Morris et al 2019 - Full Reference List and Further Reading

Summary

All 76 references of Morris, White & Crowther (2019), transcribed from the arXiv v3 PDF (pp.27-30). For each one the table gives the pages where it is cited in the text and the note in this set that covers that passage. A curated further-reading section groups the references that are follow-up reading on simulation-study methodology itself: Monte Carlo error, design, data generation, presentation, protocols and meta-research. The rest are mostly Statistics in Medicine Vol. 34 studies used as examples.

Overview

The paper cites by bracketed number. Elsewhere in this note set references appear as “Author (year) [n]“. Use this note to resolve a number to a full citation. Entries are transcribed as printed, including the source’s own slips: the “applies statistician” typo in [2], the missing initial for Haneuse in [8], the missing volume and pages in [21] and [72] and the missing volume in [32], and the “Monte Carle” spelling the paper uses in the text near [43]. Only line-break artefacts in page ranges were repaired.

How the citation pages were found

In-text citation pages were located by scanning each page of the PDF text layer for bracketed numbers such as “(6)” or “(23, 3)“. Equation and list numbers that look like citations (e.g. “depends on (1), (2) and (3)” on p.4, Eq. (1) on p.18, Eq. (2) on p.20) were checked by hand and removed. The “Note” column maps each page to the note that holds that passage. Ref [17] appears only in Table 1 (p.3).

Main Content

Curated further reading on simulation-study methodology

Follow-up reading grouped by topic

Topic Refs Why read it
Monte Carlo error Koehler, Brown & Haneuse 2009 [8]; Díaz-Emparanza 2002 [11]; Boos & Osborne 2015 [73] Assessing and reporting MCSE; how small a Monte Carlo study can be; resampling-based MCSEs for complex performance measures (§8, p.26)
Software for performance measures White 2010 simsum [12]; Gasparini & White 2018 rsimsum [67]; Wicklin 2018 zipper plot in SAS [68] Implementations of Table 6 and the zip plot
Design of simulation studies Burton et al. 2006 [7]; Skrondal 2000 [23]; Hoaglin & Andrews 1975 [4]; Hauck & Anderson 1984 [5] Closest predecessor design guide; fractional-factorial designs and meta-models for Monte Carlo experiments; early critiques of reporting
Generating data Ripley 1987 [6]; Morgan 1995 [9]; Chang 2011 [10]; Crowther & Lambert 2013 [14] General simulation texts; survival data generation
RNG streams Haramoto et al. 2008 [17] Jump-ahead for $F_{2}$ -linear generators
Simulation for power Feiveson 2002 [1] Power by simulation
Frequentist evaluation of Bayesian methods Rubin 1984 [2]; Grieve 2016 [3] Why fixed- $θ$ frequency calculations are relevant to Bayesians
Protocols and good practice Smith & Marshall 2011 [13]; Holford et al. 1999 [15]; O’Kelly et al. 2017 [16] Protocols and best practice for modelling-and-simulation projects
Presentation Rücker & Schwarzer 2014 [66] Nested-loop plot
Meta-research on simulation studies Langan, Higgins & Simmonds 2017 [75]; Boulesteix, Wilson & Hapfelmeier 2017 [76] A review of simulation studies (heterogeneity estimators); evidence-based computational statistics and real-data benchmarks (§8.1)

Topic	Refs	Why read it
Monte Carlo error	Koehler, Brown & Haneuse 2009 [8]; Díaz-Emparanza 2002 [11]; Boos & Osborne 2015 [73]	Assessing and reporting MCSE; how small a Monte Carlo study can be; resampling-based MCSEs for complex performance measures (§8, p.26)
Software for performance measures	White 2010 simsum [12]; Gasparini & White 2018 rsimsum [67]; Wicklin 2018 zipper plot in SAS [68]	Implementations of Table 6 and the zip plot
Design of simulation studies	Burton et al. 2006 [7]; Skrondal 2000 [23]; Hoaglin & Andrews 1975 [4]; Hauck & Anderson 1984 [5]	Closest predecessor design guide; fractional-factorial designs and meta-models for Monte Carlo experiments; early critiques of reporting
Generating data	Ripley 1987 [6]; Morgan 1995 [9]; Chang 2011 [10]; Crowther & Lambert 2013 [14]	General simulation texts; survival data generation
RNG streams	Haramoto et al. 2008 [17]	Jump-ahead for $F_{2}$ -linear generators
Simulation for power	Feiveson 2002 [1]	Power by simulation
Frequentist evaluation of Bayesian methods	Rubin 1984 [2]; Grieve 2016 [3]	Why fixed- $θ$ frequency calculations are relevant to Bayesians
Protocols and good practice	Smith & Marshall 2011 [13]; Holford et al. 1999 [15]; O’Kelly et al. 2017 [16]	Protocols and best practice for modelling-and-simulation projects
Presentation	Rücker & Schwarzer 2014 [66]	Nested-loop plot
Meta-research on simulation studies	Langan, Higgins & Simmonds 2017 [75]; Boulesteix, Wilson & Hapfelmeier 2017 [76]	A review of simulation studies (heterogeneity estimators); evidence-based computational statistics and real-data benchmarks (§8.1)

References [1]-[76] (pp.27-30)

Full reference list

Ref Citation (as printed) Cited on page(s) Note(s)
[1] Feiveson A. H.. Power by simulation. The Stata Journal. 2002;2(2):107–124. 2 Motivation
[2] Rubin D. B.. Bayesianly justifiable and relevant frequency calculations for the applies statistician. The Annals of Statistics. 1984;12(4):1151–1172. 2 Motivation
[3] Grieve A. P.. Idle thoughts of a ‘well-calibrated’ Bayesian in clinical drug development. Pharmaceutical Statistics. 2016;15(2):96–108. 2, 6, 26 Motivation, ADEMP, Protocols
[4] Hoaglin D. C., Andrews D. F.. The reporting of computation-based results in statistics. The American Statistician. 1975;29(3):122–126. 2 Motivation
[5] Hauck W. W., Anderson S.. A survey regarding the reporting of simulation studies. The American Statistician. 1984;38(3):214–216. 2 Motivation
[6] Ripley B. D.. Stochastic Simulation. New York: Wiley; 1987. 2, 5, 9 Motivation, ADEMP, Coding
[7] Burton A., Altman D. G., Royston P., Holder R. L.. The design of simulation studies in medical statistics. Statistics in Medicine. 2006;25(24):4279–4292. 2, 17 Motivation, n_sim
[8] Koehler E., Brown E., Haneuse [S.] [initial missing in source]. On the assessment of Monte Carlo error in simulation-based statistical analyses. The American Statistician. 2009;63:155–162. 2 Motivation
[9] Morgan B. J. T.. Elements of simulation. Boca Raton: Chapman & Hall/CRC; 1995. 2, 5 Motivation, ADEMP
[10] Chang M.. Monte Carlo simulation for the pharmaceutical industry : concepts, algorithms, and case studies. CRC Press; 2011. 2 Motivation
[11] Díaz-Emparanza I.. Is a small Monte Carlo analysis a good analysis?. Statistical Papers. 2002;43(4):567–577. 2 Motivation
[12] White I. R.. simsum: Analyses of simulation studies including Monte Carlo error. Stata Journal. 2010;10(3):369–385. 2, 26 Motivation, Protocols
[13] Smith M. K., Marshall A.. Importance of protocols for simulation studies in clinical drug development. Statistical Methods in Medical Research. 2011;20(6):613–622. 2 Motivation
[14] Crowther M. J., Lambert P. C.. Simulating biologically plausible complex survival data. Statistics in Medicine. 2013;32(23):4118–4134. 2 Motivation
[15] Holford N. H. G., Hale M., Ko H. C., et al. Simulation in drug development: good practices. 1999. 2 Motivation
[16] O’Kelly M., Anisimov V., Campbell C., Hamilton S.. Proposed best practice for projects that involve modelling and simulation. Pharmaceutical Statistics. 2017;16(2):107–113. 2 Motivation
[17] Haramoto H., Matsumoto M., Nishimura T., Panneton F., L’Ecuyer P.. Efficient jump ahead for F2-linear random number generators. INFORMS Journal on Computing. 2008;20(3):385–390. 3 Coding
[18] Kenward M. G., Roger J. H.. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics. 1997;53:983–997. 4 ADEMP
[19] White I. R.. Letter to the Editor: Survival analysis of randomized clinical trials adjusted for patients who switch treatments by M. G. Law and J. M. Kaldor, Statistics in Medicine, 15, 2069–2076 (1996). Statistics in Medicine. 1997;16(22):2619–2620. 5, 17 ADEMP, n_sim
[20] White I. R., Thompson S. G.. Adjusting for partially missing baseline measurements in randomised trials. Statistics in Medicine. 2005;24(7):993–1007. 5 ADEMP
[21] Hughes R. A., Sterne J. A. C., Tilling K.. Comparison of imputation variance estimators. Statistical Methods in Medical Research. 2014;:n/a+. 5 ADEMP
[22] Morris T. P., White I. R., Royston P.. Tuning multiple imputation by predictive mean matching and local residual draws. BMC Medical Research Methodology. 2014;14(1):75+. 5 ADEMP
[23] Skrondal A.. Design and Analysis of Monte Carlo Experiments: Attacking the Conventional Wisdom. Multivariate Behavioral Research. 2000;35(2):137–167. 6, 18 ADEMP, n_sim
[24] Graves N., Barnett A. G., Burn E., Cook D.. Smaller clinical trials for decision making; using p-values could be costly. F1000Research. 2018;7:1176+. 6 ADEMP
[25] Senn S.. Methods for assessing difference between groups in change when initial measurement is subject to intra-individual variation. Statistics in Medicine. 1994;13(21):2280–2285. 6 ADEMP
[26] Kuss O.. Statistical methods for meta-analyses including information from studies without any events—add nothing to nothing and succeed nevertheless. Statistics in Medicine. 2015;34(7):1097–1116. 6, 7, 17 ADEMP, n_sim
[27] Chaurasia A., Harel O.. Partial F-tests with multiply imputed data in the linear regression framework via coefficient of determination. Statistics in Medicine. 2015;34(3):432–443. 7 ADEMP
[28] Wu C., Shi X., Cui Y., Ma S.. A penalized robust semiparametric approach for gene–environment interactions. Statistics in Medicine. 2015;34(30):4016–4030. 7 ADEMP
[29] Ferrante L., Skrami E., Gesuita R., Cameriere R.. Bayesian calibration for forensic age estimation. Statistics in Medicine. 2015;34(10):1779–1790. 7 ADEMP
[30] Zhang Z., Wang C., Troendle J. F.. Optimizing the order of hypotheses in serial testing of multiple endpoints in clinical trials. Statistics in Medicine. 2015;34(9):1467–1482. 7 ADEMP
[31] Kahan B. C.. Bias in randomised factorial trials. Statistics in Medicine. 2013;32(26):4540–4549. 7, 13, 19 ADEMP, n_sim, Reporting
[32] Campbell H., Dean C. B.. The consequences of proportional hazards based model selection. Statistics in Medicine. 2014;:1042–1056. 7 ADEMP
[33] Robins J. M., Wang N.. Inference for imputation estimators. Biometrika. 2000;87(1):113–124. 8 ADEMP
[34] Reiter J. P.. Multiple imputation when records used for imputation are not used or disseminated for analysis. Biometrika. 2008;95(4):933–946. 8 ADEMP
[35] Hauck W. W., Anderson S., Marcus S. M.. Should we adjust for covariates in nonlinear regression analyses of randomized trials?. Controlled Clinical Trials. 1998;19:249–256. 8 ADEMP
[36] Zhang Z.. Estimating a marginal causal odds ratio subject to confounding. Communications in Statistics - Theory and Methods. 2008;38(3):309–321. 8 ADEMP
[37] Smeden M., Groot J. A. H., Moons K. G. M., et al. No rationale for 1 variable per 10 events criterion for binary logistic regression analysis. BMC Medical Research Methodology. 2016;16(1). 8 ADEMP
[38] Crowther M. J., Look M. P., Riley R. D.. Multilevel mixed effects parametric survival models using adaptive Gauss–Hermite quadrature with application to recurrent events and individual participant data meta-analysis. Statistics in Medicine. 2014;33(22):3844–3858. 8 ADEMP
[39] Royston P., Barthel F. M-S, Parmar M. K. B., Choodari-Oskooei B., Isham V.. Designs for clinical trials with time-to-event outcomes based on stopping guidelines for lack of benefit. Trials. 2011;12(1):81+. 8 ADEMP
[40] Bartlett J. W.. Combining bootstrapping with multiple imputation. 2016. 12 Coding
[41] Rubin D. B.. Inference and missing data. Biometrika. 1976;63:581–592. 13 n_sim
[42] White Ian R., Carlin John B.. Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values.. Statistics in medicine. 2010;29(28):2920–2931. 14 Performance
[43] White I. R., Royston P.. Imputing missing covariate values for the Cox model.. Statistics in Medicine. 2009;28(15):1982–1998. 14, 19 Performance, Reporting
[44] Neyman J.. On the Two Different Aspects of the Representative Method: The Method of Stratified Sampling and the Method of Purposive Selection. Journal of the Royal Statistical Society, Series A. 1934;97(4):558–625. 15 Performance
[45] Meng X. L.. Multiple-imputation inferences with uncongenial sources of input. Statistical Science. 1994;9:538–558. 15 Performance
[46] Rubin D. B.. Multiple imputation after 18+ years. Journal of the American Statistical Association. 1996;91(434):473–489. 15 Performance
[47] Morris T. P.. Rank minimization with a two-step analysis should not replace randomization in clinical trials. Journal of Clinical Epidemiology. 2012;65(7):810–811. 15 Performance
[48] Kimani P. K., Todd S., Stallard N.. Estimation after subpopulation selection in adaptive seamless trials. Statistics in Medicine. 2015;34(18):2581–2601. 16 Performance
[49] Carreras M., Gutjahr G., Brannath W.. Adaptive seamless designs with interim treatment selection: a case study in oncology. Statistics in Medicine. 2015;34(8):1317–1333. 16 Performance
[50] Efron B., Hastie T.. Computer age statistical inference. Cambridge University Press; 2016. 17 Performance
[51] Gutman R., Rubin D. B.. Robust estimation of causal effects of binary treatments in unconfounded studies with dichotomous outcomes.. Statistics in medicine. 2013;32(11):1795–1814. 17 Performance
[52] Taguri M., Chiba Y.. A principal stratification approach for evaluating natural direct and indirect effects in the presence of treatment-induced intermediate confounding. Statistics in Medicine. 2015;34(1):131–144. 17 n_sim
[53] Li P., Redden D. T.. Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes. Statistics in Medicine. 2015;34(2):281–296. 17 n_sim
[54] Marozzi M.. Multivariate multidistance tests for high-dimensional low sample size case-control studies. Statistics in Medicine. 2015;34(9):1511–1526. 17 n_sim
[55] Keogh R. H., Morris T. P.. Multiple imputation in Cox regression when there are time-varying effects of covariates. Statistics in Medicine. 2018;37(25):3661–3678. 17, 25 n_sim, Protocols
[56] Morris T. P., Fisher D. J., Kenward M. G., Carpenter J. R.. Meta-analysis of Gaussian individual patient data: two stage or not two stage?. Statistics in Medicine. in-press 2017;. 17, 25 n_sim, Protocols
[57] Cramér H. C.. Mathematical Methods of Statistics. Princeton, NJ: Princeton University Press; 1946. 18 n_sim
[58] Rao C. R.. Information and the accuracy attainable in the estimation of statistical parameters. Bulletin of the Calcutta Mathematical Society. 1945;37:81–91. 18 n_sim
[59] Williamson E. J., Forbes A., White I. R.. Variance reduction in randomised trials by inverse probability weighting using the propensity score. Statistics in Medicine. 2014;33(5):721–737. 18 n_sim
[60] He X., Whitmore G. A., Loo G. Y., Hochberg M. C., Lee M-L T.. A model for time to fracture with a shock stream superimposed on progressive degradation: the Study of Osteoporotic Fractures. Statistics in Medicine. 2015;34(4):652–663. 19 Reporting
[61] Chen Y., Hong C., Riley R. D.. An alternative pseudolikelihood method for multivariate random-effects meta-analysis. Statistics in Medicine. 2015;34(3):361–380. 19 Reporting
[62] Hsu C. H., Taylor J. M. G., Hu C.. Analysis of accelerated failure time data with dependent censoring using auxiliary variables via nonparametric multiple imputation. Statistics in Medicine. 2015;34(19):2768–2780. 19 Reporting
[63] Alonso A., Milanzi E., Molenberghs G., Buyck C., Bijnens L.. A new modeling approach for quantifying expert opinion in the drug discovery process. Statistics in Medicine. 2015;34(9):1590–1604. 19 Reporting
[64] Seaman S. R., Bartlett J. W., White I. R.. Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods.. BMC Medical Research Methodology. 2012;12(1):46+. 19 Reporting
[65] Lambert P. C., Dickman P. W., Rutherford M. J.. Comparison of different approaches to estimating age standardized net survival.. BMC Medical Research Methodology. 2015;15(1):64+. 19 Reporting
[66] Rücker G., Schwarzer G.. Presenting simulation results in a nested loop plot. BMC Medical Research Methodology. 2014;14(1):129+. 19 Reporting
[67] Gasparini A., White I. R.. rsimsum: Analysis of simulation studies including monte carlo error2018. R package version 0.3.1. 21, 26 Reporting, Protocols
[68] Wicklin R.. A zipper plot for visualizing coverage probability in simulation studies. 2018. 21, 23 Reporting
[69] Austin P. C., Stuart E. A.. Optimal full matching for survival outcomes: a method that merits more widespread use. Statistics in Medicine. 2015;34(30):3949–3967. 25 Protocols
[70] Thompson J. A., Fielding K. L., Davey C., Aiken A. M., Hargreaves J. R., Hayes R. J.. Bias and inference from misspecified mixed-effect models in stepped wedge trial analysis. Statistics in Medicine. 2017;36(23):3670–3682. 25 Protocols
[71] Sayers A., Crowther M. J., Judge A., Whitehouse M. R., Blom A. W.. Determining the sample size required to establish whether a medical device is non-inferior to an external benchmark. BMJ Open. 2017;7(8):e015397+. 25 Protocols
[72] Pham T. M., Carpenter J. R., Morris T. P., Wood A. M., Petersen I.. Population-calibrated multiple imputation for a binary/categorical covariate in categorical regression models. Statistics in Medicine. 2018;. 25 Protocols
[73] Boos D. D., Osborne J. A.. Assessing Variability of Complex Descriptive Statistics in Monte Carlo Studies Using Resampling Methods. International Statistical Review. 2015;83(2):228–238. 26 Protocols
[74] Audigier V., White I. R., Jolani S., et al. Multiple imputation for multilevel data with continuous and binary variables. 2018. 26 Protocols
[75] Langan D., Higgins J. P. T., Simmonds M.. Comparative performance of heterogeneity variance estimators in meta-analysis: a review of simulation studies. Research Synthesis Methods. 2017;8(2):181–198. 26 Protocols
[76] Boulesteix A. L., Wilson R., Hapfelmeier A.. Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies. BMC Medical Research Methodology. 2017;17(1). 26 Protocols

Ref	Citation (as printed)	Cited on page(s)	Note(s)
[1]	Feiveson A. H.. Power by simulation. The Stata Journal. 2002;2(2):107–124.	2	Motivation
[2]	Rubin D. B.. Bayesianly justifiable and relevant frequency calculations for the applies statistician. The Annals of Statistics. 1984;12(4):1151–1172.	2	Motivation
[3]	Grieve A. P.. Idle thoughts of a ‘well-calibrated’ Bayesian in clinical drug development. Pharmaceutical Statistics. 2016;15(2):96–108.	2, 6, 26	Motivation, ADEMP, Protocols
[4]	Hoaglin D. C., Andrews D. F.. The reporting of computation-based results in statistics. The American Statistician. 1975;29(3):122–126.	2	Motivation
[5]	Hauck W. W., Anderson S.. A survey regarding the reporting of simulation studies. The American Statistician. 1984;38(3):214–216.	2	Motivation
[6]	Ripley B. D.. Stochastic Simulation. New York: Wiley; 1987.	2, 5, 9	Motivation, ADEMP, Coding
[7]	Burton A., Altman D. G., Royston P., Holder R. L.. The design of simulation studies in medical statistics. Statistics in Medicine. 2006;25(24):4279–4292.	2, 17	Motivation, n_sim
[8]	Koehler E., Brown E., Haneuse [S.] [initial missing in source]. On the assessment of Monte Carlo error in simulation-based statistical analyses. The American Statistician. 2009;63:155–162.	2	Motivation
[9]	Morgan B. J. T.. Elements of simulation. Boca Raton: Chapman & Hall/CRC; 1995.	2, 5	Motivation, ADEMP
[10]	Chang M.. Monte Carlo simulation for the pharmaceutical industry : concepts, algorithms, and case studies. CRC Press; 2011.	2	Motivation
[11]	Díaz-Emparanza I.. Is a small Monte Carlo analysis a good analysis?. Statistical Papers. 2002;43(4):567–577.	2	Motivation
[12]	White I. R.. simsum: Analyses of simulation studies including Monte Carlo error. Stata Journal. 2010;10(3):369–385.	2, 26	Motivation, Protocols
[13]	Smith M. K., Marshall A.. Importance of protocols for simulation studies in clinical drug development. Statistical Methods in Medical Research. 2011;20(6):613–622.	2	Motivation
[14]	Crowther M. J., Lambert P. C.. Simulating biologically plausible complex survival data. Statistics in Medicine. 2013;32(23):4118–4134.	2	Motivation
[15]	Holford N. H. G., Hale M., Ko H. C., et al. Simulation in drug development: good practices. 1999.	2	Motivation
[16]	O’Kelly M., Anisimov V., Campbell C., Hamilton S.. Proposed best practice for projects that involve modelling and simulation. Pharmaceutical Statistics. 2017;16(2):107–113.	2	Motivation
[17]	Haramoto H., Matsumoto M., Nishimura T., Panneton F., L’Ecuyer P.. Efficient jump ahead for F2-linear random number generators. INFORMS Journal on Computing. 2008;20(3):385–390.	3	Coding
[18]	Kenward M. G., Roger J. H.. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics. 1997;53:983–997.	4	ADEMP
[19]	White I. R.. Letter to the Editor: Survival analysis of randomized clinical trials adjusted for patients who switch treatments by M. G. Law and J. M. Kaldor, Statistics in Medicine, 15, 2069–2076 (1996). Statistics in Medicine. 1997;16(22):2619–2620.	5, 17	ADEMP, n_sim
[20]	White I. R., Thompson S. G.. Adjusting for partially missing baseline measurements in randomised trials. Statistics in Medicine. 2005;24(7):993–1007.	5	ADEMP
[21]	Hughes R. A., Sterne J. A. C., Tilling K.. Comparison of imputation variance estimators. Statistical Methods in Medical Research. 2014;:n/a+.	5	ADEMP
[22]	Morris T. P., White I. R., Royston P.. Tuning multiple imputation by predictive mean matching and local residual draws. BMC Medical Research Methodology. 2014;14(1):75+.	5	ADEMP
[23]	Skrondal A.. Design and Analysis of Monte Carlo Experiments: Attacking the Conventional Wisdom. Multivariate Behavioral Research. 2000;35(2):137–167.	6, 18	ADEMP, n_sim
[24]	Graves N., Barnett A. G., Burn E., Cook D.. Smaller clinical trials for decision making; using p-values could be costly. F1000Research. 2018;7:1176+.	6	ADEMP
[25]	Senn S.. Methods for assessing difference between groups in change when initial measurement is subject to intra-individual variation. Statistics in Medicine. 1994;13(21):2280–2285.	6	ADEMP
[26]	Kuss O.. Statistical methods for meta-analyses including information from studies without any events—add nothing to nothing and succeed nevertheless. Statistics in Medicine. 2015;34(7):1097–1116.	6, 7, 17	ADEMP, n_sim
[27]	Chaurasia A., Harel O.. Partial F-tests with multiply imputed data in the linear regression framework via coefficient of determination. Statistics in Medicine. 2015;34(3):432–443.	7	ADEMP
[28]	Wu C., Shi X., Cui Y., Ma S.. A penalized robust semiparametric approach for gene–environment interactions. Statistics in Medicine. 2015;34(30):4016–4030.	7	ADEMP
[29]	Ferrante L., Skrami E., Gesuita R., Cameriere R.. Bayesian calibration for forensic age estimation. Statistics in Medicine. 2015;34(10):1779–1790.	7	ADEMP
[30]	Zhang Z., Wang C., Troendle J. F.. Optimizing the order of hypotheses in serial testing of multiple endpoints in clinical trials. Statistics in Medicine. 2015;34(9):1467–1482.	7	ADEMP
[31]	Kahan B. C.. Bias in randomised factorial trials. Statistics in Medicine. 2013;32(26):4540–4549.	7, 13, 19	ADEMP, n_sim, Reporting
[32]	Campbell H., Dean C. B.. The consequences of proportional hazards based model selection. Statistics in Medicine. 2014;:1042–1056.	7	ADEMP
[33]	Robins J. M., Wang N.. Inference for imputation estimators. Biometrika. 2000;87(1):113–124.	8	ADEMP
[34]	Reiter J. P.. Multiple imputation when records used for imputation are not used or disseminated for analysis. Biometrika. 2008;95(4):933–946.	8	ADEMP
[35]	Hauck W. W., Anderson S., Marcus S. M.. Should we adjust for covariates in nonlinear regression analyses of randomized trials?. Controlled Clinical Trials. 1998;19:249–256.	8	ADEMP
[36]	Zhang Z.. Estimating a marginal causal odds ratio subject to confounding. Communications in Statistics - Theory and Methods. 2008;38(3):309–321.	8	ADEMP
[37]	Smeden M., Groot J. A. H., Moons K. G. M., et al. No rationale for 1 variable per 10 events criterion for binary logistic regression analysis. BMC Medical Research Methodology. 2016;16(1).	8	ADEMP
[38]	Crowther M. J., Look M. P., Riley R. D.. Multilevel mixed effects parametric survival models using adaptive Gauss–Hermite quadrature with application to recurrent events and individual participant data meta-analysis. Statistics in Medicine. 2014;33(22):3844–3858.	8	ADEMP
[39]	Royston P., Barthel F. M-S, Parmar M. K. B., Choodari-Oskooei B., Isham V.. Designs for clinical trials with time-to-event outcomes based on stopping guidelines for lack of benefit. Trials. 2011;12(1):81+.	8	ADEMP
[40]	Bartlett J. W.. Combining bootstrapping with multiple imputation. 2016.	12	Coding
[41]	Rubin D. B.. Inference and missing data. Biometrika. 1976;63:581–592.	13	n_sim
[42]	White Ian R., Carlin John B.. Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values.. Statistics in medicine. 2010;29(28):2920–2931.	14	Performance
[43]	White I. R., Royston P.. Imputing missing covariate values for the Cox model.. Statistics in Medicine. 2009;28(15):1982–1998.	14, 19	Performance, Reporting
[44]	Neyman J.. On the Two Different Aspects of the Representative Method: The Method of Stratified Sampling and the Method of Purposive Selection. Journal of the Royal Statistical Society, Series A. 1934;97(4):558–625.	15	Performance
[45]	Meng X. L.. Multiple-imputation inferences with uncongenial sources of input. Statistical Science. 1994;9:538–558.	15	Performance
[46]	Rubin D. B.. Multiple imputation after 18+ years. Journal of the American Statistical Association. 1996;91(434):473–489.	15	Performance
[47]	Morris T. P.. Rank minimization with a two-step analysis should not replace randomization in clinical trials. Journal of Clinical Epidemiology. 2012;65(7):810–811.	15	Performance
[48]	Kimani P. K., Todd S., Stallard N.. Estimation after subpopulation selection in adaptive seamless trials. Statistics in Medicine. 2015;34(18):2581–2601.	16	Performance
[49]	Carreras M., Gutjahr G., Brannath W.. Adaptive seamless designs with interim treatment selection: a case study in oncology. Statistics in Medicine. 2015;34(8):1317–1333.	16	Performance
[50]	Efron B., Hastie T.. Computer age statistical inference. Cambridge University Press; 2016.	17	Performance
[51]	Gutman R., Rubin D. B.. Robust estimation of causal effects of binary treatments in unconfounded studies with dichotomous outcomes.. Statistics in medicine. 2013;32(11):1795–1814.	17	Performance
[52]	Taguri M., Chiba Y.. A principal stratification approach for evaluating natural direct and indirect effects in the presence of treatment-induced intermediate confounding. Statistics in Medicine. 2015;34(1):131–144.	17	n_sim
[53]	Li P., Redden D. T.. Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes. Statistics in Medicine. 2015;34(2):281–296.	17	n_sim
[54]	Marozzi M.. Multivariate multidistance tests for high-dimensional low sample size case-control studies. Statistics in Medicine. 2015;34(9):1511–1526.	17	n_sim
[55]	Keogh R. H., Morris T. P.. Multiple imputation in Cox regression when there are time-varying effects of covariates. Statistics in Medicine. 2018;37(25):3661–3678.	17, 25	n_sim, Protocols
[56]	Morris T. P., Fisher D. J., Kenward M. G., Carpenter J. R.. Meta-analysis of Gaussian individual patient data: two stage or not two stage?. Statistics in Medicine. in-press 2017;.	17, 25	n_sim, Protocols
[57]	Cramér H. C.. Mathematical Methods of Statistics. Princeton, NJ: Princeton University Press; 1946.	18	n_sim
[58]	Rao C. R.. Information and the accuracy attainable in the estimation of statistical parameters. Bulletin of the Calcutta Mathematical Society. 1945;37:81–91.	18	n_sim
[59]	Williamson E. J., Forbes A., White I. R.. Variance reduction in randomised trials by inverse probability weighting using the propensity score. Statistics in Medicine. 2014;33(5):721–737.	18	n_sim
[60]	He X., Whitmore G. A., Loo G. Y., Hochberg M. C., Lee M-L T.. A model for time to fracture with a shock stream superimposed on progressive degradation: the Study of Osteoporotic Fractures. Statistics in Medicine. 2015;34(4):652–663.	19	Reporting
[61]	Chen Y., Hong C., Riley R. D.. An alternative pseudolikelihood method for multivariate random-effects meta-analysis. Statistics in Medicine. 2015;34(3):361–380.	19	Reporting
[62]	Hsu C. H., Taylor J. M. G., Hu C.. Analysis of accelerated failure time data with dependent censoring using auxiliary variables via nonparametric multiple imputation. Statistics in Medicine. 2015;34(19):2768–2780.	19	Reporting
[63]	Alonso A., Milanzi E., Molenberghs G., Buyck C., Bijnens L.. A new modeling approach for quantifying expert opinion in the drug discovery process. Statistics in Medicine. 2015;34(9):1590–1604.	19	Reporting
[64]	Seaman S. R., Bartlett J. W., White I. R.. Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods.. BMC Medical Research Methodology. 2012;12(1):46+.	19	Reporting
[65]	Lambert P. C., Dickman P. W., Rutherford M. J.. Comparison of different approaches to estimating age standardized net survival.. BMC Medical Research Methodology. 2015;15(1):64+.	19	Reporting
[66]	Rücker G., Schwarzer G.. Presenting simulation results in a nested loop plot. BMC Medical Research Methodology. 2014;14(1):129+.	19	Reporting
[67]	Gasparini A., White I. R.. rsimsum: Analysis of simulation studies including monte carlo error2018. R package version 0.3.1.	21, 26	Reporting, Protocols
[68]	Wicklin R.. A zipper plot for visualizing coverage probability in simulation studies. 2018.	21, 23	Reporting
[69]	Austin P. C., Stuart E. A.. Optimal full matching for survival outcomes: a method that merits more widespread use. Statistics in Medicine. 2015;34(30):3949–3967.	25	Protocols
[70]	Thompson J. A., Fielding K. L., Davey C., Aiken A. M., Hargreaves J. R., Hayes R. J.. Bias and inference from misspecified mixed-effect models in stepped wedge trial analysis. Statistics in Medicine. 2017;36(23):3670–3682.	25	Protocols
[71]	Sayers A., Crowther M. J., Judge A., Whitehouse M. R., Blom A. W.. Determining the sample size required to establish whether a medical device is non-inferior to an external benchmark. BMJ Open. 2017;7(8):e015397+.	25	Protocols
[72]	Pham T. M., Carpenter J. R., Morris T. P., Wood A. M., Petersen I.. Population-calibrated multiple imputation for a binary/categorical covariate in categorical regression models. Statistics in Medicine. 2018;.	25	Protocols
[73]	Boos D. D., Osborne J. A.. Assessing Variability of Complex Descriptive Statistics in Monte Carlo Studies Using Resampling Methods. International Statistical Review. 2015;83(2):228–238.	26	Protocols
[74]	Audigier V., White I. R., Jolani S., et al. Multiple imputation for multilevel data with continuous and binary variables. 2018.	26	Protocols
[75]	Langan D., Higgins J. P. T., Simmonds M.. Comparative performance of heterogeneity variance estimators in meta-analysis: a review of simulation studies. Research Synthesis Methods. 2017;8(2):181–198.	26	Protocols
[76]	Boulesteix A. L., Wilson R., Hapfelmeier A.. Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies. BMC Medical Research Methodology. 2017;17(1).	26	Protocols

Examples

Resolving a citation

The performance-measures note says Kimani, Todd & Stallard conditioned bias on each possible selected estimand, and that the paper prefers this, citing [50]. The table resolves [48] to Stat Med 34(18):2581-2601 and [50] to Efron & Hastie (2016), Computer Age Statistical Inference. See Morris et al 2019 - Performance Measures and Monte Carlo Standard Errors.

Connections

Effective Sample Size and Monte Carlo Standard Error — the MCMC counterpart of the Monte Carlo error literature ([8], [11], [73]).
Designing Simulated-Data Experiments — the Bayesian-workflow treatment of fake-data simulation; Rubin [2] and Grieve [3] are the paper’s bridge to it.
Simulation-Based Calibration - Overview — prior-averaged calibration, the Bayesian alternative to fixed- $θ$ evaluation.
Zhang Vaver 2017 - Aggregate Marketing System Simulator - Overview — a data generator for marketing data, the counterpart of the data-generation references ([6], [9], [14]).

Second Brain

Explorer

Morris et al 2019 - Full Reference List and Further Reading

Morris et al 2019 - Full Reference List and Further Reading

Overview

Main Content

Curated further reading on simulation-study methodology

References [1]-[76] (pp.27-30)

Examples

Connections

See Also

Graph View

Table of Contents

Backlinks