ARRIVE Essential - Outcome measures
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ARRIVE Essential 10 - Item 6 - Outcome measures
6a. Clearly define all outcome measures assessed (e.g. cell death, molecular markers, or behavioural changes).
An outcome measure (also known as a dependent variable or a response variable) is any variable recorded during a study (e.g. volume of damaged tissue, number of dead cells, specific molecular marker) to assess the effects of a treatment or experimental intervention. Outcome measures may be important for characterising a sample (e.g. baseline data) or for describing complex responses (e.g. ‘haemodynamic’ outcome measures including heart rate, blood pressure, central venous pressure, and cardiac output).
Explicitly describe what was measured, especially when measures can be operationalised in different ways. For example, activity could be recorded as time spent moving or distance travelled. Where possible, the recording of outcome measures should be made in an unbiased manner (e.g. blinded to the treatment allocation of each experimental group; see item 2.1.7 Blinding). Specify how the outcome measure(s) assessed are relevant to the objectives of the study.
6b. For hypothesis-testing studies or research conducted in knowledge-claiming mode, specify the primary outcome measure, i.e. the outcome measure that was used to determine the sample size.
In a hypothesis-testing experiment research conducted in knowledge-claiming mode, the primary outcome measure answers the main biological question. It is the outcome of greatest importance, identified in the planning stages of the experiment and used as the basis for the sample size calculation. For exploratory studies, it is not necessary to identify a single primary outcome and often multiple outcomes are assessed.
In a hypothesis-testing study [research conducted in confirmatory mode] powered to detect an effect on the primary outcome measure, data on secondary outcomes are used to evaluate additional effects of the intervention but subsequent statistical analysis of secondary outcome measures may be underpowered, making results and interpretation less reliable. Studies that claim to test a hypothesis but do not specify a pre-defined primary outcome measure, or those that change the primary outcome measure after data were collected (also known as primary outcome switching) are liable to selectively report only statistically significant results, favouring more positive findings.
Registering a protocol in advance protects the researcher against concerns about selective outcome reporting (also known as data dredging or p-hacking) and provides evidence that the primary outcome reported in the manuscript accurately reflects what was planned (see 2.1.11 Preregistration).
If the study was designed to test a hypothesis and more than one outcome was assessed, explicitly identify the primary outcome measure and state if it was defined as such prior to data collection.
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