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Planning study visits during specific menstrual cycle phases is important if the exposure or outcome is influenced by hormonal variation. However, hormone profiles differ across cycles and across women. The value of using fertility monitors to time clinic visits was evaluated in the BioCycle Study (2005-2007). Women aged 18-44 years (mean, 27.4) with self-reported menstrual cycle lengths of 21-35 days were recruited in Buffalo, New York, for 2 cycles (n = 250). Participants were provided with home fertility monitors that measured urinary estrone-3-glucuronide and luteinizing hormone (LH). The women were instructed to visit the clinic for a blood draw when the monitor indicated an LH surge. The monitor recorded a surge during 76% of the first cycles and 78% of the second cycles. Scheduling visits by using set cycle days or algorithms based on cycle length, such as a midcycle window or a window determined by assuming a fixed luteal phase length, would be simpler. However, even with perfect attendance in a 3-day window, these methods would have performed poorly, capturing the monitor-detected LH surge only 37%-57% of the time. Fertility monitors appear to be useful in timing clinic visits in a compliant population with flexible schedules. [ABSTRACT FROM AUTHOR]

Spontaneous abortion studies that recruit pregnant women are left truncated because an unknown proportion of the source population experiences losses prior to enrollment. Unconditional logistic regression, commonly used in such studies, ignores left truncation, whereas survival analysis can accommodate left truncation and is therefore more appropriate. This study assessed the magnitude of bias introduced by fitting logistic versus Cox models using left-truncated data from a 1998 US pregnancy cohort study (n = 5,104) of trihalomethanes and spontaneous abortion. In addition, the conditions producing bias were explored by using simulated exposure data. The odds ratios and hazard ratios from the actual study differed by 10% or less. However, when the exposed women entered observation earlier on average than those unexposed, the hazard ratio was closer to the null than the odds ratio, whereas the reverse was true when the exposed entered later. The simulation suggests that bias in the odds ratio will exceed 20% when average gestational age at entry for the exposed versus the unexposed differs by 10 days or more, as has been observed regarding some socioeconomic factors, such as education and ethnicity. Cox regression can correct for left truncation and is no more difficult to perform than logistic regression. [ABSTRACT FROM AUTHOR]