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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More on Attrition Bias: Update on Missing Data Points: Difference or No Difference — Does it Matter?

Attrition Bias Update 01/14/2014: Missing Data Points: Difference or No Difference — Does it Matter?

A colleague recently wrote us to ask us more about attrition bias. We shared with him that the short answer is that there is less conclusive research on attrition bias than on other key biases. Attrition does not necessarily mean that attrition bias is present and distorting statistically significant results. Attrition may simply result in a smaller sample size which, depending upon how small the remaining population is, may be more prone to chance due to outliers or false non-significant findings due to lack of power.

If randomization successfully results in balanced groups, if blinding is successful including concealed allocation of patients to their study groups, if adherence is high, if protocol deviations are balanced and low, if co-interventions are balanced, if censoring rules are used which are unbiased, and if there are no differences between the groups except for the interventions studied, then it may be reasonable to conclude that attrition bias is not present even if attrition rates are large. Balanced baseline comparisons between completers provides further support for such a conclusion as does comparability in reasons for discontinuation, especially if many categories are reported.

On the other hand, other biases may result in attrition bias. For example, imagine a comparison of an active agent to a placebo in a situation in which blinding is not successful. A physician might encourage his or her patient to drop out of a study if they know the patient is on placebo, resulting in biased attrition that, in sufficient numbers, would potentially distort the results from what they would otherwise have been.

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Demo: Critical Appraisal of a Randomized Controlled Trial

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Demo: Critical Appraisal of a Randomized Controlled Trial

We recently had a great opportunity to listen to a live demonstration of a critical appraisal of a randomized controlled trial conducted by Dr. Brian Alper, Founder of DynaMed; Vice President of EBM Research and Development, Quality & Standards at EBSCO Information Services.

Dr. Alper is extremely knowledgeable about critical appraisal and does an outstanding job clearly describing key issues concerning his selected study for review. We are fortunate to have permission to share the recorded webinar with you.

“Learn How to Critically Appraise a Randomized Trial with Brian S. Alper, MD, MSPH, FAAFP”

Below are details of how to access the study that was used in the demo and how to access the webinar itself.

The Study
The study used for the demonstration is Primary Prevention Of Cardiovascular Disease with a Mediterranean Diet.  Full citation is here—

Estruch R, Ros E, Salas-Salvadó J, Covas MI, Corella D, Arós F, Gómez-Gracia E, Ruiz-Gutiérrez V, Fiol M, Lapetra J, Lamuela-Raventos RM, Serra-Majem L, Pintó X, Basora J, Muñoz MA, Sorlí JV, Martínez JA, Martínez-González MA; PREDIMED Study Investigators. Primary prevention of cardiovascular disease with a Mediterranean diet. N Engl J Med. 2013 Apr 4;368(14):1279-90. doi: 10.1056/NEJMoa1200303. Epub 2013 Feb 25. PubMed PMID: 23432189.

Access to the study for the critical appraisal demo is available here:

http://www.ncbi.nlm.nih.gov/pubmed/?term=N+Engl+J+Med+2013%3B+368%3A1279-1290

The Webinar: 1 Hour

For those of you who have the ability to play WebEx files or can download the software to do so, the webinar can be accessed here—

https://ebsco.webex.com/ebsco/lsr.php?AT=pb&SP=TC&rID=22616757&rKey=f7e98d3414abc8ca&act=pb

Important: It takes about 60 seconds before the webinar starts. (Be sure your sound is on.)

More Chances to Learn about Critical Appraisal

There is a wealth of freely available information to help you both learn and accomplish critical appraisal tasks as well as other evidence-based quality improvement activities. Our website is www.delfini.org. We also have a little book available for purchase for which we are getting rave reviews and which is now being used to train medical and pharmacy residents and is being used in medical, pharmacy and nursing schools.

Delfini Evidence-based Practice Series Guide Book

Basics for Evaluating Medical Research Studies: A Simplified Approach (And Why Your Patients Need You to Know This)

Find our book at—http://www.delfinigrouppublishing.com/ or on our website at www.delfini.org (see Books).

Delfini Recommends DynaMed™

We highly recommend DynaMed.  Although we urge readers to be aware that there is variation in all medical information sources, as members of the DynaMed editorial board (unpaid), we have opportunity to participate in establishing review criteria as well as getting a closer look into methods, staff skills, review outcomes, etc., and we think that DynaMed is a great resource. Depending upon our clinical question and project, DynaMed is often our starting point.

About DynaMed™ from the DynaMed Website

DynaMed™ is a clinical reference tool created by physicians for physicians and other health care professionals for use at the point-of-care. With clinically-organized summaries for more than 3,200 topics, DynaMed provides the latest content and resources with validity, relevance and convenience, making DynaMed an indispensable resource for answering most clinical questions during practice.

Updated daily, DynaMed editors monitor the content of over 500 medical journals on a daily basis. Each article is evaluated for clinical relevance and scientific validity. The new evidence is then integrated with existing content, and overall conclusions are changed as appropriate, representing a synthesis of the best available evidence. Through this process of Systematic Literature Surveillance, the best available evidence determines the content of DynaMed.

Who Uses DynaMed

DynaMed is used in hospitals, medical schools, residency programs, group practices and by individual clinicians supporting physicians, physician assistants, nurses, nurse practitioners, pharmacists, physical therapists, medical researchers, students, teachers and numerous other health care professionals at the point-of-care.

https://dynamed.ebscohost.com/

 

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Why Statements About Confidence Intervals Often Result in Confusion Rather Than Confidence

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Why Statements About Confidence Intervals Often Result in Confusion Rather Than Confidence

A recent paper by McCormack reminds us that authors may mislead readers by making unwarranted “all-or-none” statements and that readers should be mindful of this and carefully examine confidence intervals.

When examining results of a valid study, confidence intervals (CIs) provide much more information than p-values. The results are statistically significant if a confidence interval does not touch the line of no difference (zero in the case of measures of outcomes expressed as percentages such as absolute risk reduction and relative risk reduction and 1 in the case of ratios such as relative risk and odds ratios). However, in addition to providing information about statistical significance, confidence intervals also provide a plausible range for possibly true results within a margin of chance (5 percent in the case of a 95% CI). While the actual calculated outcome (i.e., the point estimate) is “the most likely to be true” result within the confidence interval, having this range enables readers to judge, in their opinion, if statistically significant results are clinically meaningful.

However, as McCormack points out, authors frequently do not provide useful interpretation of the confidence intervals, and authors at times report different conclusions from similar data. McCormack presents several cases that illustrate this problem, and this paper is worth reading.

As an illustration, assume two hypothetical studies report very similar results. In the first study of drug A versus drug B, the relative risk for mortality was 0.9, 95% CI (0.80 to 1.05). The authors might state that there was no difference in mortality between the two drugs because the difference is not statistically significant. However, the upper confidence interval is close to the line of no difference and so the confidence interval tells us that it is possible that a difference would have been found if more people were studied, so that statement is misleading. A better statement for the first study would include the confidence intervals and a neutral interpretation of what the results for mortality might mean. Example—

“The relative risk for overall mortality with drug A compared to placebo was 0.9, 95% CI (0.80 to 1.05). The confidence intervals tell us that Drug A may reduce mortality by up to a relative 20% (i.e., the relative risk reduction), but may increase mortality, compared to Drug B, by approximately 5%.”

In a second study with similar populations and interventions, the relative risk for mortality might be 0.93, 95% CI (0.83 to 0.99). In this case, some authors might state, “Drug A reduces mortality.” A better statement for this second hypothetical study would ensure that the reader knows that the upper confidence interval is close to the line of no difference and, therefore, is close to non-significance. Example—

“Although the mortality difference is statistically significant, the confidence interval indicates that the relative risk reduction may be as great as 17% but may be as small as 1%.”

The Bottom Line

  1. Remember that p-values refer only to statistical significance and confidence intervals are needed to evaluate clinical significance.
  2. Watch out for statements containing the words “no difference” in the reporting of study results. A finding of no statistically significant difference may be a product of too few people studied (or insufficient time).
  3. Watch out for statements implying meaningful differences between groups when one of the confidence intervals approaches the line of no difference.
  4. None of this means anything unless the study is valid. Remember that bias tends to favor the intervention under study.

If authors do not provide you with confidence intervals, you may be able to compute them yourself, if they have supplied you with sufficient data, using an online confidence interval calculator. For our favorites, search “confidence intervals” at our web links page: http://www.delfini.org/delfiniWebSources.htm

Reference

McCormack J, Vandermeer B, Allan GM. How confidence intervals become confusion intervals. BMC Med Res Methodol. 2013 Oct 31;13(1):134. [Epub ahead of print] PubMed PMID: 24172248.

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Biostatistical Help for Critical Appraisers

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Book Recommendation: Biostatistics for Dummies by John C. Pezzullo, PhD

We highly recommend this book.  In short—

  • An excellent resource
  • Useful to critical appraisers because it can help us understand why certain common statistical tests are used in studies
  • Provides a needed resource for answering questions about various tests
  • Helpful explanations
  • Written in a clear style with the goal of making difficult information accessible and understandable
  • Friendly style due to author’s wit and charm, and the reassurance he provides along the way

Read our full review here. Go to Amazon page and full customer reviews here.

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