Standardized protocols for characterizing women's fertility: A data-driven approach

Abstract

Experts are divided on whether women's cognition and behavior differs between fertile and non-fertile phases of the menstrual cycle. One of the biggest criticisms of this literature concerns the use of indirect, imprecise, and flexible methodologies between studies to characterize women's fertility. To resolve this problem, we provide a data-driven method of best practices for characterizing women's fertile phase. We compared the accuracy of self-reported methods and counting procedures (i.e., the forward- and backward-counting methods) in estimating ovulation using data from 140 women whose fertility was verified with luteinizing hormone tests. Results revealed that no counting method was associated with ovulation with >30% accuracy. A minimum of 39.5% of the days in the six-day fertile window predicted by the counting methods were non-fertile, and correlations between counting method conception probabilities and actual conception probability were weak to moderate, rs=0.11-0.30. Poor results persisted when using a lenient window for predicting ovulation, across alternative estimators of the onset of the next cycle, and when removing outliers to increase the homogeneity of the sample. By contrast, combining counting methods with a relatively inexpensive test of luteinizing hormone predicted fertility with accuracy >95%, but only when specific guidelines were followed. To this end, herein we provide a cost-effective, pragmatic, and standardized protocol that will allow researchers to test whether fertility effects exist or not.

Probing Ovulatory-Cycle Shifts in Women’s Preferences for Men’s Behaviors

“…Moreover, two meta-analyses have come to strikingly diverging conclusions on whether cycle effects exist or not (Gildersleeve et al, 2014;Wood, Kressel, Joshi, & Louie, 2014). Additionally, previously conducted studies have been criticized for potentially serious methodological problems, such as inappropriate sample sizes, using between-subject designs, lack of direct assessments of steroid hormones and not using luteinizing hormone (henceforth LH) tests for validating women's fertile phase (Blake, Dixson, O'Dean, & Denson 2016;Gangestad et al, 2016). In sum, to clarify the scientific discourse about the existence of ovulatory cycle shifts, there is strong need for adequately designed and powered replications, conducted in different interpersonal contexts.…”

Resolving speculations of methodological inadequacies in the standardized protocol for characterizing women's fertility: Comment on Lobmaier and Bachofner (2018)

“…I also summarize the pertinent literature on salivary ferning and cervical mucus evaluations, showing that neither method has sufficient sensitivity and specificity to characterize peak fertility. Using meta-analytic data of 10K menstrual cycles, I then show that the protocol provided by Blake et al (2016) recruits women when conception probability is at its peak and is statistically higher than the window recommended by Lobmaier and Bachofner (2018).…”

“…COMMENT ON LOBAMAIER AND BACHOFNER (2018) 3 I appreciate Lobmaier and Bachofner (2018) drawing attention to the importance of precise and careful methodology when recruiting women in the fertile phase of the menstrual cycle. Inaccuracies in their critical comments and suggestions, however, suggest a closer reading of this literature is necessary before warranting alterations to the standardized protocol for characterizing women's fertility (Blake, Dixson, O'Dean, & Denson, 2016).…”

No evidence that inbreeding avoidance is up-regulated during the ovulatory phase of the menstrual cycle

“…meta-analyses of this literature drew very different conclusions about the robustness of the evidence for ovulatory shifts in women's mate preferences (Gildersleeve et al, 2014;Wood et al, 2004). Researchers have also highlighted several potentially important methodological limitations of studies on this topic (Blake et al, 2016;Gangestad et al, 2016;Jones et al, 2018a).…”

“…Third, studies have typically used self-report methods to assess position in the menstrual cycle (e.g., self-reported number of days since last period of menstrual bleeding at time of testing). Empirical studies suggest these are imprecise and prone to bias (Blake et al, 2016), although this may not be a problem in longitudinal studies with very large samples (e.g., .…”