A regression model for the conditional probability of a competing event: application to monoclonal gammopathy of unknown significance

Competing Risks Analysis of Kidney Transplant Waitlist Outcomes: Two Important Statistical Perspectives

Modern competing risks analysis has 2 primary goals in clinical epidemiology as follows: (i) to maximize the clinician's knowledge of etiologic associations existing between potential predictor variables and various cause-specific outcomes via cause-specific hazard models, and (ii) to maximize the clinician's knowledge of noteworthy differences existing in cause-specific patient risk via cause-specific subdistribution hazard models (cumulative incidence functions [CIFs]). A perfect application exists in analyzing the following 4 distinct outcomes after listing for a deceased donor kidney transplant (DDKT): (i) receiving a DDKT, (ii) receiving a living donor kidney transplant (LDKT), (iii) waitlist removal due to patient mortality or a deteriorating medical condition, and (iv) waitlist removal due to other reasons. It is important to realize that obtaining a complete understanding of subdistribution hazard ratios (HRs) is simply not possible without first having knowledge of the multivariable relationships existing between the potential predictor variables and the cause-specific hazards (perspective #1), because the cause-specific hazards form the "building blocks" of CIFs. In addition, though we believe that a worthy and practical alternative to estimating the median waiting-time-to DDKT is to ask, "what is the conditional probability of the patient receiving a DDKT, given that he or she would not previously experience one of the competing events (known as the cause-specific conditional failure probability)," only an appropriate estimator of this conditional type of cumulative incidence should be used (perspective #2). One suggested estimator, the well-known "one minus Kaplan-Meier" approach (censoring competing events), simply does not represent any probability in the presence of competing risks and will almost always produce biased estimates (thus, it should never be used).

The role of depression in the association between mobilisation timing and live discharge after hip fracture surgery: Secondary analysis of the UK National Hip Fracture Database

PURPOSE

The aim was to compare the probability of discharge after hip fracture surgery conditional on being alive and in hospital between patients mobilised within and beyond 36-hours of surgery across groups defined by depression.

METHODS

Data were taken from the National Hip Fracture Database and included patients 60 years of age or older who underwent hip fracture surgery in England and Wales between 2014 and 2016. The conditional probability of postsurgical live discharge was estimated for patients mobilised early and for patients mobilised late across groups with and without depression. The association between mobilisation timing and the conditional probability of live discharge were also estimated separately through adjusted generalized linear models.

RESULTS

Data were analysed for 116,274 patients. A diagnosis of depression was present in 8.31% patients. In those with depression, 7,412 (76.7%) patients mobilised early. In those without depression, 84,085 (78.9%) patients mobilised early. By day 30 after surgery, the adjusted odds ratio of discharge among those who mobilised early compared to late was 1.79 (95% CI: 1.56-2.05, p<0.001) and 1.92 (95% CI: 1.84-2.00, p<0.001) for those with and without depression, respectively.

CONCLUSION

A similar proportion of patients with depression mobilised early after hip fracture surgery when compared to those without a diagnosis of depression. The association between mobilisation timing and time to live discharge was observed for patients with and without depression.

A cross-regional examination of patterns and environmental drivers of Pseudo-nitzschia harmful algal blooms along the California coast

Pseudo-nitzschia species with the ability to produce the neurotoxin domoic acid (DA) are the main cause of harmful algal blooms (HABs) along the U.S. West Coast, with major impacts on ecosystems, fisheries, and human health. While most Pseudo-nitzschia (PN) HAB studies to date have focused on their characteristics at specific sites, few cross-regional comparisons exist, and mechanistic understanding of large-scale HAB drivers remains incomplete. To close these gaps, we compiled a nearly 20-year time series of in situ particulate DA and environmental observations to characterize similarities and differences in PN HAB drivers along the California coast. We focus on three DA hotspots with the greatest data density: Monterey Bay, the Santa Barbara Channel, and the San Pedro Channel. Coastwise, DA outbreaks are strongly correlated with upwelling, chlorophyll-a, and silicic acid limitation relative to other nutrients. Clear differences also exist across the three regions, with contrasting responses to climate regimes across a north to south gradient. In Monterey Bay, PN HAB frequency and intensity increase under relatively nutrient-poor conditions during anomalously low upwelling intensities. In contrast, in the Santa Barbara and San Pedro Channels, PN HABs are favored under cold, nitrogen-rich conditions during more intense upwelling. These emerging patterns provide insights on ecological drivers of PN HABs that are consistent across regions and support the development of predictive capabilities for DA outbreaks along the California coast and beyond.

Focus on an infrequently used quantity in the context of competing risks: The conditional probability function

In clinical studies of hematologic and oncologic diseases, the outcomes of interest are generally composite time to event endpoints which are usually defined by occurrence of different event types. Nonetheless, clinicians are interested in studying only one event type, which leads to a competing risks situation. In this context, Pepe and Mori presented a quantity directly derived from the cumulative incidence: the conditional probability. This function defines the probability that a given event occurs, conditionally on not having had a competing event by that time. The objective of this paper is to present this conditional cumulative incidence function and to compare its use to the cumulative incidence in different data sets. Different scenarios highlight the importance of the competing event on the interpretation of the conditional probability. Conditional probability needs to be interpreted jointly with the cumulative incidence. This quantity can be of interest especially when the risk of the competing event is large, strongly precludes the risk of the event of interest and provides useful additional information.

Risk of second hip fracture persists for years after initial trauma

BACKGROUND

Secondary prevention often targets women who suffer from higher rates of second hip fracture than men, especially in the early years after first fracture. Yet, the occurrence of second hip fracture by certain times also depends on the death rate, which is higher in men than women. We compared the risk of sustaining second hip fracture by a certain time between women and men remaining alive at that time.

METHODS

We retrieved 38,383 hospitalization records of patients aged 60 years or older, who were discharged alive after admission for hip fracture surgery between 1990 and 2005 in British Columbia, Canada. The outcome variable was the time to a subsequent hip fracture.

RESULTS

During ten years of follow-up, 2,902 (8%) patients sustained a second hip fracture, and 21,428 (56%) died before sustaining a second hip fracture. The risk of second hip fracture in the surviving post-fracture patients was higher in women than in men: 2% vs 2%, 5% vs 4%, 9% vs 7%, 15% vs 13%, and 35% vs 30% at 1, 2, 3, 5, and 10 years after initial trauma, respectively, crude OR=1.25 (95% CI: 1.13-1.39). However, the risk did not differ between women and men after adjustment, OR=1.09 (95% CI: 0.98-1.21).

CONCLUSIONS

The risk of second hip fracture persists for at least ten years among hip fracture survivors, and therefore secondary prevention should continue beyond an early post-fracture period. Women and men have similar risks of second hip fracture and both should be considered for secondary prevention.