Investigating risk of bias: special considerations for test-treatment interventions




Poster session 1


Sunday 16 September 2018 - 12:30 to 14:00

All authors in correct order:

Ochodo E1, Naidoo S1, Schumacher S2, Nsanzabana C2, Young T3, Mallett S4, Cobelens F5, Bossuyt P6, Nicol M7, Deeks J4
1 Stellenbosch University, South Africa
2 FIND, Switzerland
3 Stellenbosch University, Cochrane South Arica, South Africa
4 University of Birmingham, UK
5 Amsterdam Institute for Global Health and Development, The Netherlands
6 University of Amsterdam, The Netherlands
7 University of Cape Town, South Africa
Presenting author and contact person

Presenting author:

Eleanor Ochodo

Contact person:

Abstract text
Evaluating the impact of diagnostic tests on patient health is complex. Multiple steps are involved between the decision to administer a test and the effect on a patient's health and a broad range of outcomes can be measured in studies that evaluate the impact of tests on a patient's health. Various forms of bias can be introduced along this pathway. The revised Cochrane 'Risk of bias' tool for randomized studies (RoB 2.0), and that for non-randomised studies of interventions (ROBINS-I), focus on risk of bias (RoB) assessment in general but do not point out issues specific to test-treatment interventions, which are a distinct type of complex intervention.

We describe our experience of using the Cochrane RoB tools to investigate bias in primary studies evaluating the impact of malaria rapid diagnostic tests (RDTs) on patient-important outcomes.

We searched relevant electronic databases and grey literature and included studies based on predefined inclusion criteria. We included any primary randomized or non-randomized study that compared a malaria RDT with one or more other diagnostic tests for malaria, with the aim of measuring the impact of these tests or strategies on patient-important outcomes. We are currently extracting data and using the RoB 2.0 tool for randomized studies and the ROBINS-I for non-randomised studies of interventions to assess the RoB of included test-treatment studies. Two authors have reviewed the search output and are currently extracting data and assessing RoB independently, resolving any disagreements by consensus. We will present our assessment of RoB across each domain and overall RoB results for included studies narratively, graphically and using descriptive statistics.

Our data set contains 27 randomised studies and 22 non-randomised studies. During the conference we will present our risk of bias results and discuss special considerations for investigating the risk of bias in test-treatment studies.

The complex interactions between diagnostic test results, associated behaviours of clinicians and patients in response to the test results and linkage to treatment need to be taken into account when assessing the risk of bias of test-treatment interventions.

Patient or healthcare consumer involvement:
The included studies evaluate patient-important outcomes.

Relevance to patients and consumers: 

We are reviewing methods for investigating risk of bias in studies evaluating the impact of diagnostic tests on patients (Test-treatment studies). We have included any primary study evaluating at least one patient-important outcome; defined as outcomes that directly reflect how an individual feels, functions or survives and outcomes that lie on the causal pathway that may precede, predict or affect a patient's health. Guidance on how to evaluate risk of bias in such studies will be useful to those using evidence to make decisions on which tests to implement for better patient outcomes. No patient helped prepare this statement.