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National Health Service Blood and Transplant and National Institute for Health Research. (PROSPERO: CRD42017072284).

Summary Organ transplantation saves and transforms lives. Failure to secure consent for organ retrieval is widely regarded as the single most important obstacle to transplantation. A soft opt‐out system of consent for deceased organ donation was introduced into Wales in December 2015, whilst England maintained the existing opt‐in system. Cumulative data on consent rates in Wales were compared with those in England, using a two‐sided sequential procedure that was powered to detect an absolute difference in consent rates between England and Wales of 10%. Supplementary risk‐adjusted logistic regression analysis examined whether any difference in consent rates between the two nations could be attributed to variations in factors known to influence UK consent rates. Between 1 January 2016 and 31 December 2018, 8192 families of eligible donors in England and 474 in Wales were approached regarding organ donation, with overall consent rates of 65% and 68%, respectively. There was a steady upward trend in the proportion of families consenting to donation after brain death in Wales as compared with England and after 33 months, this reached statistical significance. No evidence of any change in the donation after circulatory death consent rate was observed. Risk‐adjusted logistic regression analysis revealed that by the end of the study period the probability of consent to organ donation in Wales was higher than in England ( OR [95% CI ] 2.1 [1.26–3.41]). The introduction of a soft opt‐out system of consent in Wales significantly increased organ donation consent though the impact was not immediate.

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When a response variable can take one of two possible forms, such as alive or dead, the resulting data are said to be binary. The focus of this article is on models for binary response variables, and the use of logistic regression models is emphasized. The models can be fitted using most statistical software packages. Alternative models can be compared using the deviance, and for grouped binary data, this statistic may be used as a measure of goodness offit. Techniques for assessing model adequacy are outlined and the use of these methods is illustrated with an example.