aba_longpower: Run power analysis on a longitudinal-based aba model.

In aba: Automated Biomarker Analysis

Description

This function allows users to calculate power, required sample size, or percent change (i.e., treatment effect) on a fitted aba model that uses longitudinal stats such as stat_lme. Whichever argument is left as NULL will be the argument for which this function solves. Users can specify a range of values for any function arguments to test different assumptions.

Usage

aba_longpower(
  object,
  n = NULL,
  pct_change = NULL,
  power = NULL,
  t_length = NULL,
  t_freq = NULL,
  sig_level = 0.05,
  dropout = 0
)

Arguments

object	aba model. The fitted aba model on which to calculate power. This model should feature a longitudinal aba stat (e.g., stat_lme)
n	integer. The total minimum required sample size.
pct_change	double between 0 - 1. The expected treatment effect which means the percent by which the slope will decrease in the treatment group. Note that "absolute" treatment effects are not currently supported.
power	double between 0 - 1. The expected power to detect the treatment effect.
t_length	double. The expected duration of the clinical trial. Note that this value should be on the same scale as the time argument used to fit the longitudinal aba stat. A longer trial will generally result in smaller sample size or higher power.
t_freq	double. The expected frequency of endpoint sampling during the trial. This value should be less than t_length. Note that this value should also be on the same scale as the time argument used to fit the longitudinal aba stat.
sig_level	double between 0 - 1. The required alpha level. There is usually little reason to change this from the default of 0.05.
dropout	double between 0 - 1. The expected overall drop-out rate during the trial. Note that we handle dropout in a fairly crude way by simply multiplying the sample size by (1 + dropout) instead of, say, sampling dropout from some distribution at each visit.

Details

Power is calculated using the lmmpower function from the longpower package in R. Please see documentation there to get a better understanding of the actual formulas involved.

Value

An aba longpower object which can be plotted and printed in special ways and which contains all the resulting calculations.

Examples

# use only two year follow-up data; filter by some basic AD trial criteria
data <- adnimerge %>%
  dplyr::filter(
    VISCODE %in% c('bl', 'm06', 'm12', 'm24'),
    DX_bl %in% c('MCI','AD'),
    CDR_bl %in% c(0.5, 1),
    MMSE_bl >= 20, MMSE_bl <= 28
  )

# fit an aba model with an lme stat to get a longitudinal model
model <- data %>% aba_model() %>%
  set_outcomes(CDRSB, ADAS13) %>%
  set_covariates(AGE, GENDER, EDUCATION) %>%
  set_stats(stat_lme(id = 'RID', time = 'YEARS_bl')) %>%
  fit()

# summarize aba model - not necessary here but good to see results
model_summary <- model %>% summary()

# run power analysis on the fitted aba model with various assumptions
# e.g., treatment effect between 25 - 35%; power between 80 - 90%
pwr <- model %>%
  aba_longpower(
    n = NULL,
    pct_change = c(0.25, 0.30, 0.35),
    power = c(0.8, 0.85, 0.9),
    t_length = 2,
    t_freq = 0.25,
    dropout = 0.2
  )

# generate a standard results figure from the power results
fig <- pwr %>% aba_plot()

# add better inclusion criteria (CSF & CSF+MRI) to the aba model and refit.
model2 <- model %>%
  set_groups(
    everyone(),
    (CSF_ABETA_bl < 880) & (CSF_PTAU_bl > 24),
    (CSF_ABETA_bl < 880) & (CSF_PTAU_bl > 24) & (MRI_HIPP_bl < 6000),
    labels = c('DX + COG', 'DX + COG + CSF', 'DX + COG + CSF + MRI')
  ) %>%
  fit()

# summarise model fit - again, not necessary but good to see slopes
model2_summary <- model2 %>% summary()

pwr2 <- model2 %>%
  aba_longpower(
    n = NULL,
    pct_change = c(0.25, 0.3, 0.35),
    power = c(0.8, 0.85, 0.9),
    t_length = 2,
    t_freq = 0.25,
    dropout = 0.2
  )

fig2 <- pwr2 %>% aba_plot()

aba documentation built on Dec. 17, 2021, 1:06 a.m.