Competing risks modeling of cumulative effects of time-varying drug exposures

2017 ◽  
Vol 28 (1) ◽  
pp. 248-262 ◽  
Author(s):  
Coraline Danieli ◽  
Michal Abrahamowicz
Keyword(s):  
Competing Risks ◽  
Drug Exposure ◽  
Antihypertensive Drugs ◽  
Real Life ◽  
Cumulative Effects ◽  
Cumulative Exposure ◽  
Exposure Model ◽  
Weight Functions ◽  
Type I ◽  
Time Varying

An accurate assessment of drug safety or effectiveness in pharmaco-epidemiology requires defining an etiologically correct time-varying exposure model, which specifies how previous drug use affects the hazard of the event of interest. An additional challenge is to account for the multitude of mutually exclusive events that may be associated with the use of a given drug. To simultaneously address both challenges, we develop, and validate in simulations, a new approach that combines flexible modeling of the cumulative effects of time-varying exposures with competing risks methodology to separate the effects of the same drug exposure on different outcomes. To account for the dosage, duration and timing of past exposures, we rely on a spline-based weighted cumulative exposure modeling. We also propose likelihood ratio tests to test if the cumulative effects of past exposure on the hazards of the competing events are the same or different. Simulation results indicate that the estimated event-specific weight functions are reasonably accurate, and that the proposed tests have acceptable type I error rate and power. In real-life application, the proposed method indicated that recent use of antihypertensive drugs may reduce the risk of stroke but has no effect on the hazard of coronary heart disease events.

Modeling of cumulative effects of time-varying drug exposures on within-subject changes in a continuous outcome

2020 ◽  
Vol 29 (9) ◽  
pp. 2554-2568 ◽  
Author(s):  
Coraline Danieli ◽  
Therese Sheppard ◽  
Ruth Costello ◽  
William G Dixon ◽  
Michal Abrahamowicz
Keyword(s):  
Drug Use ◽  
Weight Function ◽  
Repeated Measures ◽  
Drug Exposure ◽  
Epidemiological Studies ◽  
Cumulative Effects ◽  
Cumulative Exposure ◽  
Exposure Model ◽  
Time Varying ◽  
Individual Values

An accurate assessment of the safety or effectiveness of drugs in pharmaco-epidemiological studies requires defining an etiologically correct time-varying exposure model, which specifies how previous drug use affects the outcome of interest. To address this issue, we develop, and validate in simulations, a new approach for flexible modeling of the cumulative effects of time-varying exposures on repeated measures of a continuous response variable, such as a quantitative surrogate outcome, or a biomarker. Specifically, we extend the linear mixed effects modeling to estimate how past and recent drug exposure affects the way individual values of the outcome change throughout the follow-up. To account for the dosage, duration and timing of past exposures, we rely on a flexible weighted cumulative exposure methodology to model the cumulative effects of past drug use, as the weighted sum of past doses. Weights, modeled with unpenalized cubic regression B-splines, reflect the relative importance of doses taken at different times in the past. In simulations, we evaluate the performance of the model under different assumptions concerning (i) the shape of the weight function, (ii) the sample size, (iii) the number of the longitudinal observations and (iv) the intra-individual variance. Results demonstrate the accuracy of our estimates of the weight function and of the between- and within-patients variances, and good correlation between the observed and predicted longitudinal changes in the outcome. We then apply the proposed method to re-assess the association between time-varying glucocorticoid exposure and weight gain in people living with rheumatoid arthritis.

scholarly journals Statistical Inference for a Simple Step Stress Model with Competing Risks Based on Generalized Type-I Hybrid Censoring

2021 ◽  
Vol 9 (5) ◽  
pp. 533-548
Author(s):  
Song Mao ◽  
Bin Liu ◽  
Yimin Shi
Keyword(s):  
Competing Risks ◽  
Bootstrap Method ◽  
Cumulative Exposure ◽  
Exposure Model ◽  
Type I ◽  
Unknown Parameters ◽  
Hybrid Censoring ◽  
Simple Step ◽  
Conditional Moment Generating Function ◽  
Generalized Type

Abstract This paper investigates a simple step-stress accelerated lifetime test (SSALT) model for the inferential analysis of exponential competing risks data. A generalized type-I hybrid censoring scheme is employed to improve the efficiency and controllability of the test. Firstly, the MLEs for parameters are established based on the cumulative exposure model (CEM). Then the conditional moment generating function (MGF) for unknown parameters is set up using conditional expectation and multiple integral techniques. Thirdly, confidence intervals (CIs) are constructed by the exact MGF-based method, the approximate normality-based method, and the bias-corrected and accelerated (BCa) percentile bootstrap method. Finally, we present simulation studies and an illustrative example to compare the performances of different methods.

scholarly journals E-Bayesian Estimation for the Weibull Distribution under Adaptive Type-I Progressive Hybrid Censored Competing Risks Data

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 903 ◽  
Author(s):  
Hassan Okasha ◽  
Abdelfattah Mustafa
Keyword(s):  
Weibull Distribution ◽  
Bayesian Estimation ◽  
Competing Risks ◽  
Real Data ◽  
Cumulative Exposure ◽  
Exposure Model ◽  
Type I ◽  
Competing Risks Data ◽  
Mean Square Errors ◽  
Bayesian Estimators

This article focuses on using E-Bayesian estimation for the Weibull distribution based on adaptive type-I progressive hybrid censored competing risks (AT-I PHCS). The case of Weibull distribution for the underlying lifetimes is considered assuming a cumulative exposure model. The E-Bayesian estimation is discussed by considering three different prior distributions for the hyper-parameters. The E-Bayesian estimators as well as the corresponding E-mean square errors are obtained by using squared and LINEX loss functions. Some properties of the E-Bayesian estimators are also derived. A simulation study to compare the various estimators and real data application is applied to show the applicability of the different estimators are proposed.

scholarly journals Quantifying time-varying cause-specific hazard and subdistribution hazard ratios with competing risks data

2019 ◽  
Vol 16 (4) ◽  
pp. 363-374
Author(s):  
Guoqing Diao ◽  
Joseph G Ibrahim
Keyword(s):  
Competing Risks ◽  
Weight Functions ◽  
Time Varying ◽  
Proportional Hazard ◽  
Test Statistics ◽  
Test Statistic ◽  
Subdistribution Hazard ◽  
New Methods ◽  
Hazard Ratios ◽  
Competing Risks Data

Various non-proportional hazard models have been developed in the literature for competing risks data. The regression coefficients under these models, however, typically cannot be compared directly. We propose new methods to quantify the average of the time-varying cause-specific hazard ratios and subdistribution hazard ratios through two general classes of transformations and weight functions that are chosen to reflect the relative importance of the hazard ratios in different time periods. We further propose an [Formula: see text] -norm type of test statistic that incorporates the test statistics for all possible pairs of the transformation function and weight function under consideration. Extensive simulations are conducted under various settings of the hazards and demonstrate that the proposed test performs well under all settings. An application to a clinical trial in follicular lymphoma is examined in detail.

scholarly journals Impact of time-varying cumulative bevacizumab exposures on survival: re-analysis of data from randomized clinical trial in patients with metastatic colo-rectal cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Adrien Guilloteau ◽  
Michal Abrahamowicz ◽  
Olayide Boussari ◽  
Valérie Jooste ◽  
Thomas Aparicio ◽  
...  
Keyword(s):  
Drug Use ◽  
Cumulative Dose ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Cumulative Effects ◽  
Cumulative Exposure ◽  
Time Varying ◽  
Protective Effects ◽  
Cox Proportional Hazards Models ◽  
Exposure Metrics

Abstract Background As cancer treatment, biotherapies can be as effective as chemotherapy while reducing the risk of secondary effects, so that they can be taken over longer periods than conventional chemotherapy. Thus, some trials aimed at assessing the benefit of maintaining biotherapies during chemotherapy-free intervals (CFI). For example, the recent PRODIGE9 trial assessed the effect of maintaining bevacizumab during CFI in metastatic colorectal cancer (mCRC) patients. However, its analysis was hindered by a small difference of exposure to the treatment between the randomized groups and by a large proportion of early drop outs, leading to a potentially unbalanced distribution of confounding factors among the trial completers. To address these limitations, we re-analyzed the PRODIGE9 data to assess the effects of different exposure metrics on all-cause mortality of patients with mCRC using methods originally developed for observational studies. Methods To account for the actual patterns of drug use by individual patients and for possible cumulative effects, we used five alternative time-varying exposure metrics: (i) cumulative dose, (ii) quantiles of the cumulative dose, (iii) standardized cumulative dose, (iv) Theoretical Blood Concentration (TBC), and (v) Weighted Cumulative Exposure (WCE). The last two metrics account for the timing of drug use. Treatment effects were estimated using adjusted Hazard Ratio from multivariable Cox proportional hazards models. Results After excluding 112 patients who died during the induction period, we analyzed data on 382 patients, among whom 320 (83.8%) died. All time-varying exposures improved substantially the model’s fit to data, relative to using only the time-invariant randomization group. All exposures indicated a protective effect for higher cumulative bevacizumab doses. The best-fitting WCE and TBC models accounted for both the cumulative effects and the different impact of doses taken at different times. Conclusions All time-varying analyses, regardless of the exposure metric used, consistently suggested protective effects of higher cumulative bevacizumab doses. However, the results may partly reflect the presence of a confusion bias. Complementing the main ITT analysis of maintenance trials with an analysis of potential cumulative effects of treatment actually taken can provide new insights, but the results must be interpreted with caution because they do not benefit from the randomization. Trial registration clinicaltrials.gov, NCT00952029. Registered 8 August 2009.

Assessing the cumulative effects of exposure to selected benzodiazepines on the risk of fall-related injuries in the elderly

2011 ◽  
Vol 24 (4) ◽  
pp. 577-586 ◽  
Author(s):  
Marie-Pierre Sylvestre ◽  
Michal Abrahamowicz ◽  
Radan Čapek ◽  
Robyn Tamblyn
Keyword(s):  
Cumulative Effect ◽  
The Elderly ◽  
Cumulative Effects ◽  
Cumulative Exposure ◽  
Exposure Model ◽  
Administrative Database ◽  
The Past ◽  
Increased Risk ◽  
Benzodiazepine Use ◽  
Risk Of Fall

ABSTRACTBackground: The use of benzodiazepines is associated with increased risk of fall-related injuries in the elderly. However, it is unclear if the risks vary across the products and how they depend on the pattern of use and dosage. Specifically, the possibility of cumulative effects of past benzodiazepine use has not been thoroughly investigated.Methods: We used the administrative database for a cohort of 23,765 new users of benzodiazepines, aged 65 years and older, in Quebec, Canada, between 1990 and 1994. The associations between the use of seven benzodiazepines and the risk of fall-related injuries were assessed using several statistical models, including a novel weighted cumulative exposure model. That model assigns to each dose taken in the past a weight that represents the importance of that dose in explaining the current risk of fall.Results: For flurazepam, the best-fitting model indicated a cumulative effect of doses taken in the last two weeks. Uninterrupted use of flurazepam in the past months was associated with a highly significant increase in the risk of fall-related injuries (HR = 2.83, 95% CI: 1.45–4.34). The cumulative effect of a 30-day exposure to alprazolam was 1.27 (1.13–1.42). For temazepam, the results suggested a potential withdrawal effect.Conclusions: Mechanisms affecting the risk of falls differ across benzodiazepines, and may include cumulative effects of use in the previous few weeks. Thus, benzodiazepine-specific analyses that account for individual patterns of use should be preferred over simpler analyses that group different benzodiazepines together and limit exposure to current use or current dose.

OPTIMUM MULTI-LEVEL RAMP-STRESS ALT PLAN WITH MULTIPLE-OBJECTIVES FOR BURR TYPE-XII DISTRIBUTION UNDER TYPE-I CENSORING

2012 ◽  
Vol 19 (02) ◽  
pp. 1250006 ◽  
Author(s):  
P. W. SRIVASTAVA ◽  
N. MITTAL
Keyword(s):  
Hazard Function ◽  
Accelerated Life Test ◽  
Cumulative Exposure ◽  
Exposure Model ◽  
Distribution Model ◽  
Logistic Distribution ◽  
Type I ◽  
Life Distribution ◽  
Type I Censoring ◽  
Burr Type Xii

This paper considers optimal design for ramp-stress accelerated life test (ALT) with multiple stresses and multiple estimating objectives using Burr type-XII life distribution and Type-I censoring. It is impractical to estimate only one objective parameter after conducting such costly ALT tests, therefore, an optimum ramp-stress ALT plan with multiple estimating objectives has been formulated. The need to analyze an ALT data with different life distribution models such as exponential, normal, Weibull, log logistic, etc., is necessitated since the use of correct life distribution model especially in the presence of a limited source of data-as typically occurs with modern devices, having high reliability, helps in preventing the choice of unnecessary and expensive planned replacements. The Burr type-XII distribution has a nonmonotone hazard function, which can accommodate many shapes of hazard function. The commonly used Weibull and exponential distributions are its limiting cases and log logistic distribution is its particular case. The distribution has been found appropriate for modeling failures that occur with less frequency and also when there is high occurrence of early failures. The inverse power law and a cumulative exposure model are assumed. The optimal test plan chooses the stress rates and proportion of units allocated to each stress by minimizing the weighted sum of the asymptotic variances of the maximum likelihood estimator of quantile lifetimes at design constant stress. The method developed has been illustrated using an example, sensitivity analysis carried out and comparative study has also been done to highlight the merits of the proposed model.

Simple step-stress model for an extension of the exponential distribution with type-I censoring

2015 ◽  
Vol 32 (8) ◽  
pp. 906-920 ◽  
Author(s):  
Firoozeh Haghighi
Keyword(s):  
Exponential Distribution ◽  
Maximum Likelihood Estimates ◽  
Cumulative Exposure ◽  
Exposure Model ◽  
Type I ◽  
Stress Model ◽  
Content Type ◽  
Type I Censoring ◽  
Cumulative Exposure Model ◽  
Simple Step

Purpose – The purpose of this paper is to design a simple step-stress model under type-I censoring when the failure time has an extension of the exponential distribution. Design/methodology/approach – The scale parameter of the distribution is assumed to be a log-linear function of the stress and a cumulative exposure model is hold. The maximum likelihood estimates of the parameters, as well as the corresponding Fisher information matrix are derived. Two real examples are given to show the application of an extension of the exponential distribution in reliability studies and a numerical example is presented to illustrate the method discussed here. Findings – A simple step-stress test under cumulative exposure model and type-I censoring for an extension of the exponential distribution is presented. Originality/value – The work is original.

Cumulative effects of therapies on disability in relapsing multiple sclerosis

2021 ◽  
pp. 135245852098036 ◽  
Author(s):  
Fabien Rollot ◽  
Romain Casey ◽  
Emmanuelle Leray ◽  
Marc Debouverie ◽  
Gilles Edan ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards Model ◽  
Cumulative Effects ◽  
Continuous Treatment ◽  
Cumulative Exposure ◽  
Exposure Model ◽  
Reverse Causality ◽  
Cumulative Impact

Background: Long-term effectiveness of treatment remains a key question in multiple sclerosis (MS) and the cumulative effects of past treatment have not been investigated so far. Objective: Explore the relationship between treatment exposure and disability risk in patients with relapsing-remitting multiple sclerosis (RRMS). Methods: A total of 2285 adult patients from the French nationwide cohort were included. Outcomes were irreversible EDSS4, and conversion to secondary progression of multiple sclerosis (SPMS). Associations between treatments and risk of disability were assessed using a novel weighted cumulative exposure model, assuming a 3-year lag to account for reverse causality. This flexible approach accounts for past exposure in a multivariate Cox proportional hazards model by computing a weight function. Results: At baseline, mean ± standard deviation age of patients was 33.4 ± 8.9 years and 75.0% were women. A 15-year continuous treatment starting 20 years ago was associated with a decrease in risk of 26% for irreversible EDSS4, and 34% for SPMS compared to a 5-year treatment starting 10 years ago. The risk of disability decreased with increasing duration of exposure to disease-modifying treatment (DMT). Conclusion: Long-term use of treatments in RRMS has a stronger beneficial cumulative impact than only early uses and delays the occurrence of moderate disability and conversion to SPMS.

The Impact of Angiotensin-II Receptor Blockers on Cardiovascular Events in Hypertensive Patients – Evidence from Real-life Databases

2010 ◽  
Vol 6 (3) ◽  
pp. 33
Author(s):  
Robert J Petrella ◽  
Keyword(s):  
Angiotensin Ii ◽  
Antihypertensive Drugs ◽  
Real Life ◽  
Angiotensin Ii Receptor Blockers ◽  
Angiotensin Ii Receptor ◽  
Real World Setting ◽  
Actual Experience ◽  
Receptor Blockers ◽  
Randomised Controlled ◽  
The Impact

It is widely recognised that hypertension is a major risk factor for the development of future cardiovascular (CV) events, which in turn are a major cause of morbidity and mortality. Blood pressure (BP) control with antihypertensive drugs has been shown to reduce the risk of CV events. Angiotensin-II receptor blockers (ARBs) are one such class of antihypertensive drugs and randomised controlled trials (RCTs) have shown ARB-based therapies to have effective BP-lowering properties. However, data obtained under these tightly controlled settings do not necessarily reflect actual experience in clinical practice. Real-life databases may offer alternative information that reflects an uncontrolled real-world setting and complements and expands on the findings of clinical trials. Recent analyses of practice-based real-life databases have shown ARB-based therapies to be associated with better persistence and adherence rates and with superior BP control than non-ARB-based therapies. Analyses of real-life databases also suggest that ARB-based therapies may be associated with a lower risk of CV events than other antihypertensive-drug-based therapies.

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