# Effect Sizes, Overtaking Distance for Cyclists and Mandatory Helmet Legislation

I came across an interesting comment in an article by Alan Davies on The Urbanist. Davies discusses Ian Walker’s overtaking distance study which found a statistically significant association with helmet wearing and motor vehicle overtaking distance. Like a study I published last year, Davies did not find the results convincing with regards to helmets but found other factors like vehicle size and the cyclist’s position in the lane more important.

As expected, there were several comments defending Walker’s conclusions among those with anti-helmet views. A comment left by RidesToWork (who I believe is Bicycle Helmet Research Foundation editorial board member Dorothy Robinson) states

Many readers of this blog appear keen to dismiss the arguments than the difference in passing room might make a difference. Yet they don’t apply the same critical thinking to claims about helmet laws – such as Figure 2 of http://www.cyclehelmets.org/1228.html The effect, if there is one, is nothing like as clear as the effect of helmet wearing on passing distance.

I found this comment dubious, in part, because it links to an unfounded and misleading criticism of one my articles, but also because it’s quite a bold statement comparing the results from two very different studies. Walker’s analysis was a 2×5 ANOVA for motor vehicle overtaking distance while cycling in the UK with 2 levels of helmet wearing and 5 levels of distance to the kerb. Our study, used Poisson regression to model the rate of head and limb injury hospitalizations in NSW in the the three year period surrounding the helmet laws in 1991.

Note the Figure 2 mentioned in the comment has been dubiously manipulated through rescaling and shifting two time series so they overlap to produce a seemingly null effect. This seems to be a common tactic by anti-helmet advocates as I’ve discussed before here and here.

Regarding Robinson’s claim, the question is how can someone claim the results from one study are more “clear” compared to another, disparate study?

One method is to compare their effect sizes (ES) on some common scale. It is well known that large sample sizes can make unimportant differences statistically significant (the opposite is true for small sample sizes, i.e., important differences are not statistically significant). Take the one sample z-test for the population mean $\mu$, for example. The right tailed p-value converges to 0 as n tends to infinity for fixed values of $\bar{x}$ and $\sigma$, i.e.,

$\lim\limits_{n\rightarrow\infty}\mathcal{P}\left(Z>\sqrt{n}\dfrac{\bar{x}-\mu_0}{\sigma}\right)=0$

As a result, a significant p-value is possible even when $\bar{x}$ is infinitesimally close to $\mu_0$. For that reason, it is important not to overpower a study by choosing an excessively large sample size (Walker did just that in his study computing sample size based on 98% power instead of the usual 80% or 90%). Effect sizes are essentially the observed test statistic without the influence of sample size (conversely, it can be used to compute sample size a priori).

Jacob Cohen defined the difference in sample means divided by the sample standard deviation as an effect size for the two sample t-test, i.e.,

$d=\dfrac{\bar{x}_1-\bar{x}_2}{s}$.

Cohen further proposed operationally defined values of $d\in\{0.2,0.5,0.8\}$ as smallmedium and large effect sizes respectfully[1]. The reasoning given by Cohen regarding these values is

My intent was that medium ES represent an effect likely to be visible to the naked eye of a careful observer… I set small ES to be noticeably smaller than medium but not so small as to be trivial, and I set large ES to be the same distance above medium as small was below it.

The results from both Walker’s data and our study on mandatory helmet legislation can be converted to Cohen’s d. An $F$ statistic with 1 numerator degree freedom can be converted to Cohen’s d by

$d=2\sqrt{\dfrac{F}{df_d}}$

For helmet wearing, Walker reported $F_{1,2313}=8.71$ which correponds to $d=0.12$. This categorizes helmet wearing as a trivial, and therefore unimportant, effect size by Cohen’s definition.

It is also possible to convert to Cohen’s d for a linear model with a binary predictor $x$ (takes on values 0 or 1) and corresponding coefficient $\beta$ as

$d=\dfrac{|\beta|}{se(\beta)}\sqrt{\dfrac{1}{n_1}+\dfrac{1}{n_2}}$

where $n_1$ and $n_2$ are group sample sizes. In our re-analysis of Walker’s data, the adjusted effect of helmet wearing for Walker’s data is -0.058 respectively. There were 1206 and 1149 overtaking events when not wearing or wearing a helmet respectively which translates to $d=0.16$ (a more respectable value, although I doubt Walker or Robinson would ever agree with our results). Note that in my analysis, I did not use a square root transformation or remove 35 observations as Walker did as these considerations are unnecessary given the large sample size.

In our re-analysis of Walker’s paper, we also performed logistic regression to assess whether close overtaking was associated with helmet wearing as this is more relevant to cycling safety. Using the one meter rule as a cut point, we estimated a non-significant adjusted odds ratio of 1.13 (p=0.54). Odds ratios can also be transformed to Cohen’s d by

$d=\dfrac{\sqrt{3}log(OR)}{\pi}$

Using the adjusted odds ratio, we get $d=0.07$ which is again trivial by Cohen’s definition.

In our paper regarding mandatory helmet legislation, we reported a decline in bicycle related head injury relative to limb injury at the time of the NSW helmet law of 27.5% or 31% depending on whether arm or leg injuries were used as the comparator. These results can also be written as rate ratios of 0.725 or 0.69 respectively. If we assume the rate ratio here is equivalent to an odds ratio, Cohen’s d is 0.18 and 0.20 respectively.

Our analysis made the implicit assumption that no cyclist wore helmets pre-law and all cyclists wore helmets post-law. If, instead, the comparison was relative to the approximate 55% increase in helmet wearing in NSW, we get rate ratios of 0.56 and 0.51 and transformed Cohen d’s of 0.32 and 0.37.

I also published a paper last year that proposed operationally defined effect sizes for odds ratios that did not require transformation to Cohen’s d or make distributional assumptions regarding effect sizes. These were 1.22, 1.86 and 3.00 for small, medium and large odds ratios (or, equivalently, 0.82, 0.54 and 0.33).

These results suggest the helmet wearing effect from Walker’s data is trivial and the effect of helmet legislation is about a medium effect size. Of course, these results are from one cyclist and from one Australian state. So, over-generalizations should not be made without more data. However, note that Walker did repeat aspects of his first study comparing seven types of cyclists. The CASUAL type was the only one without a helmet with a mean overtaking distance of 117.61cm. This was approximately dead center of the means (range: 114.01cm – 122.12cm) suggesting the trivial helmet effect size from the original study was no coincidence.

1. Cohen J (1992) A power primerPsychological Bulletin 112: 155–159.

# Bicycle Helmet Research Foundation: A Reliable Resource?

The Bicycle Helmet Research Foundation (BHRF), and their website cyclehelmets.org, is an organization  that serves as a hub for material regarding the efficacy of either bicycle helmets or helmet legislation. The BHRF has an editorial board who is “responsible for the content” of the website which is “subjected to multi-disciplinary peer review”.

The BHRF is not affiliated with any university or academic society, yet seems to be influential in the discussion of the efficacy of bicycle helmets and whether jurisdictions should mandate their use. For example, the recent Queensland inquiry into cycling safety contained six footnotes linking to the BHRF website. In a submission to the Victorian Parliament, Colin Clarke cites four BHRF websites which he uses as evidence regarding the effectiveness of helmet legislation in that state. Jennifer Mindell, editor-in-chief of the Journal of Transport and Health, has used BHRF graphs to argue “cycle helmet use does not yield a population level effect“. This presentation was a collaboration with BHRF editorial board members Malcolm Wardlaw and John Franklin.

On many occasions, I have come across comment boards discussing virtually anything related to bicycle helmets where someone will write something like “everything you need to know about helmets is on this website cyclehelmets.org.” This has led me to wonder about the reliability of the BHRF as a resource for bicycle helmet-related research. Can they be trusted to present a fair assessment of the available scientific evidence regarding the efficacy of bicycle helmets and helmet legislation.

In their policy statement, the BHRF exists “to undertake, encourage, and spread the scientific study of the use of bicycle helmets.” This seems quite straightforward and aligns nicely with the name of their organization. However, the policy statement then seems to devolve into a rant against helmets or helmet legislation. They state

• closer investigation has revealed serious flaws in the evidence most frequently cited in favour of helmet effectiveness“,
• helmet laws have led almost universally to large declines in the number of people who cycle“, and
• the promotion of cycle helmets has been to brand cycling as an inherently hazardous activity.”

In my opinion, this comes across as anti-bicycle helmet advocacy and does not remotely resemble a research organization.

It is not uncommon for advocacy groups to be an integral part of research. For example, the declared purpose of the National Heart Foundation of Australia is to “reduce premature death and suffering from heart, stroke and blood vessel disease in Australia.” This is accomplished, in part, by funding cardiovascular research. The Amy Gillett Foundation is another advocacy organization directed at “reducing the incidence of death and injury of bike riders”. These two groups, and many more like them, are important sources for the spread of research to a broad, non-scientific audience. The big difference between these organizations and the BHRF is they serve to facilitate research and not as the final arbiter of an issue.

I suppose my initial impression could be wrong and they aren’t an anti-helmet advocacy group. So, what can be discerned from the material on their website beyond their policy statement? Are there well-reasoned arguments discussing both sides of the argument leading them to a clear conclusion? Are commentaries provided by experts in the field who actively publish in scientific journals? How does the BHRF editorial board stack up against established research journals? These are important concerns due to the ease at which ideas are proliferated over the internet and the difficulty in discerning the reliability of resources. As HIV researcher Dr Seth Kalichman puts it “The Internet has made pseudoscience as accessible, or perhaps even more accessible, than quality medical science.”

In a series of posts, I will discuss the BHRF editorial board, the supportive/skeptical articles they list, and articles that are missing from their website. I will also respond to their criticism of one of my papers (because how else is an academic supposed to address unfounded criticisms posted on someone’s website).