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Summarize Ovulation Confirmation and Imputation

summary_ovulation.Rd

This function provides a summary of how ovulation was identified across the dataset—either through direct confirmation using biomarkers or via imputation based on menstrual cycle timing.

Usage

summary_ovulation(data)

Arguments

data

A dataframe containing the input data. Must include columns for scaled cycle day variables and symptoms/dependent variables of interest. Scaled cycle day variables are added to your dataframe after applying the pacts_scaling() function. (See examples below)

Value

A list with two data frames:

  • ovstatus_total: A dataset-level summary showing:

    1. The number of cycles with confirmed ovulation (ovtoday == 1).

    2. The number of cycles with imputed ovulation (ovtoday_impute == 1).

  • ovstatus_id: A participant-level summary showing, for each unique ID:

    1. The number of cycles with confirmed ovulation.

    2. The number of cycles with imputed ovulation.

Details

Specifically, it counts the number of cycles in which ovulation was:

  • Confirmed using objective biomarkers (i.e., a 1 in the ovtoday column), such as urinary LH surge tests or basal body temperature (BBT).

  • Imputed using the backward-count method, which estimates ovulation as 15 days before the subsequent menses onset (ovtoday_impute == 1), based on the typical length of the luteal phase.

The output includes both:

  • Overall summary counts across the entire dataset.

  • Per-individual summaries to support participant-level quality checks and reporting.

This summary is essential for understanding data quality and should be transparently reported in publications. It helps clarify how often ovulation timing was determined using biologically grounded methods versus estimated through assumptions. Whenever possible, biomarker-based confirmation (e.g., LH or BBT) is preferred, as it provides more precise and physiologically valid estimates of ovulation timing. This enhances the accuracy of cycle phase alignment and reduces potential sources of error in time-sensitive analyses.

When biomarkers are unavailable, imputation via the -15 day backward-count method offers a more biologically valid estimate than assuming ovulation occurs at the cycle midpoint. This method accounts for the relative stability of the luteal phase length and is recommended over midpoint-based estimates (see Nagpal et al., 2025).

For further guidance on ovulation identification and the rationale for the -15 day imputation method, see:

  • Nagpal et al. (2025). Studying the Menstrual Cycle as a Continuous Variable: Implementing Phase-Aligned Cycle Time Scaling (PACTS) with the menstrualcycleR package. https://doi.org/10.31219/osf.io/hd5xw_v1

  • Schmalenberger et al. (2021). How to study the menstrual cycle: Practical tools and recommendations. Psychoneuroendocrinology, 123, 104895. https://doi.org/10.1016/j.psyneuen.2020.104895

Examples

cycle_df = cycledata

data_with_scaling <- pacts_scaling(
  cycle_df, 
  id = id, 
  date = daterated, 
  menses = menses, 
  ovtoday = ovtoday, 
  lower_cyclength_bound = 21, 
  upper_cyclength_bound = 35
)
#> id: id
#> date: date
#> menses: menses
#> ovtoday: ovtoday
#> Error in ungroup(.): could not find function "ungroup"

ov_summary = summary_ovulation(data_with_scaling)
#> Error: object 'data_with_scaling' not found
print(ov_summary)
#> Error: object 'ov_summary' not found
ov_summary$ovstatus_total
#> Error: object 'ov_summary' not found
ov_summary$ovstatus_id
#> Error: object 'ov_summary' not found