Using negative control outcomes and difference-in-differences to estimate treatment effects in an entirely treated cohort: the effect of ivacaftor in cystic fibrosis.

When an entire cohort of patients receives a treatment it is difficult to estimate the treatment effect in the treated because there are no directly comparable untreated patients. Attempts can be made to find a suitable control group, (e.g. historical controls), but underlying differences between the treated and untreated can result in bias. We show how negative control outcomes (NCO) combined with difference-in-differences analysis can be used to assess bias in treatment effect estimates and obtain unbiased estimates under certain assumptions. Causal diagrams and potential outcomes are used to explain the methods and assumptions. We apply the methods to UK Cystic Fibrosis (CF) Registry data to investigate the effect of ivacaftor, introduced in 2012 for a subset of the CF population with a particular genotype, on lung function and days receiving intravenous antibiotics (IV days). We consider two NCOs: outcomes measured in the pre-ivacaftor period and outcomes in individuals ineligible for ivacaftor due to their genotype. Ivacaftor was found to improve lung function in year one (~6.5 increase in FEV1%), was associated with reduced lung function decline (~0.5 decrease in annual FEV1% decline, though confidence intervals include 0), and reduced the rate of IV days (~60% over 3 years).