Clinical Trial Design & Power Analysis — clinicaltrialdesign • ClinicoPath

Comprehensive power analysis and sample size calculation for clinical trial design. Covers the most common clinical trial types with appropriate statistical tests, effect size calculations, and regulatory considerations for clinical research.

Usage

clinicaltrialdesign(
  data,
  trial_type = "superiority",
  outcome_type = "continuous",
  test_type = "two_sample_ttest",
  calculation_type = "sample_size",
  alpha = 0.05,
  power = 0.8,
  sample_size = 100,
  effect_size = 0.5,
  mean_difference = 1,
  common_sd = 2,
  proportion1 = 0.3,
  proportion2 = 0.5,
  allocation_ratio = 1,
  two_sided = TRUE,
  continuity_correction = TRUE,
  margin = 0.1,
  margin_type = "absolute",
  dropout_rate = 10,
  interim_analyses = 0,
  multiple_comparisons = "none",
  show_assumptions = TRUE,
  show_interpretation = TRUE,
  show_sensitivity = TRUE,
  show_plots = TRUE,
  regulatory_context = "ich"
)

Arguments

data: the data as a data frame (optional for power calculations)
trial_type: Type of clinical trial design for appropriate power calculations
outcome_type: Type of primary outcome variable determining appropriate statistical test
test_type: Statistical test appropriate for the outcome type and study design
calculation_type: What to calculate - power, sample size, or detectable effect size
alpha: Significance level (typically 0.05 for superiority, 0.025 for non-inferiority)
power: Desired statistical power (typically 0.80 or 0.90)
sample_size: Total sample size for power calculation
effect_size: Standardized effect size (Cohen's d for continuous outcomes)
mean_difference: Expected difference in means between groups
common_sd: Pooled standard deviation for continuous outcomes
proportion1: Expected proportion in control/reference group
proportion2: Expected proportion in treatment/experimental group
allocation_ratio: Ratio of treatment to control group sizes (1 = equal allocation)
two_sided: Use two-sided statistical test (recommended for most trials)
continuity_correction: Apply continuity correction for proportion tests
margin: Non-inferiority or equivalence margin (absolute difference)
margin_type: Type of margin for non-inferiority/equivalence testing
dropout_rate: Expected dropout/loss to follow-up rate for sample size inflation
interim_analyses: Number of planned interim analyses (affects alpha spending)
multiple_comparisons: Adjustment for multiple testing (when applicable)
show_assumptions: Display assumptions for the selected statistical test
show_interpretation: Include clinical interpretation and regulatory considerations
show_sensitivity: Perform sensitivity analysis across parameter ranges
show_plots: Generate power/sample size relationship plots
regulatory_context: Regulatory context for power analysis considerations

Value

A results object containing:

`results$instructions`					Instructions for clinical trial design and power analysis
`results$design_summary`					Summary of trial design parameters and recommendations
`results$power_results`					Primary power analysis calculations and results
`results$sample_size_breakdown`					Detailed sample size calculations with adjustments
`results$effect_size_analysis`					Effect size calculations and clinical significance assessment
`results$assumptions_check`					Key assumptions for the selected statistical test
`results$sensitivity_analysis`					Power/sample size sensitivity across parameter ranges
`results$regulatory_considerations`					Regulatory guidance and compliance considerations
`results$power_curve`					Power curves showing relationship between sample size and statistical power
`results$effect_size_plot`					Effect size distribution and clinical significance thresholds
`results$sample_size_plot`					Sample size requirements across different effect sizes and power levels
`results$clinical_interpretation`					Clinical context, interpretation guidelines, and next steps
`results$study_protocol_template`					Template sections for study protocol statistical analysis plan

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$design_summary$asDF

as.data.frame(results$design_summary)

Details

Supports randomized controlled trials (RCTs), equivalence trials, non-inferiority trials, superiority trials, and observational studies with proper power calculations.

Examples

data('your_data')

clinicaltrialdesign(
    trial_type = "superiority",
    outcome_type = "continuous",
    test_type = "two_sample_ttest",
    effect_size = 0.5,
    alpha = 0.05,
    power = 0.80
)