Royal Statistical Society
Highlands Local Group


Local organising Committee

 

Graeme MacLennan (Chair)
Health Services Research Unit
Foresterhill
Aberdeen AB25 2ZD
Dr Lorna Aucott (Hon Secretary)
Polwarth Building,
Forresterhill,
Aberdeen AB25 2ZD 
Phone: +44 (0) 1224 553809 Phone: +44 (0) 1224 876544  

Next meeting:


We are delighted that Bendix Carstensen a senior statistician from the Department of Biostatistics, University of Copenhagen will be visiting us at the University of Aberdeen for 2 days in August. During this time there will be a series of talks on methods of Survival and a workshop as an introduction to the Lexis-machinery in the Epi-package for R, allowing users to get to grips with parametric survival and multistate models.
One of the talks has been detailed to be a RSS Local Highland Group meeting talk

DATE: Thursday 17th August 2017
SPEAKER: Bendix Carstensen, senior statistician, Depart of Biostatistics, University of Copenhagen
TIME: 16:00 (tea at 3.30 pm)
VENUE: IMS Level 7, Forresterhill, U of A, Aberdeen
TITLE: The resurrection of time as a continuous concept in biostatistics, demography and epidemiology


ABSTACT
In classical demography the actuarial estimator of the survival function has been used for centuries, typically with one year time intervals. This estimator was also the default in medical science and epidemiology till the advent of computers, when the Kaplan-Meier estimator and later the Cox-model became the de facto standard for analysis of survival data and more generally, for follow-up data from cohort studies. The reporting of survival curves in the medical literature is therefore almost exclusively as the well-known noisy step curves, which is then left to eye-ball smoothing by the reader.
In epidemiology there has also been a tradition in analysis of occurrence rates by tabulation of events and risk time in five- or ten-year intervals, and fitting models with a separate parameter in each interval. In some circumstances such as Age-Period-Cohort models based on data classified in Lexis triangles this approach gives distinctly illogical results [1].
Both of these approaches are essentially based on models where the effect of time is taken to be different in different intervals, and the intervals taken as being exchangeable | the inherent ordering of time-intervals are ignored in the model. For the very broad time-intervals the usual problems associated with categorization applies [2]. Only in the reporting of the effects is the ordering of time-intervals reintroduced, either by connecting the estimates from broad time-categories, or hiding the ragged nature of the estimates by only showing cumulative effects.
I will advocate the use of models using the quantitative nature of time in modeling of occurrence rates, by imposing restrictions on the time-effects re ecting this. This can be seen as a combination of the very ne subdivision of time used in the non-parametric modeling and assumptions about continuous, smooth effects of time. Besides the ability to show the time-effects directly on the rate-scale, this approach also includes the possibility to accommodate multiple time scales such as age and duration of disease simultaneously. It also readily extends to estimation of transition rates in multistate models [3]. Parametric estimates of transition intensities also allow derivation of demographic quantities such as residual life time and years of life lost.
An essential prerequisite for practical handling of these models is a proper way of data representation. I shall describe the philosophy of the Lexis machinery for representation of multistate data on multiple time scales implemented in the Epi package for R[4, 5]. Furthermore I will show how otherwise intractable quantities from large complex multistate models can be handled by simulation [6], even if models also involve time since entry to intermediate states as timescale. Examples will be drawn from the clinical literature, and I shall indicate directions for further work on devising proper measures of uncertainty of such quantities.

References
[1] B Carstensen. Age-Period-Cohort models for the Lexis diagram. Statistics in Medicine, 26(15):3018{3045, July 2007.
[2] Frank Harrell. Problems caused by categorizing continuous variables. http://biostat.mc.vanderbilt.edu/wiki/Main/CatContinuous, 2015.
[3] S. Iacobelli and B. Carstensen. Multiple time scales in multi-state models. Stat Med, 32(30):5315{5327, Dec 2013.
[4] Martyn Plummer and Bendix Carstensen. Lexis: An R class for epidemiological studies with long-term follow-up. Journal of Statistical Software, 38(5):1{12, 1 2011.
[5] Bendix Carstensen and Martyn Plummer. Using Lexis objects for multi-state models in R. Journal of Statistical Software, 38(6):1{18, 1 2011.
[6] Bendix Carstensen. Simulation of multistate models with multiple timescales: simLexis in the Epi package. http://BendixCarstensen.com/Epi/simLexis.pdf or Epi vignette, 2015.




There will be a RSS joint Highland Local Group and Environmental Statistics Section meeting ‘Analysing Environmental Data’ on Monday 23rd October 2017 between 1:15 pm and 4:30 pm. The meeting will be held at Scottish Natural Heritage, Great Glen House, Leachkin Road, INVERNESS, IV3 8NW.

This will provide an opportunity to present and discuss progressing or recently completed statistical analyses of physical, biological, terrestrial, marine and atmospheric environmental data. Analysis of such data is often challenging and by sharing experiences and expertise all can learn, to help progress analyses, and refine interpretations.

The first session will be presentations of four 15 minute talks on physical or biological environmental statistical or modelling analyses from throughout the north of Scotland (vaguely defined as anywhere north of and including St. Andrews). Each talk will include a brief discussion opportunity. The meeting organisers therefore invite abstracts for consideration for this part of the meeting. The abstract need not describe a definitive analysis although it would be appropriate if results were available and that at least some conclusions had been reached. The purpose of these talks is not to ‘show-off’ the work, but to generate discussion from which we will all learn and thereby improve our current and future analyses. The meeting organisers will consider these and expect to assemble a balanced selection of talks including the range of submitting institutes. The abstract should include the name of the speaker, title, institute, a 200 word summary, and a contact email address. Abstracts should be sent to malcolm.hall@gov.scot and received before 8:00 am on Monday 11th September. A decision on the presentations selected will be sent to all those proffering an abstract by 8:00 am 18th September.

The second part of the meeting comprises an invited talk by Dr Adam Butler. Dr Butler is a senior statistician at Biomathematics and Statistics Scotland in Edinburgh with extensive experience of analysing environmental data and his talk (title not yet available) will be based on his experiences of analysing environmental data.

This meeting is held under the auspices of the Royal Statistical Society’s Highland Local Group and also Environmental Statistics Section, and is being organised by Megan Towers (SNH), Roger Humphry (SRUC) and Malcolm Hall (Scottish Government). There is no charge to attend the meeting although all attendees (other than Dr Butler) will be required to meet their own travel and subsistence costs. Tea and coffee will be available at the meeting, and while there is no charge for this, a small donation of your choosing to help cover these costs (if you are not a member of the Royal Statistical Society or giving a talk), would be gratefully received. However we do not wish to embarrass anyone, all will be welcome to refreshments, and if you are unable to donate don’t let it stop you attending.

Due to the size of our venue, kindly made available to us by SNH, the meeting is limited to 60 participants (including speakers). It would help if you could send a note letting us know that you intend to attend by the 17th October to Megan.Towers@snh.gov.uk. This will ensure that space will be kept for you in the unlikely event that we are oversubscribed, and will also help us to order an appropriate number of refreshments. But if you don’t manage to send this note, you are welcome to take a chance by simply turning up.

Submission of abstracts: by 8:00 am 11th September
Decisions on abstract: by 8:00 am on 18 September
Requested registration: by 8:00 am on 17th October
Meeting date and times: 1:15-4:30 23rd October 2017



Early warning for November meeting:

Phil Crook (Secretary RSS International Development Working Group [IDWG]) has arranged to come up to Aberdeen to hold an information session, ultimately to talk about opportunities for the IDWG. They are particularly interested in some Health and Climate change statistics information (and possible collaborations). This is going to take a slightly different format to our usual meetings. There’ll be a 1 to 1½ hours talk to include questions and answers.

Title: “Measuring Sustainable Development”
Speaker: Phil Crook (Secretary RSS International Development Working Group [IDWG])
Venue: TBC
Date: November 9th 2017
Time: 3-5pm with Tea


Let us know if you would like to attend using Skype if you can’t make it to Aberdeen

Abstract.

In September 2015 the United Nations General Assembly adopted seventeen aspirational "Global Goals”, stretching from “End Poverty” through “Combat Climate” to “Peace, Justice and Strong institutions”.

The goals are associated with 169 “targets” and about 230 “global indicators”. The annual report on progress towards the targets which will cover every country, developed as well as developing. It is easy to be cynical about global initiatives and the validity of statistics on “strong institutions”, but experience in the UK and globally shows that such targets and indicators can have profound effects on behaviour. The talk will cover the proposed SDG indicator system, the process and politics of developing global indicators, experience with the preceding MDG indicators, and some of the outstanding technical statistical challenges for measuring SDG indicators.

Previous Seminars (from May 2000-)


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