Causal inference in continuous time: an example on prostate cancer therapy.
Biostatistics
; 21(1): 172-185, 2020 01 01.
Article
en En
| MEDLINE
| ID: mdl-30124773
ABSTRACT
In marginal structural models (MSMs), time is traditionally treated as a discrete parameter. In survival analysis on the other hand, we study processes that develop in continuous time. Therefore, Røysland (2011. A martingale approach to continuous-time marginal structural models. Bernoulli 17, 895-915) developed the continuous-time MSMs, along with continuous-time weights. The continuous-time weights are conceptually similar to the inverse probability weights that are used in discrete time MSMs. Here, we demonstrate that continuous-time MSMs may be used in practice. First, we briefly describe the causal model assumptions using counting process notation, and we suggest how causal effect estimates can be derived by calculating continuous-time weights. Then, we describe how additive hazard models can be used to find such effect estimates. Finally, we apply this strategy to compare medium to long-term differences between the two prostate cancer treatments radical prostatectomy and radiation therapy, using data from the Norwegian Cancer Registry. In contrast to the results of a naive analysis, we find that the marginal cumulative incidence of treatment failure is similar between the strategies, accounting for the competing risk of other death.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Evaluación de Procesos y Resultados en Atención de Salud
/
Neoplasias de la Próstata
/
Sistema de Registros
/
Modelos Estadísticos
Tipo de estudio:
Risk_factors_studies
País/Región como asunto:
Europa
Idioma:
En
Revista:
Biostatistics
Año:
2020
Tipo del documento:
Article