Training courses

Prognosis research in healthcare: concepts, methods and impact
International Summer School
Summer 2021 (to be announced), Keele University

This 3-day summer school is designed to introduce the key components and uses of prognosis research to health professionals and researchers, including:

  • a framework of four different prognosis research questions: overall prognosis, prognostic factors, prognostic models, and stratified medicine

  • key principles of study design and methods

  • interpretation of statistical results about prognosis

  • the use of prognosis research evidence at multiple stages on the translational pathway toward improving patient outcome 

  • the limitations of current prognosis research, and how the field can be improved

 

The course consists of lectures from a core faculty of epidemiologists, statisticians and clinical researchers, alongside group work and discussion sessions. Please note that no computer practicals are included with the focus instead on interpretation of statistical concepts and results of analyses. Basic knowledge of epidemiology and statistics is assumed. The course is founded on the prognosis research framework introduced by the PROGRESS partnership, described in a series of 4 articles published in BMJ/PLoS Medicine in February 2013.

 

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Statistical methods for risk prediction and prognostic models
Early 2021 (to be announced, likely to be online only), Keele University

This course provides a thorough foundation of statistical methods for developing and validating prognostic models in clinical research. The course is delivered over 3 days and focuses on model development (day 1), internal validation (day 2), and external validation and novel topics (day 3). Our focus is on multivariable models for individualised prediction of future outcomes (prognosis), although many of the concepts described also apply to models for predicting existing disease (diagnosis).

 

Computer practicals in either R or Stata are included on all three days, and participants can choose whether to focus on logistic regression examples (for binary outcomes) or Cox / flexible parametric survival examples (for time-to-event outcomes), to tailor the practicals to their own purpose.

 

The course is aimed at individuals that want to learn how to develop and validate risk prediction and prognostic models, specifically for binary or time-to-event clinical outcomes. We recommend participants have a background in statistics. An understanding of key statistical principles and measures (such as effect estimates, confidence intervals and p-values) and the ability to apply and interpret regression models is essential.

 

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Statistical methods for meta-analysis of individual participant data
(to be announced, likely to be online only late 2020), Keele University

This 3-day statistical course provides a detailed foundation of the methods and principles for meta-analysis when IPD (Individual Participant Data) are available from multiple related studies.

The course considers continuous, binary and time-to-event outcomes, and covers a variety of modelling options, including fixed effect and random effects. Days 1 and 2 mainly focus on the synthesis of IPD from randomised trials of interventions, where the aim is to summarise a treatment effect or to examine treatment-covariate interactions. We outline how to use either a two-stage framework or a one-stage framework for the meta-analysis, and compare their pros and cons. Day 3 focuses on novel extensions including multivariate and network meta-analysis of IPD to incorporate correlated and indirect evidence (e.g. from multiple outcomes or multiple treatment comparisons). Special topics will also be covered, including: (i) IPD meta-analysis to identify prognostic/risk factors, (ii) IPD meta-analysis of test accuracy studies; (iii) estimating the power of a planned IPD meta-analysis; and (iv) dealing with unavailable IPD. The course consists of a mixture of lectures and practical sessions to reinforce the underlying statistical concepts. Participants can choose either Stata or R for the practicals. 

The course is aimed at individuals that want to learn how to plan and undertake an IPD meta-analysis. We recommend that participants have a background in statistics as the course assumes a good understanding of core statistical principles and topics, such as regression methods (such as linear, logistic, and Cox), parameter estimation and interpreting software output.

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Systematic Reviews of Prognosis Studies (various online & face-to-face), Utrecht

This medical course for researchers discusses how to define your review questions, how to search the literature, how to critically assess the methodological quality of primary prognosis studies, and which statistical methods to use for their synthesis.

The course consists of plenary presentations, small-group discussions, and computer exercises.

At the end of the course, you'll should be able to:

  • Explain the rationale for performing a systematic review of prognostic studies

  • List the key steps of a systematic review of prognostic studies

  • Formulate a focused review question addressing a prognostic problem

  • Systematically search the literature

  • Critically appraise the evidence from primary prognostic studies

  • Formulate the difficulties of meta-analysis of prognostic research

  • Meta-analyses of performance of prognostic models

  • Meta-analyses of the added value of specific prognostic factors

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 Clinical prediction models (3-day course, Maastricht )

 

In the Clinical Prediction Models course, an attempt is made to teach the student a critical attitude towards prediction models for clinical practice. For this it is necessary to go through all development steps. The process starts with formulating one or more potential applications of a model to be developed. The next step is to gather the required data, by own data collection or by using an existing dataset. Important questions are: which predictors do I want to include in the model? How big should my population be? What do I do with missing values? Then follows the development and internal validation of the model. Questions that arise are: which analytical method can I best apply? What is Bootstrapping? What is shrinkage? What is the difference between discrimination and calibration? To test a prediction model once developed in a new patient group (external validation), a good dataset is also needed. Questions that will play a role in external validation are: how do I express performance now? What can I do if the performance is disappointing? Once a prediction model has overcome the previous hurdles, it remains to be seen whether the model can actually contribute to a better prognosis for the patient, more efficient care, or other intended outcomes. There are also various design options available for this.

For more info, click here