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Estimate an effect of intervention on every Viva Insights metric in input file by applying single-group Interrupted Time-Series Analysis (ITSA)

Source: R/create_itsa.R
create_itsa.Rd

r lifecycle::badge('experimental')

This function implements ITSA method described in the paper 'Conducting interrupted time-series analysis for single- and multiple-group comparisons', Ariel Linden, The Stata Journal (2015), 15, Number 2, pp. 480-500

This function further requires the installation of 'sandwich' and 'lmtest' in order to work. These packages can be installed from CRAN using install.packages().

Usage

create_itsa(
  data,
  metrics = NULL,
  before_start = NULL,
  before_end = NULL,
  after_start = NULL,
  after_end = NULL,
  ac_lags_max = 7,
  return = "table"
)

Arguments

data

Person Query as a dataframe including date column named MetricDate. This function assumes the data format is %Y-%m-%d as is standard in a Viva Insights query output.

metrics

A character vector containing the variable names to perform the interrupted time series analysis for.

before_start

String specifying the start date of the 'before' time period in %Y-%m-%d format. The 'before' time period refers to the period before the intervention (e.g. training program, re-org, shift to remote work) occurs and bounded by before_start and before_end parameters. Longer period increases likelihood of achieving more statistically significant results. Defaults to earliest date in dataset. If not provided, this defaults to the earliest date in the dataset.

before_end

String specifying the end date of 'before' time period in %Y-%m-%d format. If NULL, an error will be raised, as this value is required.

after_start

String specifying the start date of the 'after' time period in %Y-%m-%d format. If NULL, this will default to the value of before_end. The 'after' time period refers to the period after the intervention occurs and is bounded by after_start and after_end parameters. Longer periods increase the likelihood of achieving more statistically significant results.

after_end

String specifying the end date of the 'after' time period in %Y-%m-%d format. Defaults to the latest date in the dataset.

ac_lags_max

Numeric value specifying the maximum lag for the autocorrelation test. The Ljung-Box test is used to check for autocorrelation in the model residuals up to this specified number of lags. Higher values check for longer-term dependencies in the time series data.

return

String specifying what output to return. Defaults to "table". Valid return options include:

  • 'plot': return a list of plots.

  • 'table': return data.frame with estimated models' coefficients and their corresponding p-values You should look for significant p-values in beta_2 to indicate an immediate treatment effect, and/or in beta_3 to indicate a treatment effect over time

Value

When 'data' is passed to return, a data frame with the following columns:

  • metric_name: Name of the metric being analyzed.

  • beta_2: Coefficient for the immediate treatment effect.

  • beta_3: Coefficient for the treatment effect over time.

  • beta_2_pvalue: P-value for the immediate treatment effect.

  • beta_3_pvalue: P-value for the treatment effect over time.

  • AR_flag: Logical flag indicating whether autocorrelation was detected.

  • error_warning: Error or warning message if applicable.

Details

This function uses the additional package dependencies 'sandwich' and 'lmtest'. Please install these separately from CRAN prior to running the function.

As of May 2022, the 'portes' package was archived from CRAN. The dependency has since been removed and dependent functions Ljungbox() incorporated into the wpa package.

Author

Aleksey Ashikhmin alashi@microsoft.com

Examples

if (FALSE) { # \dontrun{
# Returns summary table
create_itsa(
  data = pq_data,
  metrics = c("Collaboration_span", "Internal_network_size"),
  before_end = "2024-07-01",
  after_start = "2024-07-01",
  ac_lags_max = 7,
  return = "table"
)

# Returns list of plots
plot_list <-
  create_itsa(
    data = pq_data,
    metrics = c("Collaboration_span", "Internal_network_size"),
    before_end = "2024-07-01",
    after_start = "2024-07-01",
    ac_lags_max = 7,
    return = "plot"
  )

# Extract a plot as an example
plot_list$Collaboration_span
} # }