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Journal articles on the topic 'Time-to-event outcomes'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Stensrud, Mats Julius, Pål Christie Ryalen, and Kjetil Røysland. "Sufficient Cause Interaction for Time-to-event Outcomes." Epidemiology 30, no. 2 (March 2019): 189–96. http://dx.doi.org/10.1097/ede.0000000000000958.

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2

Clark, David E., and Louise M. Ryan. "Modeling Injury Outcomes Using Time-to-event Methods." Journal of Trauma: Injury, Infection, and Critical Care 42, no. 6 (June 1997): 1129–34. http://dx.doi.org/10.1097/00005373-199706000-00025.

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3

Gao, Yan, and Yan Cui. "Clinical time-to-event prediction enhanced by incorporating compatible related outcomes." PLOS Digital Health 1, no. 5 (May 26, 2022): e0000038. http://dx.doi.org/10.1371/journal.pdig.0000038.

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Accurate time-to-event (TTE) prediction of clinical outcomes from personal biomedical data is essential for precision medicine. It has become increasingly common that clinical datasets contain information for multiple related patient outcomes from comorbid diseases or multifaceted endpoints of a single disease. Various TTE models have been developed to handle competing risks that are related to mutually exclusive events. However, clinical outcomes are often non-competing and can occur at the same time or sequentially. Here we develop TTE prediction models with the capacity of incorporating compatible related clinical outcomes. We test our method on real and synthetic data and find that the incorporation of related auxiliary clinical outcomes can: 1) significantly improve the TTE prediction performance of conventional Cox model while maintaining its interpretability; 2) further improve the performance of the state-of-the-art deep learning based models. While the auxiliary outcomes are utilized for model training, the model deployment is not limited by the availability of the auxiliary outcome data because the auxiliary outcome information is not required for the prediction of the primary outcome once the model is trained.

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Agarwal, Parul, Erin Moshier, Meng Ru, Nisha Ohri, Ronald Ennis, Kenneth Rosenzweig, and Madhu Mazumdar. "Immortal Time Bias in Observational Studies of Time-to-Event Outcomes." Cancer Control 25, no. 1 (January 1, 2018): 107327481878935. http://dx.doi.org/10.1177/1073274818789355.

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The objectives of this study are to illustrate the effects of immortal time bias (ITB) using an oncology outcomes database and quantify through simulations the magnitude and direction of ITB when different analytical techniques are used. A cohort of 11 626 women who received neoadjuvant chemotherapy and underwent mastectomy with pathologically positive lymph nodes were accrued from the National Cancer Database (2004-2008). Standard Cox regression, time-dependent (TD), and landmark models were used to compare overall survival in patients who did or did not receive postmastectomy radiation therapy (PMRT). Simulation studies showing ways to reduce the effect of ITB indicate that TD exposures should be included as variables in hazard-based analyses. Standard Cox regression models comparing overall survival in patients who did and did not receive PMRT showed a significant treatment effect (hazard ratio [HR]: 0.93, 95% confidence interval [CI]: 0.88-0.99). Time-dependent and landmark methods estimated no treatment effect with HR: 0.97, 95% CI: 0.92 to 1.03 and HR: 0.98, 95% CI, 0.92 to 1.04, respectively. In our simulation studies, the standard Cox regression model significantly overestimated treatment effects when no effect was present. Estimates of TD models were closest to the true treatment effect. Landmark model results were highly dependent on landmark timing. Appropriate statistical approaches that account for ITB are critical to minimize bias when examining relationships between receipt of PMRT and survival.

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5

Jones, Mark, and Robert Fowler. "Immortal time bias in observational studies of time-to-event outcomes." Journal of Critical Care 36 (December 2016): 195–99. http://dx.doi.org/10.1016/j.jcrc.2016.07.017.

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6

Williamson, Paula R., Catrin Tudur Smith, Jane L. Hutton, and Anthony G. Marson. "Aggregate data meta-analysis with time-to-event outcomes." Statistics in Medicine 21, no. 22 (2002): 3337–51. http://dx.doi.org/10.1002/sim.1303.

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7

Berger, Moritz, and Matthias Schmid. "Semiparametric regression for discrete time-to-event data." Statistical Modelling 18, no. 3-4 (January 17, 2018): 322–45. http://dx.doi.org/10.1177/1471082x17748084.

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Abstract: Time-to-event models are a popular tool to analyse data where the outcome variable is the time to the occurrence of a specific event of interest. Here, we focus on the analysis of time-to-event outcomes that are either intrinsically discrete or grouped versions of continuous event times. In the literature, there exists a variety of regression methods for such data. This tutorial provides an introduction to how these models can be applied using open source statistical software. In particular, we consider semiparametric extensions comprising the use of smooth nonlinear functions and tree-based methods. All methods are illustrated by data on the duration of unemployment of US citizens.

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Nielsen, Rasmus Oestergaard, Michael Lejbach Bertelsen, Daniel Ramskov, Merete Møller, Adam Hulme, Daniel Theisen, Caroline F. Finch, Lauren Victoria Fortington, Mohammad Ali Mansournia, and Erik Thorlund Parner. "Time-to-event analysis for sports injury research part 2: time-varying outcomes." British Journal of Sports Medicine 53, no. 1 (November 9, 2018): 70–78. http://dx.doi.org/10.1136/bjsports-2018-100000.

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BackgroundTime-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain.ContentIn the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete.ConclusionTime-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: ‘how much change in training load is too much before injury is sustained, among athletes with different characteristics?’ Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward.

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9

Gregson, John, Linda Sharples, Gregg W. Stone, Carl-Fredrik Burman, Fredrik Öhrn, and Stuart Pocock. "Nonproportional Hazards for Time-to-Event Outcomes in Clinical Trials." Journal of the American College of Cardiology 74, no. 16 (October 2019): 2102–12. http://dx.doi.org/10.1016/j.jacc.2019.08.1034.

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10

Sugimoto, Tomoyuki, Toshimitsu Hamasaki, Scott R. Evans, and Takashi Sozu. "Sizing clinical trials when comparing bivariate time-to-event outcomes." Statistics in Medicine 36, no. 9 (January 24, 2017): 1363–82. http://dx.doi.org/10.1002/sim.7225.

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11

Siannis,, F., J. K. Barrett,, V. T. Farewell, and J. F. Tierney. "One‐stage parametric meta‐analysis of time‐to‐event outcomes." Statistics in Medicine 29, no. 29 (October 20, 2010): 3030–45. http://dx.doi.org/10.1002/sim.4086.

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12

Walters, Stephen J. "Analyzing time to event outcomes with a Cox regression model." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 3 (February 24, 2012): 310–15. http://dx.doi.org/10.1002/wics.1197.

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13

Lee, Catherine, Stephanie J. Lee, and Sebastien Haneuse. "Time-to-event analysis when the event is defined on a finite time interval." Statistical Methods in Medical Research 29, no. 6 (August 22, 2019): 1573–91. http://dx.doi.org/10.1177/0962280219869364.

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Acute graft-versus-host disease (GVHD) is a frequent complication following hematopoietic cell transplantation (HCT). Research on risk factors for acute GVHD has tended to ignore two important clinical issues. First, post-transplant mortality is high. In our motivating data, 100-day post-HCT mortality was 15.4%. Second, acute GVHD in its classic form is only diagnosed within 100 days of the transplant; beyond 100 days, a patient may be diagnosed with late onset acute or chronic GVHD. Standard modeling of time-to-event outcomes, however, generally conceive of patients being able to experience the event at any point on the time scale. In this paper, we propose a novel multi-state model that simultaneously: (i) accounts for mortality through joint modeling of acute GVHD and death, and (ii) explicitly acknowledges the finite time interval during which the event of interest can take place. The observed data likelihood is derived, with estimation and inference via maximum likelihood. Additionally, we provide methods for estimating the absolute risk of acute GVHD and death simultaneously. The proposed framework is compared via comprehensive simulations to a number of alternative approaches that each acknowledge some but not all aspects of acute GVHD, and illustrated with an analysis of HCT data that motivated this work.

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Freeman, Suzanne, Alex Sutton, and Nicola Cooper. "OP380 A Review Of The Methodology Used To Synthesize Continuous And Time-To-Event Outcomes For Clinical And Cost-Effectiveness." International Journal of Technology Assessment in Health Care 36, S1 (December 2020): 7. http://dx.doi.org/10.1017/s0266462320001051.

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IntroductionSynthesis of continuous and time-to-event outcomes is often complicated by the use of multiple outcome scales and heterogeneous reporting of outcomes across trials. Simple methods of evidence synthesis for clinical effectiveness can fail to account for these issues and result in a reduction of the evidence base, which can be further reduced at the cost-effectiveness stage as common outcome measures, such as standardized mean differences, cannot easily be incorporated into the economic decision model. Recent methodological advances for synthesizing continuous and time-to-event outcomes aim to include a greater proportion of the available evidence base within a single coherent analysis.MethodsTo assess the statistical methods commonly used in health technology assessment (HTA) and establish whether recent advances in synthesis methods have been adopted in practice, we conducted a review of HTA reports and guidelines published in the United Kingdom (UK) between 1 April 2018 and 31 March 2019 reporting a quantitative meta-analysis (MA), network meta-analysis (NMA) or indirect treatment comparison (ITC) of at least one continuous or time-to-event outcome.ResultsForty-seven articles were considered eligible for this review. Fifty-one percent of eligible articles reported at least one continuous outcome and 55 percent at least one time-to-event outcome. Twenty-nine articles reported NMA or ITC and twenty-seven reported MA of a continuous or time-to-event outcome. Forty articles included a decision model, of which twenty-seven incorporated evidence from a synthesis of a continuous or time-to-event outcome with eleven informed by a single trial (despite synthesis being conducted).ConclusionsUptake of methods to include a greater proportion of the available evidence base within a single coherent analysis in UK HTA reports has been slow. Evaluating health technologies using an evidence-based approach often results in better outcomes for patients. Therefore, HTA analysts and decision modelers must be aware of the expanding literature for synthesis of continuous and time-to-event outcomes and appreciate the limitations of simpler approaches.

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15

Yan, Donglin, Christopher Tait, Nolan A. Wages, Tamila Kindwall-Keller, and Emily V. Dressler. "Generalization of the time-to-event continual reassessment method to bivariate outcomes." Journal of Biopharmaceutical Statistics 29, no. 4 (July 2, 2019): 635–47. http://dx.doi.org/10.1080/10543406.2019.1634087.

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16

Shaw, Pamela A., and Michael P. Fay. "A rank test for bivariate time-to-event outcomes when one event is a surrogate." Statistics in Medicine 35, no. 19 (April 5, 2016): 3413–23. http://dx.doi.org/10.1002/sim.6950.

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17

Cai, Weixin, and Mark J. Laan. "One‐step targeted maximum likelihood estimation for time‐to‐event outcomes." Biometrics 76, no. 3 (November 28, 2019): 722–33. http://dx.doi.org/10.1111/biom.13172.

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18

Freidlin, Boris, and Edward L. Korn. "Sample size adjustment designs with time-to-event outcomes: A caution." Clinical Trials 14, no. 6 (August 10, 2017): 597–604. http://dx.doi.org/10.1177/1740774517724746.

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19

Silverman, Rachel Kloss, Anastasia Ivanova, and Jason Fine. "Sequential parallel comparison design with binary and time-to-event outcomes." Statistics in Medicine 37, no. 9 (February 20, 2018): 1454–66. http://dx.doi.org/10.1002/sim.7635.

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20

Ren, Shijie, and Jeremy E. Oakley. "Assurance calculations for planning clinical trials with time-to-event outcomes." Statistics in Medicine 33, no. 1 (July 16, 2013): 31–45. http://dx.doi.org/10.1002/sim.5916.

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21

Mu, Rongji, and Jin Xu. "Predicting events in clinical trials using two time-to-event outcomes." Biometrical Journal 60, no. 4 (May 22, 2018): 815–26. http://dx.doi.org/10.1002/bimj.201700083.

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22

Tang, Yanlin, Xinyuan Song, and Grace Yun Yi. "Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes." Lifetime Data Analysis 28, no. 1 (January 2022): 139–68. http://dx.doi.org/10.1007/s10985-021-09543-3.

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23

Chakraborty, Arindom, and Kalyan Das. "Inferences for Joint Modelling of Repeated Ordinal Scores and Time to Event Data." Computational and Mathematical Methods in Medicine 11, no. 3 (2010): 281–95. http://dx.doi.org/10.1080/17486701003789096.

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In clinical trials and other follow-up studies, it is natural that a response variable is repeatedly measured during follow-up and the occurrence of some key event is also monitored. There has been a considerable study on the joint modelling these measures together with information on covariates. But most of the studies are related to continuous outcomes. In many situations instead of observing continuous outcomes, repeated ordinal outcomes are recorded over time. The joint modelling of such serial outcomes and the time to event data then becomes a bit complicated. In this article we have attempted to analyse such models through a latent variable model. In view of the longitudinal variation on the ordinal outcome measure, it is desirable to account for the dependence between ordered categorical responses and survival time for different causes due to unobserved factors. A flexible Monte Carlo EM (MCEM) method based on exact likelihood is proposed that can simultaneously handle the longitudinal ordinal data and also the censored time to event data. A computationally more efficient MCEM method based on approximation of the likelihood is also proposed. The method is applied to a number of ordinal scores and survival data from trials of a treatment for children suffering from Duchenne Muscular Dystrophy. Finally, a simulation study is conducted to examine the finite sample properties of the proposed estimators in the joint model under two different methods.

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Gebski, Val, Ian Marschner, Rebecca Asher, and Karen Byth. "Using recurrent time‐to‐event models with multinomial outcomes to generate toxicity profiles." Pharmaceutical Statistics 20, no. 4 (March 17, 2021): 840–49. http://dx.doi.org/10.1002/pst.2113.

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Ishak, K. "PRM62 Visual Assessment of Fit of Equations to Predict Time-to-Event Outcomes." Value in Health 15, no. 4 (June 2012): A169—A170. http://dx.doi.org/10.1016/j.jval.2012.03.917.

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26

Papageorgiou, Grigorios, Katya Mauff, Anirudh Tomer, and Dimitris Rizopoulos. "An Overview of Joint Modeling of Time-to-Event and Longitudinal Outcomes." Annual Review of Statistics and Its Application 6, no. 1 (March 7, 2019): 223–40. http://dx.doi.org/10.1146/annurev-statistics-030718-105048.

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In this review, we present an overview of joint models for longitudinal and time-to-event data. We introduce a generalized formulation for the joint model that incorporates multiple longitudinal outcomes of varying types. We focus on extensions for the parametrization of the association structure that links the longitudinal and time-to-event outcomes, estimation techniques, and dynamic predictions. We also outline the software available for the application of these models.

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27

Liu, Qian, Abigail R. Smith, Laura H. Mariani, Viji Nair, and Jarcy Zee. "Methods for Assessing Longitudinal Biomarkers of Time-to-Event Outcomes in CKD." Clinical Journal of the American Society of Nephrology 14, no. 9 (August 15, 2019): 1315–23. http://dx.doi.org/10.2215/cjn.00450119.

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Background and objectivesIdentifying novel biomarkers is critical to advancing diagnosis and treatment of CKD, but relies heavily on the statistical methods used. Inappropriate methods can lead to both false positive and false negative associations between biomarkers and outcomes. This study assessed accuracy of methods using computer simulations and compared biomarker association estimates in the NEPhrotic syndrome sTUdy NEtwork (NEPTUNE), a prospective cohort study of patients with glomerular disease.Design, setting, participants, & measurementsWe compared three methods for analyzing repeatedly measured biomarkers in proportional hazards models: (1) time-invariant average, that averages values over all follow-up and uses the average as a baseline covariate, (2) time-varying last observation carried forward (LOCF), that assumes the covariate is unchanged until the next observed value, and (3) time-varying cumulative average, that updates the average using values at or before each measurement.ResultsUnder both true mechanisms of LOCF and cumulative average, simulation results showed the time-invariant average method often gave extremely inaccurate results. When LOCF was the true association mechanism, the cumulative average method often gave overestimated association estimates that were further away from the null. When cumulative average was the true mechanism, LOCF always underestimated the associations, i.e., closer to the null. In NEPTUNE, compared with the LOCF or cumulative average methods, hazard ratios estimated from the time-invariant average method were always higher.ConclusionsDifferent analytic methods resulted in markedly different results. Using the time-invariant average produces inaccurate association estimates, whereas other methods can estimate additive (cumulative average) or instantaneous (LOCF) associations depending on the hypothesized underlying association mechanism and research question.

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Chiba, Yasutaka. "Kaplan–Meier curves for survivor causal effects with time-to-event outcomes." Clinical Trials: Journal of the Society for Clinical Trials 10, no. 4 (April 22, 2013): 515–21. http://dx.doi.org/10.1177/1740774513483601.

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Ishak, KJ, M. Rael, H. Phatak, C. Masseria, and T. Lanitis. "Simulated Treatment Comparison of Time-To-Event (And Other Non-Linear) Outcomes." Value in Health 18, no. 7 (November 2015): A719. http://dx.doi.org/10.1016/j.jval.2015.09.2723.

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Zhu, Lin, Qingzhao Yu, and Donald E. Mercante. "A Bayesian Sequential Design for Clinical Trials With Time-to-Event Outcomes." Statistics in Biopharmaceutical Research 11, no. 4 (July 22, 2019): 387–97. http://dx.doi.org/10.1080/19466315.2019.1629996.

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Austin, Peter C., Neal Thomas, and Donald B. Rubin. "Covariate-adjusted survival analyses in propensity-score matched samples: Imputing potential time-to-event outcomes." Statistical Methods in Medical Research 29, no. 3 (December 20, 2018): 728–51. http://dx.doi.org/10.1177/0962280218817926.

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Matching on an estimated propensity score is frequently used to estimate the effects of treatments from observational data. Since the 1970s, different authors have proposed methods to combine matching at the design stage with regression adjustment at the analysis stage when estimating treatment effects for continuous outcomes. Previous work has consistently shown that the combination has generally superior statistical properties than either method by itself. In biomedical and epidemiological research, survival or time-to-event outcomes are common. We propose a method to combine regression adjustment and propensity score matching to estimate survival curves and hazard ratios based on estimating an imputed potential outcome under control for each successfully matched treated subject, which is accomplished using either an accelerated failure time parametric survival model or a Cox proportional hazard model that is fit to the matched control subjects. That is, a fitted model is then applied to the matched treated subjects to allow simulation of the missing potential outcome under control for each treated subject. Conventional survival analyses (e.g., estimation of survival curves and hazard ratios) can then be conducted using the observed outcome under treatment and the imputed outcome under control. We evaluated the repeated-sampling bias of the proposed methods using simulations. When using nearest neighbor matching, the proposed method resulted in decreased bias compared to crude analyses in the matched sample. We illustrate the method in an example prescribing beta-blockers at hospital discharge to patients hospitalized with heart failure.

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Hebert, April E., Usha S. Kreaden, Ana Yankovsky, Dongjing Guo, Yang Li, Shih-Hao Lee, Yuki Liu, Angela B. Soito, Samira Massachi, and April E. Slee. "Methodology to standardize heterogeneous statistical data presentations for combining time-to-event oncologic outcomes." PLOS ONE 17, no. 2 (February 24, 2022): e0263661. http://dx.doi.org/10.1371/journal.pone.0263661.

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Survival analysis following oncological treatments require specific analysis techniques to account for data considerations, such as failure to observe the time of event, patient withdrawal, loss to follow-up, and differential follow up. These techniques can include Kaplan-Meier and Cox proportional hazard analyses. However, studies do not always report overall survival (OS), disease-free survival (DFS), or cancer recurrence using hazard ratios, making the synthesis of such oncologic outcomes difficult. We propose a hierarchical utilization of methods to extract or estimate the hazard ratio to standardize time-to-event outcomes so that study inclusion into meta-analyses can be maximized. We also provide proof-of concept results from a statistical analysis that compares OS, DFS, and cancer recurrence for robotic surgery to open and non-robotic minimally invasive surgery. In our example, use of the proposed methodology would allow for the increase in data inclusion from 108 hazard ratios reported to 240 hazard ratios reported or estimated, resulting in an increase of 122%. While there are publications summarizing the motivation for these analyses, and comprehensive papers describing strategies to obtain estimates from published time-dependent analyses, we are not aware of a manuscript that describes a prospective framework for an analysis of this scale focusing on the inclusion of a maximum number of publications reporting on long-term oncologic outcomes incorporating various presentations of statistical data.

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Fang, Liang, and Zheng Su. "A hybrid approach to predicting events in clinical trials with time-to-event outcomes." Contemporary Clinical Trials 32, no. 5 (September 2011): 755–59. http://dx.doi.org/10.1016/j.cct.2011.05.013.

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Phadnis, Milind A., and Matthew S. Mayo. "Group sequential design for time-to-event data using the concept of proportional time." Statistical Methods in Medical Research 29, no. 7 (October 1, 2019): 1867–90. http://dx.doi.org/10.1177/0962280219876313.

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Sequential monitoring of efficacy and safety is an important part of clinical trials. A Group Sequential design allows researchers to perform interim monitoring after groups of patients have completed the study. Statistical literature is well developed for continuous and binary outcomes and relies on asymptotic normality of the test statistic. However, in the case of time-to-event data, existing methods of sample size calculation are done either assuming proportional hazards or assuming exponentially distributed lifetimes. In scenarios where these assumptions are not true, as evidenced from historical data, these traditional methods are restrictive and cannot always be used. As interim monitoring is driven by ethical, financial, and administrative considerations, it is imperative that sample size calculations be done in an efficient manner keeping in mind the specific needs of a clinical trial with a time-to-event outcome. To address these issues, a novel group sequential design is proposed using the concept of Proportional Time. This method utilizes the generalized gamma ratio distribution to calculate the efficacy and safety boundaries and can be used for all distributions that are members of the generalized gamma family using an error spending approach. The design incorporates features specific to survival data such as loss to follow-up, administrative censoring, varying accrual times and patterns, binding or non-binding futility rules with or without skips, and flexible alpha and beta spending mechanisms. Three practical examples are discussed, followed by discussion of the important aspects of the proposed design.

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Zhao, Yue, Benjamin R. Saville, Haibo Zhou, and Gary G. Koch. "Sensitivity analysis for missing outcomes in time-to-event data with covariate adjustment." Journal of Biopharmaceutical Statistics 26, no. 2 (January 30, 2015): 269–79. http://dx.doi.org/10.1080/10543406.2014.1000549.

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Weir, I. R., G. D. Marshall, J. I. Schneider, J. A. Sherer, E. M. Lord, B. Gyawali, M. K. Paasche-Orlow, E. J. Benjamin, and L. Trinquart. "Interpretation of time-to-event outcomes in randomized trials: an online randomized experiment." Annals of Oncology 30, no. 1 (January 2019): 96–102. http://dx.doi.org/10.1093/annonc/mdy462.

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Vale, Claire L., Jayne F. Tierney, and Lesley A. Stewart. "Effects of adjusting for censoring on meta-analyses of time-to-event outcomes." International Journal of Epidemiology 31, no. 1 (February 2002): 107–11. http://dx.doi.org/10.1093/ije/31.1.107.

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Zheng, Cheng, and Yingye Zheng. "Calibrating Variations in Biomarker Measures for Improving Prediction with Time-to-event Outcomes." Statistics in Biosciences 11, no. 3 (April 5, 2019): 477–503. http://dx.doi.org/10.1007/s12561-019-09235-5.

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Díaz, I., O. Savenkov, and K. Ballman. "Targeted learning ensembles for optimal individualized treatment rules with time-to-event outcomes." Biometrika 105, no. 3 (May 7, 2018): 723–38. http://dx.doi.org/10.1093/biomet/asy017.

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Lang, BM, K. Young, LA Abderhalden, S. Dheban, D. Gelb, M. Amonkar, and M. Simmonds. "SA44 Meta-Analysis of Time-to-Event Oncology Outcomes for Health Economic Modelling." Value in Health 25, no. 12 (December 2022): S491. http://dx.doi.org/10.1016/j.jval.2022.09.2438.

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Pletscher, M., and S. Gsteiger. "PCN433 DISCRETE TIME RESTRICTED CUBIC SPLINE MODELS FOR NETWORK META-ANALYSIS OF TIME-TO-EVENT OUTCOMES." Value in Health 22 (November 2019): S520. http://dx.doi.org/10.1016/j.jval.2019.09.626.

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42

Guo, Z., T. M. Gill, and H. G. Allore. "Modeling Repeated Time-to-event Health Conditions with Discontinuous Risk Intervals." Methods of Information in Medicine 47, no. 02 (2008): 107–16. http://dx.doi.org/10.3414/me0478.

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Summary Objectives: Researchers have often used rather simple approaches to analyze repeated time-to-event health conditions that either examine time to the first event or treat multiple events as independent. More sophisticated models have been developed, although previous applications have focused largely on such outcomes having continuous risk intervals. Limitations of applying these models include their difficulty in implementation without careful attention to forming the data structures. Methods: We first review time-to-event models for repeated events that are extensions of the Cox model and frailty models. Next, we develop a way to efficiently set up the data structures with discontinuous risk intervals for such models, which are more appropriate for many applications than the continuous alternatives. Finally, we apply these models to a real dataset to investigate the effect of gender on functional disability in a cohort of older persons. For comparison, we demonstrate modeling time to the first event. Results: The GEE Poisson, the Cox counting process, and the frailty models provided similar parameter estimates of gender effect on functional disability, that is, women had increased risk of bathing disability and other disability (disability in walking, dressing, or transferring) as compared to men. These results, especially for other disabilities, were quite different from those provided by an analysis of the first-event outcomes. However, the effect of gender was no longer significant in the counting process model fully adjusted for covariates. Conclusion: Modeling time to only the first event may not be adequate. After properly setting up the data structures, repeated event models that account for the correlation between multiple events within subjects can be easily implemented with common statistical software packages.

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Mauff, Katya, Ewout Steyerberg, Isabella Kardys, Eric Boersma, and Dimitris Rizopoulos. "Joint models with multiple longitudinal outcomes and a time-to-event outcome: a corrected two-stage approach." Statistics and Computing 30, no. 4 (March 4, 2020): 999–1014. http://dx.doi.org/10.1007/s11222-020-09927-9.

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Breskin, Alexander, Andrew Edmonds, Stephen R. Cole, Daniel Westreich, Jennifer Cocohoba, Mardge H. Cohen, Seble G. Kassaye, et al. "Corrigendum to: G-computation for policy-relevant effects of interventions on time-to-event outcomes." International Journal of Epidemiology 50, no. 2 (March 24, 2021): 701. http://dx.doi.org/10.1093/ije/dyab041.

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Caetano, Samantha-Jo, David Dawe, Peter Ellis, Craig C. Earle, and Gregory R. Pond. "Methods to improve the estimation of time-to-event outcomes when data is de-identified." Statistics in Medicine 38, no. 4 (October 11, 2018): 625–35. http://dx.doi.org/10.1002/sim.7990.

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Eberly, Lynn E., James S. Hodges, Kay Savik, Olga Gurvich, Donna Z. Bliss, and Christine Mueller. "Extending the Peters-Belson approach for assessing disparities to right censored time-to-event outcomes." Statistics in Medicine 32, no. 23 (May 24, 2013): 4006–20. http://dx.doi.org/10.1002/sim.5835.

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Kotalik, Ales, Anne Eaton, Qinshu Lian, Carlos Serrano, John Connett, and James D. Neaton. "A win ratio approach to the re-analysis of Multiple Risk Factor Intervention Trial." Clinical Trials 16, no. 6 (August 7, 2019): 626–34. http://dx.doi.org/10.1177/1740774519868233.

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Abstract:

Background: Composite outcomes, which combine multiple types of clinical events into a single outcome, are common in clinical trials. The usual analysis considers the time to first occurrence of any event in the composite. The major criticisms of such an approach are (1) this implicitly treats the outcomes as if they were of equal importance, but they often vary in terms of clinical relevance and severity, (2) study participants often experience more than one type of event, and (3) often less severe events occur before more severe ones, but the usual analysis disregards any information beyond that first event. Methods: A novel approach, referred to as the win ratio, which addresses the aforementioned criticisms of composite outcomes, is illustrated with a re-analysis of data on fatal and non-fatal cardiovascular disease time-to-event outcomes reported for the Multiple Risk Factor Intervention Trial. In this trial, 12,866 participants were randomized to a special intervention group ( n = 6428) or a usual care ( n = 6438) group. Non-fatal outcomes were ranked by risk of cardiovascular disease death up to 20 years after trial. In one approach, participants in the special intervention and usual care groups were first matched on coronary heart disease risk at baseline and time of enrollment. Each matched pair was categorized as a winner or loser depending on which one experienced a cardiovascular disease death first. If neither died of cardiovascular disease causes, they were evaluated on the most severe non-fatal outcome. This process continued for all the non-fatal outcomes. A second win ratio statistic, obtained from Cox partial likelihood, was also estimated. This statistic provides a valid estimate of the win ratio using multiple events if the marginal and conditional survivor functions of each outcome satisfy proportional hazards. Loss ratio statistics (inverse of win ratios) are compared to hazard ratios from the usual first event analysis. A larger 11-event composite was also considered. Results: For the 7-event cardiovascular disease composite, the previously reported first event analysis based on 581 events in the special intervention group and 652 events in the usual care group yielded a hazard ratio (95% confidence interval) of 0.89 (0.79–0.99), compared to 0.86 (0.77–0.97) and 0.91 (0.81–1.02) for the severity ranked estimates. Results for the 11-event composite also confirmed the findings of the first event analysis. Conclusion: The win ratio analysis was able to leverage information collected past the first experienced event and rank events by severity. The results were similar to and confirmed previously reported traditional first event analysis. The win ratio statistic is a useful adjunct to the traditional first event analysis for trials with composite outcomes.

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Ohyama, Katsuhiro, Hiroyuki Tanaka, and Yusuke Hori. "Effect of Concomitant Drug Use on the Onset and Exacerbation of Diabetes Mellitus in Everolimus-Treated Cancer." Journal of Pharmacy & Pharmaceutical Sciences 25 (July 29, 2022): 245–52. http://dx.doi.org/10.18433/jpps32908.

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Purpose: Everolimus-induced diabetes mellitus (DM) outcomes include everolimus-resistant tumors and poor hyperglycemia outcomes, which lead to various other negative clinical outcomes. This study aimed to evaluate the effect of associations between concomitant drug treatment and time to DM event occurrence (onset or exacerbation) on the outcomes of everolimus-induced DM in patients with cancer. Methods: Data from the Japanese Adverse Drug Event Report database (JADER) were used, and patient drug use, time of DM event occurrence, and DM outcomes were determined from patient records. Associations between concomitant drug groups with everolimus and DM event occurrence were then evaluated for patients with both good and poor DM outcomes. Results: Top ten groups used concomitantly were drugs for the treatment of hypertension (HT), controlled DM, constipation, hypothyroidism, kidney disease, insomnia, hyperlipidemia, hyperuricemia, anemia, and gastritis. Among them, only HT, controlled DM, and hyperlipidemia were associated with DM event occurrence. These three drug groups were examined by the outcome of everolimus concomitant usage and revealed a significantly shorter time to DM event occurrence for patients with poor outcomes than for those with good outcomes (p = 0.015) among patients without a concomitant drug for DM. Each of these three drug groups was analyzed on patients who were concomitantly administered with one of each drug group with everolimus and revealed a significantly shorter time to DM event occurrence for patients with poor outcomes than for those with good outcomes in patients who received concomitant HT drugs (p = 0.006). Moreover, among the four HT drug categories, calcium channel blockers were significantly associated with poor outcomes (odds ratio, 2.18 [1.09–4.34], p = 0.028). Conclusion: To prevent everolimus-induced poor DM outcomes, early DM detection and treatment are necessary, and the effect of the concomitant drug should be considered before initiating everolimus treatment.

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Bofill Roig, Marta, and Guadalupe Gómez Melis. "A class of two-sample nonparametric statistics for binary and time-to-event outcomes." Statistical Methods in Medical Research 31, no. 2 (December 6, 2021): 225–39. http://dx.doi.org/10.1177/09622802211048030.

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We propose a class of two-sample statistics for testing the equality of proportions and the equality of survival functions. We build our proposal on a weighted combination of a score test for the difference in proportions and a weighted Kaplan–Meier statistic-based test for the difference of survival functions. The proposed statistics are fully non-parametric and do not rely on the proportional hazards assumption for the survival outcome. We present the asymptotic distribution of these statistics, propose a variance estimator, and show their asymptotic properties under fixed and local alternatives. We discuss different choices of weights including those that control the relative relevance of each outcome and emphasize the type of difference to be detected in the survival outcome. We evaluate the performance of these statistics with small sample sizes through a simulation study and illustrate their use with a randomized phase III cancer vaccine trial. We have implemented the proposed statistics in the R package SurvBin, available on GitHub ( https://github.com/MartaBofillRoig/SurvBin ).

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Yuan, Ying, and Guosheng Yin. "Bayesian dose finding by jointly modelling toxicity and efficacy as time-to-event outcomes." Journal of the Royal Statistical Society: Series C (Applied Statistics) 58, no. 5 (December 2009): 719–36. http://dx.doi.org/10.1111/j.1467-9876.2009.00674.x.

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