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Objective: To determine whether hormone replacement therapy (HRT) after treatment for breast cancer is associated with increased risk of recurrence and mortality. Design: Retrospective observational study. Participants and setting: Postmenopausal women diagnosed with breast cancer and treated by five Sydney doctors between 1964 and 1999. Outcome measures: Times from diagnosis to cancer recurrence or new breast cancer, to death from all causes and to death from primary tumour were compared between women who used HRT for menopausal symptoms after diagnosis and those who did not. Relative risks (RRs) were determined from Cox regression analyses, adjusted for patient and tumour characteristics. Results: 1122 women were followed up for 0–36 years (median, 6.08 years); 154 were lost to follow‐up. 286 women used HRT for menopausal symptoms for up to 26 years (median, 1.75 years). Compared with non‐users, HRT users had reduced risk of cancer recurrence (adjusted relative risk [RR], 0.62; 95% CI, 0.43–0.87), all‐cause mortality (RR, 0.34; 95% CI, 0.19–0.59) and death from primary tumour (RR, 0.40; 95% CI, 0.22–0.72). Continuous combined HRT was associated with a reduced risk of death from primary tumour (RR, 0.32; 95% CI, 0.12–0.88) and all‐cause mortality (RR, 0.27; 95% CI, 0.10–0.73). Conclusion: HRT use for menopausal symptoms by women treated for primary invasive breast cancer is not associated with an increased risk of breast cancer recurrence or shortened life expectancy.

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Long-range forecasting of intermittent streamflow in semi-arid Australia poses a number of major challenges. One of the challenges relates to modelling zero, skewed, non-stationary, and non-linear data. To address this, a probabilistic statistical model to forecast streamflow 12 months ahead is applied to five semi-arid catchments in South Western Queensland. The model uses logistic regression through Generalised Additive Models for Location, Scale and Shape (GAMLSS) to determine the probability of flow occurring in any of the systems. We then use the same regression framework in combination with a right-skewed distribution, the Box-Cox t distribution, to model the intensity (depth) of the non-zero streamflows. Time, seasonality and climate indices, describing the Pacific and Indian Ocean sea surface temperatures, are tested as covariates in the GAMLSS model to make probabilistic 12-month forecasts of the occurrence and intensity of streamflow. The output reveals that in the study region the occurrence and variability of flow is driven by sea surface temperatures and therefore forecasts can be made with some skill