Researchers do not always make the same decisions concerning confounding factors, so the method used to control for confounding is an important source of heterogeneity between studies. There may be differences in the confounding factors considered, the method used to control for confounding and the precise way in which confounding factors were measured and included in analyses. Many (but not all) NRS describe the confounding factors that were considered and whether confounding was taken into account by the study design or analysis; most also report the baseline characteristics of the groups being compared. However, assessing what researchers actually did to control for confounding may be difficult; far fewer studies describe precisely how confounding factors were measured or fitted as covariates in regression models (e.g. as a continuous, ordinal, or grouped categorical variable).
Some specific suggestions for assessing risk of selection bias are:
At the stage of writing the protocol, list potential confounding factors;
Identify the confounding factors that the researchers have considered and those that have been omitted. Note the ways in which they have been measured (the ability to control for a confounding factor depends on the precision with which the factor is measured);
Assess the balance between comparator groups at baseline with respect to the main prognostic or confounding factors;
Identify what researchers did to control for selection bias, i.e. any design features used for this purpose (e.g. matching or restriction to particular subgroups) and the methods of analysis (e.g. stratification or regression modelling with propensity scores or covariates).
There is no established method for identifying a pre-specified set of important confounders. Listing potential confounding factors should certainly be done ‘independently’ and, one might argue, ‘systematically’. The list should not be generated solely on the basis of factors considered in primary studies included in the review (at least, not without some form of independent validation), since the number of potential confounders is likely to increase over time (hence, older studies may be out of date) and researchers themselves may simply choose to measure confounders considered in previous studies (hence, such a list could be selective). (Researchers investigating aetiological associations often do not explain their choice of confounding factors (Pocock 2004).) Rather, the list should be based on evidence (although undertaking a systematic review to identify all potential prognostic factors is extreme) and expert opinion from members of the review team and advisors.
Reporting results of assessments of confounders in a Cochrane review may best be achieved by creating additional tables listing the pre-stated confounders as columns, the studies as rows, and indicating whether each study: (i) restricted participant selection so that all groups had the same value for the confounder (e.g. restricting the study to male participants only); (ii) demonstrated balance between groups for the confounder; (iii) matched on the confounder; or (iv) adjusted for the confounder in statistical analyses to quantify the effect size.