A nonparametric proportional risk model to assess a treatment effect in time-to-event data

Time-to-event analysis often relies on prior parametric assumptions, or, if a semiparametric approach is chosen, Cox's model. This is inherently tied to the assumption of proportional hazards, with the analysis potentially invalidated if this assumption is not fulfilled. In addition, most interpretations focus on the hazard ratio, that is often misinterpreted as the relative risk (RR), the ratio of the cumulative distribution functions. In this paper, we introduce an alternative to current methodology for assessing a treatment effect in a two-group situation, not relying on the proportional hazards assumption but assuming proportional risks. Precisely, we propose a new nonparametric model to directly estimate the RR of two groups to experience an event under the assumption that the risk ratio is constant over time. In addition to this relative measure, our model allows for calculating the number needed to treat as an absolute measure, providing the possibility of an easy and holistic interpretation of the data. We demonstrate the validity of the approach by means of a simulation study and present an application to data from a large randomized controlled trial investigating the effect of dapagliflozin on all-cause mortality.

Genome-wide time-to-event analysis on smoking progression stages in a family-based study

BACKGROUND

Various pivotal stages in smoking behavior can be identified, including initiation, conversion from experimenting to established use, development of tolerance, and cessation. Previous studies have shown high heritability for age of smoking initiation and cessation; however, time-to-event genome-wide association studies aiming to identify underpinning genes that accelerate or delay these transitions are missing to date.

METHODS

We investigated which single nucleotide polymorphisms (SNPs) across the whole genome contribute to the hazard ratio of transition between different stages of smoking behavior by performing time-to-event analyses within a large Finnish twin family cohort (N = 1962), and further conducted mediation analyses of plausible intermediate traits for significant SNPs.

RESULTS

Genome-wide significant signals were detected for three of the four transitions: (1) for smoking cessation on 10p14 (P = 4.47e-08 for rs72779075 flanked by RP11-575N15 and GATA3), (2) for tolerance on 11p13 (P = 1.29e-08 for rs11031684 in RP1-65P5.1), mediated by smoking quantity, and on 9q34.12 (P = 3.81e-08 for rs2304808 in FUBP3), independent of smoking quantity, and (3) for smoking initiation on 19q13.33 (P = 3.37e-08 for rs73050610 flanked by TRPM4 and SLC6A16) in analysis adjusted for first time sensations. Although our top SNPs did not replicate, another SNP in the TRPM4-SLC6A16 gene region showed statistically significant association after region-based multiple testing correction in an independent Australian twin family sample.

CONCLUSION

Our results suggest that the functional effect of the TRPM4-SLC6A16 gene region deserves further investigation, and that complex neurotransmitter networks including dopamine and glutamate may play a critical role in smoking initiation. Moreover, comparison of these results implies that genetic contributions to the complex smoking behavioral phenotypes vary among the transitions.