|
There is a minority but extremely interesting view that holds that sensitivity and specificity are misleading parameters. Much of the conventional discussion of this in terms of prior probability assumes that these are permanent features of the test unaffected by prevalence so that therefore what one has to do is put them together with prevalence to calculate PPV and NPV. Suppose that this was not the case. Given PPV and NPV you could then calculate sensitivity and specificity as a function of prevalence.
See the following papers
1. Dawid AP. Properties of Diagnostic Data Distributions. Biometrics 1976; 32: 647-658.
2. Guggenmoos-Holzmann I, van Houwelingen HC. The (in)validity of sensitivity and specificity. Statistics in Medicine 2000; 19: 1783-1792.
3. Miettinen OS, Caro JJ. Foundations of Medical Diagnosis - What Actually Are the Parameters Involved in Bayes Theorem. Statistics in Medicine 1994; 13: 201-209.
Stephen Senn
Competence Center for Methodology and Statistics
Luxembourg Institute of Health
1a Rue Thomas Edison
L-1445 Luxembourg
Tel:(+352) 26970 894
Email [log in to unmask]
Follow me on twitter @stephensenn
-----Original Message-----
From: Evidence based health (EBH) [mailto:[log in to unmask]] On Behalf Of EVIDENCE-BASED-HEALTH automatic digest system
Sent: 01 February 2015 01:00
To: [log in to unmask]
Subject: EVIDENCE-BASED-HEALTH Digest - 30 Jan 2015 to 31 Jan 2015 (#2015-25)
There are 7 messages totaling 2931 lines in this issue.
Topics of the day:
1. Genetic tests and Predictive validity (7)
----------------------------------------------------------------------
Date: Sat, 31 Jan 2015 02:08:24 +0000
From: "Mayer, Dan" <[log in to unmask]>
Subject: Re: Genetic tests and Predictive validity
Hi Teresa,
You are absolutely correct. This is why we should demand that diagnostic studies ONLY present the results of Sensitivity, Specificity and Likelihood ratios.
This issue has been a serious problem for many years and it is about time that more people spoke up about it. Also, journal editors and peer reviewers should be up in arms against the practice of reporting PPV and NPV.
Best wishes
Dan
________________________________
From: Evidence based health (EBH) [[log in to unmask]] on behalf of Benson, Teresa [[log in to unmask]]
Sent: Friday, January 30, 2015 11:59 AM
To: [log in to unmask]
Subject: Genetic tests and Predictive validity
I’ve just started reading the literature on genetic tests, and noticing how many of them tend to focus on predictive value—that is, if a certain test accurately predicts whether a patient will or won’t get a particular phenotype (condition), the authors suggest the test should be used. But if we’re deciding whether to order the test in the first place, shouldn’t we be focused on sensitivity and specificity instead, not PPV and NPV? Predictive value is so heavily dependent on disease prevalence. For example, if I want to get tested for a disease with a 2% prevalence in people like me, I could just flip a coin and regardless of the outcome, my “Coin Flip Test” would show an NPV of 98%! So what does NPV alone really tell me, if I’m not also factoring out prevalence—which would be easier done by simply looking at sensitivity and specificity? Someone please tell me where my thinking has gone awry!
For a concrete example, look at MammaPrint, a test which reports binary results. In addition to hazard ratios, study authors often tout statistically significant differences between the probabilities of recurrence-free survival in the MammaPrint-High Risk vs. MammaPrint-Low Risk groups (essentially the test’s predictive values). In the RASTER study (N = 427), 97% of the patients with a “Low Risk” test result did not experience metastasis in the next 5 years. Sounds great, right? But when you look at Sensitivity, you see that of the 33 patients in the study who did experience metastasis, only 23 of them were classified as “High Risk” by MammaPrint, for a 70% sensitivity. If patients and clinicians are looking for a test to inform their decision about adjuvant chemotherapy for early stage breast cancer, wouldn’t the fact that the test missed 10 out of 33 cases be more important than the 97% NPV, an artifact of the extremely low 5-year prevalence of metastasis in this cohort (only 33 out of 427, or 0.7%)?
Drukker et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int J Cancer 2013. 133(4):929-36. http://www.ncbi.nlm.nih.gov/pubmed/23371464
Retel et al. Prospective cost-effectiveness analysis of genomic profiling in breast cancer. Eur J Cancer 2013. 49:3773-9. http://www.ncbi.nlm.nih.gov/pubmed/23992641 (Provides actual true/false positive/negative results)
Thanks so much!
Teresa Benson, MA, LP
Clinical Lead, Evidence-Based Medicine
McKesson Health Solutions
18211 Yorkshire Ave
Prior Lake, MN 55372
[log in to unmask]<mailto:[log in to unmask]>
Phone: 1-952-226-4033
Confidentiality Notice: This e-mail message, including any attachments, is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message.
-----------------------------------------
CONFIDENTIALITY NOTICE: This email and any attachments may contain confidential information that is protected by law and is for the sole use of the individuals or entities to which it is addressed. If you are not the intended recipient, please notify the sender by replying to this email and destroying all copies of the communication and attachments. Further use, disclosure, copying, distribution of, or reliance upon the contents of this email and attachments is strictly prohibited. To contact Albany Medical Center, or for a copy of our privacy practices, please visit us on the Internet at www.amc.edu.
------------------------------
Date: Sat, 31 Jan 2015 06:28:59 -0000
From: Michael Power <[log in to unmask]>
Subject: Re: Genetic tests and Predictive validity
Hi Dan and Teresa
Clinicians and patients need PPVs and NPVs.
So, rather than banning them, why not show graphs of PPV/NPV against the range of clinically relevant prevalences?
Michael
From: Evidence based health (EBH) [mailto:[log in to unmask]] On Behalf Of Mayer, Dan
Sent: 31 January 2015 02:08
To: [log in to unmask]
Subject: Re: Genetic tests and Predictive validity
Hi Teresa,
You are absolutely correct. This is why we should demand that diagnostic studies ONLY present the results of Sensitivity, Specificity and Likelihood ratios.
This issue has been a serious problem for many years and it is about time that more people spoke up about it. Also, journal editors and peer reviewers should be up in arms against the practice of reporting PPV and NPV.
Best wishes
Dan
_____
From: Evidence based health (EBH) [[log in to unmask]] on behalf of Benson, Teresa [[log in to unmask]]
Sent: Friday, January 30, 2015 11:59 AM
To: [log in to unmask]
Subject: Genetic tests and Predictive validity
I’ve just started reading the literature on genetic tests, and noticing how many of them tend to focus on predictive value—that is, if a certain test accurately predicts whether a patient will or won’t get a particular phenotype (condition), the authors suggest the test should be used. But if we’re deciding whether to order the test in the first place, shouldn’t we be focused on sensitivity and specificity instead, not PPV and NPV? Predictive value is so heavily dependent on disease prevalence. For example, if I want to get tested for a disease with a 2% prevalence in people like me, I could just flip a coin and regardless of the outcome, my “Coin Flip Test” would show an NPV of 98%! So what does NPV alone really tell me, if I’m not also factoring out prevalence—which would be easier done by simply looking at sensitivity and specificity? Someone please tell me where my thinking has gone awry!
For a concrete example, look at MammaPrint, a test which reports binary results. In addition to hazard ratios, study authors often tout statistically significant differences between the probabilities of recurrence-free survival in the MammaPrint-High Risk vs. MammaPrint-Low Risk groups (essentially the test’s predictive values). In the RASTER study (N = 427), 97% of the patients with a “Low Risk” test result did not experience metastasis in the next 5 years. Sounds great, right? But when you look at Sensitivity, you see that of the 33 patients in the study who did experience metastasis, only 23 of them were classified as “High Risk” by MammaPrint, for a 70% sensitivity. If patients and clinicians are looking for a test to inform their decision about adjuvant chemotherapy for early stage breast cancer, wouldn’t the fact that the test missed 10 out of 33 cases be more important than the 97% NPV, an artifact of the extremely low 5-year prevalence of metastasis in this cohort (only 33 out of 427, or 0.7%)?
Drukker et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int J Cancer 2013. 133(4):929-36. http://www.ncbi.nlm.nih.gov/pubmed/23371464
Retel et al. Prospective cost-effectiveness analysis of genomic profiling in breast cancer. Eur J Cancer 2013. 49:3773-9. http://www.ncbi.nlm.nih.gov/pubmed/23992641 (Provides actual true/false positive/negative results)
Thanks so much!
Teresa Benson, MA, LP
Clinical Lead, Evidence-Based Medicine
McKesson Health Solutions
18211 Yorkshire Ave
Prior Lake, MN 55372
[log in to unmask] <mailto:[log in to unmask]>
Phone: 1-952-226-4033
Confidentiality Notice: This e-mail message, including any attachments, is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message.
_____
----------------------------------------- CONFIDENTIALITY NOTICE: This email and any attachments may contain confidential information that is protected by law and is for the sole use of the individuals or entities to which it is addressed. If you are not the intended recipient, please notify the sender by replying to this email and destroying all copies of the communication and attachments. Further use, disclosure, copying, distribution of, or reliance upon the contents of this email and attachments is strictly prohibited. To contact Albany Medical Center, or for a copy of our privacy practices, please visit us on the Internet at www.amc.edu.
------------------------------
Date: Sat, 31 Jan 2015 09:35:31 -0200
From: Moacyr Roberto Cuce Nobre <[log in to unmask]>
Subject: Re: Genetic tests and Predictive validity
I disagree Dan, if the NPV is 100% the sensitivity is 100% also, mutatis mutandis, if the PPV is 100% the specificity is 100%.
I think that the problem is to present the results of accuracy, since this measure interest those involved with the test procedure. What's matter the clinician is a positive or negative test result.
Cheers
--
Moacyr
_______________________________________
Moacyr Roberto Cuce Nobre, MD, MS, PhD.
Diretor da Unidade de Epidemiologia Clínica Instituto do Coração (InCor) Hospital das Clínicas Faculdade de Medicina da Universidade de São Paulo
55 11 2661 5941 (fone/fax)
55 11 9133 1009 (celular)
----- Mensagem original -----
De: "Dan Mayer" <[log in to unmask]>
Para: [log in to unmask]
Enviadas: Sábado, 31 de Janeiro de 2015 0:08:24
Assunto: Re: Genetic tests and Predictive validity
Hi Teresa,
You are absolutely correct. This is why we should demand that diagnostic studies ONLY present the results of Sensitivity, Specificity and Likelihood ratios.
This issue has been a serious problem for many years and it is about time that more people spoke up about it. Also, journal editors and peer reviewers should be up in arms against the practice of reporting PPV and NPV.
Best wishes
Dan
From: Evidence based health (EBH) [[log in to unmask]] on behalf of Benson, Teresa [[log in to unmask]]
Sent: Friday, January 30, 2015 11:59 AM
To: [log in to unmask]
Subject: Genetic tests and Predictive validity
I’ve just started reading the literature on genetic tests, and noticing how many of them tend to focus on predictive value—that is, if a certain test accurately predicts whether a patient will or won’t get a particular phenotype (condition), the authors suggest the test should be used. But if we’re deciding whether to order the test in the first place, shouldn’t we be focused on sensitivity and specificity instead, not PPV and NPV? Predictive value is so heavily dependent on disease prevalence. For example, if I want to get tested for a disease with a 2% prevalence in people like me, I could just flip a coin and regardless of the outcome, my “Coin Flip Test” would show an NPV of 98%! So what does NPV alone really tell me, if I’m not also factoring out prevalence—which would be easier done by simply looking at sensitivity and specificity? Someone please tell me where my thinking has gone awry!
For a concrete example, look at MammaPrint, a test which reports binary results. In addition to hazard ratios, study authors often tout statistically significant differences between the probabilities of recurrence-free survival in the MammaPrint-High Risk vs. MammaPrint-Low Risk groups (essentially the test’s predictive values). In the RASTER study (N = 427), 97% of the patients with a “Low Risk” test result did not experience metastasis in the next 5 years. Sounds great, right? But when you look at Sensitivity, you see that of the 33 patients in the study who did experience metastasis, only 23 of them were classified as “High Risk” by MammaPrint, for a 70% sensitivity. If patients and clinicians are looking for a test to inform their decision about adjuvant chemotherapy for early stage breast cancer, wouldn’t the fact that the test missed 10 out of 33 cases be more important than the 97% NPV, an artifact of the extremely low 5-year prevalence of metastasis in this cohort (only 33 out of 427, or 0.7%)?
Drukker et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int J Cancer 2013. 133(4):929-36. http://www.ncbi.nlm.nih.gov/pubmed/23371464
Retel et al. Prospective cost-effectiveness analysis of genomic profiling in breast cancer. Eur J Cancer 2013. 49:3773-9. http://www.ncbi.nlm.nih.gov/pubmed/23992641 (Provides actual true/false positive/negative results)
Thanks so much!
Teresa Benson, MA, LP
Clinical Lead, Evidence-Based Medicine
McKesson Health Solutions
18211 Yorkshire Ave
Prior Lake, MN 55372
[log in to unmask]
Phone: 1-952-226-4033
Confidentiality Notice: This e-mail message, including any attachments, is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message.
----------------------------------------- CONFIDENTIALITY NOTICE: This email and any attachments may contain confidential information that is protected by law and is for the sole use of the individuals or entities to which it is addressed. If you are not the intended recipient, please notify the sender by replying to this email and destroying all copies of the communication and attachments. Further use, disclosure, copying, distribution of, or reliance upon the contents of this email and attachments is strictly prohibited. To contact Albany Medical Center, or for a copy of our privacy practices, please visit us on the Internet at www.amc.edu.
------------------------------
Date: Sat, 31 Jan 2015 11:39:12 +0000
From: "Huw Llewelyn [hul2]" <[log in to unmask]>
Subject: Re: Genetic tests and Predictive validity
Hi Theresa, Dan, Michael , Moacyr and everyone It is not only a medical finding’s PPV (positive predictive value) that may change in another population with a different prevalence of target disease. The specificity will also change with the prevalence. For example, the specificity may be lower if another population contains diseases that can also ’cause’ the finding and the specificity may be higher (and much higher) if the population contains more people without the finding or the target disease. The sensitivity may also change if the severity of the target disease is different in a different population. It is also possible for a test result to have a likelihood ratio of ‘one’ and still be very powerful when used in combination with another finding to make a prediction.
So it is a fallacy to assume that sensitivity and specificity are constant in different populations – they vary as much as PPVs. In any case, provided one knows the prevalence, sensitivity and PPV for finding and diagnosis in a population, it is a simple matter to calculate the specificity, the NPP, etc. However, these should not be applied to another population without checking that they are the same. In addition, experienced doctors usually interpret intuitively the actual value of an individual patient’s observation e.g. BP of 196/132 and do not divide it into ‘negative’ and ‘positive’ ranges. Under these circumstances, there is no such thing as ‘specificity’. I explain this in detail in the final chapter of the 3rd edition of Oxford Handbook of Clinical Diagnosis (see also abstract #13 in http://preventingoverdiagnosis.net/documents/POD-Abstracts.docx).
Huw
________________________________
From: Evidence based health (EBH) [[log in to unmask]] on behalf of Michael Power [[log in to unmask]]
Sent: 31 January 2015 06:28
To: [log in to unmask]
Subject: Re: Genetic tests and Predictive validity
Hi Dan and Teresa
Clinicians and patients need PPVs and NPVs.
So, rather than banning them, why not show graphs of PPV/NPV against the range of clinically relevant prevalences?
Michael
From: Evidence based health (EBH) [mailto:[log in to unmask]] On Behalf Of Mayer, Dan
Sent: 31 January 2015 02:08
To: [log in to unmask]
Subject: Re: Genetic tests and Predictive validity
Hi Teresa,
You are absolutely correct. This is why we should demand that diagnostic studies ONLY present the results of Sensitivity, Specificity and Likelihood ratios.
This issue has been a serious problem for many years and it is about time that more people spoke up about it. Also, journal editors and peer reviewers should be up in arms against the practice of reporting PPV and NPV.
Best wishes
Dan
________________________________
From: Evidence based health (EBH) [[log in to unmask]] on behalf of Benson, Teresa [[log in to unmask]]
Sent: Friday, January 30, 2015 11:59 AM
To: [log in to unmask]<mailto:[log in to unmask]>
Subject: Genetic tests and Predictive validity I’ve just started reading the literature on genetic tests, and noticing how many of them tend to focus on predictive value—that is, if a certain test accurately predicts whether a patient will or won’t get a particular phenotype (condition), the authors suggest the test should be used. But if we’re deciding whether to order the test in the first place, shouldn’t we be focused on sensitivity and specificity instead, not PPV and NPV? Predictive value is so heavily dependent on disease prevalence. For example, if I want to get tested for a disease with a 2% prevalence in people like me, I could just flip a coin and regardless of the outcome, my “Coin Flip Test” would show an NPV of 98%! So what does NPV alone really tell me, if I’m not also factoring out prevalence—which would be easier done by simply looking at sensitivity and specificity? Someone please tell me where my thinking has gone awry!
For a concrete example, look at MammaPrint, a test which reports binary results. In addition to hazard ratios, study authors often tout statistically significant differences between the probabilities of recurrence-free survival in the MammaPrint-High Risk vs. MammaPrint-Low Risk groups (essentially the test’s predictive values). In the RASTER study (N = 427), 97% of the patients with a “Low Risk” test result did not experience metastasis in the next 5 years. Sounds great, right? But when you look at Sensitivity, you see that of the 33 patients in the study who did experience metastasis, only 23 of them were classified as “High Risk” by MammaPrint, for a 70% sensitivity. If patients and clinicians are looking for a test to inform their decision about adjuvant chemotherapy for early stage breast cancer, wouldn’t the fact that the test missed 10 out of 33 cases be more important than the 97% NPV, an artifact of the extremely low 5-year prevalence of metastasis in this cohort (only 33 out of 427, or 0.7%)?
Drukker et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int J Cancer 2013. 133(4):929-36. http://www.ncbi.nlm.nih.gov/pubmed/23371464
Retel et al. Prospective cost-effectiveness analysis of genomic profiling in breast cancer. Eur J Cancer 2013. 49:3773-9. http://www.ncbi.nlm.nih.gov/pubmed/23992641 (Provides actual true/false positive/negative results)
Thanks so much!
Teresa Benson, MA, LP
Clinical Lead, Evidence-Based Medicine
McKesson Health Solutions
18211 Yorkshire Ave
Prior Lake, MN 55372
[log in to unmask]<mailto:[log in to unmask]>
Phone: 1-952-226-4033
Confidentiality Notice: This e-mail message, including any attachments, is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message.
________________________________
----------------------------------------- CONFIDENTIALITY NOTICE: This email and any attachments may contain confidential information that is protected by law and is for the sole use of the individuals or entities to which it is addressed. If you are not the intended recipient, please notify the sender by replying to this email and destroying all copies of the communication and attachments. Further use, disclosure, copying, distribution of, or reliance upon the contents of this email and attachments is strictly prohibited. To contact Albany Medical Center, or for a copy of our privacy practices, please visit us on the Internet at www.amc.edu<http://www.amc.edu>.
------------------------------
Date: Sat, 31 Jan 2015 12:31:29 +0000
From: Brian Alper MD <[log in to unmask]>
Subject: Re: Genetic tests and Predictive validity
Reporting diagnostic and prognostic parameters to help clinicians and policy makers understood the use of tests is challenging. There are many possible parameters to report and varied understanding such that one measure is not uniformly understood by all.
The most sophisticated may want likelihood ratios and understand how to apply them with respect to pretest likelihood. But many want simpler yes/no type of information. For the simpler approach sensitivity and specificity are too often confused for rule in and rule out, and are too often confused for representing the likelihood of an answer in practice (which does not account for the pretest likelihood at all).
For these reasons positive and negative predictive values appear to be the simplest yet clinically meaningful results to report. However they are only accurate if the pretest likelihood is similar in practice to the source where they are being reported from. It is possible to report PPV and NPV for different pretest likelihoods or different representative populations.
In determining whether a test is useful, the PPV and NPV in isolation is insufficient. Only if PPV or NPV is different enough from the pretest likelihood to cross a decision threshold (treatment threshold or testing threshold) does the test change action. In the example below with a pretest likelihood of 2% and a coin flip resulting in PPV 2% and NPV 98% the post-test results are no different than the pretest likelihood.
Other parameters (accuracy, diagnostic odds ratio) that combine sensitivity and specificity into a single measure are easier to combine and report statistically but I generally avoid these parameters because they are less informative for clinical use - in most clinical situations sensitivity and specificity are not equally important; a useful one-sided test (eg useful if positive, ignored if negative) becomes less clear when viewing it for overall accuracy.
But another consideration is sometimes tests are used for "diagnostic" purposes - Does the patient have or not have a certain diagnosis? - an in these cases sensitivity, specificity, PPV*, NPV*, positive likelihood ratio, and negative likelihood ratio (* with prevalence to put into perspective) are clear.
Sometimes tests are used for prognostic purposes, and may be used as a continuous measure with different predictions at different test results. In these cases presenting the expected prevalence (or probability or likelihood) at different test results may be easier to interpret than selecting a single cutoff and converting it to sensitivity/specificity/etc.
In general all of the comments above reflect diagnostic and prognostic testing, whether genetic or not. If the test is genetic the results can usually be handled similarly if the clinical question is specific to the person being tested. There may be some scenarios where familial implications (cross-generation interpretation) or combinatorial implications (multigene testing) make the overall reporting more challenging.
Brian S. Alper, MD, MSPH, FAAFP
Founder of DynaMed
Vice President of EBM Research and Development, Quality & Standards dynamed.ebscohost.com
From: Evidence based health (EBH) [mailto:[log in to unmask]] On Behalf Of Benson, Teresa
Sent: Friday, January 30, 2015 12:00 PM
To: [log in to unmask]
Subject: Genetic tests and Predictive validity
I've just started reading the literature on genetic tests, and noticing how many of them tend to focus on predictive value-that is, if a certain test accurately predicts whether a patient will or won't get a particular phenotype (condition), the authors suggest the test should be used. But if we're deciding whether to order the test in the first place, shouldn't we be focused on sensitivity and specificity instead, not PPV and NPV? Predictive value is so heavily dependent on disease prevalence. For example, if I want to get tested for a disease with a 2% prevalence in people like me, I could just flip a coin and regardless of the outcome, my "Coin Flip Test" would show an NPV of 98%! So what does NPV alone really tell me, if I'm not also factoring out prevalence-which would be easier done by simply looking at sensitivity and specificity? Someone please tell me where my thinking has gone awry!
For a concrete example, look at MammaPrint, a test which reports binary results. In addition to hazard ratios, study authors often tout statistically significant differences between the probabilities of recurrence-free survival in the MammaPrint-High Risk vs. MammaPrint-Low Risk groups (essentially the test's predictive values). In the RASTER study (N = 427), 97% of the patients with a "Low Risk" test result did not experience metastasis in the next 5 years. Sounds great, right? But when you look at Sensitivity, you see that of the 33 patients in the study who did experience metastasis, only 23 of them were classified as "High Risk" by MammaPrint, for a 70% sensitivity. If patients and clinicians are looking for a test to inform their decision about adjuvant chemotherapy for early stage breast cancer, wouldn't the fact that the test missed 10 out of 33 cases be more important than the 97% NPV, an artifact of the extremely low 5-year prevalence of metastasis in this cohort (only 33 out of 427, or 0.7%)?
Drukker et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int J Cancer 2013. 133(4):929-36. http://www.ncbi.nlm.nih.gov/pubmed/23371464
Retel et al. Prospective cost-effectiveness analysis of genomic profiling in breast cancer. Eur J Cancer 2013. 49:3773-9. http://www.ncbi.nlm.nih.gov/pubmed/23992641 (Provides actual true/false positive/negative results)
Thanks so much!
Teresa Benson, MA, LP
Clinical Lead, Evidence-Based Medicine
McKesson Health Solutions
18211 Yorkshire Ave
Prior Lake, MN 55372
[log in to unmask]<mailto:[log in to unmask]>
Phone: 1-952-226-4033
Confidentiality Notice: This e-mail message, including any attachments, is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message.
------------------------------
Date: Sat, 31 Jan 2015 14:59:57 +0000
From: "Huw Llewelyn [hul2]" <[log in to unmask]>
Subject: Re: Genetic tests and Predictive validity
Hi Moacyr, Theresa, Dan, Michael, Brian and everyone I agree that prior probabilities are important. However, these should not be non-evidence-based subjective guesses but probabilities (i.e. positive predictive values) of diagnoses in a traditional differential diagnostic list. This list and the probabilities are suggested by a presenting complaint or another finding (e.g. a chest x ray appearance) with a shorter list of possibilities. One of these diagnoses are chosen, and other findings looked for that occur commonly in that diagnosis (i.e. the sensitivity is high) and rarely or never in at least one other diagnosis in the list (i.e. the sensitivity is low or zero). If the finding were present, this would make the probability of the chosen diagnosis higher and the probability of the other diagnosis lower or zero. Other findings are sought to try to make all but one of the differential diagnoses improbable.
In this reasoning process, which experienced doctors use when explaining their reasoning, we use ratios of sensitivities, NOT the ratio of sensitivity to false positive rate (i.e. one minus the specificity). Differential diagnostic reasoning uses a statistical dependence assumption which safely underestimates diagnostic probabilities whereas using likelihood ratios uses a statistical independence assumption that overestimates probabilities (and thus may over-diagnose). I explain this reasoning process in the Oxford Handbook of Clinical Diagnosis (see page 6 and 622 with the mathematical proof on page 638) in ‘look inside’ on the Amazon web-site: http://www.amazon.co.uk/Handbook-Clinical-Diagnosis-Medical-Handbooks/dp/019967986X#reader_019967986X.
Best wishes
Huw
________________________________
From: Moacyr Roberto Cuce Nobre [[log in to unmask]]
Sent: 31 January 2015 12:15
To: Huw Llewelyn [hul2]
Subject: Re: Genetic tests and Predictive validity
Hi Huw, dont you think that the output to the clinician is the pretest probability of the patient, that better reflects the individual uncertainty, rather than the prevalence as a population data.
--
Moacyr
_______________________________________
Moacyr Roberto Cuce Nobre, MD, MS, PhD.
Diretor da Unidade de Epidemiologia Clínica Instituto do Coração (InCor) Hospital das Clínicas Faculdade de Medicina da Universidade de São Paulo
55 11 2661 5941 (fone/fax)
55 11 9133 1009 (celular)
________________________________
De: "Huw Llewelyn [hul2]" <[log in to unmask]>
Para: [log in to unmask]
Enviadas: Sábado, 31 de Janeiro de 2015 9:39:12
Assunto: Re: Genetic tests and Predictive validity
Hi Theresa, Dan, Michael , Moacyr and everyone It is not only a medical finding’s PPV (positive predictive value) that may change in another population with a different prevalence of target disease. The specificity will also change with the prevalence. For example, the specificity may be lower if another population contains diseases that can also ’cause’ the finding and the specificity may be higher (and much higher) if the population contains more people without the finding or the target disease. The sensitivity may also change if the severity of the target disease is different in a different population. It is also possible for a test result to have a likelihood ratio of ‘one’ and still be very powerful when used in combination with another finding to make a prediction.
So it is a fallacy to assume that sensitivity and specificity are constant in different populations – they vary as much as PPVs. In any case, provided one knows the prevalence, sensitivity and PPV for finding and diagnosis in a population, it is a simple matter to calculate the specificity, the NPP, etc. However, these should not be applied to another population without checking that they are the same. In addition, experienced doctors usually interpret intuitively the actual value of an individual patient’s observation e.g. BP of 196/132 and do not divide it into ‘negative’ and ‘positive’ ranges. Under these circumstances, there is no such thing as ‘specificity’. I explain this in detail in the final chapter of the 3rd edition of Oxford Handbook of Clinical Diagnosis (see also abstract #13 in http://preventingoverdiagnosis.net/documents/POD-Abstracts.docx).
Huw
________________________________
From: Evidence based health (EBH) [[log in to unmask]] on behalf of Michael Power [[log in to unmask]]
Sent: 31 January 2015 06:28
To: [log in to unmask]
Subject: Re: Genetic tests and Predictive validity
Hi Dan and Teresa
Clinicians and patients need PPVs and NPVs.
So, rather than banning them, why not show graphs of PPV/NPV against the range of clinically relevant prevalences?
Michael
From: Evidence based health (EBH) [mailto:[log in to unmask]] On Behalf Of Mayer, Dan
Sent: 31 January 2015 02:08
To: [log in to unmask]
Subject: Re: Genetic tests and Predictive validity
Hi Teresa,
You are absolutely correct. This is why we should demand that diagnostic studies ONLY present the results of Sensitivity, Specificity and Likelihood ratios.
This issue has been a serious problem for many years and it is about time that more people spoke up about it. Also, journal editors and peer reviewers should be up in arms against the practice of reporting PPV and NPV.
Best wishes
Dan
________________________________
From: Evidence based health (EBH) [[log in to unmask]] on behalf of Benson, Teresa [[log in to unmask]]
Sent: Friday, January 30, 2015 11:59 AM
To: [log in to unmask]<mailto:[log in to unmask]>
Subject: Genetic tests and Predictive validity I’ve just started reading the literature on genetic tests, and noticing how many of them tend to focus on predictive value—that is, if a certain test accurately predicts whether a patient will or won’t get a particular phenotype (condition), the authors suggest the test should be used. But if we’re deciding whether to order the test in the first place, shouldn’t we be focused on sensitivity and specificity instead, not PPV and NPV? Predictive value is so heavily dependent on disease prevalence. For example, if I want to get tested for a disease with a 2% prevalence in people like me, I could just flip a coin and regardless of the outcome, my “Coin Flip Test” would show an NPV of 98%! So what does NPV alone really tell me, if I’m not also factoring out prevalence—which would be easier done by simply looking at sensitivity and specificity? Someone please tell me where my thinking has gone awry!
For a concrete example, look at MammaPrint, a test which reports binary results. In addition to hazard ratios, study authors often tout statistically significant differences between the probabilities of recurrence-free survival in the MammaPrint-High Risk vs. MammaPrint-Low Risk groups (essentially the test’s predictive values). In the RASTER study (N = 427), 97% of the patients with a “Low Risk” test result did not experience metastasis in the next 5 years. Sounds great, right? But when you look at Sensitivity, you see that of the 33 patients in the study who did experience metastasis, only 23 of them were classified as “High Risk” by MammaPrint, for a 70% sensitivity. If patients and clinicians are looking for a test to inform their decision about adjuvant chemotherapy for early stage breast cancer, wouldn’t the fact that the test missed 10 out of 33 cases be more important than the 97% NPV, an artifact of the extremely low 5-year prevalence of metastasis in this cohort (only 33 out of 427, or 0.7%)?
Drukker et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int J Cancer 2013. 133(4):929-36. http://www.ncbi.nlm.nih.gov/pubmed/23371464
Retel et al. Prospective cost-effectiveness analysis of genomic profiling in breast cancer. Eur J Cancer 2013. 49:3773-9. http://www.ncbi.nlm.nih.gov/pubmed/23992641 (Provides actual true/false positive/negative results)
Thanks so much!
Teresa Benson, MA, LP
Clinical Lead, Evidence-Based Medicine
McKesson Health Solutions
18211 Yorkshire Ave
Prior Lake, MN 55372
[log in to unmask]<mailto:[log in to unmask]>
Phone: 1-952-226-4033
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Date: Sat, 31 Jan 2015 16:45:44 +0000
From: "McCormack, James" <[log in to unmask]>
Subject: Re: Genetic tests and Predictive validity
Hi Everyone - here is a useful rough rule of thumb for Likelihood Ratios that a clinician can use which puts numbers to disease probability and you can see when an LR can basically rule in our out a disease.
The only caveat is this is ONLY accurate if the pretest probability lies between 10% and 90% so it doesn’t apply to low prevalent (<10%) conditions so it may not be all that applicable to this discussion.
A LR of 2 increases the absolute probability of disease FROM your pre-test probability by 15% - for every additional one increase in LR you add another 5% absolute change to your probability - so a LR of 3 increases your probability of disease by 15% plus 2 X 5% or a total increase of 25% over your pre-test probability.
Going in the other direction
A LR of 0.5 decreases the absolute probability of disease FROM your pre-test probability by 15% - for every additional 0.1 lowering in LR you add another 5% absolute change to your probability - so a LR of 0.3 decreases probability by 15% plus 2 X 5% or a total decrease of 25% from your pre-test probability.
The original idea for this came from this article http://onlinelibrary.wiley.com/doi/10.1046/j.1525-1497.2002.10750.x/full
and we published a discussion on probabilistic reasoning in primary care here http://goo.gl/Rnl2q2
Thanks
James
On Jan 31, 2015, at 4:31 AM, Brian Alper MD <[log in to unmask]<mailto:[log in to unmask]>> wrote:
Reporting diagnostic and prognostic parameters to help clinicians and policy makers understood the use of tests is challenging. There are many possible parameters to report and varied understanding such that one measure is not uniformly understood by all.
The most sophisticated may want likelihood ratios and understand how to apply them with respect to pretest likelihood. But many want simpler yes/no type of information. For the simpler approach sensitivity and specificity are too often confused for rule in and rule out, and are too often confused for representing the likelihood of an answer in practice (which does not account for the pretest likelihood at all).
For these reasons positive and negative predictive values appear to be the simplest yet clinically meaningful results to report. However they are only accurate if the pretest likelihood is similar in practice to the source where they are being reported from. It is possible to report PPV and NPV for different pretest likelihoods or different representative populations.
In determining whether a test is useful, the PPV and NPV in isolation is insufficient. Only if PPV or NPV is different enough from the pretest likelihood to cross a decision threshold (treatment threshold or testing threshold) does the test change action. In the example below with a pretest likelihood of 2% and a coin flip resulting in PPV 2% and NPV 98% the post-test results are no different than the pretest likelihood.
Other parameters (accuracy, diagnostic odds ratio) that combine sensitivity and specificity into a single measure are easier to combine and report statistically but I generally avoid these parameters because they are less informative for clinical use – in most clinical situations sensitivity and specificity are not equally important; a useful one-sided test (eg useful if positive, ignored if negative) becomes less clear when viewing it for overall accuracy.
But another consideration is sometimes tests are used for “diagnostic” purposes – Does the patient have or not have a certain diagnosis? – an in these cases sensitivity, specificity, PPV*, NPV*, positive likelihood ratio, and negative likelihood ratio (* with prevalence to put into perspective) are clear.
Sometimes tests are used for prognostic purposes, and may be used as a continuous measure with different predictions at different test results. In these cases presenting the expected prevalence (or probability or likelihood) at different test results may be easier to interpret than selecting a single cutoff and converting it to sensitivity/specificity/etc.
In general all of the comments above reflect diagnostic and prognostic testing, whether genetic or not. If the test is genetic the results can usually be handled similarly if the clinical question is specific to the person being tested. There may be some scenarios where familial implications (cross-generation interpretation) or combinatorial implications (multigene testing) make the overall reporting more challenging.
Brian S. Alper, MD, MSPH, FAAFP
Founder of DynaMed
Vice President of EBM Research and Development, Quality & Standards dynamed.ebscohost.com<http://dynamed.ebscohost.com/>
From: Evidence based health (EBH) [mailto:[log in to unmask]] On Behalf Of Benson, Teresa
Sent: Friday, January 30, 2015 12:00 PM
To: [log in to unmask]<mailto:[log in to unmask]>
Subject: Genetic tests and Predictive validity
I’ve just started reading the literature on genetic tests, and noticing how many of them tend to focus on predictive value—that is, if a certain test accurately predicts whether a patient will or won’t get a particular phenotype (condition), the authors suggest the test should be used. But if we’re deciding whether to order the test in the first place, shouldn’t we be focused on sensitivity and specificity instead, not PPV and NPV? Predictive value is so heavily dependent on disease prevalence. For example, if I want to get tested for a disease with a 2% prevalence in people like me, I could just flip a coin and regardless of the outcome, my “Coin Flip Test” would show an NPV of 98%! So what does NPV alone really tell me, if I’m not also factoring out prevalence—which would be easier done by simply looking at sensitivity and specificity? Someone please tell me where my thinking has gone awry!
For a concrete example, look at MammaPrint, a test which reports binary results. In addition to hazard ratios, study authors often tout statistically significant differences between the probabilities of recurrence-free survival in the MammaPrint-High Risk vs. MammaPrint-Low Risk groups (essentially the test’s predictive values). In the RASTER study (N = 427), 97% of the patients with a “Low Risk” test result did not experience metastasis in the next 5 years. Sounds great, right? But when you look at Sensitivity, you see that of the 33 patients in the study who did experience metastasis, only 23 of them were classified as “High Risk” by MammaPrint, for a 70% sensitivity. If patients and clinicians are looking for a test to inform their decision about adjuvant chemotherapy for early stage breast cancer, wouldn’t the fact that the test missed 10 out of 33 cases be more important than the 97% NPV, an artifact of the extremely low 5-year prevalence of metastasis in this cohort (only 33 out of 427, or 0.7%)?
Drukker et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int J Cancer 2013. 133(4):929-36.http://www.ncbi.nlm.nih.gov/pubmed/23371464
Retel et al. Prospective cost-effectiveness analysis of genomic profiling in breast cancer. Eur J Cancer 2013. 49:3773-9. http://www.ncbi.nlm.nih.gov/pubmed/23992641 (Provides actual true/false positive/negative results)
Thanks so much!
Teresa Benson, MA, LP
Clinical Lead, Evidence-Based Medicine
McKesson Health Solutions
18211 Yorkshire Ave
Prior Lake, MN 55372
[log in to unmask]<mailto:[log in to unmask]>
Phone: 1-952-226-4033
Confidentiality Notice: This e-mail message, including any attachments, is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message.
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End of EVIDENCE-BASED-HEALTH Digest - 30 Jan 2015 to 31 Jan 2015 (#2015-25)
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