Abstract
Association studies form the backbone of biomedical research, with almost every effort in the field ultimately boiling down to a comparison between groups, coupled with some form of statistical test intended to determine whether or not any observed difference is more or less than would be expected by chance. Unfortunately, although the paradigm is powerful and frequently effective, it is often forgotten that false positive association can easily arise if there is any bias or systematic difference in the way in which study subjects are selected into the considered groups. To protect against such confounding, researchers generally try to match cases and controls for extraneous variables thought to correlate with the exposures of interest. However, if seemingly homogenously distributed exposures are actually more heterogeneous than appreciated, then matching may be inadequate and false positive results can still arise. In this review, we will illustrate these fundamental issues by considering the previously proposed relationship between month of birth and multiple sclerosis. This much discussed but false positive association serves as a reminder of just how heterogeneous even easily measured environmental risk factors can be, and how easily case control studies can be confounded by seemingly minor differences in ascertainment.Electronic supplementary materialThe online version of this article (doi:10.1007/s00415-014-7241-y) contains supplementary material, which is available to authorized users.
Highlights
Heterogeneity in the timing of birthThere are many local and personal factors that might influence an individual’s month of birth (MOB), it seems reasonable to imagine that across the population in any given country these effects would likely average out; intuitively it feels unlikely that the probability of being born in any given month would vary between different parts of the same country, and unlikely that this probability might be significantly different in different years
Association studies form the backbone of biomedical research, with almost every effort in the field boiling down to a comparison between groups, coupled with some form of statistical test intended to determine whether or not any observed difference is more or less than would be expected by chance
It is unsurprising that the studies that have looked for association between multiple sclerosis and month of birth (MOB) have all assumed some degree of such homogeneity [2, 8, 11, 12, 16, 23,24,25, 27,28,29, 32, 33]
Summary
There are many local and personal factors that might influence an individual’s MOB, it seems reasonable to imagine that across the population in any given country these effects would likely average out; intuitively it feels unlikely that the probability of being born in any given month would vary between different parts of the same country, and unlikely that this probability might be significantly different in different years. Each of these 824 records is statistically significantly different from that expected assuming a constant birth rate, with all but three records remaining significant even after stringent Bonferroni correction (i.e. having p \ 6 9 10-5). Cohorts collected through the efforts of interested researchers are likely to be even less representative of the country as a whole, as such collections are invariably biased in favour of prevalent cases from regions local to the interested investigator(s)
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