Evidence-based Flying: A New Paradigm for Frequent Flyers

L. Citrome


Int J Clin Pract. 2010;64(6):667-668. 

Abstract and Introduction


In recent years, increasing emphasis has been placed on the practice of evidence-based medicine (EBM). Originally proposed by Sackett et al., EBM exhorts clinicians to incorporate into medical decision-making the best available research evidence together with an individualised assessment and simultaneously considering the preferences of the patient.[1] One of the important tools of EBM is the notion of number needed to treat (NNT).[2] The concept of EBM and NNT can be easily translated to help with other human activities, such as airplane travel, especially for frequent flyers. Academics 'on the circuit' have their own extensive travel experience (albeit anecdotal), and fairly well defined values and preferences when it comes to airline selection. What has been missing in the flyer decision-making process is robust research evidence. This obstacle to the proper practice of evidence-based flying (EBF) is now disappearing, thanks to cut-throat competition amongst the airlines and advertisements that tout low rates of departure delays.

Introducing Number Needed to Fly (NNF)

With the disclosure of on-time departure rates, this dichotomous outcome can be used to calculate the number of flights one has to take with one airline vs. another before expecting to encounter (or avoid) one additional departure delay. The data from Table 1 were extracted from a newspaper advertisement in USA Today (ostensibly the most commonly read national newspaper amongst US frequent flyers).[3] NNF was determined by taking the difference in on-time departure rates between the two airlines of interest, calculating the reciprocal, and then rounding up to the next highest whole number. Caveats to this crude measure is that the specific airport one is flying out of is not considered – adjustment for this baseline risk (and others) requires methodological refinements that have yet to be worked out with the data currently publically available. Regardless of the emphatic claims by each airline, the NNF may not be entirely compelling; for example, the comparison between US Airways vs. Delta reveals that about 30 flights would need to be taken to encounter one additional delayed departure. One can easily calculate other pair-wise comparisons for NNF. If the denominators are known, a 95% confidence interval can also be calculated. Although the NNF for the comparison between US Airways (ranked #1 on this list) vs. American (ranked last) is 10, this may still not be a compelling effect size given the perks an American Airlines frequent flyer may enjoy. This latter point can be quantified if one examines another metric outlined below.

Introducing Number Needed to Upgrade (NNU)

This statistic can only be guesstimated as the actual rates of achieving a successful upgrade from coach to first class can be highly variable, depending on baseline factors such as city from which one is flying, time of day, day of week, class of ticket purchased and individual traveller characteristics such as frequent flyer loyalty club status level. In this author's experience, flying his airline of choice the NNU is in the range of 2–4. The likelihood to be upgraded or delayed (LUD) can thus be calculated, with the result being of some utility in flying decision-making.

In any of these calculations, using any frequent flyer outcome measures, the personal preferences and values of the flyer are key to making flyer-relevant decisions (Figure 1). These include the type of food that is served (free or not), pillow and blanket policy, and so on. Cost considerations (same as alternative airlines or not, extras for checked baggage) and availability of flights also enter the decision-making process. Using baseline characteristics to refine our calculations will help make the NNF and NNU estimates more precise. We all look forward to greater transparency and the posting of delay and upgrade rates in publically accessible airline registries, further enhancing the amount of data available to help us make wise decisions.

Figure 1.

What is evidence-based flying (EBF)?


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