scholarly journals Approximate Reciprocal Relationship Between Two Cause-Specific Hazard Ratios in COVID-19 Data With Mutually Exclusive Events

2021 ◽  
Author(s):  
Sirin Cetin ◽  
Ayse Ulgen ◽  
Hakan Sivgin ◽  
Wentian Li
Keyword(s):  
Survival Data ◽  
Direct Consequence ◽  
Reciprocal Relationship ◽  
Quantitative Relation ◽  
Reciprocal Relation ◽  
Time To Event ◽  
Slow Recovery ◽  
Special Situation ◽  
Hazard Ratios ◽  
Definition Of

ABSTRACTCOVID-19 survival data presents a special situation where not only the time-to-event period is short, but also the two events or outcome types, death and release from hospital, are mutually exclusive, leading to two cause-specific hazard ratios (csHRd and csHRr). The eventual mortality/release outcome can also be analyzed by logistic regression to obtain odds-ratio (OR). We have the following three empirical observations concerning csHRd, csHRr and OR: (1) The magnitude of OR is an upper limit of the csHRd: | log(OR) | ≥ | log(csHRd)|. This relationship between OR and HR might be understood from the definition of the two quantities; (2) csHRd and csHRr point in opposite directions: log(csHRd)· log(csHRr) < 0; This relation is a direct consequence of the nature of the two events; and (3) there is a tendency for a reciprocal relation between csHRd and csHRr: csHRd ∼ 1/csHRr. Though an approximate reciprocal trend between the two hazard ratios is in indication that the same factor causing faster death also lead to slow recovery by a similar mechanism, and vice versa, a quantitative relation between csHRd and csHRr in this context is not obvious. These resutls may help future analyses of COVID-19 data, in particular if the deceased samples are lacking.

scholarly journals Sampling strategies to evaluate the prognostic value of a new biomarker on a time-to-event end-point

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Francesca Graziano ◽  
Maria Grazia Valsecchi ◽  
Paola Rebora
Keyword(s):  
Survival Data ◽  
Prognostic Value ◽  
Cox Model ◽  
Lymphoblastic Leukemia ◽  
Fixed Ratio ◽  
Case Control ◽  
Time To Event ◽  
Full Cohort ◽  
Number Of Patients ◽  
Nested Case Control

Abstract Background The availability of large epidemiological or clinical data storing biological samples allow to study the prognostic value of novel biomarkers, but efficient designs are needed to select a subsample on which to measure them, for parsimony and economical reasons. Two-phase stratified sampling is a flexible approach to perform such sub-sampling, but literature on stratification variables to be used in the sampling and power evaluation is lacking especially for survival data. Methods We compared the performance of different sampling designs to assess the prognostic value of a new biomarker on a time-to-event endpoint, applying a Cox model weighted by the inverse of the empirical inclusion probability. Results Our simulation results suggest that case-control stratified (or post stratified) by a surrogate variable of the marker can yield higher performances than simple random, probability proportional to size, and case-control sampling. In the presence of high censoring rate, results showed an advantage of nested case-control and counter-matching designs in term of design effect, although the use of a fixed ratio between cases and controls might be disadvantageous. On real data on childhood acute lymphoblastic leukemia, we found that optimal sampling using pilot data is greatly efficient. Conclusions Our study suggests that, in our sample, case-control stratified by surrogate and nested case-control yield estimates and power comparable to estimates obtained in the full cohort while strongly decreasing the number of patients required. We recommend to plan the sample size and using sampling designs for exploration of novel biomarker in clinical cohort data.

scholarly journals Mitigation of biases in estimating hazard ratios under non-sensitive and non-specific observation of outcomes–applications to influenza vaccine effectiveness

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Ulrike Baum ◽  
Sangita Kulathinal ◽  
Kari Auranen
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Vaccine Effectiveness ◽  
Partial Likelihood ◽  
Event Data ◽  
Time To Event ◽  
Positive Events ◽  
Time To Event Data ◽  
Hazard Ratios ◽  
Positive Rate

Abstract Background Non-sensitive and non-specific observation of outcomes in time-to-event data affects event counts as well as the risk sets, thus, biasing the estimation of hazard ratios. We investigate how imperfect observation of incident events affects the estimation of vaccine effectiveness based on hazard ratios. Methods Imperfect time-to-event data contain two classes of events: a portion of the true events of interest; and false-positive events mistakenly recorded as events of interest. We develop an estimation method utilising a weighted partial likelihood and probabilistic deletion of false-positive events and assuming the sensitivity and the false-positive rate are known. The performance of the method is evaluated using simulated and Finnish register data. Results The novel method enables unbiased semiparametric estimation of hazard ratios from imperfect time-to-event data. False-positive rates that are small can be approximated to be zero without inducing bias. The method is robust to misspecification of the sensitivity as long as the ratio of the sensitivity in the vaccinated and the unvaccinated is specified correctly and the cumulative risk of the true event is small. Conclusions The weighted partial likelihood can be used to adjust for outcome measurement errors in the estimation of hazard ratios and effectiveness but requires specifying the sensitivity and the false-positive rate. In absence of exact information about these parameters, the method works as a tool for assessing the potential magnitude of bias given a range of likely parameter values.

scholarly journals Revisiting Methods For Modeling Longitudinal and Survival Data: The Framingham Heart Study

2021 ◽  
Author(s):  
Julius S Ngwa ◽  
Howard J Cabral ◽  
Debbie M Cheng ◽  
David R Gagnon ◽  
Michael P LaValley ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Longitudinal Data ◽  
Survival Data ◽  
Framingham Heart Study ◽  
Time Dependent ◽  
Time To Event ◽  
Longitudinal Measure ◽  
Heart Study ◽  
Longitudinal And Survival Data ◽  
Joint Method

Abstract Background: Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. Methods: In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. Results: Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years.Conclusions: Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

scholarly journals Concordance probability as a meaningful contrast across disparate survival times

2020 ◽  
pp. 096228022097369
Author(s):  
Sean M Devlin ◽  
Glenn Heller
Keyword(s):  
Prognostic Score ◽  
Area Under The Curve ◽  
Risk Scores ◽  
Survival Times ◽  
Time To Event ◽  
Cell Renal Cell Carcinoma ◽  
Concordance Probability ◽  
Direct Input ◽  
The Times ◽  
Definition Of

The performance of time-to-event models is frequently assessed in part by estimating the concordance probability, which evaluates the probabilistic pairwise ordering of the model-based risk scores and survival times. The standard definition of this probability conditions on any survival time pair ordering, irrespective of whether the times are meaningfully separated. Inclusion of survival times that would be deemed clinically similar attenuates the concordance and moves the estimate away from the contrast-of-interest: comparing the risk scores between individuals with disparate survival times. In this manuscript, we propose a concordance definition and corresponding method to estimate the probability conditional on survival times being separated by at least a minimum difference. The proposed estimate requires direct input from the analyst to identify a separable survival region and, in doing so, is analogous to the clinically defined subgroups used for binary outcome area under the curve estimates. The method is illustrated in two cancer examples: a prognostic score in clear cell renal cell carcinoma and two biomarkers in metastatic prostate cancer.

scholarly journals Learning to Be Employable or Being Employable to Learn: The Reciprocal Relation Between Perceived Employability and Work-Related Learning

2019 ◽  
pp. 089484531986743
Author(s):  
Ellen Houben ◽  
Nele De Cuyper ◽  
Eva Kyndt ◽  
Anneleen Forrier
Keyword(s):  
Labor Market ◽  
Structural Equation ◽  
Reciprocal Relationship ◽  
Equation Modeling ◽  
Reciprocal Relation ◽  
Work Related ◽  
Knowledge Based ◽  
Significant Relationships ◽  
Perceived Employability ◽  
The Relationship

Learning to become employable is a catch phrase often used to highlight the importance of upskilling in today’s knowledge-based labor market. Yet, evidence on the relationship between work-related learning and employability is limited and does not account for potential reciprocity. This is important though: if employability also promotes work-related learning, labor market segmentation could be enhanced. Accordingly, this study investigates the reciprocal relationship between (formal and informal) work-related learning and perceived (internal and external) employability. Hypotheses are based on the attribution-based theory of intrapersonal motivation, which has not yet figured in employability research. Structural equation modeling was performed on three-wave survey data of Belgian employees. The pattern of results showed a reciprocal, albeit weak, relationship between formal work-related learning and perceived internal employability. No other significant relationships were established. Hence, the relationship between work-related learning and perceived employability might not be as straightforward as generally assumed.

scholarly journals Statistics Notes: Time to event (survival) data

BMJ ◽  
1998 ◽  
Vol 317 (7156) ◽  
pp. 468-469 ◽  
Author(s):  
D. G Altman ◽  
J M. Bland
Keyword(s):  
Survival Data ◽  
Time To Event

scholarly journals A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Stefan Leger ◽  
Alex Zwanenburg ◽  
Karoline Pilz ◽  
Fabian Lohaus ◽  
Annett Linge ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Survival Data ◽  
Time To Event ◽  
Learning Methods ◽  
Risk Modelling ◽  
Machine Learning Methods

scholarly journals Bayesian Joint Models for Longitudinal and Multi-state Survival Data

2019 ◽  
Vol 8 (2) ◽  
pp. 34
Author(s):  
Josua Mwanyekange ◽  
Samuel Musili Mwalili ◽  
Oscar Ngesa
Keyword(s):  
Clinical Trials ◽  
Survival Data ◽  
Phase Iii ◽  
Dependence Structure ◽  
Linear Mixed Effect Model ◽  
Time To Event ◽  
Joint Models ◽  
Mixed Effect ◽  
State Process ◽  
Proportional Intensity

Joint models for longitudinal and time to event data are frequently used in many observational studies such as clinical trials with the aim of investigating how biomarkers which are recorded repeatedly in time are associated with time to an event of interest. In most cases, these joint models only consider a univariate time to event process. However, many clinical trials of patients with cancer, involve multiple recurrences of a single event together with a single terminal event experienced by patients over time. Therefore, this article proposes joint modelling approachs for longitudinal and multi-state data. The approach considers two sub-models that are linked by a common latent random variable. The first sub-model is linear mixed effect model that defines the longitudinal process and the second sub-model is a proportional intensity function for the multi-state process. Furthermore, on the proportional intensity model, two different formulations are used to define dependence structure between longitudinal and multi-state processes. In this article, a semi-Markov process that consider the time spent in the current state is defined for the transitions between states. Moreover, the time spent in each transient state is assumed to have Gompertz distribution. A Bayesian method using Markov Chain Monte Carlo (MCMC) is developed for parameter estimation and inferences. The deviance information criterion (DIC) is also derived for Bayesian model selection and comparison. Finally, our proposed joint modeling approach is evaluated through a simulation study and is applied to real datasets (colorectal and colorectal.Longi) which present a random selection of 150 patients from a multi-center randomized phase III clinical trial FFCD 2000-05 of patients diagnosed with metastatic colorectal cancer.

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