The cumulative incidence function describes the probability that a specific event has occurred by a certain time, taking into account competing events. It is estimated separately for each event type, thereby showing the actually observed proportion of individuals who experience the target event. The Aalen-Johansen estimator models the sequence of different first events and the remaining event-free time for this purpose. <\/p>\n
In the presence of multiple possible first events, a patient can only have reached one of these states at a given time. The cumulative incidence function of the target event therefore not only counts its occurrence but also considers that competing events permanently remove individuals from the set of possible future target events. It answers the question of event probability in the real competition of causes. <\/p>\n
A curve starts at zero and increases when the target event is observed. Its height at a given time is not the probability that an event would have occurred with continued risk, but rather the probability under the observed system of competing events. For separate causes, several curves can be shown side by side; together with the remaining event-free time, they represent the possible initial courses. <\/p>\n
The Aalen-Johansen estimator extends the principle of observed transitions to multiple event types. It assigns each first event time to its cause and updates the proportions at risk accordingly. Administrative censoring is treated separately because it does not indicate that a competing event has occurred. <\/p>\n
The area under such a curve up to a chosen time point can be interpreted as the event-free time lost due to the target event, accumulated within the time window. This interpretation requires a clear state definition. It is not to be equated with restricted mean survival time, because with multiple event types, it must be precisely specified which state is considered lost. <\/p>\n
The cumulative incidence function is not the counterpart to a Kaplan-Meier curve, but rather its appropriate replacement for event probability in the presence of competing risks. Kaplan-Meier treats competing events as censoring and thus estimates a hypothetical probability without their exclusionary effect. In contrast, the cumulative incidence function incorporates the competition into the estimator. <\/p>\n
It is closely related to the entry on Competing Risks and Subdistribution Hazard. This explains regression on the subdistribution hazard; the cumulative incidence function is the descriptive event probability to which this modeling refers. The terms hazard ratio and survival analysis do not automatically capture this cause-specific perspective. <\/p>\n
Direct group comparison can be performed using a test adapted for cumulative incidences or by a pre-selected regression model. The curves alone do not answer whether an observed difference is consistent with the intended uncertainty assessment. For confirmatory questions, the function’s role in the multiplicity and decision plan must therefore also be established. <\/p>\n
The number at risk remains important accompanying information. If it decreases sharply, steps at the end of the curve can arise from few events, and the impression of large group differences can be misleading. An appropriate graph therefore shows the time axis, event type, understanding of censoring, and the underlying numbers at risk. <\/p>\n
In studies on recurrences, organ failure, or specific adverse events, death before the target event can significantly influence clinical frequency. Case report forms and coding rules must therefore reliably distinguish the cause of the first event. During data review, contradictory data, such as a later-dated target event after documented death, must be clarified before evaluation. <\/p>\n
Full-service CROs like Mediconomics support the definition of event categories, the verification of temporal consistency of event and death data, and the creation of Aalen-Johansen-based incidence curves. The statistical outputs can tabulate target and competing events separately, so that the graphical representation and the clinical event balance reflect the same data basis. <\/p>\n
A precise data derivation always preserves the first clinically relevant reason for the event unchanged.<\/p>\n
Can the cumulative incidence function be calculated for multiple causes simultaneously?<\/strong><\/p>\n Yes. A separate function is estimated for each cause, provided the causes are defined as mutually exclusive first events. The analysis must specify how combined or simultaneously reported events are ordered chronologically. <\/p>\n Why is censoring not a competing event?<\/strong><\/p>\n Censoring merely means that no observation is available thereafter, for example, due to study end or loss to follow-up. It does not make the target event impossible. A competing event, however, permanently changes the patient’s possible future state. <\/p>\n Is a higher cumulative incidence always a worse outcome?<\/strong><\/p>\n That depends on the target event. For a relapse, a higher incidence may be detrimental; for a desired response, it may be favorable. The direction of clinical evaluation results from the endpoint definition and not from the mathematical designation. <\/p>\n The cumulative incidence function describes the probability that a specific event has occurred by a certain time, taking into account competing events. It is estimated separately for each event type, thereby showing the actually observed proportion of individuals who experience the target event. The Aalen-Johansen estimator models the sequence of different first events and the […]<\/p>\n","protected":false},"author":10,"featured_media":0,"parent":0,"template":"","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"glossary-cat":[22],"class_list":["post-7804","glossary","type-glossary","status-publish","hentry","glossary-cat-biostatistik-methodik"],"acf":[],"related_terms":"","external_url":"","internal_reference_id":"","_links":{"self":[{"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary\/7804","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary"}],"about":[{"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/types\/glossary"}],"author":[{"embeddable":true,"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/users\/10"}],"version-history":[{"count":0,"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary\/7804\/revisions"}],"wp:attachment":[{"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/media?parent=7804"}],"wp:term":[{"taxonomy":"glossary-cat","embeddable":true,"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary-cat?post=7804"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Regulatory References<\/h2>\n
\n