & Peters, S. (1988).

*On the Impact of Variable Selection in Fitting Regression Equations*(pp. 1–16). Springer-Verlag.
Friedman, J. H. (1984).

*A variable span smoother*(Technical Report No. 5). Laboratory for Computational Statistics, Department of Statistics, Stanford University.
Gail, M. H., & Pfeiffer, R. M. (2005). On criteria for evaluating
models of absolute risk.

*Biostatistics*,*6*(2), 227–239.
Gardiner, J. C., Luo, Z., & Roman, L. A. (2009). Fixed effects,
random effects and GEE: What are the
differences?

*Stat Med*,*28*, 221–239.nice comparison of models; econometrics; different use
of the term "fixed effects model"

Giannoni, A., Baruah, R., Leong, T., Rehman, M. B., Pastormerlo, L. E.,
Harrell, F. E., Coats, A. J., & Francis, D. P. (2014). Do optimal
prognostic thresholds in continuous physiological variables really
exist? Analysis of origin of apparent thresholds, with
systematic review for peak oxygen consumption, ejection fraction and
BNP.

*PLoS ONE*,*9*(1). https://doi.org/10.1371/journal.pone.0081699
Giudice, J. H., Fieberg, J. R., & Lenarz, M. S. (2011). Spending
degrees of freedom in a poor economy: A case study of
building a sightability model for moose in northeastern minnesota.

*J Wildlife Manage*. https://doi.org/10.1002/jwmg.213
Gneiting, T., & Raftery, A. E. (2007). Strictly proper scoring
rules, prediction, and estimation.

*J Am Stat Assoc*,*102*, 359–378.wonderful review article
except missing references from Scandanavian and German medical decision
making literature

Goldstein, H. (1989). Restricted unbiased iterative generalized
least-squares estimation.

*Biometrika*,*76*(3), 622–623.derivation of REML

Govindarajulu, U. S., Spiegelman, D., Thurston, S. W., Ganguli, B.,
& Eisen, E. A. (2007). Comparing smoothing techniques in
Cox models for exposure-response relationships.

*Stat Med*,*26*, 3735–3752.authors wrote a
SAS macro for restricted cubic splines even though such a macro as
existed since 1984; would have gotten more useful results had simulation
been used so would know the true regression shape;measure of agreement
of two estimated curves by computing the area between them, standardized
by average of areas under the two;penalized spline and rcs were closer
to each other than to fractional polynomials

Grambsch, P. M., & O’Brien, P. C. (1991). The effects of
transformations and preliminary tests for non-linearity in regression.

*Stat Med*,*10*, 697–709.
Grambsch, P., & Therneau, T. (1994). Proportional hazards tests and
diagnostics based on weighted residuals.

*Biometrika*,*81*, 515–526.
Gray, R. J. (1992). Flexible methods for analyzing survival data using
splines, with applications to breast cancer prognosis.

*J Am Stat Assoc*,*87*, 942–951.
Gray, R. J. (1994). Spline-based tests in survival analysis.

*Biometrics*,*50*, 640–652.
Greenacre, M. J. (1988). Correspondence analysis of multivariate
categorical data by weighted least-squares.

*Biometrika*,*75*, 457–467.
Greenland, S. (2000). When should epidemiologic regressions use random
coefficients?

*Biometrics*,*56*, 915–921. https://doi.org/10.1111/j.0006-341X.2000.00915.xuse of statistics in epidemiology is largely
primitive;stepwise variable selection on confounders leaves important
confounders uncontrolled;composition matrix;example with far too many
significant predictors with many regression coefficients absurdly
inflated when overfit;lack of evidence for dietary effects mediated
through constituents;shrinkage instead of variable selection;larger
effect on confidence interval width than on point estimates with
variable selection;uncertainty about variance of random effects is just
uncertainty about prior opinion;estimation of variance is
pointless;instead the analysis should be repeated using different
values;"if one feels compelled to estimate $\tau{̂2}$, I would recommend
giving it a proper prior concentrated amount contextually reasonable
values";claim about ordinary MLE being unbiased is misleading because it
assumes the model is correct and is the only model entertained;shrinkage
towards compositional model;"models need to be complex to capture
uncertainty about the relations...an honest uncertainty assessment
requires parameters for all effects that we know may be present. This
advice is implicit in an antiparsimony principle often attributed to L.
J. Savage ’All models should be as big as an elephant (see Draper,
1995)’". See also gus06per.

Guo, J., James, G., Levina, E., Michailidis, G., & Zhu, J. (2011).
Principal component analysis with sparse fused loadings.

*J Comp Graph Stat*,*19*(4), 930–946.incorporates blocking structure in the
variables;selects different variables for different
components;encourages loadings of highly correlated variables to have
same magnitude, which aids in interpretation

Gurka, M. J., Edwards, L. J., & Muller, K. E. (2011). Avoiding bias
in mixed model inference for fixed effects.

*Stat Med*,*30*(22), 2696–2707. https://doi.org/10.1002/sim.4293
Hand, D., & Crowder, M. (1996).

*Practical Longitudinal Data Analysis*. Chapman & Hall.
Harel, O., & Zhou, X.-H. (2007). Multiple imputation:
Review of theory, implementation and software.

*Stat Med*,*26*, 3057–3077.failed to review
aregImpute;excellent overview;ugly S code;nice description of different
statistical tests including combining likelihood ratio tests (which
appears to be complex, requiring an out-of-sample log likelihood
computation);congeniality of imputation and analysis models;Bayesian
approximation or approximate Bayesian bootstrap overview;"Although
missing at random (MAR) is a non-testable assumption, it has been
pointed out in the literature that we can get very close to MAR if we
include enough variables in the imputation models ... it would be
preferred if the missing data modelling was done by the data
constructors and not by the users... MI yields valid inferences not only
in congenial settings, but also in certain uncongenial ones as
well—where the imputer’s model (1) is more general (i.e. makes fewer
assumptions) than the complete-data estimation method, or when the
imputer’s model makes additional assumptions that are
well-founded."

Harrell, F. E. (1986). The LOGIST Procedure. In

*SUGI Supplemental Library Users Guide*(Version 5, pp. 269–293). SAS Institute, Inc.
Harrell, Frank E. (2015).

*Regression Modeling Strategies, with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis*(Second edition). Springer. https://doi.org/10.1007/978-3-319-19425-7
Harrell, F. E., Lee, K. L., Califf, R. M., Pryor, D. B., & Rosati,
R. A. (1984). Regression modeling strategies for improved prognostic
prediction.

*Stat Med*,*3*, 143–152.
Harrell, Frank E., Lee, K. L., & Mark, D. B. (1996). Multivariable
prognostic models: Issues in developing models, evaluating
assumptions and adequacy, and measuring and reducing errors.

*Stat Med*,*15*, 361–387.
Harrell, F. E., Lee, K. L., Matchar, D. B., & Reichert, T. A.
(1985). Regression models for prognostic prediction:
Advantages, problems, and suggested solutions.

*Ca Trt Rep*,*69*, 1071–1077.
Harrell, F. E., Lee, K. L., & Pollock, B. G. (1988). Regression
models in clinical studies: Determining relationships
between predictors and response.

*J Nat Cancer Inst*,*80*, 1198–1202.
Harrell, Frank E., Margolis, P. A., Gove, S., Mason, K. E., Mulholland,
E. K., Lehmann, D., Muhe, L., Gatchalian, S., & Eichenwald, H. F.
(1998). Development of a clinical prediction model for an ordinal
outcome: The World Health Organization ARI Multicentre
Study of clinical signs and etiologic agents of pneumonia,
sepsis, and meningitis in young infants.

*Stat Med*,*17*, 909–944. http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1097-0258(19980430)17:8%3C909::AID-SIM753%3E3.0.CO;2-O/abstract
Hastie, T. J., & Tibshirani, R. J. (1990).

*Generalized Additive Models*. Chapman & Hall/CRC.
Hastie, T., & Tibshirani, R. (1990).

*Generalized Additive Models*. Chapman and Hall.
Hastie, Trevor, Tibshirani, R., & Friedman, J. H. (2008).

*The Elements of Statistical Learning*(second). Springer.
Hazewinkel, A., Tilling, K., Wade, K. H., & Palmer, T. (2023). Trial
arm outcome variance difference after dropout as an indicator of
missing‐not‐at‐random bias in randomized controlled trials.

*Biometrical J*, 2200116. https://doi.org/10.1002/bimj.202200116Indirect assessment of MNAR by comparing variances of
continuous outcome variable. Not sure if authors discussed
transformation assumptions about Y.

He, Y., & Zaslavsky, A. M. (2012). Diagnosing imputation models by
applying target analyses to posterior replicates of completed data.

*Stat Med*,*31*(1), 1–18. https://doi.org/10.1002/sim.4413
Herndon, J. E., & Harrell, F. E. (1990). The restricted cubic spline
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*Comm Stat Th Meth*,*19*, 639–663.
Herndon, J. E., & Harrell, F. E. (1995). The restricted cubic spline
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*Stat Med*,*14*, 2119–2129.
Hilsenbeck, S. G., & Clark, G. M. (1996). Practical p-value
adjustment for optimally selected cutpoints.

*Stat Med*,*15*, 103–112.
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*Ann Math Stat*,*19*, 546–557.
Holländer, N., Sauerbrei, W., & Schumacher, M. (2004). Confidence
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“optimal” cutpoint.

*Stat Med*,*23*, 1701–1713. https://doi.org/10.1002/sim.1611true type I error can be much greater than nominal
level;one example where nominal is 0.05 and true is 0.5;minimum P-value
method;CART;recursive partitioning;bootstrap method for correcting
confidence interval;based on heuristic shrinkage coefficient;"It should
be noted, however, that the optimal cutpoint approach has disadvantages.
One of these is that in almost every study where this method is applied,
another cutpoint will emerge. This makes comparisons across studies
extremely difficult or even impossible. Altman et al. point out this
problem for studies of the prognostic relevance of the S-phase fraction
in breast cancer published in the literature. They identified 19
different cutpoints used in the literature; some of them were solely
used because they emerged as the “optimal” cutpoint in a
specific data set. In a meta-analysis on the relationship between
cathepsin-D content and disease-free survival in node-negative breast
cancer patients, 12 studies were in included with 12 different cutpoints
... Interestingly, neither cathepsin-D nor the S-phase fraction are
recommended to be used as prognostic markers in breast cancer in the
recent update of the American Society of Clinical Oncology.";
dichotomization; categorizing continuous variables; refs alt94dan,
sch94out, alt98sub

Horton, N. J., & Kleinman, K. P. (2007). Much ado about nothing:
A comparison of missing data methods and software to fit
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*Am Statistician*,*61*(1), 79–90.
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*Am Statistician*,*44*, 214–217.
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*Risk Adjustment for Measuring Health Outcomes*(pp. 29–118). Foundation of the American College of Healthcare Executives.dimensions of risk factors to include in models

Janssen, K. J., Donders, A. R., Harrell, F. E., Vergouwe, Y., Chen, Q.,
Grobbee, D. E., & Moons, K. G. (2010). Missing covariate data in
medical research: To impute is better than to ignore.

*J Clin Epi*,*63*, 721–727.
Jolliffe, I. T. (2010).

*Principal Component Analysis*(Second). Springer-Verlag.
Jones, M. P. (1996). Indicator and stratification methods for missing
explanatory variables in multiple linear regression.

*J Am Stat Assoc*,*91*, 222–230.
Kalbfleisch, J. D., & Prentice, R. L. (1973). Marginal likelihood
based on Cox’s regression and life model.

*Biometrika*,*60*, 267–278.
Karrison, T. G. (1997). Use of Irwin’s restricted mean as
an index for comparing survival in different treatment
groups—Interpretation and power considerations.

*Controlled Clin Trials*,*18*, 151–167.nice power comparisons with Wilcoxon;power with and
without covariable adjustment

Karvanen, J., & Harrell, F. E. (2009). Visualizing covariates in
proportional hazards model.

*Stat Med*,*28*, 1957–1966.
Kass, R. E., & Raftery, A. E. (1995). Bayes factors.

*J Am Stat Assoc*,*90*, 773–795.
Kay, R. (1986). Treatment effects in competing-risks analysis of
prostate cancer data.

*Biometrics*,*42*, 203–211.
Kenward, M. G., White, I. R., & Carpener, J. R. (2010). Should
baseline be a covariate or dependent variable in analyses of change from
baseline in clinical trials? (Letter to the editor).

*Stat Med*,*29*, 1455–1456.sharp rebuke of
liu09sho

Keselman, H. J., Algina, J., Kowalchuk, R. K., & Wolfinger, R. D.
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structures in the analysis of repeated measurements.

*Comm Stat - Sim Comp*,*27*, 591–604.use of AIC and
BIC for selecting the covariance structure in repeated
measurements;serial data;longitudinal data;when chosing from 11
covariance patterns, AIC selected the correct structure 0.47 of the
time; BIC was correct in 0.35

Kim, S., Sugar, C. A., & Belin, T. R. (2015). Evaluating model-based
imputation methods for missing covariates in regression models with
interactions.

*Stat Med*,*34*(11), 1876–1888. https://doi.org/10.1002/sim.6435
Knaus, W. A., Harrell, F. E., Lynn, J., Goldman, L., Phillips, R. S.,
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hospitalized adults.

*Ann Int Med*,*122*, 191–203. https://doi.org/10.7326/0003-4819-122-3-199502010-00007
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*J Clin Epi*,*63*, 728–736.
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*Quantreg: Quantile Regression*. http://CRAN.R-project.org/package=quantreg
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*Econometrica*,*46*, 33–50.
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*J Am Stat Assoc*,*90*, 78–94.
Kuhfeld, W. F. (2009). The PRINQUAL Procedure. In

*SAS/STAT 9.2 User’s Guide*(Second). SAS Publishing. http://support.sas.com/documentation/onlinedoc/stat
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*Biometrics*,*42*, 507–519.
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*J Am Stat Assoc*,*79*, 61–83.
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*Appl Stat*,*34*, 201–211.
Lausen, B., & Schumacher, M. (1996). Evaluating the effect of
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*Comp Stat Data Analysis*,*21*(3), 307–326. https://doi.org/10.1016/0167-9473(95)00016-X
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*Biometrics*,*34*, 318–327.
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*Appl Stat*,*41*, 191–201.
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*Stat Med*,*7*, 983–995.
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multiple imputation: A simulation study.

*Emerg Themes Epi*,*9*(1), 3+. https://doi.org/10.1186/1742-7622-9-3Not sure that the authors satisfactorily dealt with
nonlinear predictor effectsin the absence of strong auxiliary
information, there is little to gain from multiple imputation with
missing data in the exposure-of-interest. In fact, the authors went
further to say that multiple imputation can introduce bias not present
in a complete case analysis if a poorly fitting imputation model is used
[from Yong Hao Pua]

Lee, S., Huang, J. Z., & Hu, J. (2010). Sparse logistic principal
components analysis for binary data.

*Ann Appl Stat*,*4*(3), 1579–1601.
Leng, C., & Wang, H. (2009). On general adaptive sparse principal
component analysis.

*J Comp Graph Stat*,*18*(1), 201–215.
Li, C., & Shepherd, B. E. (2012). A new residual for ordinal
outcomes.

*Biometrika*,*99*(2), 473–480. https://doi.org/10.1093/biomet/asr073
Liang, K.-Y., & Zeger, S. L. (2000). Longitudinal data analysis of
continuous and discrete responses for pre-post designs.

*Sankhyā*,*62*, 134–148.makes an
error in assuming the baseline variable will have the same univariate
distribution as the response except for a shift;baseline may have for
example a truncated distribution based on a trial’s inclusion
criteria;if correlation between baseline and response is zero, ANCOVA
will be twice as efficient as simple analysis of change scores;if
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Lindsey, J. K. (1997).

*Models for Repeated Measurements*. Clarendon Press.
Lipsitz, S., Parzen, M., & Zhao, L. P. (2002). A
Degrees-Of-Freedom approximation in Multiple
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*J Stat Comp Sim*,*72*(4), 309–318. https://doi.org/10.1080/00949650212848
Little, R. J. A., & Rubin, D. B. (2002).

*Statistical Analysis with Missing Data*(second). Wiley.
Liu, G. F., Lu, K., Mogg, R., Mallick, M., & Mehrotra, D. V. (2009).
Should baseline be a covariate or dependent variable in analyses of
change from baseline in clinical trials?

*Stat Med*,*28*, 2509–2530.seems to miss several important points,
such as the fact that the baseline variable is often part of the
inclusion/exclusion criteria and so has a truncated distribution that is
different from that of the follow-up measurements;sharp rebuke in
ken10sho

Lockhart, R., Taylor, J., Tibshirani, R. J., & Tibshirani, R.
(2013).

*A significance test for the lasso*. arXiv. http://arxiv.org/abs/1301.7161
Luchman, J. N. (2014). Relative Importance Analysis With
Multicategory Dependent Variables:: An Extension and
Review of Best Practices.

*Organizational Research Methods*,*17*(4), 452–471. https://doi.org/10.1177/1094428114544509Measures based on pseudo R2̂ and considering all
possible subsets of covariates. Good background section with review of
pseudo R2̂ measures.

Luo, X., Stfanski, L. A., & Boos, D. D. (2006). Tuning variable
selection procedures by adding noise.

*Technometrics*,*48*, 165–175.adding a known amount of
noise to the response and studying σ² to tune the stopping rule to avoid
overfitting or underfitting;simulation setup

Madley-Dowd, P., Hughes, R., Tilling, K., & Heron, J. (2019). The
proportion of missing data should not be used to guide decisions on
multiple imputation.

*Journal of Clinical Epidemiology*,*110*, 63–73. https://doi.org/10.1016/j.jclinepi.2019.02.016
Mamouris, P., Nassiri, V., Verbeke, G., Janssens, A., Vaes, B., &
Molenberghs, G. (2023). A longitudinal transition imputation model for
categorical data applied to a large registry dataset.

*Statistics in Medicine*,*42*(29), 5405–5418. https://doi.org/10.1002/sim.9919
Mantel, N. (1970). Why stepdown procedures in variable selection.

*Technometrics*,*12*, 621–625.
Manuguerra, M., & Heller, G. Z. (2010). Ordinal Regression
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*Int J Biostat*,*6*(1). https://doi.org/10.2202/1557-4679.1230mislabeled a flexible parametric model as
semi-parametric; does not cover semi-parametric approach with lots of
intercepts

Mark, D. B., Hlatky, M. A., Harrell, F. E., Lee, K. L., Califf, R. M.,
& Pryor, D. B. (1987). Exercise treadmill score for predicting
prognosis in coronary artery disease.

*Ann Int Med*,*106*, 793–800.
Maxwell, S. E., & Delaney, H. D. (1993). Bivariate median splits and
spurious statistical significance.

*Psych Bull*,*113*, 181–190. https://doi.org/10.1037//0033-2909.113.1.181
McCabe, G. P. (1984). Principal variables.

*Technometrics*,*26*, 137–144.
Michailidis, G., & de Leeuw, J. (1998). The Gifi system
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*Stat Sci*,*13*, 307–336.
Moons, K. G. M., Donders, R. A. R. T., Stijnen, T., & Harrell, F. E.
(2006). Using the outcome for imputation of missing predictor values was
preferred.

*J Clin Epi*,*59*, 1092–1101. https://doi.org/10.1016/j.jclinepi.2006.01.009use of outcome variable; excellent graphical summaries
of simulations

Morris, T. P., White, I. R., Carpenter, J. R., Stanworth, S. J., &
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multiple imputation: T. P. Morris

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Moser, B. K., & Coombs, L. P. (2004). Odds ratios for a continuous
outcome variable without dichotomizing.

*Stat Med*,*23*, 1843–1860.large loss of efficiency and
power;embeds in a logistic distribution, similar to proportional odds
model;categorization;dichotomization of a continuous response in order
to obtain odds ratios often results in an inflation of the needed sample
size by a factor greater than 1.5

Muenz, L. R. (1983). Comparing survival distributions: A
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*Ca Invest*,*1*, 537–545.
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crossing hazards problem.

*Stat Med*,*29*, 1947–1957.failed to reference per06red or per07app

Myers, R. H. (1990).

*Classical and Modern Regression with Applications*. PWS-Kent.
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coefficient of determination.

*Biometrika*,*78*, 691–692.
Nick, T. G., & Hardin, J. M. (1999). Regression modeling strategies:
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*Am J Occ Ther*,*53*, 459–470.
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*Computational Statistics & Data Analysis*,*54*(12), 3227–3241. https://doi.org/10.1016/j.csda.2010.01.036
Paul, D., Bair, E., Hastie, T., & Tibshirani, R. (2008).
“Preconditioning” for feature selection and
regression in high-dimensional problems.

*Ann Stat*,*36*(4), 1595–1619. https://doi.org/10.1214/009053607000000578develop consistent Y using a latent variable
structure, using for example supervised principal components. Then run
stepwise regression or lasso predicting Y (lasso worked better). Can run
into problems when a predictor has importance in an adjusted sense but
has no marginal correlation with Y;model approximation;model
simplification

Peduzzi, P., Concato, J., Feinstein, A. R., & Holford, T. R. (1995).
Importance of events per independent variable in proportional hazards
regression analysis. II. Accuracy and
precision of regression estimates.

*J Clin Epi*,*48*, 1503–1510.
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A. R. (1996). A simulation study of the number of events per variable in
logistic regression analysis.

*J Clin Epi*,*49*, 1373–1379.
Peek, N., Arts, D. G. T., Bosman, R. J., van der Voort, P. H. J., &
de Keizer, N. F. (2007). External validation of prognostic models for
critically ill patients required substantial sample sizes.

*J Clin Epi*,*60*, 491–501.large sample sizes
need to obtain reliable external validations;inadequate power of DeLong,
DeLong, and Clarke-Pearson test for differences in correlated ROC areas
(p. 498);problem with tests of calibration accuracy having too much
power for large sample sizes

Pencina, M. J., D’Agostino, R. B., & Demler, O. V. (2012). Novel
metrics for evaluating improvement in discrimination: Net
reclassification and integrated discrimination improvement for normal
variables and nested models.

*Stat Med*,*31*(2), 101–113. https://doi.org/10.1002/sim.4348
Pencina, M. J., D’Agostino, R. B., & Steyerberg, E. W. (2011).
Extensions of net reclassification improvement calculations to measure
usefulness of new biomarkers.

*Stat Med*,*30*, 11–21. https://doi.org/10.1002/sim.4085lack of need for NRI to be
category-based;arbitrariness of categories;"category-less or continuous
NRI is the most objective and versatile measure of improvement in risk
prediction;authors misunderstood the inadequacy of three categories if
categories are used;comparison of NRI to change in C index;example of
continuous plot of risk for old model vs. risk for new model

Pencina, M. J., D’Agostino Sr, R. B., D’Agostino Jr, R. B., & Vasan,
R. S. (2008). Evaluating the added predictive ability of a new marker:
From area under the ROC curve to
reclassification and beyond.

*Stat Med*,*27*, 157–172.small differences in ROC area can still
be very meaningful;example of insignificant test for difference in ROC
areas with very significant results from new method;Yates’
discrimination slope;reclassification table;limiting version of this
based on whether and amount by which probabilities rise for events and
lower for non-events when compare new model to old;comparing two
models;see letter to the editor by Van Calster and Van Huffel, Stat in
Med 29:318-319, 2010 and by Cook and Paynter, Stat in Med 31:93-97,
2012

Penning, de V. B. B. L., van, S. M., & Groenwold, R. H. H. (2018).
Propensity Score Estimation Using Classification and
Regression Trees in the Presence of
Missing Covariate Data.

*Epidemiologic Methods*,*7*(1). https://doi.org/10.1515/em-2017-0020
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*J Am Stat Assoc*,*86*, 770–778.
Pepe, M. S., Longton, G., & Thornquist, M. (1991). A qualifier
Q for the survival function to describe the prevalence of a
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*Stat Med*,*10*, 413–421.
Pepe, Margaret S., & Mori, M. (1993). Kaplan–Meier,
marginal or conditional probability curves in summarizing competing
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*Stat Med*,*12*, 737–751.
Perperoglou, A., le Cessie, S., & van Houwelingen, H. C. (2006).
Reduced-rank hazard regression for modelling non-proportional hazards.

*Stat Med*,*25*, 2831–2845.natural
structural way to allow for varying degrees of freedom in modeling
non-PH

Peters, S. A., Bots, M. L., den Ruijter, H. M., Palmer, M. K., Grobbee,
D. E., Crouse, J. R., O’Leary, D. H., Evans, G. W., Raichlen, J. S.,
Moons, K. G., Koffijberg, H., & METEOR study group. (2012). Multiple
imputation of missing repeated outcome measurements did not add to
linear mixed-effects models.

*J Clin Epi*,*65*(6), 686–695. https://doi.org/10.1016/j.jclinepi.2011.11.012
Peterson, B., & George, S. L. (1993). Sample size requirements and
length of study for testing interaction in a 1 k factorial design when
time-to-failure is the outcome.

*Controlled Clin Trials*,*14*, 511–522.
Peterson, B., & Harrell, F. E. (1990). Partial proportional odds
models for ordinal response variables.

*Appl Stat*,*39*, 205–217.
Pike, M. C. (1966). A method of analysis of certain class of experiments
in carcinogenesis.

*Biometrics*,*22*, 142–161.
Pinheiro, J. C., & Bates, D. M. (2000).

*Mixed-Effects Models in S and S-PLUS*. Springer.
Potthoff, R. F., & Roy, S. N. (1964). A generalized multivariate
analysis of variance model useful especially for growth curve problems.

*Biometrika*,*51*, 313–326.included an AR1 example

Pryor, David B., Harrell, F. E., Lee, K. L., Califf, R. M., &
Rosati, R. A. (1983). Estimating the likelihood of significant coronary
artery disease.

*Am J Med*,*75*, 771–780.
Pryor, D. B., Harrell, F. E., Rankin, J. S., Lee, K. L., Muhlbaier, L.
H., Oldham, H. N., Hlatky, M. A., Mark, D. B., Reves, J. G., &
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*Circ (Supplement V)*,*76*, 13–21.
Putter, H., Sasako, M., Hartgrink, H. H., van de Velde, C. J. H., &
van Houwelingen, J. C. (2005). Long-term survival with non-proportional
hazards: Results from the Dutch Gastric Cancer Trial.

*Stat Med*,*24*, 2807–2821.
Radchenko, P., & James, G. M. (2008). Variable inclusion and
shrinkage algorithms.

*J Am Stat Assoc*,*103*(483), 1304–1315.solves problem caused by lasso using
the same penalty parameter for variable selection and shrinkage which
causes lasso to have to keep too many variables in the model to avoid
overshrinking the remaining predictors;does not handle scaling issue
well

Ragland, D. R. (1992). Dichotomizing continuous outcome variables:
Dependence of the magnitude of association and statistical
power on the cutpoint.

*Epi*,*3*, 434–440. https://doi.org/10.1097/00001648-199209000-00009
Reilly, B. M., & Evans, A. T. (2006). Translating clinical research
into clinical practice: Impact of using prediction rules to
make decisions.

*Ann Int Med*,*144*, 201–209.impact analysis;example of decision aid being ignored
or overruled making MD decisions worse;assumed utilities are constant
across subjects by concluding that directives have more impact than
predictions;Goldman-Cook clinical prediction rule in AMI

Reiter, J. P. (2007). Small-sample degrees of freedom for
multi-component significance tests with multiple imputation for missing
data.

*Biometrika*,*94*(2), 502–508. https://doi.org/10.1093/biomet/asm028
Riley, R. D., Snell, K. I., Ensor, J., Burke, D. L., Harrell, F. E.,
Moons, K. G., & Collins, G. S. (2019). Minimum sample size for
developing a multivariable prediction model: PART II -
binary and time-to-event outcomes.

*Stat Med*,*38*(7), 1276–1296. https://doi.org/10.1002/sim.7992
Riley, R. D., Snell, K. I., Ensor, J., Burke, D. L., Jr, F. E. H.,
Moons, K. G., & Collins, G. S. (2019). Minimum sample size for
developing a multivariable prediction model: PART II -
binary and time-to-event outcomes.

*Statistics in Medicine*,*38*(7), 1276–1296. https://doi.org/10.1002/sim.7992
Roecker, E. B. (1991). Prediction error and its estimation for
subset-selected models.

*Technometrics*,*33*, 459–468.
Royston, P., Altman, D. G., & Sauerbrei, W. (2006). Dichotomizing
continuous predictors in multiple regression: A bad idea.

*Stat Med*,*25*, 127–141. https://doi.org/10.1002/sim.2331destruction of statistical inference when cutpoints
are chosen using the response variable; varying effect estimates when
change cutpoints;difficult to interpret effects when dichotomize;nice
plot showing effect of categorization; PBC data

Rubin, D., & Schenker, N. (1991). Multiple imputation in health-care
data bases: An overview and some applications.

*Stat Med*,*10*, 585–598.
Sarle, W. (1990). The VARCLUS Procedure. In

*SAS/STAT User’s Guide*(fourth, Vol. 2, pp. 1641–1659). SAS Institute, Inc. http://support.sas.com/documentation/onlinedoc/stat
Sauerbrei, W., & Schumacher, M. (1992). A bootstrap resampling
procedure for model building: Application to the
Cox regression model.

*Stat Med*,*11*, 2093–2109.
Schafer, J. L., & Graham, J. W. (2002). Missing data:
Our view of the state of the art.

*Psych Meth*,*7*, 147–177.excellent review and overview
of missing data and imputation;problems with MICE;less technical
description of 3 types of missing data

Schemper, M., & Heinze, G. (1997). Probability imputation revisited
for prognostic factor studies.

*Stat Med*,*16*, 73–80.imputation of missing covariables using
logistic model;comparison with multiple imputation;analysis of prostate
cancer dataset

Schoenfeld, D. (1982). Partial residuals for the proportional hazards
regression model.

*Biometrika*,*69*, 239–241.
Schulgen, G., Lausen, B., Olsen, J., & Schumacher, M. (1994).
Outcome-oriented cutpoints in quantitative exposure.

*Am J Epi*,*120*, 172–184.
Selvin, E., Steffes, M. W., Zhu, H., Matsushita, K., Wagenknecht, L.,
Pankow, J., Coresh, J., & Brancati, F. L. (2010). Glycated
hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults.

*NEJM*,*362*(9), 800–811. https://doi.org/10.1056/NEJMoa0908359
Senn, S. (2006). Change from baseline and analysis of covariance
revisited.

*Stat Med*,*25*, 4334–4344.shows that claims that in a 2-arm study it is not true
that ANCOVA requires the population means at baseline to be
identical;refutes some claims of lia00lon;problems with
counterfactuals;temporal additivity ("amounts to supposing that despite
the fact that groups are difference at baseline they would show the same
evolution over time");causal additivity;is difficult to design trials
for which simple analysis of change scores is unbiased, ANCOVA is
biased, and a causal interpretation can be given;temporally and
logically, a "baseline cannot be a <i>response</i> to treatment", so baseline and
response cannot be modeled in an integrated framework as Laird and
Ware’s model has been used;"one should focus clearly on
“outcomes” as being the only values that can be influenced
by treatment and examine critically any schemes that assume that these
are linked in some rigid and deterministic view to
“baseline” values. An alternative tradition sees a baseline
as being merely one of a number of measurements capable of improving
predictions of outcomes and models it in this way.";"You cannot
establish necessary conditions for an estimator to be valid by
nominating a model and seeing what the model implies unless the model is
universally agreed to be impeccable. On the contrary it is appropriate
to start with the estimator and see what assumptions are implied by
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overlap between groups

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*BMC Medical Research Methodology*,*14*(1), 137+. https://doi.org/10.1186/1471-2288-14-137Would be better to use proper accuracy scores in the
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of eliciting the expected harm of a missed diagnosis;use of a threshold
on the probability of disease for taking some action;decision curve;has
other good references to decision analysis

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*Chance*,*19*(1), 49–56.can find bins that yield
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the outcome, since, in practice, many genes work together to effect a
particular phenotype. LPC effectively down-weights individual genes that
are associated with the outcome but that do not share an expression
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binary variables using techniques for continuous data;uses the method to
solve for the cutpoint for a continuous estimate to be converted into a
binary value;method should be useful in more general situations;idea is
to duplicate the entire dataset and in the second half of the new
datasets to set all non-missing values of the target variable to
missing;multiply impute these now-missing values and compare them to the
actual values

Zhang, H. H., & Lu, W. (2007). Adaptive lasso for Cox’s
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*Biometrika*,*94*, 691–703.penalty function has ratios against
original MLE;scale-free lasso

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shrinks some loadings to zero

Zhou, X., & Reiter, J. P. (2012). A Note on
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