references.bib

@Manual{r2021,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2021},
    url = {https://www.R-project.org/},
  }

@article{law2002,
    author = {Law, Malcolm R. and Watt, Hilary C. and Wald, Nicholas J.},
    title = "{The Underlying Risk of Death After Myocardial Infarction in the Absence of Treatment}",
    journal = {Archives of Internal Medicine},
    volume = {162},
    number = {21},
    pages = {2405-2410},
    year = {2002},
    month = {11},
    abstract = "{The underlying risk of death in the absence of treatment after a myocardial infarction (MI) is poorly documented.Analysis of 23 published studies in which 14 211 patients were followed prospectively after MI; 6817 deaths were recorded. We restricted the analysis to studies in which follow-up was completed by 1980 to quantify the underlying risk in the absence of effective treatments.After a first MI, on average, 23\\% of patients died before reaching the hospital and another 13\\% died during hospital admission; these rates increased with age. After hospital discharge cardiovascular mortality was approximately 10\\% in the first year and 5\\% per year thereafter, rates that were unrelated to age or sex. The yearly death rate of 5\\% persisted indefinitely; after 15 years, cumulative cardiovascular mortality was 70\\%. After a subsequent MI, 33\\% of patients died before reaching the hospital, and 20\\% died in hospital. After discharge, cardiovascular mortality was approximately 20\\% in the first year and 10\\% per year thereafter, rates again unrelated to age and sex. Approximately a third of all heart disease deaths occurred minutes after the first MI, a sixth during the first hospitalization, and half after a subsequent MI, which could occur many years after the first.In persons with a history of MI, cardiovascular mortality in the absence of treatment is high—5\\% per year after a first MI and 10\\% per year after a subsequent MI, persisting for many years and probably for the rest of a person's life. The high mortality rate emphasizes the need to ensure that everyone who has had an MI, even years previously, receives effective preventive treatment.Arch Intern Med. 2002;162:2405-2410-->}",
    issn = {0003-9926},
    doi = {10.1001/archinte.162.21.2405},
    url = {https://doi.org/10.1001/archinte.162.21.2405},
    eprint = {https://jamanetwork.com/journals/jamainternalmedicine/articlepdf/214413/ioi10881.pdf},
}

@Article{presize,
    title = {`presize`: An R-package for precision-based sample size calculation in clinical research},
    author = {Alan G. Haynes and Armando Lenz and Odile Stalder and Andreas Limacher},
    year = {2021},
    journal = {Journal of Open Source Software},
    volume = {6},
    number = {60},
    pages = {3118},
    doi = {10.21105/joss.03118},
  }

@Manual{sse,
    title = {sse: Sample Size Estimation},
    author = {Thomas Fabbro},
    year = {2021},
    note = {R package version 0.7-17},
    url = {https://CRAN.R-project.org/package=sse},
  }

@article{https://doi.org/10.1111/1471-0528.15498,
author = {Marlin, Nadine and Allotey, John},
title = {Baseline 101 – who is who?},
journal = {BJOG: An International Journal of Obstetrics \& Gynaecology},
volume = {126},
number = {11},
pages = {1336-1336},
doi = {https://doi.org/10.1111/1471-0528.15498},
url = {https://obgyn.onlinelibrary.wiley.com/doi/abs/10.1111/1471-0528.15498},
eprint = {https://obgyn.onlinelibrary.wiley.com/doi/pdf/10.1111/1471-0528.15498},
year = {2019}
}

@article{VONELM20071453,
title = {The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies},
journal = {The Lancet},
volume = {370},
number = {9596},
pages = {1453-1457},
year = {2007},
issn = {0140-6736},
doi = {https://doi.org/10.1016/S0140-6736(07)61602-X},
url = {https://www.sciencedirect.com/science/article/pii/S014067360761602X},
author = {Erik {von Elm} and Douglas G Altman and Matthias Egger and Stuart J Pocock and Peter C Gøtzsche and Jan P Vandenbroucke},
abstract = {Summary
Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September, 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. 18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed explanation and elaboration document is published separately and is freely available on the websites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE statement will contribute to improving the quality of reporting of observational studies.}
}

@article{Vandenbroucke_strobe,
Author = {Vandenbroucke, Jan P. and von Elm, Erik and Altman, Douglas G. and
   Gotzsche, Peter C. and Mulrow, Cynthia D. and Pocock, Stuart J. and
   Poole, Charles and Schlesselman, James J. and Egger, Matthias and STROBE
   Initiative},
Title = {Strengthening the Reporting of Observational Studies in Epidemiology
   (STROBE) Explanation and Elaboration},
Journal = {EPIDEMIOLOGY},
Year = {2007},
Volume = {18},
Number = {6},
Pages = {805-835},
Month = {NOV},
DOI = {10.1097/EDE.0b013e3181577511},
ISSN = {1044-3983},
EISSN = {1531-5487},
ResearcherID-Numbers = {Vandenbroucke, Jan/AAM-3928-2020
   },
ORCID-Numbers = {Vandenbroucke, Jan/0000-0001-5668-6716
   von Elm, Erik/0000-0002-7412-0406},
Unique-ID = {WOS:000262285900028},
}

@article{Palesch,
author = {Yuko Y. Palesch },
title = {Some Common Misperceptions About <i>P</i> Values},
journal = {Stroke},
volume = {45},
number = {12},
pages = {e244-e246},
year = {2014},
doi = {10.1161/STROKEAHA.114.006138},
URL = {https://www.ahajournals.org/doi/abs/10.1161/STROKEAHA.114.006138},
eprint = {https://www.ahajournals.org/doi/pdf/10.1161/STROKEAHA.114.006138}
}

@article{MOHER201228,
title = {CONSORT 2010 explanation and elaboration: Updated guidelines for reporting parallel group randomised trials},
journal = {International Journal of Surgery},
volume = {10},
number = {1},
pages = {28-55},
year = {2012},
issn = {1743-9191},
doi = {https://doi.org/10.1016/j.ijsu.2011.10.001},
url = {https://www.sciencedirect.com/science/article/pii/S1743919111005656},
author = {David Moher and Sally Hopewell and Kenneth F. Schulz and Victor Montori and Peter C. Gøtzsche and P.J. Devereaux and Diana Elbourne and Matthias Egger and Douglas G. Altman},
keywords = {Clinical trials (epidemiology), Epidemiology, Health informatics, Internet, Quantitative research},
abstract = {Overwhelming evidence shows the quality of reporting of randomised controlled trials (RCTs) is not optimal. Without transparent reporting, readers cannot judge the reliability and validity of trial findings nor extract information for systematic reviews. Recent methodological analyses indicate that inadequate reporting and design are associated with biased estimates of treatment effects. Such systematic error is seriously damaging to RCTs, which are considered the gold standard for evaluating interventions because of their ability to minimise or avoid bias. A group of scientists and editors developed the CONSORT (Consolidated Standards of Reporting Trials) statement to improve the quality of reporting of RCTs. It was first published in 1996 and updated in 2001. The statement consists of a checklist and flow diagram that authors can use for reporting an RCT. Many leading medical journals and major international editorial groups have endorsed the CONSORT statement. The statement facilitates critical appraisal and interpretation of RCTs. During the 2001 CONSORT revision, it became clear that explanation and elaboration of the principles underlying the CONSORT statement would help investigators and others to write or appraise trial reports. A CONSORT explanation and elaboration article was published in 2001 alongside the 2001 version of the CONSORT statement. After an expert meeting in January 2007, the CONSORT statement has been further revised and is published as the CONSORT 2010 Statement. This update improves the wording and clarity of the previous checklist and incorporates recommendations related to topics that have only recently received recognition, such as selective outcome reporting bias. This explanatory and elaboration document—intended to enhance the use, understanding, and dissemination of the CONSORT statement—has also been extensively revised. It presents the meaning and rationale for each new and updated checklist item providing examples of good reporting and, where possible, references to relevant empirical studies. Several examples of flow diagrams are included. The CONSORT 2010 Statement, this revised explanatory and elaboration document, and the associated website (www.consort-statement.org) should be helpful resources to improve reporting of randomised trials.}
}

@article{HAYESLARSON2019125,
title = {Who is in this study, anyway? Guidelines for a useful Table 1},
journal = {Journal of Clinical Epidemiology},
volume = {114},
pages = {125-132},
year = {2019},
issn = {0895-4356},
doi = {https://doi.org/10.1016/j.jclinepi.2019.06.011},
url = {https://www.sciencedirect.com/science/article/pii/S0895435618309867},
author = {Eleanor Hayes-Larson and Katrina L. Kezios and Stephen J. Mooney and Gina Lovasi},
keywords = {Descriptive statistics, Tables, Epidemiologic methods, External validity, Internal validity, Generalizability, Clinical research},
abstract = {Objective
Epidemiologic and clinical research papers often describe the study sample in the first table. If well-executed, this “Table 1” can illuminate potential threats to internal and external validity. However, little guidance exists on best practices for designing a Table 1, especially for complex study designs and analyses. We aimed to summarize and extend the literature related to reporting descriptive statistics.
Study Design and Setting
In consultation with existing guidelines, we synthesized and developed reporting recommendations driven by study design and focused on transparency related to potential threats to internal and external validity.
Results
We describe a basic structure for Table 1 and discuss simple modifications in terms of columns, rows, and cells to enhance a reader's ability to judge both internal and external validity. We further highlight several analytic complexities common in epidemiologic research (missing data, sample weights, clustered data, and interaction) and describe possible variations to Table 1 to maintain and add clarity about study validity in light of these issues. We discuss considerations and tradeoffs in Table 1 related to breadth and comprehensiveness vs. parsimony and reader-friendliness.
Conclusion
We anticipate that our work will guide authors considering layouts for Table 1, with attention to the reader's perspective.}
}

@misc{gtsummary_tuto,
title={Tutorial: Tbl_summary},
url={https://www.danieldsjoberg.com/gtsummary/articles/tbl_summary.html},
journal={gtsummary}}

 @misc{gtsummary_web,
title={gtsummary},
url={https://www.danieldsjoberg.com/gtsummary/index.html},
journal={gtsummary}}

 @misc{gtsummary_format,
title={gtsummary and rmarkdown},
url={https://www.danieldsjoberg.com/gtsummary/articles/rmarkdown.html},
journal={gtsummary}}


@article{Strbel2019atableCT,
  title={atable: Create Tables for Clinical Trial Reports},
  author={Armin Michael Str{\"o}bel},
  journal={R J.},
  year={2019},
  volume={11},
  pages={137}
}

 @misc{atable_vignette,
title={atable vignette},
url={https://cran.r-project.org/web/packages/atable/vignettes/atable_usage.pdf},
}

@article{COLE200445,
title = {Adjusted survival curves with inverse probability weights},
journal = {Computer Methods and Programs in Biomedicine},
volume = {75},
number = {1},
pages = {45-49},
year = {2004},
issn = {0169-2607},
doi = {10.1016/j.cmpb.2003.10.004},
url = {https://www.sciencedirect.com/science/article/pii/S0169260703001378},
author = {Stephen R. Cole and Miguel A. Hernán},
keywords = {Graphics, Standardization, Stratification, Survival analysis},
abstract = {Kaplan–Meier survival curves and the associated nonparametric log rank test statistic are methods of choice for unadjusted survival analyses, while the semiparametric Cox proportional hazards regression model is used ubiquitously as a method for covariate adjustment. The Cox model extends naturally to include covariates, but there is no generally accepted method to graphically depict adjusted survival curves. The authors describe a method and provide a simple worked example using inverse probability weights (IPW) to create adjusted survival curves. When the weights are non-parametrically estimated, this method is equivalent to direct standardization of the survival curves to the combined study population.}
}

@book{hernan_book,
    title = {Causal Inference: What If},
    author = {MA Hernán and JM Robins},
    year = {2022},
    publisher = {Chapman & Hall/CRC},
    address = {Boca Raton}
}