Checklist for Biostatistical Review of Research Protocols

Hypotheses/Objectives

  • Clearly articulated and consistent with aims, both the null and alternative hypothesis needs to be specified.

General design

  • General study design (observational, RCT, pragmatic etc) will effectively answer the question
  • General study design is appropriate for current state of knowledge

Population and sample

  • Population from which the sample will be drawn is representative of the population of interest (e.g. disease severity, time point during disease progression), or the study is correlational / comparative in a way that is likely to cancel out any shifts in distributions from population to sample
  • Inclusion and exclusion criteria appropriate for state of knowledge
  • Screening and enrollment processes do not introduce bias

Variables and measurements

  • Choice of measurements, especially the response variable, are justified and consistent with the hypothesis/objectives
  • Measurements are of maximum feasible resolution with no unecessary categorization
  • Continuous responses, or ordinal responses with several well-populated levels, are used wherever possible
  • If a binary response variable is used, it should be justified and the effect on power and precision acknowledged
  • Algorithms used to derive variables or score outcome assessments are appropriate

Treatment assignment

  • Method for assigning treatments (e.g. randomization) minimizes bias or potential confounding factors are carefully thought out.

Data integrity

  • Data collection methods are sufficient to result in accurate measurement of primary variable(s)
  • Data management uses appropriate tools (e.g. REDCap)
  • Plans for monitoring or interim data checks are appropriate

Data handling rules

  • Range and consistency checks are included
  • Handling of missing data is addressed

Statistical environment

  • Statistical collaborator is engaged
  • Software is acceptable (not Excel)
  • Plans for reproducibility are stated

Analysis plan

  • Statistical approach is consistent with hypothesis and objectives
  • A plan for describing the dataset is given
  • Unit of analysis is clearly described for each analysis
  • Analysis populations clearly described (intent to treat set, per protocol set, full analysis set etc)
  • Analytical approach is justified, including consideration of assumptions and alternative approaches
  • Approach minimizes unnecessary exposure of participants to risk

Sample size justification

  • Alpha and 1-beta given for all statistical tests and justified based on state of current knowledge
  • Tests are identified as one or two-tailed and justification provided
  • Justification is based on clinically important differences or on precison (margin of error) with a margin of error that is acceptable to advance learning, or at least the PI has acknowledged the limited learning from a poorly powered study and can justify the risk given the limited benefit.
Topic revision: r4 - 21 Sep 2017, ChangYu
 

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