Thanks to all who came to Ben's talk yesterday. Here's a link to the full-text of the paper which formed the basis of the discussion:
Lifetime body size and reproductive factors: comparisons of data recorded prospectively with self reports in middle age
BMC Medical Research Methodology 2011, 11:7
Thursday, 21 April 2011
Wednesday, 16 March 2011
Summary of journal club discussion - Coste and Pouchot, 'A grey zone for quantitative diagnostic and screening tests'
Thanks to those who contributed to today's journal club. Here is a short summary for anyone who couldn't make it.
We started the discussion by considering what clinicians do when they conduct a quantitative test. In general practice, it is rare for GPs to make a diagnosis based on one test result; often clinicians use multiple sources of information like signs, symptoms or other tests. In some cases, such as PSA testing, where there is a large grey zone, the doctor may sometimes ask what the patient wants to do based on a test reading (or series of readings).
One of the ideas we had was the the grey zone depicts the unacceptable levels of false positives or false negatives. This needs to be balanced against the harms occuring from each test/diagnosis based on the false positives or negatives. This led to the idea of the grey zone as giving us an idea of the value of a test; i.e. the proportion of people for whom you can get a conclusive result or not.
We thought a bit about how you would construct the grey zone for a specific diagnostic or screening test, and wondered if this could be achieved through clinical Delphi consensus, a systematic review of the literature, etc. One member said that she was involved in a study where patients were asked to set the grey zone, and to trade off how many false negatives were acceptable in order to gain one true positive. We agreed that a patient-set grey zone would be different than a clinician's, as patients tend to be more tolerant of false positives.
Beth Shinkins kindly prepared some discussion questions to go with this paper which are posted below:
Discussion Points
1) Coste and Pouchot extend the standard binary positive/negative test result framework to include an intermediate range of values where the diagnostic test is unable to determine disease status with any certainty. This is based on the argument made by Feinstein (1990) that the use of a single threshold is not representative of the reality of clinical decision making.
a.Statement by Battaglia and Pewsner (2003): “In practice clinicians hardly ever interpret results of continuous tests as being only ‘normal’ or ‘pathological’. They always take into consideration ‘how positive’ or ‘how negative’ the result is.” Battaglia and Pewsner (2003) – Do you agree with this statement? If so, why do we insist on using a binary framework if these interpretations do not translate into day-to-day practice?
b.Do you agree with the concept of classifying people as diseased or not diseased? Or are we talking more about people who require treatment/monitoring/therapy etc. as opposed to those who don’t?
2)“Pre-test probabilities may vary according to the epidemiological context, the care facility, information already gathered about diagnostic risk factors, and other factors; furthermore ‘subjective probabilities’ produced by clinicians or experts may be unreliable”
a.Does this completely undermine the clinical usefulness of the methodology?
b.How comfortable do you think clinicians are in using measures such as likelihood ratios to describe the accuracy of a test?
3)Excluding those in the ‘grey’ zone from diagnostic accuracy measures such as sensitivity and specificity are meaningless in isolation and potentially very misleading.
a.Do you agree? How could this be overcome?
b.How can we determine what proportion of the sample falling into the grey zone is acceptable?
4)Coste and Pouchot spend very little time explaining how a doctor should proceed in the face of an intermediate test result.
a.What do you think a doctor should do/is currently being done? Repeat the test? Use an alternative test?
b.Lemoine (2009) questions the assumption that a patient is either ‘diseased’ or ‘not diseased’ claiming that “the uncertainty is therefore always attributed to the measurement or to the test procedure itself” rather than “the qualitative complexity of a biological situation”. Coste and Pouchot do not discuss the possibility that an intermediate test result may be an indicator of a subclinical or early stage of disease – is this an important oversight?
5)“The proposal to enrich the interpretation of test results by measuring continuous parameters in shades of grey is an important step in the right direction. The introduction of different shades of grey may help to improve the interpretation of diagnostic test results and, more importantly, improve clinical outcomes.” Battaglia and Pewsner (2003)
a.Do you agree? How would this translate into clinical practice?
We started the discussion by considering what clinicians do when they conduct a quantitative test. In general practice, it is rare for GPs to make a diagnosis based on one test result; often clinicians use multiple sources of information like signs, symptoms or other tests. In some cases, such as PSA testing, where there is a large grey zone, the doctor may sometimes ask what the patient wants to do based on a test reading (or series of readings).
One of the ideas we had was the the grey zone depicts the unacceptable levels of false positives or false negatives. This needs to be balanced against the harms occuring from each test/diagnosis based on the false positives or negatives. This led to the idea of the grey zone as giving us an idea of the value of a test; i.e. the proportion of people for whom you can get a conclusive result or not.
We thought a bit about how you would construct the grey zone for a specific diagnostic or screening test, and wondered if this could be achieved through clinical Delphi consensus, a systematic review of the literature, etc. One member said that she was involved in a study where patients were asked to set the grey zone, and to trade off how many false negatives were acceptable in order to gain one true positive. We agreed that a patient-set grey zone would be different than a clinician's, as patients tend to be more tolerant of false positives.
Beth Shinkins kindly prepared some discussion questions to go with this paper which are posted below:
Discussion Points
1) Coste and Pouchot extend the standard binary positive/negative test result framework to include an intermediate range of values where the diagnostic test is unable to determine disease status with any certainty. This is based on the argument made by Feinstein (1990) that the use of a single threshold is not representative of the reality of clinical decision making.
a.Statement by Battaglia and Pewsner (2003): “In practice clinicians hardly ever interpret results of continuous tests as being only ‘normal’ or ‘pathological’. They always take into consideration ‘how positive’ or ‘how negative’ the result is.” Battaglia and Pewsner (2003) – Do you agree with this statement? If so, why do we insist on using a binary framework if these interpretations do not translate into day-to-day practice?
b.Do you agree with the concept of classifying people as diseased or not diseased? Or are we talking more about people who require treatment/monitoring/therapy etc. as opposed to those who don’t?
2)“Pre-test probabilities may vary according to the epidemiological context, the care facility, information already gathered about diagnostic risk factors, and other factors; furthermore ‘subjective probabilities’ produced by clinicians or experts may be unreliable”
a.Does this completely undermine the clinical usefulness of the methodology?
b.How comfortable do you think clinicians are in using measures such as likelihood ratios to describe the accuracy of a test?
3)Excluding those in the ‘grey’ zone from diagnostic accuracy measures such as sensitivity and specificity are meaningless in isolation and potentially very misleading.
a.Do you agree? How could this be overcome?
b.How can we determine what proportion of the sample falling into the grey zone is acceptable?
4)Coste and Pouchot spend very little time explaining how a doctor should proceed in the face of an intermediate test result.
a.What do you think a doctor should do/is currently being done? Repeat the test? Use an alternative test?
b.Lemoine (2009) questions the assumption that a patient is either ‘diseased’ or ‘not diseased’ claiming that “the uncertainty is therefore always attributed to the measurement or to the test procedure itself” rather than “the qualitative complexity of a biological situation”. Coste and Pouchot do not discuss the possibility that an intermediate test result may be an indicator of a subclinical or early stage of disease – is this an important oversight?
5)“The proposal to enrich the interpretation of test results by measuring continuous parameters in shades of grey is an important step in the right direction. The introduction of different shades of grey may help to improve the interpretation of diagnostic test results and, more importantly, improve clinical outcomes.” Battaglia and Pewsner (2003)
a.Do you agree? How would this translate into clinical practice?
Friday, 18 February 2011
New schedule for March to September 2011
I'm happy to announce a new schedule of paper discussions, seminars and talks for the statistics and epidemiology methodology sessions.
16 March at 1pm
Journal club
A grey zone for quantitative diagnostic and screening tests
Joël Coste and Jacques Pouchot
International Journal of Epidemiology 2003;32:304-313
Location: Teaching Room A, Rosemary Rue Building
20 April, 1pm
Talk by Ben Cairns, Cancer Epidemiology Unit, University of Oxford
Agreeing to disagree: self-report vs. measurement of body size in epidemiological studies
Location: CTSU Main Meeting Room, 1st floor, Richard Doll Building
25 May, 1pm
Talk by Jim Lewsey, Department of Public Health, University of Glasgow
The utility of advanced survival analysis methods for epidemiological and health economic modelling
Location: Seminar Room 1, Rosemary Rue Building
15 June, 1pm
Talk by Jill Dawson, Department of Public Health, University of Oxford
Use of PROMs in healthcare settings
Location: MSc Teaching Room, 1st floor, Rosemary Rue Building
13 July, 1pm
Talk by Gill Lancaster, Department of Maths and Statistics, Lancaster University
Design, conduct and evaluation of complex interventions
Location: Department of Primary Care, Hythe Bridge Street
21 September, 1pm
Talk by Clare Relton, ScHARR, University of Sheffield
The cohort multiple randomised controlled trial: a new study design
Location: TBC
The talks by Jill Dawson, Gill Lancaster and Clare Relton will be based on recent papers by the speakers which I'll circulate closer to the seminar date.
Please note the location of each seminar...apologies for the lack of a dedicated meeting space but things have been slightly complicated by our department moving to a different location!
16 March at 1pm
Journal club
A grey zone for quantitative diagnostic and screening tests
Joël Coste and Jacques Pouchot
International Journal of Epidemiology 2003;32:304-313
Location: Teaching Room A, Rosemary Rue Building
20 April, 1pm
Talk by Ben Cairns, Cancer Epidemiology Unit, University of Oxford
Agreeing to disagree: self-report vs. measurement of body size in epidemiological studies
Location: CTSU Main Meeting Room, 1st floor, Richard Doll Building
25 May, 1pm
Talk by Jim Lewsey, Department of Public Health, University of Glasgow
The utility of advanced survival analysis methods for epidemiological and health economic modelling
Location: Seminar Room 1, Rosemary Rue Building
15 June, 1pm
Talk by Jill Dawson, Department of Public Health, University of Oxford
Use of PROMs in healthcare settings
Location: MSc Teaching Room, 1st floor, Rosemary Rue Building
13 July, 1pm
Talk by Gill Lancaster, Department of Maths and Statistics, Lancaster University
Design, conduct and evaluation of complex interventions
Location: Department of Primary Care, Hythe Bridge Street
21 September, 1pm
Talk by Clare Relton, ScHARR, University of Sheffield
The cohort multiple randomised controlled trial: a new study design
Location: TBC
The talks by Jill Dawson, Gill Lancaster and Clare Relton will be based on recent papers by the speakers which I'll circulate closer to the seminar date.
Please note the location of each seminar...apologies for the lack of a dedicated meeting space but things have been slightly complicated by our department moving to a different location!
Thursday, 10 February 2011
Radio Silence
Apologies again for the lack of updates - we have got a schedule of speakers/paper discussions from March 2011 onwards, so watch this space!
Tuesday, 19 October 2010
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.
Monday, 18 October 2010
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!
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!
Friday, 1 October 2010
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.
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.
Monday, 6 September 2010
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
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
Thursday, 10 June 2010
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
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
Tuesday, 27 April 2010
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
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
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