Photos
Photos are available on Facebook (click here) and Flickr (click here). Note, you’ll need to request to join the Facebook group.
Presentations
Should you not want your slides or poster made available, please inform Vanessa Cave (vanessa.cave@agresearch.co.nz).
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Oral Presentations
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1: Tuesday morning
- Assessing model adequacy in phylogenetics - are the tools powerful? (Daisy Shepherd)
- Establishing a Biostatistics Unit at the University of Otago (Robin Turner & Claire Cameron)
- www.MixedModel.Academy as a SaaS learning tool (Donghui Ma)
- New Unicode Facilities in Genstat (David Baird)
- Updates to Genstat 19 (David Baird)
- Ceci n'est pas une pipe. . . yet: Building data & analysis pipelines (Peter Jaksons)
- Statistical Methods for the Analysis of High-Dimensional and Massive Data using R (Benoit Liquet)
- Large Scale & Real Time Data Analytics (Chris Auld)
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2: Tuesday afternoon
- A one-sided test to simultaneously compare the predictive values (Kouji Yamamoto)
- Desert island papers - a life in variance parameter and quantitative genetic parameters estimation reviewed using ten papers (Robin Thompson)
- ASReml moving forward (Arthur Gilmour)
- Mimicking anova in reml mixed modelling of comparative experiments using the R-package asremlPlus (Chris Brien)
- Shared latent fields for mark-location dependence in a log Gaussian Cox process (Charlotte Jones-Todd)
- Instrumental variable estimation in the Cox Proportional Hazards Model (James O'Malley)
- The impact of covariates on the grouping structure of a Bayesian spatio-temporal localised model (Aswi)
- A notion of depth for curve data (Pierre Lafaye de Micheaux)
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3: Wednesday morning
- Design Tableau: An aid to specifying the linear mixed model for a comparative experiment (Alison Smith)
- Efficient designs for early generation variety trials using genetic relationships (Nicole Cocks)
- Connectivity, does it impact genetic variance parameter estimates in a Multi Environment Trial analysis? (Chris Lisle)
- Statistical issues arising from the Australian Royal Commission into Institutional Responses to Child Sexual Abuse (Graham Hepworth )
- A depth-adjusted Hardy-Weinberg test for low-depth sequencing data (Ken Dodds)
- Deprivation, hospital admissions and previous dental appointment records in Early Childhood: A comparative use of traditional statistical modelling with machine learning (Sarah Sonal)
- Hockey sticks and broken sticks - a design for a single-treatment, placebo-controlled, double-blind, randomized clinical trial suitable for chronic diseasese (Hans Hockey)
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4: Thursday morning
- Computing tools for a Don't Repeat Yourself data analysis work flow and reproducible research (Peter Baker)
- Beyond statistical consulting: What role should statisticians play in ensuring good practice in the application and interpretation of statistics by disciplinary practitioners? (Janet Chaseling)
- Answering the research question by identifying balanced embedded factorials in messy combined trials (Kerry Bell)
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5: Statistical consultancy session
- PFR templates and associated documents (Ruth Butler)
- "From cradle to grave": making an impact from conception to publishing (Ruth Butler)
- Statistical inference and management decisions (Helene Thygesen)
- The view from the other side: a biologist's view of communicating statistics (Linley Jesson)
- From heaven to hell. . . and how to find a way back! (Gabriela Borgognone)
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6 : Thursday afternoon
- Getting the most of my mixed model (and specially ASReml): applications in quantitative genetics and breeding (Salvador Gezan)
- Crown Rot Tolerance in Durum Wheat (Jess Meza)
- Don't be so negative about negative estimates of variance components (Emi Tanaka)
- Bayesian Network as a Modelling Tool for Increasing Knowledge on the Factors Influencing Vineyard Longevity and Sustainability (Jelena Cosic)
- Statistical theory of rare event detection applied to forensic database establishment (Kyle James)
- Estimating the Extent of Underreporting in Disease Counts (Rodelyn Jaksons)
- Finding your feet: modelling the batting abilities of cricketers using Gaussian processes (Oliver Stevenson)
- Using Bayesian Growth Models to Predict Grape Yield (Rory Ellis)
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7: Friday morning
- 50 Years of Genstat ANOVA (Roger Payne)
- Identifying hotspots of rat activity and how they affect the risk of leptospirosis in urban slums (Poppy Miller)
- Developing methods to improve the accuracy of classification-based crowd-sourcing (Julie Mugford)
- A predictive model for nodal metastases among oral cancer patients (Ari Samaranayaka)
- Analysing compositional time-use data in paediatric populations (Jillian Haszard)
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1: Tuesday morning
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Posters
- Jiaxu Zeng: Model-Averaged Confidence Distribution
- Muyiwa Olayemi: Covariate selection using penalized regression analysis in mill mud data analysis
- Malgorzata Hirsz: Associations between symptoms and colorectal cancer outcome in GP/hospital e-referrals
- Ludwig Hothorn: Association between different-scaled multiple metabolomics endpoints and clinical predictors
- Kouji Tahata: On comparison between two square tables using index of marginal inhomogeneity
- Dongwen Luo: Recent Development of R package `predictmeans'
- Pauline O'Shaughnessy: Bootstrapping F test for testing random effects in Linear Mixed models
- Michael Mumford: How do you like them predictions? My experiences with obtaining predictions from GLMMs
- Lingyu Li: Likelihood based Clustering via Finite Mixtures
Consulting in the real world: Communicating statistics to scientists
During this special session, there were four invited talks on statistical consultancy followed by a discussion. To assist the discussion, evpoll was used to capture anonymous comments (with voting on the comments). These comments are given below.
“Moving beyond p”: How can we best promote awareness around the aim of a statistical analysis?
Comments | Up votes | Down votes |
Data patterns, distributions, confidence intervals | 12 | 0 |
Help people understand what their research question actually is. | 12 | 1 |
Get “statistical thinking” embedded rather than just what’s the answer | 10 | 0 |
Focus on practical significance | 10 | 0 |
Providing courses in statistics that are delivered by staff from the statistics school as opposed to staff in other faculties with both minimal understanding and enthusiasm for the subject | 5 | 0 |
Massively improve how we teach stats in the first place | 6 | 1 |
How do we make non-significant results publishable? | 9 | 5 |
Change thinking from how to get results to what we want to know. | 2 | 0 |
Relate output back to how it addresses the aim | 2 | 0 |
Simply ask the question first: can you see the effect on the graph… | 2 | 0 |
What about Bayesian approaches? | 3 | 1 |
Convince editors that in some cases a picture is far more informative than any p-value or complicated stats. | 0 | 0 |
Don’t provide p-values 😁 | 2 | 6 |
Basic Statistical ideas: Ways to encourage sound understanding ideas/ philosophies (e.g. replication vs pseudo replication)?
Comments | Up votes | Down votes |
Training using the researchers examples | 15 | 0 |
Develop a virtual reality maze with statistical tests along the way and only talk to them when they find their way out 😉 | 6 | 0 |
Experimental games that users participate in and see variability between their results and others | 8 | 2 |
A chocolate bar for thinking about design for just one minute | 7 | 1 |
Use computing simulations | 6 | 1 |
Get involved in the experiment running stage, going to the lab, that way you can better understand critical considerations they have, and address these. | 6 | 2 |
We should go to none stats conferences and vice versa | 3 | 0 |
Ensure the basic concept of variability and “effect vs noise” is understood | 3 | 0 |
Support concepts using practical and relevant examples | 4 | 1 |
Explain with examples | 3 | 0 |
Use fun, engaging examples! | 3 | 0 |
Show striking counter examples | 2 | 0 |
Graphical interpretation of interaction effects. | 1 | 0 |
Teach school kids statistical ideas like this – they will get it | 2 | 1 |
Communicating results: Relating things back to the original question; Encouraging good standards in stat aspects of results presentation?
Comments | Up votes | Down votes |
Statisticians should be involved in writing papers and producing the presentations | 20 | 0 |
Encourage people to think about what the results are showing and do they make sense. Is there a scientific explanation. | 12 | 0 |
Support good writing of statistical results and conclusions | 9 | 0 |
Have the original hypothesis explicitly written down and ask how does this answer that | 7 | 0 |
Publish research/analysis protocols before data collected (cf Clinical Trials) | 5 | 0 |
discuss the limitations, why and where it wont work | 5 | 0 |
Reporting guidelines used in the design phase eg consort statement | 2 | 0 |
Use graphics. | 2 | 0 |
Create graphs with captions for the sci | 2 | 0 |
use layman terms | 1 | 0 |
Give examples!good results vs bad result | 1 | 0 |
Teaching that to students in first year courses. | 1 | 0 |
Culpability | 1 | 0 |