Comparative Effectiveness Research (CER), “Big Data” & Causality

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Comparative Effectiveness Research (CER), “Big Data” & Causality

For a number of years now, we’ve been concerned that the CER movement and the growing love affair with “big data,” will lead to many erroneous conclusions about cause and effect.  We were pleased to see the following blog from Austin Frakt, an editor-in-chief of The Incidental Economist: Contemplating health care with a focus on research, an eye on reform

Ten impressions of big data: Claims, aspirations, hardly any causal inference

http://theincidentaleconomist.com/wordpress/ten-impressions-of-big-data-claims-aspirations-hardly-any-causal-inference/

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Five more big data quotes: The ambitions and challenges

http://theincidentaleconomist.com/wordpress/five-more-big-data-quotes/

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Cochrane Risk Of Bias Tool For Non-Randomized Studies

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Cochrane Risk Of Bias Tool For Non-Randomized Studies

Like many others, our position is that, with very few exceptions, cause and effect conclusions regarding therapeutic interventions can only be drawn when valid RCT data exists. However, there are uses for observational studies which may be used to answer additional questions, and non-randomized studies (NRS) are often included in systematic reviews.

In September 2014, Cochrane published a tool for assessing bias in NRS for systematic review authors [1]. It may be of interest to our colleagues. The tool is called ACROBAT-NRSI (“A Cochrane Risk Of Bias Assessment Tool for Non-Randomized Studies”) and is designed to assist with evaluating the risk of bias (RoB) in the results of NRS that compare the health effects of two or more interventions.

The tool focuses on internal validity. It covers seven domains through which bias might be introduced into a NRS. The domains provide a framework for considering any type of NRS, and are summarized in the table below, and many of the biases listed here are described and explanations of how they may cause bias are presented in the full document, and you can see our rough summary here: http://www.delfini.org/delfiniClick_Observations.htm#robtable

Response options for each bias include: low risk of bias; moderate risk of bias; serious risk of bias; critical risk of bias; and no information on which to base a judgment.

Details are available in the full document which can be downloaded at—https://sites.google.com/site/riskofbiastool/

Delfini Comment
We again point out that non-randomized studies often report seriously misleading results even when treated and control groups appear similar in prognostic variables and agree with Deeks that, for therapeutic interventions ,“non-randomised studies should only be undertaken when RCTs are infeasible or unethical”[2]—and even then, buyer beware. Studies do not get “validity grace” because of scientific or practical challenges.

Furthermore, we are uncertain that this tool is of great value when assessing NRS. Deeks [2] identified 194 tools that could be or had been used to assess NRS. Do we really need another one? While it’s a good document for background reading, we are more comfortable approaching the problem of observational data by pointing out that, when it comes to efficacy, high quality RCTs have a positive predictive value of about 85% whereas well-done observational trials have a positive predictive value of about 20% [3].

References

Sterne JAC, Higins JPT, Reves BC on behalf of the development group for ACROBAT- NRSI. A Cochrane Risk Of Bias Asesment Tol: for Non-Randomized Studies of Interventions (ACROBAT- NRSI), Version 1.0.0, 24 September 2014. Available from htp:/www.riskofbias.info [accessed 10/11/14.

Deeks JJ, Dinnes J, D’Amico R, Sowden AJ, Sakarovitch C, Song F, Petticrew M, Altman DG; International Stroke Trial Collaborative Group; European Carotid Surgery Trial Collaborative Group. Evaluating non-randomised intervention studies. Health Technol Assess. 2003;7(27):iii-x, 1-173. Review. PubMed PMID: 14499048.

Ioannidis JPA. Why Most Published Research Findings are False. PLoS Med 2005; 2(8):696-701 PMID: 16060722.

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Comparison of Risk of Bias Ratings in Clinical Trials—Journal Publications Versus Clinical Study Reports

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Comparison of Risk of Bias Ratings in Clinical Trials—Journal Publications Versus Clinical Study Reports

Many critical appraisers assess bias using tools such as the Cochrane risk of bias tool (Higgins 11) or tools freely available from us (http://www.delfini.org/delfiniTools.htm). Internal validity is assessed by evaluating important items such as generation of the randomization sequence, concealment of allocation, blinding, attrition and assessment of results.

Jefferson et al. recently compared the risk of bias in 14 oseltamivir trials using information from previous assessments based on the study publications and the newly acquired, more extensive clinical study reports (CSRs) obtained from the European Medicines Agency (EMA) and the manufacturer, Roche.

Key findings include the following:

  • Evaluations using more complete information from the CSRs resulted in no difference in the number of previous assessment of “high” risk of bias.
  • However, over half (55%, 34/62) of the previous “low” risk of bias ratings were reclassified as “high.”
  • Most of the previous “unclear” risk of bias ratings (67%, 28/32) were changed to “high” risk of bias ratings when CSRs were available.

The authors discuss the idea that the risk of bias tools are important because they facilitate the process of critical appraisal of medical evidence. They also call for greater availability of the CSRs as the basic unit available for critical appraisal.

Delfini Comment

We believe that both sponsors and researchers need to provide more study detail so that critical appraisers can provide more precise ratings of risk of bias. Study publications frequently lack information needed by critical appraisers.

We agree that CSRs should be made available so they can be used to improve their assessments of clinical trials.  However, our experience has been the opposite of that experienced by the authors.  When companies have invited us to work with them to assess the reliability of their studies and made CSRs available to us, frequently we have found important information not otherwise available in the study publication.  When this happens, studies otherwise given a rating at higher risk of bias have often been determined to be at low risk of bias and of high quality.

References

1. Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, Savovic J, Schulz KF, Weeks L, Sterne JA; Cochrane Bias Methods Group; Cochrane Statistical  Methods Group. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ. 2011 Oct 18;343:d5928. doi: 10.1136/bmj.d5928. PubMed PMID: 22008217.

2. Jefferson T, Jones MA, Doshi P, Del Mar CB, Hama R, Thompson MJ, Onakpoya I, Heneghan CJ. Risk of bias in industry-funded oseltamivir trials: comparison of core reports versus full clinical study reports. BMJ Open. 2014 Sep 30;4(9):e005253. doi: 10.1136/bmjopen-2014-005253. PubMed PMID: 25270852.

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Clinical Guidelines and Elderly Patients—Proceed with Caution

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Clinical Guidelines and Elderly Patients—Proceed with Caution

We were alerted to this important study of clinical practice guidelines (CPGs) in elderly patients with co-morbidities [1] by Demetra Antimisiaris, an associate professor who directs the polypharmacy initiative at the University of Louisville School of Medicine.

The authors report that most CPGs did not modify recommendations for older patients with multiple comorbidities. Most also did not comment the quality of the underlying scientific evidence or provide guidance for incorporating patient preferences into treatment plans. If the relevant CPGs were followed, one hypothetical patient described in the report would be prescribed 12 medications (costing her $406 per month). Use of guidelines for this patient would result in a complicated medication schedule, and adverse events would be likely. The authors state that, “Although CPGs provide detailed guidance for managing single diseases, they fail to address the needs of older patients with complex comorbid illness.” CPGs rarely address treatment of patients with 3 or more chronic diseases—a group that includes half of the population older than 65 years. Adhering to current CPGs in caring for an older person with several comorbidities may have multiple undesirable effects and could result in net harms.

  1. Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005 Aug 10;294(6):716-24. PubMed PMID: 16091574.
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Comparative Study Designs: Claiming Superiority, Equivalence and Non-inferiority—A Few Considerations & Practical Approaches

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Comparative Study Designs: Claiming Superiority, Equivalence and Non-inferiority—A Few Considerations & Practical Approaches

This is a complex area, and we recommend downloading our freely available 1-page summary to help assess issues with equivalence and non-inferiority trials. Here is a short sampling of some of the problems in these designs: lack of sufficient evidence confirming efficacy of referent treatment, (“referent” refers to the comparator treatment); study not sufficiently similar to referent study; inappropriate Deltas (meaning the margin established for equivalence or non-inferiority); or significant biases or analysis methods that would tend to diminish an effect size and “favor” no difference between groups (e.g., conservative application of ITT analysis, insufficient power, etc.), thus pushing toward non-inferiority or equivalence.

However, we do want to say a few more things about non-inferiority trials based on some recent questions and readings.

Is it acceptable to claim superiority in a non-inferiority trial? Yes. The Food and Drug Administration (FDA) and the European Medicines Agency (EMA), among others, including ourselves, all agree that declaring superiority in a non-inferiority trial is acceptable. What’s more, there is agreement that multiplicity adjusting does not need to be done when first testing for non-inferiority and then superiority.

Snappin even recommends that “…most, if not all, active-controlled clinical trial protocols should define a noninferiority margin and include a noninferiority hypothesis.” We agree. Clinical trials are expensive to do, take time, have opportunity costs, and—most importantly—are of impact on the lives of the human subjects who engage in them. This is a smart procedure that costs nothing especially as multiplicity adjusting is not needed.

What does matter is having an appropriate population for doing a superiority analysis. For superiority, in studies with dichotomous variables, the population should be Intention-to-Treat (ITT) with an appropriate imputation method that does not favor the intervention under study. In studies with time-to-event outcomes, the population should be based on the ITT principle (meaning all randomized patients should be used in the analysis by the group to which they were randomized) with unbiased censoring rules.

Confidence intervals (CIs) should be evaluated to determine superiority. Some evaluators seem to suggest that superiority can be declared only if the CIs are wholly above the Delta. Schumi et al. express their opinion that you can declare superiority if the confidence interval for the new treatment is above the line of no difference (i.e.., is statistically significant). They state, “The calculated CI does not know whether its purpose is to judge superiority or non-inferiority. If it sits wholly above zero [or 1, depending upon the measure of outcome], then it has shown superiority.” EMA would seem to agree. We agree as well. If one wishes to take a more conservative approach, one method we recommend is to judge whether the Delta seems clinically reasonable (you should always do this) and if not, establishing your own through clinical judgment. Then determine if the entire CI meets or exceeds what you deem to be clinically meaningful. To us, this method satisfies both approaches and makes practical and clinical sense.

Is it acceptable to claim non-inferiority trial superiority? It depends. This area is controversial with some saying no and some saying it depends. However, there is agreement amongst those on the “it depends” side that it generally should not be done due to validity issues as described above.

References
US Department of Health and Human Services, Food and Drug Administration: Guidance for Industry Non-Inferiority Clinical Trials (DRAFT). 2010.
http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/ Guidances/UCM202140.pdf

European Agency for the Evaluation of Medicinal Products Committee for Proprietary Medicinal Products (CPMP): Points to Consider on Switching Between Superiority and Non-Inferiority. 2000. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2014556/

http://www.delfini.org/delfiniReading.htm#equivalence

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Medical Literature Searching Update

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Searching Update

We’ve updated our searching tips.  You can download our Searching the Medical Literature Tool, along with others freely available, at our library of Tools & Educational Materials by Delfini:

http://www.delfini.org/delfiniTools.htm

1. Quick Way To Find Drug Information On The FDA Site

If you are looking for information about a specific drug, (e.g.,  a drug recently approved by the FDA) you it may be faster to use Google to find the information you want. Type “FDA [drug name].

2.  Also see Searching With Symbols in the tool.

 

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American College of Cardiology/American Heart Association Guidelines: Numbers-Needed-to-Treat (NNTs) for Statin Treatment in Primary Prevention of Cardiovascular Disease (CVD)

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American College of Cardiology/American Heart Association Guidelines: Numbers-Needed-to-Treat (NNTs) for Statin Treatment in Primary Prevention of Cardiovascular Disease (CVD)

Following publication of the November 2013 American College of Cardiology/American Heart Association (ACC/AHA) guideline [1], concern was expressed that, in the area of primary prevention for CVD, the 10 year guideline estimates of risk were overestimated [2]. Furthermore, the ACC/AHA criteria could result in more than 45 million middle-aged Americans without cardiovascular disease being recommended for consideration of statin therapy.

While the amount of risk overestimation is still being debated, Alper and Drabkin of DynaMed, have created very nice decision-support based on their evaluation of the most current and reliable systematic reviews available for estimating the effects of statins in individuals with various 10 year risks [3].

The risk estimates below will prove quite useful for individual decision-making providing the NNTs over 5 years for the use of statins by individual risk. More detailed information regarding the evidence of statins in preventing CVD events on is available on the DynaMed website [4].

For a person with an estimated 7.5% 10-year risk, the 5-year NNT was 108 for CVD events, 186 for MI, and 606 for stroke. At 15% 10-year risk, 5-year NNTs were 54 for CVD events, 94 for MI, 204 for stroke, and 334 for overall mortality. At 20% 10-year risk, 5-year NNTs were 40 for CVD events, 70 for MI, 228 for stroke, and 250 for overall mortality.

References
1. Stone NJ, Robinson J, Lichtenstein AH et al. 2013 ACC/AHA Guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013. [Epub ahead of print] [PMID: 24239923]

2. Ridker PM, Cook NR. Statins: new American guidelines for prevention of cardiovascular disease. Lancet. 2013 Nov 30;382(9907):1762-5. doi: 10.1016/S0140-6736(13)62388-0. Epub 2013 Nov 20. PubMed PMID: 24268611.

3. Click on the Comments Tab here: http://annals.org/article.aspx?articleid=1817258

4. Search “statins” at the link below:”
http://archive.constantcontact.com/fs132/1102736301344/

archive/1116074054121.html

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Involving Patients in Their Care Decisions and JAMA Editorial: The New Cholesterol and Blood Pressure Guidelines: Perspective on the Path Forward

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Involving Patients in Their Care Decisions and JAMA Editorial: The New Cholesterol and Blood Pressure Guidelines: Perspective on the Path Forward

Krumholz HM. The New Cholesterol and Blood Pressure Guidelines: Perspective on the Path Forward. JAMA. 2014 Mar 29. doi: 10.1001/jama.2014.2634. [Epub ahead of print] PubMed PMID: 24682222.

http://jama.jamanetwork.com/article.aspx?articleid=1853201

Here is an excellent editorial that highlights the importance of patient decision-making.  We thank the wonderful Dr. Richard Lehman, MA, BM, BCh, Oxford, & Blogger, BMJ Journal Watch, for bringing this to our attention. [Note: Richard’s wonderful weekly review of medical journals—informative, inspiring and oh so droll—is here.]

We have often observed that evidence can be a neutralizing force. This editorial highlights for us that this means involving the patient in a meaningful way and finding ways to support decisions based on patients’ personal requirements. These personal “patient requirements” include health care needs and wants and a recognition of individual circumstances, values and preferences.

To achieve this, we believe that patients should receive the same information as clinicians including what alternatives are available, a quantified assessment of potential benefits and harms of each including the strength of evidence for each and potential consequences of making various choices including things like vitality and cost.

Decisions may differ between patients, and physicians may make incorrect assumption about what most matters to patients of which there are many examples in the literature such as in the citations below.

O’Connor A. Using patient decision aids to promote evidence-based decision making. ACP J Club. 2001 Jul-Aug;135(1):A11-2. PubMed PMID: 11471526.

O’Connor AM, Wennberg JE, Legare F, Llewellyn-Thomas HA,Moulton BW, Sepucha KR, et al. Toward the ‘tipping point’: decision aids and informed patient choice. Health Affairs 2007;26(3):716-25.

Rothwell PM. External validity of randomised controlled trials: “to whom do the results of this trial apply?”. Lancet. 2005 Jan 1-7;365(9453):82-93. PubMed PMID: 15639683.

Stacey D, Bennett CL, Barry MJ, Col NF, Eden KB, Holmes-Rovner M, Llewellyn-Thomas H, Lyddiatt A, Légaré F, Thomson R. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2011 Oct 5;(10):CD001431. Review. PubMed PMID: 21975733.

Wennberg JE, O’Connor AM, Collins ED, Weinstein JN. Extending the P4P agenda, part 1: how Medicare can improve patient decision making and reduce unnecessary care. Health Aff (Millwood). 2007 Nov-Dec;26(6):1564-74. PubMed PMID: 17978377.

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Estimating Relative Risk Reduction from Odds Ratios

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Estimating Relative Risk Reduction from Odds Ratios

Odds are hard to work with because they are the likelihood of an event occurring compared to not occurring—e.g., odds of two to one mean that likelihood of an event occurring is twice that of not occurring. Contrast this with probability which is simply the likelihood of an event occurring.

An odds ratio (OR) is a point estimate used for case-control studies which attempts to quantify a mathematical relationship between an exposure and a health outcome. Odds must be used in case-control studies because the investigator arbitrarily controls the population; therefore, probability cannot be determined because the disease rates in the study population cannot be known. The odds that a case is exposed to a certain variable are divided by the odds that a control is exposed to that same variable.

Odds are often used in other types of studies as well, such as meta-analysis, because of various properties of odds which make them easy to use mathematically. However, increasingly authors are discouraged from computing odds ratios in secondary studies because of the difficulty translating what this actually means in terms of size of benefits or harms to patients.

Readers frequently attempt to deal with this by converting the odds ratio into relative risk reduction by thinking of the odds ratio as similar to relative risk. Relative risk reduction (RRR) is computed from relative risk (RR) by simply subtracting the relative risk from one and expressing that outcome as a percentage (1-RR).

Some experts advise readers that this is safe to do if the prevalence of the event is low. While it is true that odds and probabilities of outcomes are usually similar if the event rate is low, when possible, we recommend calculating both the odds ratio reduction and the relative risk reduction in order to compare and determine if the difference is clinically meaningful. And determining if something is clinically meaningful is a judgment, and therefore whether a conversion of OR to RRR is distorted depends in part upon that judgment.

a = group 1 outcome occurred
b = group 1 outcome did not occur
c = group 2 outcome occurred
d = group 2 outcome did not occur

OR = (a/b)/(c/d)
Estimated RRR from OR (odds ratio reduction) = 1-OR

RR = (a/ group 1 n)/(c/ group 2 n)
RRR – 1-RR

 

 

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