Subtleties in the interpretation of hazard contrasts
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Subtleties in the interpretation of hazard contrasts. / Martinussen, Torben; Vansteelandt, Stijn; Andersen, Per Kragh.
In: Lifetime Data Analysis, Vol. 26, 2020, p. 833–855.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Subtleties in the interpretation of hazard contrasts
AU - Martinussen, Torben
AU - Vansteelandt, Stijn
AU - Andersen, Per Kragh
PY - 2020
Y1 - 2020
N2 - The hazard ratio is one of the most commonly reported measures of treatment effect in randomised trials, yet the source of much misinterpretation. This point was made clear by Hernan (Epidemiology (Cambridge, Mass) 21(1):13-15, 2010) in a commentary, which emphasised that the hazard ratio contrasts populations of treated and untreated individuals who survived a given period of time, populations that will typically fail to be comparable-even in a randomised trial-as a result of different pressures or intensities acting on different populations. The commentary has been very influential, but also a source of surprise and confusion. In this note, we aim to provide more insight into the subtle interpretation of hazard ratios and differences, by investigating in particular what can be learned about a treatment effect from the hazard ratio becoming 1 (or the hazard difference 0) after a certain period of time. We further define a hazard ratio that has a causal interpretation and study its relationship to the Cox hazard ratio, and we also define a causal hazard difference. These quantities are of theoretical interest only, however, since they rely on assumptions that cannot be empirically evaluated. Throughout, we will focus on the analysis of randomised experiments.
AB - The hazard ratio is one of the most commonly reported measures of treatment effect in randomised trials, yet the source of much misinterpretation. This point was made clear by Hernan (Epidemiology (Cambridge, Mass) 21(1):13-15, 2010) in a commentary, which emphasised that the hazard ratio contrasts populations of treated and untreated individuals who survived a given period of time, populations that will typically fail to be comparable-even in a randomised trial-as a result of different pressures or intensities acting on different populations. The commentary has been very influential, but also a source of surprise and confusion. In this note, we aim to provide more insight into the subtle interpretation of hazard ratios and differences, by investigating in particular what can be learned about a treatment effect from the hazard ratio becoming 1 (or the hazard difference 0) after a certain period of time. We further define a hazard ratio that has a causal interpretation and study its relationship to the Cox hazard ratio, and we also define a causal hazard difference. These quantities are of theoretical interest only, however, since they rely on assumptions that cannot be empirically evaluated. Throughout, we will focus on the analysis of randomised experiments.
KW - Causality
KW - Cox regression
KW - Hazard difference
KW - Hazard ratio
KW - Randomised study
KW - Survival analysis
KW - PRINCIPAL STRATIFICATION
KW - SURVIVAL
KW - COX
U2 - 10.1007/s10985-020-09501-5
DO - 10.1007/s10985-020-09501-5
M3 - Journal article
C2 - 32654089
VL - 26
SP - 833
EP - 855
JO - Lifetime Data Analysis
JF - Lifetime Data Analysis
SN - 1380-7870
ER -
ID: 245036170