Site Map

Plan a study

Finding and working with a biostatistician

• Best practices: Effective statistical collaborations in research
• What to expect: Collaborating with a biostatistician
• How to work with a statistician
• How to build a successful long-term collaboration with a biostatistician

Sample size calculators

• Sample size calculators (sample-size.net)
• Sealed envelope - power (sample size) calculators
• Cancer research and biostatistics statistical tools

Power and sample size concepts

• The Abdul Latif Jameel Poverty Action Lab’s sampling and sample size lecture
• University of Oklahoma Health Sciences Center sample size justification lecture
• University of Alabama-Birmingham power, type II error and sample size lecture
• CRCAIH Methodology Core sample size calculations webinar
• UCLA IDRE Statistical Consulting’s Introduction to Power Analysis webpage
• University of California-Davis sample size and power lecture

Study designs

• Overview of study designs
• An overview of clinical research: the lay of the land
• Evidence-Based Practice: Levels of Evidence and Study Designs
• Randomised controlled trials-the gold standard for effectiveness research 
• Randomized Controlled Trials (RCTs)
• Randomised controlled trials

Conduct a study

Data collection and management

• Research Electronic Data Capture (REDCap) training videos
• University of California’s Data Management Plan Tool (DMPTool)
• University of Minnesota BDAC’s Data Tips
• Data Collection in Spreadsheets

Analyze data

Non-parametric methods

• Statistics review 6: Nonparametric methods
• Nonparametric statistical tests for the continuous data: the basic concept and the practical use
• Parametric and Nonparametric: Demystifying the Terms
• Uses and Abuses of Non-parametric Statistics in Medical Research

Multiple testing

• Duke Core Guide: Multiple Testing Part 1
• Multiple Comparisons
• The Problem of Multiple Testing

Basic statistical tests

• Chi-squared Test of Independence
• The t-Test
• The McNemar Test

Interpret results

Hypothesis Tests and P-Values

• OpenIntro Statistics’s Introduction to Hypothesis Testing
• What to Say (and Not Say) When P > 0.05?
• Hypothesis Testing and P-Value Pitfalls
• The Scientific Process II: Analysis and Inference
• Why P-values are like puppies

Screening and diagnostic tests

• Statistics Notes: Diagnostic Tests 2: Predictive Values
• Statistics Notes: Diagnostic Tests 1: Sensitivity and Specificity
• Beyond ROC Curves: Recent Statistical Advances in the Evaluation of Diagnostic and Prognostic Tests
• ROC curves
• The Trade-off between Sensitivity & Specificity

Post-hoc Power Analyses

• What is post-hoc power anyway?
• Why post-hoc power analyses are nonsensical