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. 2016 Nov;27(6):848-51.
doi: 10.1097/EDE.0000000000000539.

Brief Report: Estimating Differences and Ratios in Median Times to Event

Elizabeth T Rogawski  1 , Daniel J Westreich, Gagandeep Kang, Honorine D Ward, Stephen R Cole
Affiliations

Brief Report: Estimating Differences and Ratios in Median Times to Event

Elizabeth T Rogawski et al. Epidemiology. 2016 Nov.

Abstract

Time differences and time ratios are often more interpretable estimates of effect than hazard ratios for time-to-event data, especially for common outcomes. We developed a SAS macro for estimating time differences and time ratios between baseline-fixed binary exposure groups based on inverse probability-weighted Kaplan-Meier curves. The macro uses pooled logistic regression to calculate inverse probability of censoring and exposure weights, draws Kaplan-Meier curves based on the weighted data, and estimates the time difference and time ratio at a user-defined survival proportion. The macro also calculates the risk difference and risk ratio at a user-specified time. Confidence intervals are constructed by bootstrap. We provide an example assessing the effect of exclusive breastfeeding during diarrhea on the incidence of subsequent diarrhea in children followed from birth to 3 years in Vellore, India. The SAS macro provided here should facilitate the wider reporting of time differences and time ratios.

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Figures

Figure 1
Figure 1
Schematic showing the calculation of the median time difference and time ratio from inverse probability-weighted Kaplan-Meier curves.
Figure 2
Figure 2
Inverse probability-weighted Kaplan-Meier curves for time to second diarrhea episode by exclusive breastfeeding at the first episode among 982 children in birth cohorts from Vellore, Tamil Nadu, India 2002–2013. A – Crude (unweighted by covariates); B – Weighted by age at first episode, malnutrition status at first episode, child sex, low birth weight, socioeconomic status, maternal education, and household hygiene.
See this image and copyright information in PMC

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