9Bias Adjustment Methods

9.1 Introduction

Ideally all RCTs included in a meta-analysis or network meta-analysis will have been conducted with a high standard of methodological rigour on exactly the target population we are interested in making treatment recommendations and decisions for. In practise, however, this is unlikely to be the case, and the results of individual trials may provide biased estimates of the relative treatment effects that we would expect to see in the target population of interest. Bias arises as a result of effect-modifying mechanisms, in other words interactions between relative treatment effects and trial-level variables that have not been accounted for.

As in Chapter 8, we distinguish between two types of interaction effect, those that threaten the external validity of a trial and those that threaten the internal validity. Where effect modifiers have been measured and reported, covariate adjustment using meta-regression techniques may be used to adjust for bias caused by issues to do with external validity (Chapter 8). In this chapter we focus on interaction effects due to deficiencies in the way the trial was conducted or reported, which threaten internal validity (Rothman et al., 2012). Here, the trial delivers a biased estimate of the treatment effects in the target population for the trial, defined by that trial’s inclusion/exclusion criteria. Typically, biases due to lack of internal validity are considered to vary randomly in size over trials, ...

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