seq_wrapper {SequentialDesign}

Execute Simulated Exact Sequential Analysis in Multi-Site Observational Database Studies

Description

SequentialDesign is designed for planning observational database studies that use exact sequential analysis. It is designed to be used in conjunction with the R package Sequential. This package is appropriate to use when one is performing a multi-site observational database study (i.e., an epidemiologic study) and planning to use sequential statistical analysis. This package supports two types of observational study designs:

• a self-controlled risk interval design which creates binomial data, and

• a current v. historical design which creates Poisson data.

The goal of this package is to allow the investigator to plan for the optimal study.

Usage

seq_wrapper(seed, N, t0, tf, NStrata, strataRatio, EventRate, sensitivity,
  PPVest, RR, MatchRatio, maxSampleSize, maxTest, totalAlpha, minEvents,
  AlphaSpendType, AlphaParameter, rate, offset, address, ...)

Arguments

seed

Seed used for randomization

N

Number of simulations to be created. Because adverse event assignment is stochastic, this number is usually at least 10,000.

t0

Initial time point, a number in units of either days, weeks, months, or years. It is important to be consistent.

tf

Final time point, a number in units of either days, weeks, months, or years.

NStrata

Number of strata in the observational study design, where a "stratum" can be defined by age categories, sex, and any other defining characteristics. Event rate of the adverse event of interest is also segregated by strata and database population size is also segregated by strata. For example, a single strata might 0-17 year old females.

strataRatio

Ratio of individuals within a single strata for exposed and unexposed individuals. The number of elements in this list should be 2*NStrata.

EventRate

Rate of event accrual given in events /person-time where the time constant is the same constant being used throughout the study. Additionally, the number of elements in the EventRate matrix should be equal to the NStrata.

sensitivity

True sensitivity of the outcome of interest. sensitivity = (true positive case) / (true positive case + false negative case).

PPVest

True positive predictive value of outcome in the unexposed group. PPV = (true positive case) / (true positive case + false positive case).

RR

Intended relative risk to detect (and therefore to simulate) in the dataset.

MatchRatio

Single numeric value. In a self-controlled risk interval design, it is the ratio of the length of the control window to the length of the risk window.

maxSampleSize

Maximum number of events before sequential analysis is ended or the upper limit on sample size expressed in terms of total number of events. This is the same variable as N from R Sequential.

maxTest

Number of tests to perform on simulation data.

totalAlpha

Total amount of alpha available to spend.

minEvents

Minimum number of events needed before the null hypothesis can be rejected. Represented as M in R Sequential.

AlphaSpendType

Method of alpha spenditure. Available values are "Wald" or "power-type". This is the same as AlphaSpending R Sequential.

AlphaParameter

Rho parameter for power-type alpha spending function. This is the same as rho in R Sequential.

rate

Rate of exposure/cohort accrual.

offset

Offset for exposure/cohort accrual.

address

Output folder where Sequential TXT files are to be stored. These should be preserved between runs, as detailed within the Sequential package.

...

additional arguments to be passed to or from methods.

Details

The simulation has the following steps:

1. Sample Size Calculations for the study using the R Sequential package

2. Given these sample size calculations and an exposure uptake function, calculate new exposure accrual in calendar time for the exposures of interest.

3. Given the simulated exposure information, generate adverse events of interest according to a pre-specified effect size.

4. Perform sequential analysis on these simulated data.

5. Generate calendar time descriptive statistics with respect to stopping points.

These steps will be discussed in more detail.
First, the investigator should work with the R package Sequential in order to calculate design parameters for their study. These are the statistical parameters that govern stopping points in statistical analysis. The relevant ones required for this analysis are: maxSampleSize, totalAlpha, minEvents, AlphaSpendType, AlphaParameter. If binomial data is being used for sequential analysis of a self-controlled risk interval design, then MatchRatio is also needed.
Second, this function will generate incident exposure to a simulated study population based on the parameters of an exposure accrual function.
Third, with incremental exposure accrual information, new adverse events will be assigned based on user-specified characteristics. This function also allows for outcome misclassification so true positive adverse events, false positive adverse events, and false negative adverse events are all simualted.
Fourth, Sequential analysis is implemented on these simulated data using function in R Sequential.
Fifth, the investigator is able to generate descriptive statistics in calendar time to enable the investigator to plan for their analysis.
Simulating sequential analysis in observational data requires many parameter inputs about

• the parameters that control the epidemiologic study design,

• the parameters that describe the characteristics of the databases, and

• the parameters of the simulation.

In addition to the parameter inputs, there are many sub-functions that are needed to perform different steps in the simulation. These sub-functions are not intended to be run as stand-alone functions but rather always in the sequence specified in this function.

Examples

#paramtest <- initialize.data(seed=8768, N=1, t0=0, tf=2, NStrata=2, 
#strataRatio=c(0.2, 0.3, 0.3, 0.2), EventRate=c(0.4, 0.5), sensitivity=0.9, PPVest=0.9, RR=3.0, 
#MatchRatio=1, maxSampleSize=200, maxTest=1, totalAlpha=0.05, minEvents=5, AlphaSpendType="Wald",
#AlphaParameter=0.5, address=getwd(), rate=20, offset=30)
#exposed1 <- create.exposure(paramtest)
#exposed2 <- sim.exposure(exposed1, paramtest)
#SCRI.seq(exposed2, paramtest)