Planes, trains and automobiles

For some reason, Kobi's favourite thing in the world is flying on an airplane, with making paper airplanes a very closed second and playing airport pretending to check (real) suitcases in and setting off through security as a rather close third.

So it's not surprising that he was quite upset when I told him I would go on an airplane not once, not twice, but three times in the space of just a couple of weeks (in fact, I'll fly to Pisa, then Paris, come back on a train, ride a train again to Brussels and back and finally fly to Bologna and back, all to give talks at several places. From Bologna, I'll actually need to hire a car, because my talk is in nearby Parma). 

I think for a moment Kobi did consider stop loving me. But luckily, I think the crisis has been averted and I got him back on good terms when I told him it's not too long until he can fly again...

Yesterday I was Glasgow to give a talk at the Conference of the Royal Statistical Society in the first leg of my September travels-for-talks. My talk was in a session on missing data in health economic evaluation, with Andrew Briggs and James Carpenter also speaking. I think the session was really interesting and we had a rather good audience, so I was pleased with that.

My talk was basically stealing from Andrea's PhD work $-$ we (this includes also Alexina and Rachael who are co-supervising the project) have been doing some interesting stuff on modelling costs and benefit individual level data accounting for correlation between the outcomes; skeweness in the distributions; and "structural" values (eg spikes at QALY values of 1, which cannot be modelled directly using a Beta distribution).

Andrea has done some very good work also in programming the relevant functions in BUGS/JAGS (and he's having a stub at Stan too) into a beta-version of what we'll be our next package (we have called it missingHE) $-$ I'll say more on this when we have a little more established material ready.

The next trip is to Paris on Monday to give a talk at the Department of Biostatistics, in the Institut Gustav Roussy, where I'll speak about (you guessed it...)  Bayesian methods in health economics. I'll link to my presentation (that is when I'm finished tweaking it...).

A couple of things...

Just a couple of interesting things...


1. Petros sends me this advert for a post as Biostatistician at the Hospital for Sick Children in Toronto

The Child Health Evaluative Sciences Program at the Hospital for Sick Children in Toronto is recruiting a PhD Biostatistician to lead the execution of a CIHR funded clinical trial methodology project, and the planning of upcoming trials with a focus on:

• improving and using methods of Bayesian Decision analysis and Value of Information in pediatric trial design and analysis;
• using patient and caregiver preference elicitation methods (e.g. discrete choice experiments) in pediatrics;
• developing of statistical plan and conducting the statistical analysis for pediatric clinical trials.

The Biostatistician will collaborate with the Principal Investigators (PIs) of four trials that are in the design stage, and with two senior biostatisticians and methodologists within the CHES program. The successful candidate will have protected time for independent methods development. A cross appointment with the Dalla Lana School of Public Health at the University of Toronto will be sought.

Here’s What You’ll Get To Do:

In collaboration with the trials’ Principal Investigators (PIs), develop the study protocols;

Contribute in the conceptualization and development of decision analytic models;

Contribute in conducting literature reviews and keep current with study literature;

Assist with design/development and implementation of value of information methods;

Contribute to preparation of reports, presentations, and manuscripts.

Here’s What You’ll Need:

Graduate degree in Statistics, Biostatistics, Health Economics or a related discipline;

Ability to function independently yet collaboratively within a team;

Excellent statistical programming skills predominantly using R software; 

Experience with report and manuscript writing;

Effective communication, interpersonal, facilitation and organizational skills;

Meticulous attention to detail.

Employment Type: 

Temporary, Full-Time (3 year contract with possibilities for renewal)

Contacts: Dr. Petros Pechlivanoglou and Dr. Martin Offringa 

2. And Manuel has an advert for a very interesting short course on Missing Data in health economic evaluations (I will do my bit on Bayesian methods to do this, which is also very much related to the talk I'll give at the RSS conference in Glasgow, later in September $-$ this is part of Andrea's PhD work). I'll post more on this later.

Two-day short course: Methods for addressing missing data in health economic evaluation

Dates: 21-22 September, 2017

Venue: University College London

Overview

Missing data are ubiquitous in health economic evaluation. The major concern that arises with missing data is that individuals with missing information tend to be systematically different from those with complete data. As a result, cost-effectiveness inferences based on complete cases are often misleading. These concerns face health economic evaluation based on a single study, and studies that synthesise data from several sources in decision models. While accessible, appropriate methods for addressing the missing data are available in most software packages, their uptake in health economic evaluation has been limited.

Taught by leading experts in missing data methodology, this course offers an in-depth description of both introductory and advanced methods for addressing missing data in economic evaluation. These will include multiple imputation, hierarchical approaches, sensitivity analysis using pattern mixture models and Bayesian methods. The course will introduce the statistical concepts and underlying assumptions of each method, and provide extensive guidance on the application of the methods in practice. Participants will engage in practical sessions illustrating how to implement each technique with user-friendly software (Stata).

At the end of the course, the participants should be able to develop an entire strategy to address missing data in health economic studies, from describing the problem, to choosing an appropriate statistical approach, to conducting sensitivity analysis to standard missing data assumptions, to interpreting the cost-effectiveness results in light of those assumptions.

Who should apply?

The course is aimed at health economists, statisticians, policy advisors or other analysts with an interest in health economic evaluation, who would like to expand their toolbox. It is anticipated that participants will be interested in undertaking or interpreting cost-effectiveness analyses that use patient-level data, either from clinical trials or observational data.

Course fees: £600 (Commercial/Industry); £450 (Public sector); £200 (Students); payable by the 8th September 2017.

To register for the course or for further information, please see here. 

 

When simple becomes complicated...

A while ago, Anna and I published an editorial in Global & Regional Health Technology Assessment. In the paper, we discuss one of my favourite topics $-$ how models for health technology assessment and cost-effectiveness analysis should increasingly move away from using spreadsheet (basically, Excel) and towards proper statistical software.

The main arguments that historically have been used to support spreadsheet-based modelling are those of "simplicity and transparency" $-$ which really grinds my gears. In the paper we also argue that, may be, as statisticians we should invest in efforts towards designing our models using user-interfaces, or GUIs $-$ the obvious example is web-apps. This would expand and extend work done, eg in SAVI, or BCEAweb or bmetaweb, just to name a few (that I'm more familiar with...). 

Picky people (2)

I've complained here about the fonts for some parts of the computer code in our book . Eva (our publisher) has picked up on this and has been brilliant and very quick in trying to fix the issue. I think they will update the fonts so that at least on the ebooks version all will look nice!

Conflict of interest

I am fully aware that this post is seriously affected by a conflict of interest, because what I'm about to discuss (in positive terms!) is work by Anthony, who's doing a very good job on his PhD (which I co-supervise).

But, I thought I'd do like our former PM (BTW: see this; I really liked the series) and sort conflict of interests by effectively ignoring them (to be fair, this seems to be a popular strategy, so let's not be too harsh on Silvio...).

Anyway, Anthony has written an editorial, which has received some traction in the mainstream media (for example here, here or here). Not much that I disagree with in Anthony's piece, except that I am really sceptical of any bake & eat situation $-$ the only exception is when I actually make pizza from scratch...

Picky people

Our book on Bayesian cost-effectiveness analysis using BCEA is out (I think as of last week). This has been a long process (I've talked about this here, here and here). 

Today I've come back to the office and have open the package with my copies. The book looks nice $-$ I am only a bit disappointed about a couple of formatting things, specifically the way in which computer code got badly formatted in chapter 4. 
We had originally used specific font, but for some reason in that chapter all computer code is formatted in Times New Romans. I think we did check in the proofs and I don't recall seeing this (which, to be fair, isn't necessarily to swear that we didn't miss it, while checking...).

Not a biggie. But it bothers me, a bit. Well, OK: a lot. But then again, I am a(n annoyingly) picky person...

Homecoming (of sort...)

I spent last week in Florence for our Summer School. Of course, it was home-coming for me and I really enjoyed being back to Florence $-$ although it was really hot. I would say I'm not used to that level of heat anymore, if it wasn't for the fact that I have caught my brother (who still lives there) huffing and complaining about it several times!...

I think it was a very good week $-$ we had capped the number of participants at 27; everybody showed up and I think had a good time. I think I can speak for myself as well as for Chris, Nicky, Mark and Anna and say that we certainly enjoyed being around people who were so committed and interested! We did joke at several points that we didn't even have to ask the questions $-$ they were starting the discussion almost without us prompting it...

The location was also very good and helped make sure everybody was enjoying it. The Centro Studi in Fiesole is an amazing place $-$ not too close to Florence that people always disappears after the lectures, but not too far either. So there was always somebody there even for dinner and a chat in the beautiful garden, although some people would venture down the hill (notably, many did so by walking!). We also went to Florence a couple of times (the picture is one of my favourite spots of the city, which I obviously brought everybody to...).

Surprise?

So: for once I woke up this morning feeling slightly quite tired for the late night, but also rather upbeat after an election. The final results of the general election are out and have produced quite some shock. 

Throughout yesterday, it looked as though the final polls were returning an improved majority for the Conservative party $-$ this would have been consistent with the "shy Tory" effect. Even Yougov had presented their latest poll suggesting a seven points lead and improved Tory majority. So I guess many people were unprepared for the exit polls, which suggested a very different figure...

First off, I think that the actual results have vindicated Yougov's model (rather than the poll), based on a hierarchical model informed by over 50,000 individual-level data on voting intention as well as several other covariates. They weren't spot on, but quite close. 

Also, the exit polls (based on a sample of over 30,000) were remarkably good. To be fair, however, I think that exit polls are different than the pre-election polls, because unlike them they do not ask about "voting intentions", but the actual vote that people have just cast.

And now, time for the post-mortem. My final prediction using all the polls at June 8th was as follows:

                mean       sd 2.5% median 97.5%     OBSERVED
Conservative 346.827 3.411262  339    347   354          318
Labour       224.128 3.414861  218    224   233          261
UKIP           0.000 0.000000    0      0     0            0
Lib Dem       10.833 2.325622    7     11    15           12
SNP           49.085 1.842599   45     49    51           35
Green          0.000 0.000000    0      0     0            1
PCY            1.127 1.013853    0      2     3            4

Not all bad, but not quite spot on either and to be fair, less spot on than Yougov's (as I said, I was hoping they were closer to the truth than my model, so not too many complaints there!...).

I've thought a bit about the discrepancies and I think a couple of issues stand out:

1. I (together with several other predictions and in fact even Yougov) have overestimated the vote and, more importantly, the number of seats won by the SNP. I think in my case, the main issue had to do with the polls I have used to build my model. As it has happened, the battleground in Scotland has been rather different than the rest of the country, I think. But what was feeding into my model were the data from national polls. I had tried to bump up my prior for the SNP to counter this effect. But most likely this has exaggerated the result, producing an estimate that was too optimistic.
2. Interestingly, the error for the SNP is 14 seats; 12 of these, I think, have (rather surprisingly) gone to the Tories. So, basically, I've got the Tory vote wrong by (347-318+12)=41 seats $-$ which if you actually allocate to Labour would have brought my prediction to 224+41=265. 
3. Post-hoc adjustements aside, it is obvious that my model had overestimated the result for the Tories, while underestimating Labour's performance. In this case, I think the problem was that the structure I had used was mainly based on the distinction between leave and remain areas at last year's referendum. And of course, these were highly related to the vote that in 2015 had gone to UKIP. Now: like virtually everybody, I have correctly predicted that UKIP would get "zip, nada, zilch" seats. In my case, this was done by combining the poor performance in the polls with a strongly informative prior (which, incidentally, was not strong enough and combined with the polls, I did overestimate UKIP vote share). However, I think that the aggregate data observed in the polls had consistently tended to indicate that in leave areas the Tories would have had massive gains. What actually happened was in fact that the former UKIP vote has split nearly evenly between the two major parties. So, in strong leave areas, the Tories have gained marginally more than Labour, but that was not enough to swing and win the marginal Labour seats. Conversely, in remain areas, Labour has done really well (as the polls were suggesting) and this has in many cases produced a change in colours in some Conservative marginal seats.
4. I missed the Green's success in Brighton. This was, I think, down to being a bit lazy and not bothering telling the model that in Caroline Lucas' seat the Lib Dem had not fielded a candidate. This in turn meant that the model was predicting a big surge in the vote for the Lib Dems (because Brighton Pavilion is a strong remain area), which would eat into the Green's majority. And so my model was predicting a change to Labour, which never happened (again, I'm quite pleased to have got it wrong here, because I really like Ms Lucas!).
5. My model had correctly guessed that the Conservatives would regain Richmond Park, but that the Lib Dems had got back Twickenham and Labour would have held Copeland. In comparison to Electoralcalculus's prediction, I've done very well in predicting the number of seats for the Lib Dems. I am not sure about the details of their model, but I am guessing that they had some strong prior to (over)discount the polls, which has lead to a substantial underestimation. In contrast, I think that my prior for the Lib Dems was spot on.
6. Back to Yougov's model, I think that the main, huge difference, has been the fact that they could rely on a very large number of individual level data. The published polls would only provide aggregated information, which almost invariably would only cross-tabulate one variable at a time (ie voting intention in Leave vs Remain, or in London vs other areas, etc $-$ but not both). To actually be able to analyse the individual level data (combined of course with a sound modelling structure!) has allowed Yougov to get some of the true underlying trends right, which models based on the aggregated polls simply couldn't, I think.

It's been a fun process $-$ and all in all, I'm enjoying the outcome...