
🔬 Research & Academia · Flowchart
The CONSORT flow diagram for a randomised trial: enrolment, allocation to two arms, follow-up and analysis, with the numbers lost at each stage.
Drawing diagram…
CONSORT flow diagram for a two-arm randomised controlled trial. Assessed for eligibility (n = ). Excluded (n = ): not meeting inclusion criteria, declined to participate, other reasons. Randomised (n = ). Allocated to intervention (n = ): received allocated intervention, did not receive it with reasons. Allocated to control (n = ): received allocated control, did not receive it with reasons. Follow-up: lost to follow-up and discontinued interventions with reasons for each arm. Analysed (n = ) in each arm, with number excluded from analysis and reasons.
flowchart TD A["Assessed for eligibility<br/>n = "] B["Excluded n = <br/>Not meeting criteria n = <br/>Declined n = <br/>Other reasons n = "] C["Randomised<br/>n = "] A --> B A --> C C --> D1["Allocated to intervention<br/>n = <br/>Received allocated intervention n = <br/>Did not receive, with reasons n = "] C --> D2["Allocated to control<br/>n = <br/>Received allocated control n = <br/>Did not receive, with reasons n = "] D1 --> E1["Follow-up<br/>Lost to follow-up n = <br/>Discontinued, with reasons n = "] D2 --> E2["Follow-up<br/>Lost to follow-up n = <br/>Discontinued, with reasons n = "] E1 --> F1["Analysed<br/>n = <br/>Excluded from analysis n = "] E2 --> F2["Analysed<br/>n = <br/>Excluded from analysis n = "]
The standard flow diagram for a systematic review: records identified, screened, assessed for eligibility and included. Replace each n with your own counts.
Study-design figure for a randomised controlled trial: population, randomisation, two arms, measurements and outcomes.
Design figure for a prospective cohort study: exposed and unexposed groups followed over time to compare outcomes.
A conceptual framework with independent, mediating, moderating and dependent variables, plus control variables. Rename the boxes for your own study.
The experiment pipeline figure for a machine-learning paper: data, preprocessing, splits, model training, evaluation, ablation and statistical testing.
The six steps of thematic analysis (Braun and Clarke) as a process figure, from familiarisation to the written report.