• What do we currently know about the research topic?
  • What type of study are you conducting (descriptive, analytical, comparative, interventional, observational, etc.)?
  • What variables will you use, and which subjects will be exposed to which variables?
  • How often and when will the data be collected?
  • How can you control all the factors that might affect prediction models and outcomes?
  • Do you have to adjust design because of some feasibility issues? If so, what factors?
  • Ethical considerations (all animal or human studies must discuss factors like ethics committee approval of research protocol, informed consent of human subjects, etc.
  • Study setting (time, place, etc.), where applicable.
  • For living subjects (humans and animals): demographic and clinical conditions, gender, weight, species, age, special characteristics, living conditions, etc.
  • Any preparations of subjects made before starting experiments.
  • Sampling method, including target population, sampling frame, instruments used, and any stratification, clustering or weighting.
  • Recruitment method and its effectiveness, including profiles of any subjects that refused (where applicable).
  • For comparative studies: group allocation and randomization procedures.
  • Follow-up procedures for longitudinal studies.
  • Variables measured (identify only the key predictors and all outcomes of those variables).
  • Methods and instruments used to collect data (include information such as an instrument’s manufacturer and model, calibration procedures, and other information necessary to allow others to reproduce your experimental results).
  • Bias controls (e.g., blinding procedures).
  • Descriptive statistics (e.g., means, medians, standard deviations, etc.).
  • Inferential methods (include confidence intervals, hypothetical testing methods, and regression models or other modeling procedures used).
  • Methods used to address confounding observational studies.
  • Interim analysis methods.
  • Adjustments made to sampling methods and weighting procedures.
  • Missing data.
  • Sample size.
  • Power determination.
  • Software used.
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