Long-Term Outcomes of Invasive vs Conservative Strategies for Older Patients With Non-ST-Segment Elevation Acute Coronary Syndromes.

BACKGROUND: Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is a frequent cause of hospital admission in older people, but clinical trials targeting this population are scarce. OBJECTIVES: The After Eighty Study assessed the effect of an invasive vs a conservative treatment strategy in a very old population with NSTE-ACS. METHODS: Between 2010 and 2014, the investigators randomized 457 patients with NSTE-ACS aged ≥80 years (mean age 85 years) to an invasive strategy involving early coronary angiography with immediate evaluation for revascularization and optimal medical therapy or to a conservative strategy (ie, optimal medical therapy). The primary endpoint was a composite of myocardial infarction, need for urgent revascularization, stroke, and death. The long-term outcomes are presented. RESULTS: After a median follow up of 5.3 years, the invasive strategy was superior to the conservative strategy in the reduction of the primary endpoint (incidence rate ratio: 0.76; 95% CI: 0.63-0.93; P = 0.0057). The invasive strategy demonstrated a significant gain in event-free survival of 276 days (95% CI: 151-400 days; P = 0.0001) at 5 years and 337 days (95% CI: 123-550 days; P = 0.0001) at 10 years. These results were consistent across subgroups of patients with respect to major cardiovascular prognostic factors. CONCLUSIONS: In patients aged ≥80 years with NSTE-ACS, the invasive strategy was superior to the conservative strategy in the reduction of composite events and demonstrated a significant gain in event-free survival. (The After Eighty Study: a randomized controlled trial; NCT01255540).

Causal inference in continuous time: an example on prostate cancer therapy.

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.

Sufficient Cause Interaction for Time-to-event Outcomes.

Methods to assess sufficient cause interactions are well developed for binary outcomes. We extend these methods to handle time-to-event outcomes, which occur frequently in medicine and epidemiology. Based on theory for marginal structural models in continuous time, we show how to assess sufficient cause interaction nonparametrically, allowing for censoring and competing risks. We apply the method to study interaction between intensive blood pressure therapy and statin treatment on all-cause mortality.