P0811CONFUSION BETWEEN ETIOLOGY AND PREDICTION IN CLINICAL OBSERVATIONAL STUDIES; HOW OFTEN DOES IT ARISE AND HOW CAN WE AVOID IT? A SCOPING REVIEW
Authors: Ramspek Chava , Dekker Friedo W , Van Diepen Merel 
Published: June 2020

Background and Aims

In etiological research the aim is to uncover the causal effect of a specific exposure on an outcome. The aim in prediction research is to predict an outcome with the best accuracy, irrespective of possible causality. Although in observational research both types of studies use similar statistical methods (generally multivariable modelling), the interpretation differs. For example, when researching the causal effect of BMI on kidney failure, it is important to correct for confounders such as age (i.e. factors that causally affect both BMI and kidney failure). Judgement of confounders must be made on pre-existing knowledge. If we select confounders based on the data, we might also correct for hypertension and atherosclerosis (as they are associated with BMI and kidney failure). As this correction is in the causal pathway we would erroneously conclude that BMI does not affect the risk of kidney failure. Vice versa, a mortality prediction model for dialysis patients may include antihypertensive medication use as a predictor. It would be wrong to conclude that patients should discontinue this medication to improve prognosis; the medication use is a marker for a certain health status and we cannot interpret it in a causal manner. Similar to these examples, we’ve found that characteristics from etiology and prediction are often confused, leaving us with studies that may be misinterpreted. The aim of the current study is to quantify the amount of confusion between etiology and prediction in clinical observational studies and identify common mistakes that lead to this confusion.

Method

Studies published in January 2018 in the top journals of four distinct medical fields: General & Internal medicine, Surgery, Cardiology and Nephrology were screened for inclusion. Original research studies on observational cohorts of humans were included. A list of key study characteristics of etiological and prediction research was developed by CLR and MvD in an iterative fashion. From these characteristics signaling questions for confusion were developed to score included studies.

Results

The developed key study characteristics of etiology and prediction are shown in the table. In total, 286 studies were screened of which 123 were included. Overall, 27% (n=33) of included articles contained some form of confusion between etiology and prediction. In the figure the journal impact factor is mapped against proportion of confusion per included journal. We can see a trend that as impact factors increase the amount of confusion decreases. In etiological studies, the most frequent (n=15) form of confusion was adjustment for variables based on predictive performance in the data, instead of known causal structure. The majority selected ‘confounders’ purely based on p-values and therefore potentially adjusted for intermediate variables, resulting in incorrect effect estimates. Another mistake in etiological studies (n=5) was the reporting of predictive performance measures such as the C-statistic for an etiological model. In prediction studies the most confusion occurred in the discussion (n=14). Seven studies interpreted predictors causally, for example by concluding that these predictors should be modified in order to improve patient outcomes. As the effect estimates of these studies do not account for confounding, this interpretation is invalid. Lastly, seven studies mention residual confounding as a limitation, which is only a problem in etiological research.

Conclusion

Confusion between etiology and prediction is a wide-spread methodological flaw in medical observational studies, particularly those published in lower impact clinical journals. As confusion may lead to erroneous conclusions, the distinction between causal and predictive research deserves more attention in medical research and scientific education.