References

Agresti, A. (2013). Categorical data analysis (3rd ed.). Wiley.
Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton University Press.
Austin, P. C., Lee, D. S., & Fine, J. P. (2016). Introduction to the analysis of survival data in the presence of competing risks. Circulation, 133(6), 601–609.
Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962–972.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Wiley.
Buuren, S. van. (2018). Flexible imputation of missing data (2nd ed.). Chapman; Hall/CRC. https://stefvanbuuren.name/fimd/
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68.
Committee for Proprietary Medicinal Products. (2002). Points to consider on multiplicity issues in clinical trials (CPMP/EWP/908/99). https://www.ema.europa.eu/en/multiplicity-issues-clinical-trials-scientific-guideline
Conley, T. G., Hansen, C. B., & Rossi, P. E. (2012). Plausibly exogenous. The Review of Economics and Statistics, 94(1), 260–272.
DerSimonian, R., & Laird, N. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials, 7(3), 177–188.
Diggle, P. J., Heagerty, P., Liang, K.-Y., & Zeger, S. L. (2002). Analysis of longitudinal data (2nd ed.). Oxford University Press.
Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634.
Fitzmaurice, G. M., Laird, N. M., & Ware, J. H. (2011). Applied longitudinal analysis (2nd ed.). Wiley.
Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2015). Fundamentals of clinical trials (5th ed.). Springer.
Guyatt, G. H., Oxman, A. D., Vist, G. E., Kunz, R., Falck-Ytter, Y., Alonso-Coello, P., & Schünemann, H. J. (2008). GRADE: An emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 336(7650), 924–926.
Harrell, F. E. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis (2nd ed.). Springer.
Hernán, M. A. (2018). The C-word: Scientific euphemisms do not improve causal inference from observational data. American Journal of Public Health, 108(5), 616–619.
Hernán, M. A., & Robins, J. M. (2016). Using big data to emulate a target trial when a randomized trial is not available. American Journal of Epidemiology, 183(8), 758–764.
Hernán, M. A., & Robins, J. M. (2020). Causal inference: What if. Chapman; Hall/CRC. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2019). Cochrane handbook for systematic reviews of interventions (2nd ed.). Wiley.
Higgins, J. P. T., Thompson, S. G., & Spiegelhalter, D. J. (2009). A re-evaluation of random-effects meta-analysis. Journal of the Royal Statistical Society, Series A, 172(1), 137–159.
Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15(4), 309–334.
International Council for Harmonisation. (2019). ICH E9(R1) addendum on estimands and sensitivity analysis in clinical trials. https://database.ich.org/sites/default/files/E9-R1_Step4_Guideline_2019_1203.pdf
IntHout, J., Ioannidis, J. P. A., & Borm, G. F. (2014). The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Medical Research Methodology, 14, 25.
Jennison, C., & Turnbull, B. W. (2000). Group sequential methods with applications to clinical trials. Chapman; Hall/CRC.
Kleinbaum, D. G., & Klein, M. (2012). Survival analysis: A self-learning text (3rd ed.). Springer.
Knol, M. J., Groenwold, R. H. H., & Grobbee, D. E. (2012). P-values in baseline tables of randomised controlled trials are inappropriate but still common in high impact journals. European Journal of Preventive Cardiology, 19(2), 231–232.
Laan, M. J. van der, & Rubin, D. (2006). Targeted maximum likelihood learning. The International Journal of Biostatistics, 2(1).
Lash, T. L., VanderWeele, T. J., Haneuse, S., & Rothman, K. J. (2021). Modern epidemiology (4th ed.). Wolters Kluwer.
Loudon, K., Treweek, S., Sullivan, F., Donnan, P., Thorpe, K. E., & Zwarenstein, M. (2015). The PRECIS-2 tool: Designing trials that are fit for purpose. BMJ, 350, h2147.
McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society, Series B, 42(2), 109–142.
National Research Council. (2010). The prevention and treatment of missing data in clinical trials. The National Academies Press. https://doi.org/10.17226/12955
Neyman, J. (1923). On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Statistical Science (Translated Reprint), 5, 465–472.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71.
Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82(4), 669–688.
Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press.
Pepe, M. S. (2003). The statistical evaluation of medical tests for classification and prediction. Oxford University Press.
Piantadosi, S. (2017). Clinical trials: A methodologic perspective (3rd ed.). Wiley.
Rizopoulos, D. (2012). Joint models for longitudinal and time-to-event data: With applications in R. Chapman; Hall/CRC.
Robins, J. (1986). A new approach to causal inference in mortality studies with a sustained exposure period: Application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393–1512.
Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846–866.
Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Rothstein, H. R., Sutton, A. J., & Borenstein, M. (2005). Publication bias in meta-analysis: Prevention, assessment and adjustments. Wiley.
Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701.
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592.
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. Wiley.
Schulz, K. F., Altman, D. G., Moher, D., & the CONSORT Group. (2010). CONSORT 2010 statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.
Shi, B., Choirat, C., Coull, B. A., VanderWeele, T. J., & Valeri, L. (2021). CMAverse: A suite of functions for reproducible causal mediation analyses. Epidemiology, 32(5), e20–e22.
Taves, D. R. (2010). The use of minimization in clinical trials. Contemporary Clinical Trials, 31(2), 180–184.
Therneau, T. M., & Grambsch, P. M. (2000). Modeling survival data: Extending the Cox model. Springer.
Uno, H., Claggett, B., Tian, L., Inoue, E., Gallo, P., Miyata, T., Schrag, D., Takeuchi, M., Uyama, Y., Zhao, L., Skali, H., Solomon, S., Jacobus, S., Hughes, M., Packer, M., & Wei, L.-J. (2014). Moving beyond the hazard ratio in quantifying the between-group difference in survival analysis. Journal of Clinical Oncology, 32(22), 2380–2385.
U.S. Food and Drug Administration. (2010). Guidance for the use of Bayesian statistics in medical device clinical trials. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/guidance-use-bayesian-statistics-medical-device-clinical-trials
U.S. Food and Drug Administration. (2019). Adaptive designs for clinical trials of drugs and biologics: Guidance for industry. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/adaptive-design-clinical-trials-drugs-and-biologics-guidance-industry
VanderWeele, T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press.
VanderWeele, T. J., & Ding, P. (2017). Sensitivity analysis in observational research: Introducing the E-value. Annals of Internal Medicine, 167(4), 268–274.