@misc{dyck2024bpgwsppreprint,
      title={The BPgWSP test: a Bayesian Weibull Shape Parameter signal detection test for adverse drug reactions}, 
      author={Julia Dyck and Odile Sauzet},
      year={2025},
      eprint={2412.05463},
      archivePrefix={arXiv},
      primaryClass={stat.ME},
      url={https://arxiv.org/abs/2412.05463}, 
     note={preprint},
}

@article{cornelius2012,
  title={A signal detection method to detect adverse drug reactions using a parametric time-to-event model in simulated cohort data},
  author={Cornelius, Victoria R and Sauzet, Odile and Evans, Stephen JW},
  journal={Drug safety},
  volume={35},
  pages={599--610},
  year={2012},
  publisher={Springer}
}



@Article{sauzet2022,
  author   = {Odile Sauzet and Victoria Cornelius},
  journal  = {Frontiers in Pharmacology},
  title    = {Generalised weibull model-based approaches to detect non-constant hazard to signal adverse drug reactions in longitudinal data},
  year     = {2022},
  abstract = {Pharmacovigilance is the process of monitoring the emergence of harm from a 
      medicine once it has been licensed and is in use. The aim is to identify new 
      adverse drug reactions (ADRs) or changes in frequency of known ADRs. The last 
      decade has seen increased interest for the use of electronic health records 
      (EHRs) in pharmacovigilance. The causal mechanism of an ADR will often result in 
      the occurrence being time dependent. We propose identifying signals for ADRs 
      based on detecting a variation in hazard of an event using a time-to-event 
      approach. Cornelius et al. proposed a method based on the Weibull Shape Parameter 
      (WSP) and demonstrated this to have optimal performance for ADRs occurring 
      shortly after taking treatment or delayed ADRs, and introduced censoring at 
      varying time points to increase performance for intermediate ADRs. We now propose 
      two new approaches which combined perform equally well across all time periods. 
      The performance of this new approach is illustrated through an EHR 
      Bisphosphonates dataset and a simulation study. One new approach is based on the 
      power generalised Weibull distribution (pWSP) introduced by Bagdonavicius and 
      Nikulin alongside an extended version of the WSP test, which includes one 
      censored dataset resulting in improved detection across time period (dWSP). In 
      the Bisphosphonates example, the pWSP and dWSP tests correctly signalled two 
      known ADRs, and signal one adverse event for which no evidence of association 
      with the drug exist. A combined test involving both pWSP and dWSP is reliable 
      independently of the time of occurrence of ADRs.},
  doi      = {10.3389/fphar.2022.889088 },
  url      = {https://pubmed.ncbi.nlm.nih.gov/36081935/}
}


@article{sauzet2024,
  abstract     = {BACKGROUND AND OBJECTIVES: Statistical methods for signal detection of adverse drug reactions (ADRs) in electronic health records (EHRs) need information about optimal significance levels and sample sizes to achieve sufficient power. Sauzet and Cornelius proposed tests for signal detection based on the hazard functions of Weibull type distributions (WSP tests) which use the time-to-event information available in EHRs. Optimal significance levels and sample sizes for the application of the WPS tests are derived.; METHOD: A simulation study was performed with a range of scenarios for sample size, rate of event due (ADRs), and not due to the drug and random time to ADR occurrence. Based on the area under the curve of the receiver operating characteristic graph, we obtain optimal significance levels of the different WSP tests for the implementation in a hypothesis free signal detection setting and approximate sample sizes required to reach a power of 80% or 90%.; RESULTS: The dWSP-pPWSP (combination of double WSP and power WSP) test with a significance level of 0.004 was recommended. Sample sizes needed for a power of 80% were found to start at 60 events for an ADR rate equal to the background rate of 0.1. The number of events required for a background rate of 0.05 and an ADR rate equal to a 20% increase of the background rate was 900.; CONCLUSION: Based on this study, it is recommended to use the dWSP-pWSP test combination for signal detection with a significance level of 0.004 when the same test is applied to all adverse events not depending on rates. © 2024. The Author(s).},
  author       = {Sauzet, Odile and Dyck, Julia Alexandra and Cornelius, Victoria},
  issn         = {1179-1942},
  journal      = {Drug Safety },
  publisher    = {Springer},
  title        = {{Optimal Significance Levels and Sample Sizes for Signal Detection Methods Based on Non-constant Hazards}},
  url          = {https://pub.uni-bielefeld.de/record/2991311},
  doi          = {10.1007/s40264-024-01460-2},
  year         = {2024},
}


@book{nikulin2016,
  title={The Cox model and its applications},
  author={Nikulin, Mikhail and Wu, Hong-Dar Isaac and others},
  year={2016},
  publisher={Springer}
}

@article{sauzet2013illustration,
  title={Illustration of the weibull shape parameter signal detection tool using electronic healthcare record data},
  author={Sauzet, Odile and Carvajal, Alfonso and Escudero, Antonio and Molokhia, Mariam and Cornelius, Victoria R},
  journal={Drug safety},
  volume={36},
  pages={995--1006},
  year={2013},
  publisher={Springer}
}

@article{fawcett2004,
  title={ROC graphs: Notes and practical considerations for researchers},
  author={Fawcett, Tom},
  journal={Machine learning},
  volume={31},
  number={1},
  pages={1--38},
  year={2004},
  publisher={Citeseer}
}

@article{lloyd1998,
  title={Using smoothed receiver operating characteristic curves to summarize and compare diagnostic systems},
  author={Lloyd, Chris J},
  journal={Journal of the American Statistical Association},
  volume={93},
  number={444},
  pages={1356--1364},
  year={1998},
  publisher={Taylor \\& Francis}
}

@Article{ROCR,
  entry = {article},
  title = {ROCR: visualizing classifier performance in R},
  author = {T. Sing and O. Sander and N. Beerenwinkel and T. Lengauer},
  year = {2005},
  journal = {Bioinformatics},
  volume = {21},
  number = {20},
  pages = {7881},
  url = {http://rocr.bioinf.mpi-sb.mpg.de},
  version = {1.0.11},
}

@Book{kruschke2015,
  author    = {Kruschke, John},
  publisher = {Academic Press},
  title     = {Doing Bayesian Data Analysis (Second Edition)},
  year      = {2015},
  address   = {Boston},
  added-at  = {2016-12-29T09:25:25.000+0100},
  biburl    = {https://www.bibsonomy.org/bibtex/277f97f6f84d077790b702e30ed86be5f/becker},
  interhash = {5e4043e24f7f58a8de076d7956ca08ea},
  intrahash = {77f97f6f84d077790b702e30ed86be5f},
  keywords  = {diss imported inthesis mixedtrails},
  timestamp = {2017-06-19T10:12:05.000+0200},
}

@article{kruschke2018,
author = {John K. Kruschke},
title ={Rejecting or Accepting Parameter Values in Bayesian Estimation},
journal = {Advances in Methods and Practices in Psychological Science},
volume = {1},
number = {2},
pages = {270-280},
year = {2018},
doi = {10.1177/2515245918771304},
URL = {https://doi.org/10.1177/2515245918771304},
eprint = {https://doi.org/10.1177/2515245918771304},
    abstract = { This article explains a decision rule that uses Bayesian posterior distributions as the basis for accepting or rejecting null values of parameters. This decision rule focuses on the range of plausible values indicated by the highest density interval of the posterior distribution and the relation between this range and a region of practical equivalence (ROPE) around the null value. The article also discusses considerations for setting the limits of a ROPE and emphasizes that analogous considerations apply to setting the decision thresholds for p values and Bayes factors. }
}


