This is an archived version of the Handbook. For the current version, please go to or search for this chapter here.  Comprehensiveness of search strategy

When a review aims to include randomized trials only, a key principle of searching for eligible studies is that review authors should try as hard as possible to identify all randomized trials of the review question that have ever been started. Therefore, review authors are recommended to search trial registers, conference abstracts, grey literature, etc, as well as standard bibliographic databases such as MEDLINE, PUBMED, EMBASE (see Chapter 6, Section 6.2). It is argued that a systematic review needs to search comprehensively in order to avoid publication biases. It is easy to argue that authors of a review that includes NRS should do the same (Petticrew 2001). However, it is important to set out the premises underpinning the original rationale for a comprehensive search and to consider very carefully whether they apply to reviews of NRS. The premises are as follows.

a)  A finite population exists of randomized trials that investigate the review question.

b)  All randomized trials in this population can be identified through a search that is sufficiently comprehensive because randomized trials are relatively easily identified, registers of them are available, and they are difficult to do without funding and ethics approval, which also create an ‘audit trail’ (Chan 2004).

c)  All randomized trials in this population, if well conducted, provide valuable information.

d)  Ease of access to information about these randomized trials is related to their findings, so that the most readily identified trials may be a biased subset. This is publication bias: studies with statistically significant and favourable findings are more likely to be published in accessible places (see Chapter 10, Section 10.2). Because smaller studies are less likely to produce such findings, failure to identify all studies may result in funnel plot asymmetry. An unbiased answer can in theory be reached by identifying all randomized trials, i.e. by a comprehensive search to uncover the small, non-significant or unfavourable studies. Smaller studies may also suffer differentially from other biases, giving rise to an alternative cause of funnel plot asymmetry. The risks of these biases are reasonably well understood and may be assessed (Chapter 10, Section 10.4).

It is not clear that these premises apply equally to NRS.


Section points out that NRS include diverse designs, and that there is difficulty in categorizing them. Even if review authors are able to set specific study design criteria against which potential NRS should be assessed for inclusion, many of the potentially eligible NRS will report insufficient information to allow them to be classified.


There is a further problem in defining exactly when a NRS comes into existence. For example, is a cohort study that has collected data on the interventions and outcome of interest, but that has not examined their association, an eligible NRS? Is computer output in a filing cabinet that includes a calculated odds ratio for the relevant association an eligible NRS? Consequently, it is difficult to define a ‘finite population of NRS’ for a particular review question. Some NRS that have been done may not be traceable at all, i.e. they are not to be found even in the proverbial ‘bottom drawer’.


Notwithstanding the problems in defining what constitutes an eligible NRS, the actual identification of NRS provides important challenges. This is not just to do with poor reporting but also to do with:


There is no evidence that reporting biases affect randomized trials and NRS differentially. However, it is difficult to believe that reporting biases could affect NRS less than randomized trials, given the increasing number of features associated with carrying out and reporting randomized trials that act to prevent reporting biases which are frequently absent in NRS (pre-specified protocol, ethical approval including progress and final reports, the CONSORT statement (Moher 2001), trial registers and indexing of publication type in bibliographic databases). Unlike the situation for randomized trials, the likely magnitude and determinants of publication bias are not known.


The benefits of comprehensive searching for NRS are unclear, and this is a topic that requires further research. It is possible that the studies which are the hardest to find may be the most biased, if being hard to identify relates to poor design and small size. With reviews of randomized trials, comprehensive searching offers potential protection against bias because a defined population of eligible studies exists, so small studies with non-significant findings should, ultimately, be identified. With reviews of NRS, even if a theoretical finite population of eligible studies can be defined, one does not have similar confidence that missing studies with non-significant findings can be identified.