Sexual activity and weekly contraceptive use among young adult women in Michigan

Past studies on the influence of sexual activity on contraceptive behaviours are inconclusive, relying heavily on cross-sectional data. We used a population-based longitudinal sample of young women in Michigan to evaluate weekly associations between sexual activity and contraceptive use at three levels of measurement: comparing between women, among individual women’s partnerships, and from week to week within partnerships. We used multinomial logistic regression accounting for correlations within partnerships and women. Relative to use of least effective methods, weekly sexual activity was significantly associated with increased use of condoms, pills, and highly effective methods. For pills and highly effective methods, partnership-, woman-, and week-level effects were similar. For condoms, there was no significant woman-level effect. Evidence of immediate effects of sexual activity on contraceptive use highlights the importance of longitudinal data. These dynamics may be diluted or missed altogether when relying on cross-sectional data approaches that compare groups of individuals.     

Improved precision in the analysis of randomized trials with survival outcomes,without assuming proportional hazards

We present a new estimator of the restricted mean survival time in randomized trials where there is right censoring that may depend on treatment and baseline variables. The proposed estimator leverages prognostic baseline variables to obtain equal or better asymptotic precision compared to traditional estimators. Under regularity conditions and random censoring within strata of treatment and baseline variables, the proposed estimator has the following features: (i) it is interpretable under violations of the proportional hazards assumption; (ii) it is consistent and at least as precise as the Kaplan–Meier and inverse probability weighted estimators, under identifiability conditions; (iii) it remains consistent under violations of independent censoring (unlike the Kaplan–Meier estimator) when either the censoring or survival distributions, conditional on covariates, are estimated consistently; and (iv) it achieves the nonparametric efficiency bound when both of these distributions are consistently estimated. We illustrate the performance of our method using simulations based on resampling data from a completed, phase 3 randomized clinical trial of a new surgical treatment for stroke; the proposed estimator achieves a 12% gain in relative efficiency compared to the Kaplan–Meier estimator. The proposed estimator has potential advantages over existing approaches for randomized trials with time-to-event outcomes, since existing methods either rely on model assumptions that are untenable in many applications, or lack some of the efficiency and consistency properties (i)–(iv). We focus on estimation of the restricted mean survival time, but our methods may be adapted to estimate any treatment effect measure defined as a smooth contrast between the survival curves for each study arm. We provide R code to implement the estimator.