U7.
Use Appropriate Language
The language used to describe aims, methods, results, and implications should match the research task. Misaligned language can lead readers to interpret descriptive, predictive, or signal-discovery studies as causal, or to mistake causal effect estimates for definitive proof.
This step helps researchers describe their study accurately and avoid vague associational language, unsupported causal claims, unclear “risk factor” framing, or predictive language when prediction is not the goal.
Description
Describe and interpret aims, methods, and results in terms of target occurrence measures and how these vary between pre-specified strata, avoiding associational, predictive, risk factor, or causal language.
-
Aim: 'We aimed to estimate the prevalence of X in population Y and how this varied by Z'
Methods: 'We estimated the age-standardised prevalence of X and calculated prevalence ratios across strata of Z'
Results: 'The prevalence of X was higher in group Z than group Z*, with five-year survival rates of…'
Discussion: 'These findings suggest that the prevalence of X is higher among Z than Z*, increasing with age from… to…'
-
Aim: 'We aimed to estimate the association between X and Y' (associational language obscures the descriptive aim and target occurrence measure)
Methods: 'We modelled the association between X and Y adjusting for Z' (associational language obscures the descriptive aim and target occurrence measure)
Results: 'X was associated with Y' (associational language obscures the descriptive aim and target occurrence measure)
Discussion: 'These findings suggest X is associated with Y' (associational language obscures the descriptive aim and target occurrence measure)
Throughout:
'X predicted Y' (predictive language implies a model-based prediction task)
'X caused/influenced/modified Y' (causal language implies a causal effect estimation task)
'X was a risk factor for Y' (risk factor language is unclear and should be avoided
Signal Discovery
Describe and interpret aims, methods, and results in terms of detected signals and their statistical strength, avoiding predictive, causal, or definitive language, and emphasizing the hypothesis-generating nature of findings.
-
Aim: 'We aimed to identify candidate signals for Y across a broad set of exposures/variants/drugs in population Z'
Methods: 'Associations between each exposure and Y were estimated using Method Z, with signals identified based on a pre-specified threshold of X'
Results: 'A potential signal was detected between X and Y (effect estimate, 95% CI…)'
Discussion: 'These findings suggest X may be a candidate signal for Y'
-
Throughout:
'X predicted Y' (predictive language implies a model-based prediction task)
'X caused/influenced/modified Y' (causal language implies a causal effect estimation task)
'X was a risk factor for Y' (risk factor language is unclear and should be avoided)
Factual Prediction
Describe and interpret aims, methods, and results in terms of predicted outcomes and model performance, avoiding associational, risk factor, or causal language.
-
Aim: 'We aimed to develop a model to predict 5-year risk of Y in population Z'
Methods: 'Model discrimination and calibration were assessed using the C-statistic and calibration plots across risk deciles'
Results: 'The predicted 5-year risk of Y was X% (95% CI…); the model achieved good discrimination (C-statistic …) and was well-calibrated across risk deciles'.
Discussion: 'These findings suggest our model can accurately predict 5-year risk of Y in population Z, with the addition of biomarker X improving discrimination (change in C-statistic:…)'
-
Throughout:
'We aimed to identify risk factors for Y' (risk factor language is unclear and should be avoided)
'X was associated with higher predicted risk of Y' (associational language obscures the predictive aim)
'X was an independent predictor of Y' (implies causal interpretation of model coefficients)
'These findings suggest X increases the risk of Y' (causal language inappropriate for predictive study)
Counterfactual Prediction
Describe and interpret aims, methods, and results in terms of predicted outcomes under hypothetical treatment strategies and counterfactual model performance, avoiding associational, risk factor, or definitive causal language.
-
Aim: 'We aimed to predict 5-year risk of Y under hypothetical treatment strategies X and X* in population Z'
Methods: 'Counterfactual model discrimination and calibration were assessed using the C-statistic and calibration plots under each treatment strategy'
Results: 'The predicted 5-year risk of Y under strategy X* was lower than under strategy X (X% vs X%); the model showed good discrimination and calibration under both strategies'
Discussion: 'These findings suggest our model can accurately predict risk of Y under hypothetical treatment strategies X and X*'
-
Throughout:
'X was associated with Y' (associational language obscures the counterfactual estimand)
'X was a risk factor for Y' (risk factor language is unclear and should be avoided)
'X caused Y' (definitive causal language presents model-based estimates as established facts rather than quantitative estimates based on assumptions)
Causal Effect Estimation
Describe and interpret aims, methods, and results in terms of target estimands and estimated causal effects, avoiding associational, predictive, risk factor, or definitive causal language.
-
Aim: ‘We aimed to estimate the causal effect of X* vs X on Y’
Methods: ‘The average treatment effect of X* vs X on Y was estimated using Method Z’
Results: ‘The estimated causal effect of X* vs X on Y was …’
Discussion: ‘Our findings suggest that exposure to X* vs X may have a small harmful causal effect on Y’
-
Aim: ‘We aimed to estimate the association between X and Y adjusting for covariates Z’ (unclear language in the aim obscures the true aim and target estimand)
Methods: ‘We estimated the association between X and Y using Methods Z...’ (unclear language in the methods obscures the true aim and target estimand)
Results: ‘The association between X and Y was...’ (unclear language in the results obscures the true aim and target estimand)
Discussion: ‘Our findings suggest that X may have a modest association with Y’ (unclear language in the discussion obscures the true aim and target estimand)
Throughout:
"X predicted higher levels of Y" (predictive language obscures causal aim and target estimand)
‘X was a risk factor for Y’ (risk factor language is unclear and should be avoided)
‘X caused Y’ (definitive causal language presents the estimate as an established fact rather than a quantitative estimate based on assumptions)
By the end of this step, you should have:
Reviewed the study aim, methods, results, and discussion language
Replaced vague or mismatched language with task-appropriate wording
Avoided unsupported causal, predictive, associational, or “risk factor” claims
Created approved language for the abstract, results, and discussion
Confirmed that interpretation aligns with the estimand and assumptions