Hi Liam,

Certainly the RoB II tool would not be applicable.

Perhaps it may be more practical to assess your bias based on key domains that would influence the outcome of your study instead of solely relying on a structured tool.

Regards,

Godfrey Habil Mudhune I misspoke before, I was intending to use the RoB II for the few RCTs that I am including. Any suggestions for resources on the advice you mentioned?

Hi Liam,

You could opt to thematically assess bias in three domains: selection bias, measurement bias and confounding. From this you may develop a scoring pattern for ranking the studies, obviously depending on the nature of your outcome.

Alternatively, perhaps you may want to take a look at the Newcastle-Ottawa Tool. If modified, this tool may be applicable in your study.

Regards,

Hi Liam, how did you solve the problem you were asking about? I've a similar situation in the risk of bias assessment for a systematic review I'm conducting

Studies in which: (i) units are non-randomly allocated to a group that receives an intervention or to an alternative group that receives nothing or a comparator intervention; and (ii) at least one measurement of the outcome variable is made in both groups before and after implementation of the intervention are often known as controlled before-after studies (CBAs) (Eccles et al 2003, Polus et al 2017). The comparator group(s) may be contemporaneous or not. This category also includes controlled interrupted time series (CITSs) (Lopez Bernal et al 2018). The units included in the study may be individuals, clusters of individuals, or administrative units. The intervention may be at the level of the individual unit or at some aggregate (cluster) level. Studies may follow the same units over time (sometimes referred to as within-person or within-unit longitudinal designs) or look at (possibly) different units at the different time points (sometimes referred to as repeated cross-sectional designs, where the population of interest may be defined geographically or through interaction with a health service, and may include different individuals over time).

A common analysis of CBA studies is a ‘difference in differences’ analysis, in which before-after differences in the outcome (possibly averaged over multiple units) are contrasted between the intervention and comparator groups. The outcome measurements before and after intervention may be single observations, means, or measures of trend or pattern. The assumption underlying such an analysis is that the before-after change in the intervention group is equivalent to the before-after change in the comparator group, except for any causal effects of the intervention; that is, that the pre-post intervention difference in the comparator group reflects what would have happened in the intervention group had the intervention not taken place.