Description Usage Arguments Value Examples
View source: R/assess_performance.R
Calculates the expected power of an environmental epidemiology time series analysis based
on simulated datasets. This function uses the simulation provided by eesim
to
simulate multiple environmental epidemiology datasets under different scenarios (e.g.,
total days in study, size of association between exposure and outcome, or baseline
average daily count of the outcome in the study) and estimates the power of a specified
analysis to detect the hypothesized association.
1 2 3 4 5 6 7  power_calc(varying, values, n_reps, custom_model, central, exposure_type,
n = NULL, sd = NULL, exposure_trend = "no trend", exposure_amp = NULL,
average_outcome = NULL, outcome_trend = "no trend", outcome_amp = NULL,
rr = NULL, start.date = "20000101", cust_exp_func = NULL,
cust_exp_args = NULL, cust_base_func = NULL, cust_lambda_func = NULL,
cust_base_args = NULL, cust_lambda_args = NULL,
custom_model_args = NULL, plot = FALSE)

varying 
A character string specifying the parameter to be varied. Choices are

values 
A numeric vector with the values you would like to test for the varying
parameters. For example, 
n_reps 
An integer specifying the number of datasets to simulate (e.g.,

custom_model 
The object name of an R function that defines the code that will be used to fit the model. This object name should not be in quotations. See Details for more. 
central 
A numeric value specifying the mean probability of exposure (for binary data) or the mean exposure value (for continuous data). 
exposure_type 
A character string specifying the type of exposure. Choices are "binary" or "continuous". 
n 
An integer specifying the number of days to simulate (e.g., 
sd 
A nonnegative numeric value giving the standard deviation of the exposure values from the exposure trend line (not the total standard deviation of the exposure values). 
exposure_trend 
A character string specifying a seasonal and / or longterm trend for
expected mean exposure. See the vignette for
Options for binary exposure are:

exposure_amp 
A numeric value specifying the amplitude of the exposure trend. Must be between 1 and 1 for continuous exposure or between 0.5 and 0.5 for binary exposure. Positive values will simulate a pattern with higher values at the time of the year of the start of the dataset (typically January) and lowest values six months following that (typically July). Negative values can be used to simulate a trend with lower values at the time of year of the start of the dataset and higher values in the opposite season. 
average_outcome 
A nonnegative numeric value specifying the average daily outcome count. 
outcome_trend 
A character string specifying the seasonal trend in health outcomes. Options are the same as for continuous exposure data. 
outcome_amp 
A numeric value specifying the amplitude of the outcome trend. Must be between 1 and 1. 
rr 
A nonnegative numeric value specifying the relative risk (i.e., the relative risk per unit increase in the exposure). 
start.date 
A date of the format "yyyymmdd" from which to begin simulating daily exposures 
cust_exp_func 
An R object name specifying the name of a custom trend function to generate exposure data 
cust_exp_args 
A list of arguments and their values for the userspecified custom exposure function. 
cust_base_func 
A R object name specifying a usermade custom function for baseline trend. 
cust_lambda_func 
An R object name specifying a usermade custom function for relating baseline, relative risk, and exposure 
cust_base_args 
A list of arguments and their values used in the userspecified custom baseline function 
cust_lambda_args 
A list of arguments and their values used in the userspecified custom lambda function 
custom_model_args 
A list of arguments and their values for a custom
model. These arguments are passed through to the function specified with 
plot 
"TRUE" or "FALSE" for whether to produce a plot 
Data frame with the values of the varying parameter and the estimated power
for each. If the plot
argument is set to TRUE
, it also returns a power
curve plot as a side effect. Because these estimates are based on simulations, there
will be some random variation in estimates of power. Estimates will be more stable
if a higher value is used for n_reps
, although this will increase the time it
takes the function to run.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  # Calculate power for studies that vary in the total length of the study period
# (between one and twentyone years of data) for the association between a continuous
# exposure with a seasonal trend (mean = 100, sd from seasonal baseline = 10) and a count
# outcome (e.g., daily number of deaths, mean daily value across the study period of 22).
# The alternative hypothesis is that there is a relative rate of the outcome of 1.001 for
# every oneunit increase in exposure. The null hypothesis is that there is no association
# between the exposure and the outcome. The model used to test for an association is a
# casecrossover model
## Not run:
pow < power_calc(varying = "n", values = floor(365.25 * seq(1, 21, by = 5)), n_reps = 20,
central = 100, sd = 10, rr = 1.001, exposure_type = "continuous",
exposure_trend = "cos1", exposure_amp = .6, average_outcome = 22,
outcome_trend = "no trend", outcome_amp = .6,
custom_model = spline_mod, plot = TRUE)
## End(Not run)

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