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Step-by-Step Analysis Workflow

DRAFT VERSION

Source: vignettes/DataAnalysis.Rmd
DataAnalysis.Rmd

Introduction

This vignette walks users through the mechanics of the functions that produce all of the Analysis workflow output within the gsm package. gsm leverages Key Risk Indicators (KRIs) and thresholds to conduct study-level and site-level Risk Based Monitoring for clinical trials.

These functions provide data frames, visualizations, and metadata to be used in reporting and error checking at clinical sites. The image below illustrates the supporting functions that feed into the yaml workflow that is specified in each analysis workflow.

All of these functions will run automatically and sequentially when a user calls upon the RunWorkflow() function with a specified yaml file for KRI metrics.

Each of these individual functions can also be run independently outside of a specified yaml workflow.

For the purposes of this documentation, we will evaluate the input(s) and output(s) of each individual function for a specific KRI to show the stepwise progression of how a yaml workflow is set up to handle and process data.

Case Study - Step-by-Step Adverse Event KRI

We will use sample clinical data from the {clindata} package to run the Adverse Events (AE) Assessment, i.e., AE_Assess(), using the normal approximation method.

Additional statistical methods and supporting functions are explored in Appendix 1.

1. Create dfInput

Start by creating dfInput using sample rawplus data from clindata. Note that Input_Rate() requires three specific datasets from clindata, which include a subject-level demographics/exposure dataset (dfSubjects) and a domain-level dataset (dfNumerator) that records every adverse event per subject.

Since Input_Rate() is a generalized function, it is also required that you specify the relevant column names for the Subject (strSubjectCol), Group (strGroupCol) and optionally the Denominator (strDenominatorCol) and Numerator (strNumeratorCol) when it is not simply “Denominator” or “Numerator”, respectively.

Finally, the method for calculating the Numerator and Denominator is specified in strNumeratorMethod and strDenominatorMethod as either “Count” or “Sum”. If the method is “Count”, the function simply counts the number of rows in the provided data frame. If the numerator method is “Sum”, the function takes the sum of the values in the specified column (strNumeratorCol or strDenominatorCol).

dfInput <- Input_Rate(
              dfSubjects = clindata::rawplus_dm,
              dfNumerator = clindata::rawplus_ae,
              dfDenominator = clindata::rawplus_dm,
              strSubjectCol = "subjid",
              strGroupCol = "siteid",
              strNumeratorMethod = "Count",
              strDenominatorMethod = "Sum",
              strDenominatorCol = "timeonstudy"
)

The data frame dfInput for an AE assessment will be created by running Input_Rate() and will have one record per subject, with the following columns:

  • SubjectID: Subject Identifier
  • GroupID: Group Identifier
  • GroupLevel: Type of Group specified in GroupID (Country, Site)
  • Numerator: Total Time on Treatment (measured in days; per subject)
  • Denominator: Total Number of Event(s) of Interest (in this example, the number of AEs reported; per subject)
  • Metric: Rate of Event Incidence (calculated as Exposure/Count; per subject)

2. Create dfTransformed

The data frame dfTransformed is derived from dfInput using a Transform() function. In our example, the analysis pipeline pulls in Transform_Rate() since the default metric for AEs is the number of AEs reported over the course of treatment per site, i.e., a rate.

dfTransformed <- Transform_Rate(dfInput)

The resulting dfTransformed data frame will contain site-level transformed data, including KRI calculation. Using our example AE data, dfTransformed contains the following columns:

  • GroupID: Group Identifier (default is Site ID)
  • GroupLevel: Type of Group specified in GroupID (Country, Site)
  • Numerator: Cumulative Number of Event(s) of Interest (in this example, number of AEs reported across subjects)
  • Denominator: Cumulative Time on Treatment (in days, across subjects)
  • Metric: Rate of Event(s) of Interest (in this example, number of AEs reported over the course of treatment in days)

3. Create dfAnalyzed

The data frame dfAnalyzed is derived from dfTransformed using an Analyze() function, which incorporates a specific statistical model. The resulting dfAnalyzed data frame will contain site-level analysis results data. The normal approximation method is the default statistical model for AE data, so the analysis pipeline automatically runs Analyze_NormalApprox().

dfAnalyzed <- Analyze_NormalApprox(dfTransformed)
#> `OverallMetric`, `Factor`, and `Score` columns created from normal
#> approximation.

Using our example AE data, dfAnalyzed contains the following columns:

  • GroupID: Group Identifier (default is Site ID)
  • GroupLevel: Type of Group specified in GroupID (Country, Site)
  • Numerator: Cumulative Number of Event(s) of Interest (in this example, number of AEs reported across subjects); Carried from dfTransformed.
  • Denominator: Cumulative Time on Treatment (in days, across subjects); Carried from dfTransformed.
  • Metric: Rate of Event(s) of Interest (in this example, number of AEs reported over the course of treatment in days); Carried from dfTransformed.
  • OverallMetric: Aggregate metric for the group that is being assessed. ( sum(Numerator) / sum(Denominator) ).
  • Factor: Calculated over-dispersion adjustment factor (mean of the z-score sum of squares calculated in the analysis functions).
  • Score: Calculated Residual (per site).

4. Create dfFlagged

The data frame dfFlagged is derived from dfAnalyzed using a Flag() function. The resulting dfFlagged data frame will contain site-level analysis results data with flagging incorporated based on a pre-specified statistical threshold to highlight possible outliers.

dfFlagged <- Flag_NormalApprox(dfAnalyzed, vThreshold = c(-3, -2, 2, 3))

The default flagging function for the normal approximation method is Flag_NormalApprox() and the default threshold is (-3, -2, 2, 3). Using our example AE data, dfFlagged contains the following columns:

  • GroupID: Group Identifier (default is Site ID)
  • GroupLevel: Type of Group specified in GroupID (Country, Site)
  • Numerator: Cumulative Number of Event(s) of Interest (in this example, number of AEs reported across subjects); Carried from dfAnalyzed
  • Denominator: Cumulative Time on Treatment (in days, across subjects); Carried from dfAnalyzed
  • Metric: Rate of Event(s) of Interest (in this example, number of AEs reported over the course of treatment in days); Carried from dfAnalyzed
  • OverallMetric: Aggregate metric for the group that is being assessed. ( sum(Numerator) / sum(Denominator) ).
  • Factor: Calculated over-dispersion adjustment factor (mean of the z-score sum of squares calculated in the analysis functions); Carried from dfAnalyzed.
  • Score: Calculated Residual (per site); Carried from dfAnalyzed
  • Flag: Flag Indicating Possible Statistical Outliers; Valid values for this variable include -2, -1, 0, 1, and 2, which determine the “extremeness” of the outlier. -2 and 2 represent more extreme outliers, -1 and 1 represent less extreme outliers, and 0 represents a non-outlier.

5. Create dfSummary

The data frame dfSummary is derived from dfFlagged using the Summarize() function. The resulting dfSummary data frame will contain the most relevant columns from dfFlagged with data sorted in a meaningful way to provide a concise overview of the assessment. Flagged sites will sort earlier than non-flagged sites, with the more “extreme” outliers displayed first. The columns in dfSummary include:

  • GroupID: Group Identifier (default is Site ID)
  • GroupLevel: Type of Group specified in GroupID (Country, Site)
  • Numerator: Cumulative Number of Event(s) of Interest (in this example, number of AEs reported across subjects); Carried from dfAnalyzed
  • Denominator: Cumulative Time on Treatment (in days, across subjects); Carried from dfAnalyzed
  • Metric: Rate of Event(s) of Interest (in this example, number of AEs reported over the course of treatment in days)
  • Score: Calculated Residual (per site)
  • Flag: Flag Indicating Possible Statistical Outliers; Valid values for this variable include -2, -1, 0, 1, and 2, which determine the “extremeness” of the outlier. -2 and 2 represent more extreme outliers, -1 and 1 represent less extreme outliers, and 0 represents a non-outlier.
dfSummary <- Summarize(dfFlagged)

6. Data Visualization

For a single Normal Approximation AE assessment, a scatter plot of Total Exposure (in days, on log scale) vs Total Number of Event(s) of Interest (on linear scale) is created. Each data point represents one site with outliers displayed in yellow or red, depending on the “extremeness” of the KRI value. The Visualize_Scatter() function takes inputs of dfBounds– which defines bounds based on the statistical model, dfTransformed and thresholds using Analyze_NormalApprox_PredictBounds()– and dfFlagged to plot the data. Using our example AE data, we see the following scatter plot:

dfBounds <- Analyze_NormalApprox_PredictBounds(dfTransformed, vThreshold = c(-3, -2, 2, 3))
#> nStep was not provided. Setting default step to 203.584.

chart <- Visualize_Scatter(dfFlagged, dfBounds)

A full explanation of all Data Visualizations in the gsm package is outlined in the vignette("DataReporting").

Recap - Normal Approximation Adverse Event KRI

Appendix 1 - Supporting Functions

The following sections include various examples of supporting functions and statistical models that can be employed in the Analysis workflow. Please note that this is not an exhaustive list, but includes some of the most commonly called upon functions.

Mapping Functions

  • RunQuery(): Run a SQL query to create new data.frames with filtering and column name specifications.
  • Input_Rate(): Calculate a subject level rate from raw numerator and denominator data

Transform Functions

Analyze Functions

  • Analyze_NormalApprox(): Uses funnel plot method with normal approximation to create analysis results for percentage/rate.
  • Analyze_Fisher(): Uses Fisher’s Exact Test to determine if there are non-random associations between a site and a given KRI
  • Analyze_Identity(): Used in the data pipeline between Transform() and Flag() functions to rename KRI and Score columns
  • Analyze_Poisson(): Uses a Poisson model to describe the distribution of events in the overall site population, i.e., determine how many times an event is likely to occur at a site over a specified treatment period

Flag Functions

Visualization Functions

  • Visualize_Scatter(): Creates scatter plot of Total Exposure (in days, on log scale) vs Total Number of Event(s) of Interest (on linear scale). Each data point represents one site. Outliers are plotted in red with the site label attached. This plot is only created when statistical method is not defined as identity.
  • Visualize_Score(): Provides a standard visualization for Score or KRI.
  • Visualize_Metric(): Creates all available charts for a metric using the data provided.

What Statistical Models Are Available For Each Assessment?