IntroductionTraditional phase IIIb randomised trials may not reflect routine clinical practice. The Salford Lung Study in chronic obstructive pulmonary disease (SLS COPD) allowed broad inclusion criteria and followed patients in routine practice. We assessed whether SLS COPD approximated the England COPD population and evidence for a Hawthorne effect.MethodsThis observational cohort study compared patients with COPD in the usual care arm of SLS COPD (2012–2014) with matched non-trial patients with COPD in England from the Clinical Practice Research Datalink database. Generalisability was explored with baseline demographics, clinical and treatment variables; outcomes included COPD exacerbations in adjusted models and pretrial versus peritrial comparisons.ResultsTrial participants were younger (mean, 66.7 vs 71.1 years), more deprived (most deprived quintile, 51.5% vs 21.4%), more current smokers (47.5% vs 32.1%), with more severe Global initiative for chronic Obstructive Lung Disease stages but less comorbidity than non-trial patients. There were no material differences in other characteristics. Acute COPD exacerbation rates were high in the trial population (98.37th percentile).ConclusionThe trial population was similar to the non-trial COPD population. We observed some evidence of a Hawthorne effect, with more exacerbations recorded in trial patients; however, the largest effect was observed through behavioural changes in patients and general practitioner coding practices.

Background Analysis of competing risks is commonly achieved through a cause specific or a subdistribution framework using Cox or Fine & Gray models, respectively. The estimation of treatment effects in observational data is prone to unmeasured confounding which causes bias. There has been limited research into such biases in a competing risks framework. Methods We designed simulations to examine bias in the estimated treatment effect under Cox and Fine & Gray models with unmeasured confounding present. We varied the strength of the unmeasured confounding (i.e. the unmeasured variable’s effect on the probability of treatment and both outcome events) in different scenarios. Results In both the Cox and Fine & Gray models, correlation between the unmeasured confounder and the probability of treatment created biases in the same direction (upward/downward) as the effect of the unmeasured confounder on the event-of-interest. The association between correlation and bias is reversed if the unmeasured confounder affects the competing event. These effects are reversed for the bias on the treatment effect of the competing event and are amplified when there are uneven treatment arms. Conclusion The effect of unmeasured confounding on an event-of-interest or a competing event should not be overlooked in observational studies as strong correlations can lead to bias in treatment effect estimates and therefore cause inaccurate results to lead to false conclusions. This is true for cause specific perspective, but moreso for a subdistribution perspective. This can have ramifications if real-world treatment decisions rely on conclusions from these biased results. Graphical visualisation to aid in understanding the systems involved and potential confounders/events leading to sensitivity analyses that assumes unmeasured confounders exists should be performed to assess the robustness of results. Electronic supplementary material The online version of this article (10.1186/s12874-019-0808-7) contains supplementary material, which is available to authorized users.

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