An Overview on the Complement of Kaplan-Meir Estimation and Cumulative Incidence Estimation in the Presence of Competing Risks_Simulation Approach

2015 ◽  
Vol 1 (06) ◽  
pp. 61-65
Author(s):  
Chinnaiyan Ponnuraja
Keyword(s):  
Cumulative Incidence ◽  
Incidence Estimation

Excess cumulative incidence estimation for matched cohort survival studies

2020 ◽  
Vol 39 (20) ◽  
pp. 2606-2620
Author(s):  
Cristina Boschini ◽  
Klaus K. Andersen ◽  
Hélène Jacqmin‐Gadda ◽  
Pierre Joly ◽  
Thomas H. Scheike
Keyword(s):  
Cumulative Incidence ◽  
Matched Cohort ◽  
Incidence Estimation ◽  
Survival Studies

scholarly journals Improving SARS-CoV-2 cumulative incidence estimation through mixture modelling of antibody levels

2021 ◽  
Author(s):  
C. Bottomley ◽  
M. Otiende ◽  
S. Uyoga ◽  
K. Gallagher ◽  
E.W. Kagucia ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Antibody Level ◽  
Cumulative Incidence ◽  
Model Estimate ◽  
Mixture Modelling ◽  
Seroprevalence Data ◽  
Incidence Estimation ◽  
Using Data ◽  
Serological Data ◽  
Antibody Levels

AbstractAs countries decide on vaccination strategies and how to ease movement restrictions, estimates of cumulative incidence of SARS-CoV-2 infection are essential in quantifying the extent to which populations remain susceptible to COVID-19. Cumulative incidence is usually estimated from seroprevalence data, where seropositives are defined by an arbitrary threshold antibody level, and adjusted for sensitivity and specificity at that threshold. This does not account for antibody waning nor for lower antibody levels in asymptomatic or mildly symptomatic cases. Mixture modelling can estimate cumulative incidence from antibody-level distributions without requiring adjustment for sensitivity and specificity. To illustrate the bias in standard threshold-based seroprevalence estimates, we compared both approaches using data from several Kenyan serosurveys. Compared to the mixture model estimate, threshold analysis underestimated cumulative incidence by 31% (IQR: 11 to 41) on average. Until more discriminating assays are available, mixture modelling offers an approach to reduce bias in estimates of cumulative incidence.One-Sentence SummaryMixture models reduce biases inherent in the standard threshold-based analysis of SARS-CoV-2 serological data.

scholarly journals Impact of and Correction for Outcome Misclassification in Cumulative Incidence Estimation

PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0137454 ◽  
Author(s):  
Giorgos Bakoyannis ◽  
Constantin T. Yiannoutsos
Keyword(s):  
Cumulative Incidence ◽  
Incidence Estimation

Stabilizing cumulative incidence estimation of pregnancy outcome with delayed entries

2018 ◽  
Vol 61 (5) ◽  
pp. 1290-1302 ◽  
Author(s):  
Valentin Rousson ◽  
Arthur Allignol ◽  
Alexandre Aurousseau ◽  
Ursula Winterfeld ◽  
Jan Beyersmann
Keyword(s):  
Pregnancy Outcome ◽  
Cumulative Incidence ◽  
Incidence Estimation

Clinical Factors Associated with Progression to AIDS in the Italian Cohort of HIV-Positive Hemophiliacs

1994 ◽  
Vol 72 (01) ◽  
pp. 033-038 ◽  
Author(s):  
N Schinaia ◽  
A M G Ghirardini ◽  
M G Mazzucconi ◽  
G Tagariello ◽  
M Morfini ◽  
...  
Keyword(s):  
Cumulative Incidence ◽  
National Registry ◽  
Factor Ix ◽  
Clotting Factor ◽  
Coagulation Disorders ◽  
Factors Associated ◽  
Kaplan Meier ◽  
Significant Difference ◽  
Congenital Coagulation Disorders ◽  
Clotting Factor Concentrates

SummaryThis study updates estimates of the cumulative incidence of AIDS among Italian patients with congenital coagulation disorders (mostly hemophiliacs), and elucidates the role of age at seroconversion, type and amount of replacement therapy, and HBV co-infection in progression. Information was collected both retrospectively and prospectively on 767 HIV-1 positive patients enrolled in the on-going national registry of patients with congenital coagulation disorders. The seroconversion date was estimated as the median point of each patient’s seroconversion interval, under a Weibull distribution applied to the overall interval. The independence of factors associated to faster progression was assessed by multivariate analysis. The cumulative incidence of AIDS was estimated using the Kaplan-Meier survival analysis at 17.0% (95% Cl = 14.1-19.9%) over an 8-year period for Italian hemophiliacs. Patients with age greater than or equal to 35 years exhibited the highest cumulative incidence of AIDS over the same time period, 32.5% (95% Cl = 22.2-42.8%). Factor IX recipients (i.e. severe B hemophiliacs) had higher cumulative incidence of AIDS (23.3% vs 14.2%, p = 0.01) than factor VIII recipients (i.e. severe A hemophiliacs), as did severe A hemophiliacs on less-than-20,000 IU/yearly of plasma-derived clotting factor concentrates, as opposed to A hemophiliacs using an average of more than 20,000 IU (18.8% vs 10.9%, p = 0.02). No statistically significant difference in progression was observed between HBsAg-positive vs HBsAg-negative hemophiliacs (10.5% vs 16.4%, p = 0.10). Virological, immunological or both reasons can account for such findings, and should be investigated from the laboratory standpoint.

Can increases in Twitter posts predict increases in cumulative incidence of COVID-19 in the United States? Evidence that social media can inform epidemic surveillance. (Preprint)

2020 ◽  
Author(s):  
Ruoyan Sun ◽  
Henna Budhwani
Keyword(s):  
Public Health ◽  
United States ◽  
Cumulative Incidence ◽  
Hot Spots ◽  
Adult Population ◽  
State Level ◽  
The United States ◽  
Education Attainment ◽  
Public Health Systems ◽  
Per Capita

BACKGROUND Though public health systems are responding rapidly to the COVID-19 pandemic, outcomes from publicly available, crowd-sourced big data may assist in helping to identify hot spots, prioritize equipment allocation and staffing, while also informing health policy related to “shelter in place” and social distancing recommendations. OBJECTIVE To assess if the rising state-level prevalence of COVID-19 related posts on Twitter (tweets) is predictive of state-level cumulative COVID-19 incidence after controlling for socio-economic characteristics. METHODS We identified extracted COVID-19 related tweets from January 21st to March 7th (2020) across all 50 states (N = 7,427,057). Tweets were combined with state-level characteristics and confirmed COVID-19 cases to determine the association between public commentary and cumulative incidence. RESULTS The cumulative incidence of COVID-19 cases varied significantly across states. Ratio of tweet increase (p=0.03), number of physicians per 1,000 population (p=0.01), education attainment (p=0.006), income per capita (p = 0.002), and percentage of adult population (p=0.003) were positively associated with cumulative incidence. Ratio of tweet increase was significantly associated with the logarithmic of cumulative incidence (p=0.06) with a coefficient of 0.26. CONCLUSIONS An increase in the prevalence of state-level tweets was predictive of an increase in COVID-19 diagnoses, providing evidence that Twitter can be a valuable surveillance tool for public health.

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