Why most research findings are false - Paper discussion on 20 October

The winning paper for the paper discussion on 20 October was 'Why most published research findings are false' by John Ioannidis. Please do join us at 1pm in Teaching Room A (Rosemary Rue Building) for an informal discussion of a controversial and thought-provoking paper.

Stats and epidemiology paper discussion - Wednesday October 20

Dear all,

This Wednesday, we plan to hold a paper discussion for the stats and
epidemiology methods seminar. If you would like to come, please take
a look at the following papers and vote here:

Doodle poll link


to choose a paper to discuss. The most popular paper will be chosen
for discussion - I will announce the results tomorrow.

1. Research Methods & Reporting: Is a subgroup effect believable?
Updating criteria to evaluate the credibility of subgroup analyses
Xin Sun et al

2. Bias in identifying and recruiting participants in cluster
randomised trials: what can be done?
S Eldridge, S Kerry, DJ Torgerson - BMJ, 200

3. Why most research findings are false
John P. A. Ioannidis

We plan to meet in Teaching Room A in the Rosemary Rue Building at 1pm
this Wednesday October 20. Everyone is welcome!

Summary of talk by Sue Mallett

Thanks to all who came for Sue's informative talk on quality in reporting for prognostic models. Her talk was based on two publications, and if you would like any further information please see the following papers:

1. Reporting methods in studies developing prognostic models in cancer: a review.
Sue Mallett, Patrick Royston, Sue Dutton, Rachel Waters, Douglas G Altman. BMC Medicine, 2010

2. Reporting performance of prognostic models in cancer: a review
Susan Mallett, Patrick Royston, Rachel Waters, Susan Dutton and Douglas G Altman. BMC Medicine 2010, 8:21

Our next session will be a paper discussion on Wednesday 20 October.

New seminar series for Michaelmas term

With summer over, we are re-starting the statistics and epidemiology methods seminar series. Please find a schedule below for the next few months.

The sessions are a chance to discuss methodology in epidemiological research, and are intended as a platform for learning and discussion. Everyone is welcome, and we would welcome any suggestions for future sessions. I would also be happy to hear from you if you are conducting research using a new or under-utilized approach and would like to lead an upcoming meeting.

Summaries for all sessions will be up on this website.

Sue Mallet, What makes a prognostic model more reliable for use in clinical practice?
22 September 2010
1-2pm
Rosemary Rue Building, Teaching room A

Paper discussion, paper to be decided
20 October 2010
1-2pm
Rosemary Rue Building, Teaching room A

Richard Peto, Rubbishing random effects
17 November 2010
12:30-1:30pm
CTSU Main Meeting Room 1st floor Richard Doll Building

Ly-mee Yu, How to handle missing data in trials
15 December
1-2pm
Rosemary Rue Building, Teaching room A

Update on seminar series

If you are here looking for the next scheduled seminar, sorry for being so quiet!

We have teamed up with the Clinical Trial Service Unit (CTSU) and aim to bring a new schedule of statistical primers, seminars and paper discussions starting from September 2010. I will circulate more details in September.

In the meantime, if you have an idea for a seminar or would like to present some of your own work, please do email me and let me know!

Nada

The power of graphs in meta analysis

We met today (April 26) to discuss the paper 'More than numbers: The power of graphs in meta-analysis' by Leon Bax and colleagues.

We went through the different plots types (short powerpoint presentation below). A few of the points I picked up on included:

- Limitations of the funnel plot, including the need for at least 25 studies or more to determine whether any studies are 'missing'.
- The need for accurate and complete trial registers to estimate publication bias
- Reporting the funnel plot statistic instead of including the plot in a paper
- The usefulness of L'Abbe plot, which can be extended to other studies with continuous variables. It was also pointed out that L'Abbe plot demonstrates whether the different studies report a constant risk reduction/increase which may be useful. There is a 'bubble plot' in Excel which can be used to draw a L'Abbe plot.

Many of the attendees said that they generally didn't report any plots other than a forest plot in a meta-analysis. Paul G. said that he sometimes tries a L'Abbe plot, and has also used 'Rosenthal's file drawer N' method to estimate publication bias by estimating how many studies of no effect would be needed to change the summary estimate.

We had some concerns about the simulation studies in this paper. The researchers who scored the graphs' ability to demonstrate hetereogeneity or bias may have benefitted from a training period, and may have preferred the forest plot due to familiarity. We thought that it may have been better to have more raters scoring fewer graphs, and agreed that most of the different plots were poor at assessing publication bias (Table 4).

Our next meeting is scheduled for May 19.

Meta Analysis Plots

April 21 meeting options

Dear all,

I hope that some of you will be able to join us next week for the
stats and epidemiology journal club on Wednesday April 21. The
meeting will be held at 1pm in the 2nd floor meeting room in the
Rosemary Rue Building.

I have attached three possible papers for discussion. If you are
interested in coming, please send me back your vote for which paper
you'd like to discuss. The options are:

1. Bias in identifying and recruiting participants in cluster
randomised trials: what can be done? Sandra Eldridge and colleagues,
BMJ. 2009 Oct 9;339:b4006.
A discussion of how to design cluster randomized trials to minimize
selection bias

2. More Than Numbers: The Power of Graphs in Meta-Analysis. Leon Bax
and colleagues, Am J Epidemiol. 2009 Jan 15;169(2):249-55
What graphs should we use to report the results from a meta analysis?
A comparison for some of the popular options and limitations in each.

3. Power for studies with random group sizes. Walter T. Ambrosius,
Stat Med. 2010 Mar 11.
How to appropriately conduct a power calculation for observational studies

Let me know by next Monday and I will circulate the winning paper! Just email me at nada.khan@dphpc.ox.ac.uk.

Meeting on 17 March 2010 - Capture/Recapture

Thanks to Geraldine Surman and Matthias Pierce from NPEU for a great talk on capture/recapture methods and applications in their own projects. I've uploaded their talk rfom Wednesday, which includes some Stata code and may be of interest to other people who wish to apply this method in their own research. Both Geraldine and Matthias are happy for people to contact them to discuss capture/recapture in further detail. Next meeting is scheduled for 21 April.

Capture Recapture Mar 10

Meeting on 20 January - Missing data and Mendelian randomization

Dear all,

Hope you enjoyed Nicola Fitz- Simon's discussion on missing data and Mendelian randomization. I've uploaded her slides (see below) so do take a look if you missed the meeting. Our next meeting is scheduled for Wednesday February 17.

Missing Data and Mendelian Random is at Ion