Estimating a population cumulative incidence under calendar time trends

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Estimating a population cumulative incidence under calendar time trends. / Hansen, Stefan N.; Overgaard, Morten; Andersen, Per K.; Parner, Erik T.

In: B M C Medical Research Methodology, Vol. 17, 7, 11.01.2017.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Hansen, SN, Overgaard, M, Andersen, PK & Parner, ET 2017, 'Estimating a population cumulative incidence under calendar time trends', B M C Medical Research Methodology, vol. 17, 7. https://doi.org/10.1186/s12874-016-0280-6

APA

Hansen, S. N., Overgaard, M., Andersen, P. K., & Parner, E. T. (2017). Estimating a population cumulative incidence under calendar time trends. B M C Medical Research Methodology, 17, [7]. https://doi.org/10.1186/s12874-016-0280-6

Vancouver

Hansen SN, Overgaard M, Andersen PK, Parner ET. Estimating a population cumulative incidence under calendar time trends. B M C Medical Research Methodology. 2017 Jan 11;17. 7. https://doi.org/10.1186/s12874-016-0280-6

Author

Hansen, Stefan N. ; Overgaard, Morten ; Andersen, Per K. ; Parner, Erik T. / Estimating a population cumulative incidence under calendar time trends. In: B M C Medical Research Methodology. 2017 ; Vol. 17.

Bibtex

@article{014f16676bbf4b3bad7f5af562161a7c,
title = "Estimating a population cumulative incidence under calendar time trends",
abstract = "BACKGROUND: The risk of a disease or psychiatric disorder is frequently measured by the age-specific cumulative incidence. Cumulative incidence estimates are often derived in cohort studies with individuals recruited over calendar time and with the end of follow-up governed by a specific date. It is common practice to apply the Kaplan-Meier or Aalen-Johansen estimator to the total sample and report either the estimated cumulative incidence curve or just a single point on the curve as a description of the disease risk.METHODS: We argue that, whenever the disease or disorder of interest is influenced by calendar time trends, the total sample Kaplan-Meier and Aalen-Johansen estimators do not provide useful estimates of the general risk in the target population. We present some alternatives to this type of analysis.RESULTS: We show how a proportional hazards model may be used to extrapolate disease risk estimates if proportionality is a reasonable assumption. If not reasonable, we instead advocate that a more useful description of the disease risk lies in the age-specific cumulative incidence curves across strata given by time of entry or perhaps just the end of follow-up estimates across all strata. Finally, we argue that a weighted average of these end of follow-up estimates may be a useful summary measure of the disease risk within the study period.CONCLUSIONS: Time trends in a disease risk will render total sample estimators less useful in observational studies with staggered entry and administrative censoring. An analysis based on proportional hazards or a stratified analysis may be better alternatives.",
author = "Hansen, {Stefan N.} and Morten Overgaard and Andersen, {Per K.} and Parner, {Erik T.}",
year = "2017",
month = "1",
day = "11",
doi = "10.1186/s12874-016-0280-6",
language = "English",
volume = "17",
journal = "B M C Medical Research Methodology",
issn = "1471-2288",
publisher = "BioMed Central Ltd.",

}

RIS

TY - JOUR

T1 - Estimating a population cumulative incidence under calendar time trends

AU - Hansen, Stefan N.

AU - Overgaard, Morten

AU - Andersen, Per K.

AU - Parner, Erik T.

PY - 2017/1/11

Y1 - 2017/1/11

N2 - BACKGROUND: The risk of a disease or psychiatric disorder is frequently measured by the age-specific cumulative incidence. Cumulative incidence estimates are often derived in cohort studies with individuals recruited over calendar time and with the end of follow-up governed by a specific date. It is common practice to apply the Kaplan-Meier or Aalen-Johansen estimator to the total sample and report either the estimated cumulative incidence curve or just a single point on the curve as a description of the disease risk.METHODS: We argue that, whenever the disease or disorder of interest is influenced by calendar time trends, the total sample Kaplan-Meier and Aalen-Johansen estimators do not provide useful estimates of the general risk in the target population. We present some alternatives to this type of analysis.RESULTS: We show how a proportional hazards model may be used to extrapolate disease risk estimates if proportionality is a reasonable assumption. If not reasonable, we instead advocate that a more useful description of the disease risk lies in the age-specific cumulative incidence curves across strata given by time of entry or perhaps just the end of follow-up estimates across all strata. Finally, we argue that a weighted average of these end of follow-up estimates may be a useful summary measure of the disease risk within the study period.CONCLUSIONS: Time trends in a disease risk will render total sample estimators less useful in observational studies with staggered entry and administrative censoring. An analysis based on proportional hazards or a stratified analysis may be better alternatives.

AB - BACKGROUND: The risk of a disease or psychiatric disorder is frequently measured by the age-specific cumulative incidence. Cumulative incidence estimates are often derived in cohort studies with individuals recruited over calendar time and with the end of follow-up governed by a specific date. It is common practice to apply the Kaplan-Meier or Aalen-Johansen estimator to the total sample and report either the estimated cumulative incidence curve or just a single point on the curve as a description of the disease risk.METHODS: We argue that, whenever the disease or disorder of interest is influenced by calendar time trends, the total sample Kaplan-Meier and Aalen-Johansen estimators do not provide useful estimates of the general risk in the target population. We present some alternatives to this type of analysis.RESULTS: We show how a proportional hazards model may be used to extrapolate disease risk estimates if proportionality is a reasonable assumption. If not reasonable, we instead advocate that a more useful description of the disease risk lies in the age-specific cumulative incidence curves across strata given by time of entry or perhaps just the end of follow-up estimates across all strata. Finally, we argue that a weighted average of these end of follow-up estimates may be a useful summary measure of the disease risk within the study period.CONCLUSIONS: Time trends in a disease risk will render total sample estimators less useful in observational studies with staggered entry and administrative censoring. An analysis based on proportional hazards or a stratified analysis may be better alternatives.

U2 - 10.1186/s12874-016-0280-6

DO - 10.1186/s12874-016-0280-6

M3 - Journal article

VL - 17

JO - B M C Medical Research Methodology

JF - B M C Medical Research Methodology

SN - 1471-2288

M1 - 7

ER -

ID: 195511106