[Competing death risks].

J Llorca; M Delgado-Rodríguez

doi:10.1016/s0213-9111(99)71391-9

Home/Archive/PubMed

PubMed · 10564852

[Competing death risks].

J Llorca·M Delgado-Rodríguez

View original source ↗Export metadata ↓

Abstract

The death of an individual is not a repetitive event, so if a cause of death overtakes another cause in producing death, mortality rates from the overtaken cause decrease. This phenomenon is known as competing risks of death and it must be taken into account in any cause-specific mortality analysis. In this work the competing risks concept is formalized and some historical data are described. The main parametric tools to analyze competing risks are displayed, with a special look at the Gompertz and Weibull functions. Regarding non-parametric models, the Chiang method is shown and its applicability on both dependent and independent causes of death is discussed. Finally, other tools specially useful in clinical epidemiology are enumerated, including Cox regression, Kaplan-Meier and log-rank methods, as well as the interactions between competing risks and misclassification and selection biases.

Explore related subjects

Bias Cause of Death Humans Life Tables Models, Theoretical Mortality Proportional Hazards Models Risk Risk Assessment Comparative Study Journal Article Review

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

J Llorca, M Delgado-Rodríguez. [Competing death risks].. https://doi.org/10.1016/s0213-9111(99)71391-9

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Assessment of blinding in pharmacotherapy and noninvasive neuromodulation randomized controlled trials for neuropathic pain in adults.

In randomized controlled trials (RCTs), study participants and research personnel are often blinded to minimize biases related to knowing treatment allocation. To determine if blinding was effective, participants may be asked which treatment they believe they received ("treatment guess"). This descriptive review characterized blinding assessment (BA) reporting in pharmacotherapy and neuromodulation neuropathic pain RCTs. Of 288 papers, 36 (12.5%) reported a BA. One paper reported the results of 2 studies, so in total 37 studies with a BA were assessed. Of these, 19 were crossover, 17 parallel, and 1 partial crossover in design. All 37 studies assessed participant blinding, and 10 also assessed investigator blinding. Approximately 27% included an "unsure" answer option for treatment guess, and 38% asked the reason for the guess. There were no clear patterns in BA reporting across time nor based on treatment type. Seventeen trials provided sufficient data to calculate Bang Blinding Index (BI) to determine blinding success. Participants remained blinded (BI = 0 ± 0.2) in 10/17 placebo and 10/17 treatment arms, 6 placebo and 5 treatment arms had a BI > 0.2 suggesting possible unblinding, whereas 1 placebo and 2 treatment arms had a BI < -0.2 suggesting misinformed guessing. Overall, we found that BAs are done in a minority of published neuropathic pain trials and with variable methodology. Given the importance of minimizing risk of bias because of treatment unblinding, future studies should consider including BAs, and further consensus building is necessary to determine if and how BAs should be conducted and interpreted in analgesic clinical trials.

Bias↗

PubMed2001

Causation of bias: the episcope.

A risk ratio or difference from a meta-analysis is as many as ten steps away from the unobservable causal risk ratios and differences in target populations. The steps are like lenses, filters, or other fallible components of the epidemiologist's "telescope" for observing populations. Each step is another domain where different biases can be caused. How biases combine across domains in the production of epidemiologic evidence can be quickly explained to nonepidemiologists by using a sequence of causal arrow diagrams with easy notation: (a) agent of interest, (b) background risk factors, (c) correlated causes, (d) diagnosis, (e) exposure measurement, (f) filing of data, (g) grouping of cohorts, (h) harvesting of cases and controls, (i) interpretations of investigators, (j) judgments of journals, and (k) knowledge of meta-analysts. For epidemiologists, this article serves as a review of ideas about confounding, information bias, and selection bias and underscores the need for routinely analyzing the sensitivity of study findings to multiple hypothesized biases.

Bias↗

PubMed2000

Assessing the potential for bias in meta-analysis due to selective reporting of subgroup analyses within studies.

Subgroup analysis is frequently used to investigate heterogeneity in meta-analysis. Subgroup data are not always available, and researchers should record what data were available for each trial. If data were not available, and it is known that the subgroup data were collected, the potential for selective reporting should be considered. Bias due to selective publishing of reports is widely recognized in meta-analysis. In contrast, selective reporting within studies is little discussed but potentially important. We explored this problem by evaluating the effect of potential bias in subgroup analysis due to within-study selective reporting with an existing meta-analysis. The review addressed malaria chemoprophylaxis in pregnancy. The conclusion in the original review, that benefit is limited to primigravidae, was based on subgroup analysis using the three trials out of five which reported on subgroups. We developed a method of sensitivity analysis that imputes data for the missing subgroups to assess the robustness of the results and the conclusions drawn. In this particular example, our analysis indicates that the estimate of effect reported in the review is most likely to overestimate the true effect and the conclusion that benefit is limited to primigravidae may be false.

Bias↗