Welcome to the homepage of Frank Miller

I am Professor in Statistics at the University of Linköping where I work since 2022. Between 2013 and 2023, I was (Associate) Professor at the University of Stockholm. From 2003 to 2012, I applied and developed statistical methods in the pharmaceutical industry (at AstraZeneca in Södertälje, Sweden). I received my Ph.D. in 2002 from the University of Karlsruhe (Germany).

• Mar-May 2025: Ph.D. course Advanced computational statistics
• Jan 2025 - Feb 2025: Computational statistics
• Sep 2024 - Jan 2025: Course organisator of master's thesis course
• Sep 2024 - Jan 2025: Course organisator of the course research project
• Oct 2023 - Jan 2024: Computational statistics
• Mar-May 2023: Ph.D. course Advanced computational statistics
• Jan 2023: One lecture about Computer based methods: randomization, bootstrap within the course Biological statistics III
• Jan/Feb 2022: Computational statistics
• Jan 2022: One lecture about Computer based methods: randomization, bootstrap within the course Biological statistics III
• Nov 2021: Experimental design
• May 2021: Two lectures about Active machine learning within the course Machine learning
• Mar/Apr 2021: Ph.D. course Optimisation algorithms in statistics II

Currently, I am supervising the PhD student Karl Sigfrid as main supervisor. His research topic is: Design of computerized adaptive tests.

• From 2016 to 2021, I have supervised Renata Tsirpitzi. She successfully defended her PhD thesis Optimal design for dose-finding studies on December 17, 2021.
• During the same years, I have supervised Jonas Bjermo. He successfully defended his PhD thesis Test design for mean ability growth and optimal item calibration for achievement tests on December 10, 2021.
• From 2014 to 2019, I have supervised Mahmood Ul Hassan. He successfully defended his PhD thesis Achievement tests and optimal design for pretesting of questions on November 15, 2019.

I was co-supervisor for Oskar Gustafsson from 2015-2020. Pär Stockhammar was main supervisor; here the PhD thesis.

• 2020 to 2023: Optimal calibration of questions in computerized achievement tests. Grant by the Swedish Research Council (Vetenskapsrådet). I am main applicant. Link to the project homepage. Here an article in Swedish from the start of the project.
  
• 2014 to May 2017: Innovative methodology for small populations research (InSPiRe). Grant by European Union's Seventh Framework Programme. Main applicant: Nigel Stallard (University of Warwick). I was co-applicant. Link to the project homepage.
         
• 2015 and 2016: Integrated DEsign and AnaLysis of small population group trials (IDEAL). Grant by European Union's Seventh Framework Programme. Main applicant: Ralf-Dieter Hilgers (University Hospital Aachen). I contributing to a workstream lead by co-applicant Carl-Fredrik Burman (AstraZeneca). Link to the project homepage.
         

• Miller F, Fackle-Fornius E (2024).
  Parallel optimal calibration of mixed-format items for achievement tests.
  Psychometrika, 89, 903 928.
• Ul Hassan M, Miller F (2024).
  Optimal calibration of items for multidimensional achievement tests.
  Journal of Educational Measurement, 61, 274-302.
• Tsirpitzi RE, Miller F, Burman CF (2023).
  Robust optimal designs using a model misspecification term.
  Metrika, 86, 781-804.
• Bjermo J, Miller F (2021).
  Efficient estimation of mean ability growth using vertical scaling.
  Applied Measurement in Education, 34, 163-178.
• Tsirpitzi RE, Miller F (2021).
  Optimal dose-finding for efficacy-safety-models.
  Biometrical Journal, 63, 1185-1201.
• Ul Hassan M, Miller F (2021).
  An exchange algorithm for optimal calibration of items in computerized achievement tests.
  Computational Statistics and Data Analysis, 157: 107177.
• Ul Hassan M, Miller F (2019).
  Optimal item calibration for computerized achievement tests.
  Psychometrika, 84, 1101-1128.
• Miller F, Zohar S, Stallard N, Madan J, Posch M, Hee SW, Pearce M, Vågerö M, Day S (2018).
  Approaches to sample size calculation for clinical trials in rare diseases.
  Pharmaceutical Statistics, 17, 214-230.
  A pre-peer reviewed version is available at Warwick University.
• Posch M, Klinglmueller F, König F, Miller F (2018).
  Estimation after blinded sample size reassessment.
  Statistical Methods in Medical Research, 27, 1830-1846.
• Broberg P, Miller F (2017).
  Conditional estimation in two-stage adaptive designs.
  Biometrics, 73, 895-904.
  The author version of the paper is available on arXiv.
• Fackle-Fornius E, Miller F, Nyquist H (2015).
  Implementation of maximin efficient designs in dose-finding studies.
  Pharmaceutical Statistics, 14, 63-73.
• Friede T, Miller F (2012).
  Blinded continuous monitoring of the nuisance parameter in clinical trials.
  J. Royal Stat. Soc. Series C, 61, 601-618.
• Miller F (2010).
  Adaptive dose-finding: proof of concept with type I error control.
  Biometrical Journal, 52, 577-589.
• Miller F, Friede T, Kieser M (2009).
  Blinded assessment of treatment effects utilizing information about the randomization block length.
  Statistics in Medicine, 28, 1690-1706.
• Miller F, Guilbaud O, Dette H (2007).
  Optimal designs for estimating the interesting part of a dose-effect curve.
  Journal of Biopharmaceutical Statistics, 17, 1097-1115.
  Link to preprint (at Ruhr-Universit t Bochum).
• Bischoff W, Miller F (2006).
  Optimal designs which are efficient for lack of fit tests.
  Annals of Statistics, 34, 2015-2025.
• Miller F (2005).
  Variance estimation in clinical studies with interim sample size re-estimation.
  Biometrics, 61, 355-361.

• Parallel optimal calibration of mixed-format items for achievement tests
  Conference: FREMO (Oslo, Norway, 2023)
• About c- and D-optimal dose-finding designs for bivariate outcomes
  Conference: mODa 13 (Southampton, UK, 2023)
• Parallel optimal calibration for Swedish national tests in school
  Conference: 36th IRT workshop (Twente, The Netherlands, 2022)
• Optimal pretesting of questions for Swedish national tests in school
  Webex of the Cramérsällskapet, section of the Swedish Statistical Association (Sweden, 2022)
• Optimal pretesting of questions for Swedish national tests in school
  Conference: COMPSTAT (Bologna, Italy, 2022)
• Optimal dose finding for efficacy-safety-models of Emax-type
  Conference: DAGStat (Hamburg, Germany, 2022)
• Optimal pretesting of questions in large achievement tests
  Seminar talk at KU Eichstätt-Ingolstadt, Department of Mathematics (Eichstätt, Germany, 2019)
• Optimal item calibration designs for computerized achievment tests
  Conference: DAGStat (Munich, Germany, 2019)