Skanda Rajasundaram1,2, Dalia Abdulhussein2*, Minak Bhalla3
Received:February 5, 2022; Published:February 21, 2022
Corresponding author: Dalia Abdulhussein, The Imperial College Ophthalmic Research Group (ICROG), Imperial College London, London, United Kingdom
Understanding the relationship between an exposure and an outcome of interest is the central challenge of most research in the medical sciences, including in Ophthalmology. The exposure may be aetiological in nature, taking the form of a putative risk biological factor, or therapeutic, in the form of a proposed treatment. Randomised Controlled Trials (RCTs) sit at the top of the canonical hierarchy of evidence, primarily because the process of randomisation balances both known and unknown confounders between the exposed vs the unexposed arm, where the ‘exposure’ is a treatment of some kind [1,2]. Given a large enough sample size, the two arms will on average differ in only one respect: allocation to receive or not to receive the exposure under investigation. Consequently, we can attribute any differences in outcomes in the exposed vs unexposed arms as being a causal effect of the exposure itself. In essence, we can attribute causality to the exposure because the very process of randomisation minimizes bias due to confounding.
In practice, however, RCTs are extremely resource-intensive in terms of time, money and personnel. We could not feasibly perform an RCT for every possible exposure-outcome relationship of ophthalmological interest. Indeed, for many aetiological exposures, it would be unethical to perform an RCT. Take, for example, attempting to measure the effects of any exposure that we have good prior evidence or reasonable inclination to believe, has harmful effects: smoking, diabetes, hyperlipidaemia, raised intra-ocular pressure (IOP), corneal thinning, prior ophthalmic surgery. Clearly, it would be unethical to take a population of people and randomise half of them to receive an intervention that increased their intra-ocular pressure, simply because we wanted to measure the causal effect and corresponding clinical outcomes of having raised IOP. Yet the clinical imperative to understand the downstream clinical sequalae of raised IOP is no less pertinent. Accordingly, despite the absence of randomisation and the inherent vulnerability to confounding (amongst a range of other biases), we are left to observe differences in IOP that arise naturally in the population. We then look for associated factors in individuals with raised IOP that might cause the raised IOP in the first place, as well as ocular sequelae that might result as a consequence of the raised IOP. Ethics, economics and effort explain why observational studies continue to be important tools for studying exposure-outcome relationships in Ophthalmological research. Moreover, a well-designed observational study can be highly informative. We illustrate this below using a recent study published in JAMA Ophthalmology.
Bai et al. conducted a retrospective cohort analysis of medical records in Olmsted County, Minnesota in order to evaluate the risk of diabetes-associated ocular complications (DAOCs) amongst children with Type 2 Diabetes Mellitus (T2DM) vs Type 1 Diabetes Mellitus (T1DM) during a 50-year period . The study was motivated by the increasing prevalence of T2DM diabetic retinopathy in children and the relative paucity of evidence guiding the screening and management of T2DM diabetic retinopathy in comparison to that of T1DM diabetic retinopathy. In comparison to the risk in children with T1DM, there was a 1.88-fold increased risk of developing any diabetic retinopathy, a 2.33-fold increased risk of developing proliferative diabetic retinopathy and a 4.06-fold increased risk of requiring pars plana vitrectomy in children with T2DM, based on their univariate Cox regression model. The authors suggest that these results may warrant the need for ophthalmoscopic examination of children with T2DM at least as frequently, if not more frequently, than children with T1DM in order to prevent DAOCs. Firstly, it’s important to acknowledge the merits in this study. The study investigates a clinically important question for which little published data currently exist. The fact that such an analysis was possible is a testament to the consistent record-keeping over a 50-year period in Olmsted County. However, there are a no. of important limitations, some of which have been discussed previously. For example, Sun comments that Kaplan- Meier estimates were unstable beyond 15 years of follow-up owing to the small numbers of children with T2DM, insufficient duration of follow-up and right-censoring of observations, either due to participants dropping out of the study (e.g., moving away from Olmsted County) or because they did not incur any DAOC by the time the study was terminated . Consequently, the longer-term estimates of DAOC risk, arguably the most important estimates given the progressive and chronic nature of T2DM, are least reliable. Further, Sun highlights the challenges of trying to control for confounding due to differences in HbA1c levels between T1DM and T2DM populations, due to insufficient recording of HbA1c prior to 1996. But attempting to statistically adjust for one parameter of possible differences in disease severity between two exposure groups can only account for one, imperfectly measured parameter of disease severity, itself only one aspect of possible confounding. Bai et al. also produce sex and ethnicity-adjusted risk estimates. This approach does nothing to address known confounders that cannot feasibly be included as co-variates in a multivariable model and, more importantly, does nothing to account for the multitude of unknown confounders that may be exacerbating or attenuating the apparent difference in rates of DAOCs in children with T2DM vs T1DM. By definition, unknown confounders are unknown, and even the most elaborate statistical models are helpless to address them. Indeed, given the small event rate, as reflected by the presence of only 17 of the 64 children with T2DM developing any DAOC over the 50-year period, the presence of only a small degree of confounding can markedly skew final point estimates toward the exposure of interest (T2DM), comparator group (T1DM) or null. Moreover, we cannot know in which direction unknown confounding will skew are estimates because, again, it’s unknown(!).
Bai et al. deserve credit for investigating the diabetes-associated ocular sequalae amongst the largest sample and longest follow-up of children diagnosed with diabetes mellitus. Nevertheless, despite yielding statistically significant associations, observational studies of this or indeed any design are typically incapable of demonstrating causality between an exposure (T2DM) and outcome of interest (increased risk of DAOCs). The beauty of randomisation is that it offers a means of quelling the distortive effect of both known and unknown confounding, thus facilitating a degree of causal inference not attainable by observational studies [1,2]. In upcoming articles, we describe how the advent of large-scale genetic data can introduce randomisation to our attempts to disentangle causation from association in observational epidemiology, where randomised controlled trials are not ethically or practically feasible.
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