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Introduction This paper aimed to assess purchasing and drinking behaviour during the first COVID‐19 pandemic restrictions in New Zealand. Method A convenience sample was collected via Facebook from 2173 New Zealanders 18+ years during pandemic restrictions April/May 2020. Measures included: the quantity typically consumed during a drinking occasion and heavier drinking (6+ drinks on a typical occasion) in the past week; place of purchase including online alcohol delivery. Descriptive statistics were generated, logistic and linear regression models predicted heavier drinking and typical occasion quantity, respectively. Weighting was not applied. Results During pandemic restrictions, around 75% of respondents purchased from supermarkets, 40% used online alcohol delivery services (18% for the first time during COVID‐19). Purchasing online alcohol delivery during pandemic restrictions was associated with heavier drinking (75% higher odds) in the past week, while purchasing from supermarkets was not. About 58% of online purchasers under 25 reported no age checks. Sixteen percent of those purchasing online repeat ordered online to keep drinking after running out. Of respondents who had tried to buy alcohol and food online, 56% reported that alcohol was easier to get delivered than fresh food. Advertising for online alcohol delivery was seen by around 75% of the sample. Half of the sample reported drinking more alcohol during the restrictions. Discussion and Conclusions Online alcohol delivery during the COVID‐19 pandemic restrictions was associated with heavier drinking in the past week. The rapid expansion of online alcohol delivery coupled with a lack of regulatory control requires public health policy attention.

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Copula models have become increasingly popular for modelling the dependence structure in multivariate survival data. The two-parameter Archimedean family of Power Variance Function (PVF) copulas includes the Clayton, Positive Stable (Gumbel) and Inverse Gaussian copulas as special or limiting cases, thus offers a unified approach to fitting these important copulas. Two-stage frequentist procedures for estimating the marginal distributions and the PVF copula have been suggested by Andersen (Lifetime Data Anal 11:333-350, 2005), Massonnet et al. (J Stat Plann Inference 139(11):3865-3877, 2009) and Prenen et al. (J R Stat Soc Ser B 79(2):483-505, 2017) which first estimate the marginal distributions and conditional on these in a second step to estimate the PVF copula parameters. Here we explore an one-stage Bayesian approach that simultaneously estimates the marginal and the PVF copula parameters. For the marginal distributions, we consider both parametric as well as semiparametric models. We propose a new method to simulate uniform pairs with PVF dependence structure based on conditional sampling for copulas and on numerical approximation to solve a target equation. In a simulation study, small sample properties of the Bayesian estimators are explored. We illustrate the usefulness of the methodology using data on times to appendectomy for adult twins in the Australian NH&MRC Twin registry. Parameters of the marginal distributions and the PVF copula are simultaneously estimated in a parametric as well as a semiparametric approach where the marginal distributions are modelled using Weibull and piecewise exponential distributions, respectively.