Tools & Techniques - Statistics: Dealing with time-varying covariates in survival analysis – joint models versus Cox models

2014 ◽  
Vol 10 (2) ◽  
pp. 285-288 ◽  
Author(s):  
Dimitris Rizopoulos ◽  
Johanna J.M. Takkenberg
Keyword(s):  
Survival Analysis ◽  
Time Varying ◽  
Joint Models ◽  
Cox Models ◽  
Time Varying Covariates

Association of changes in circulating cell-free plasma DNA (cfDNA) and circulating tumor cells (CTC) during treatment with clinical outcome from olaparib in castration-resistant prostate cancer (CRPC): Exploratory analyses from the TOPARP-A trial.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 141-141 ◽  
Author(s):  
Joaquin Mateo ◽  
Helen Mossop ◽  
Jane Goodall ◽  
David Lorente ◽  
Nuria Porta ◽  
...  
Keyword(s):  
Progression Free Survival ◽  
P Value ◽  
Time Varying ◽  
Castration Resistant Prostate Cancer ◽  
Plasma Dna ◽  
Cox Models ◽  
Castration Resistant ◽  
Treatment Switch ◽  
Response Biomarkers ◽  
Time Varying Covariates

141 Background: Response biomarkers are needed to optimize treatment switch decisions in CRPC patients. CTC and cfDNA may have clinical utility as response biomarkers; we studied them during olaparib treatment in a phase II trial in CRCP (Mateo et al NEJM 2015). Methods: CTC were enumerated using CellSearch (Jannsen Diagnostics) and cfDNA was extracted with the QIASymphony circulating DNA kit (Qiagen) from blood samples taken at baseline, 4- and 8-weeks (wk) of therapy. Radiological progression-free survival (rPFS) was defined as time from starting treatment to progression by RECIST 1.1, bone scan (PCWG2) or death. Overall survival (OS) was defined as time from starting treatment to death. CTC changes were categorized based on conversion from ≥ 5 to < 5 CTC/7.5ml blood and on ≥ 30% decline (Lorente et al Eur Urol 2016). cfDNA changes were evaluated as percentage change from baseline (continuous and binary). The prognostic value of CTC and cfDNA changes were assessed by Landmark analysis and Cox models with time-varying covariates; p-value < 0.01 were considered significant to account for multiple tests. Results: Overall, 13/47 (28%) and 16/42 (38%) evaluable patients had a CTC conversion at 4- and 8-wk respectively. A CTC conversion after 4-wk of olaparib associated with longer rPFS (median 8.9 vs 2.7 months [m], p = 0.001); a similar association was found at 8-wk. A 30% CTC decline at 4-wk also associated with longer rPFS (median 4.4 vs 2.6 m, p = 0.004). CTC conversion as a time-varying covariate associated with longer OS (HR 0.26, 95%CI 0.14-0.50, p < 0.001). Median baseline cfDNA was 31.6 ng/ml (IQR 19.4-57.1); 46 and 42 patients were evaluable for cfDNA changes at 4- and 8-wk. Percentage changes in cfDNA at 4- and 8- wk associated with rPFS (HR 1.01 and 1.005; p = 0.005 and 0.002 respectively) but association with OS was not significant. cfDNA declines ≥ 50% at 8- wk associated with longer rPFS (median 8.9 vs 2.7 m, p = 0.007) and OS (17.0 vs 10.1 m, p = 0.004). Conclusions: Decreases in CTC counts and cfDNA concentration associate with improved outcome from olaparib (rPFS, OS) in the TOPARP-A trial. Clinical trial information: NCT01682772.

Exponential decay for binary time-varying covariates in Cox models

2017 ◽  
Vol 37 (5) ◽  
pp. 776-788
Author(s):  
Charles Donald George Keown-Stoneman ◽  
Julie Horrocks ◽  
Gerarda Darlington
Keyword(s):  
Exponential Decay ◽  
Time Varying ◽  
Cox Models ◽  
Time Varying Covariates

scholarly journals Survival Analysis with Error-Prone Time-Varying Covariates: A Risk Set Calibration Approach

Biometrics ◽  
2010 ◽  
Vol 67 (1) ◽  
pp. 50-58 ◽  
Author(s):  
Xiaomei Liao ◽  
David M. Zucker ◽  
Yi Li ◽  
Donna Spiegelman
Keyword(s):  
Survival Analysis ◽  
Time Varying ◽  
Time Varying Covariates ◽  
Calibration Approach

Survival Analysis of Time to Opioid Use and the Role of PTSD-Linked Stress Sensitivity for Predicting Onset Risk for Juvenile Offenders Following Adjudication

2021 ◽  
pp. 003288552110693
Author(s):  
Thomas W. Wojciechowski
Keyword(s):  
Survival Analysis ◽  
Juvenile Offenders ◽  
Stress Sensitivity ◽  
Hazard Models ◽  
Time Varying ◽  
Opioid Use ◽  
Time Varying Covariates ◽  
Moderating Role

This study sought to understand how PTSD predicts opioid use onset rates and how subsequent exposures to violence also influence this risk following adjudication. Survival analysis was used to examine the moderating role that baseline PTSD status plays for predicting rates of opioid use onset risk following adjudication. Hazard models used to examine the role of time-varying covariates for predicting opioid onset risk following adjudication. PTSD was found to predict significantly greater odds of opioid use initiation. Hazard of introducing opioid use was greater during observation periods in which participants witnessed violence. This effect was greater for PTSD sufferers.

Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models

2021 ◽  
pp. 191-196
Author(s):  
Matteo Di Maso ◽  
Monica Ferraroni ◽  
Pasquale Ferrante ◽  
Serena Delbue ◽  
Federico Ambrogi
Keyword(s):  
Mortality Risk ◽  
Fixed Effects ◽  
Cox Model ◽  
Time Dependent ◽  
Neutrophil Granulocytes ◽  
Time Varying ◽  
C Reactive Protein ◽  
Joint Models ◽  
Reactive Protein ◽  
Time Varying Covariates

In survival analysis, time-varying covariates are endogenous when their measurements are directly related to the event status and incomplete information occur at random points during the follow-up. Consequently, the time-dependent Cox model leads to biased estimates. Joint models (JM) allow to correctly estimate these associations combining a survival and longitudinal sub-models by means of a shared parameter (i.e., random effects of the longitudinal sub-model are inserted in the survival one). This study aims at showing the use of JM to evaluate the association between a set of inflammatory biomarkers and Covid-19 mortality. During Covid-19 pandemic, physicians at Istituto Clinico di Città Studi in Milan collected biomarkers (endogenous time-varying covariates) to understand what might be used as prognostic factors for mortality. Furthermore, in the first epidemic outbreak, physicians did not have standard clinical protocols for management of Covid-19 disease and measurements of biomarkers were highly incomplete especially at the baseline. Between February and March 2020, a total of 403 COVID-19 patients were admitted. Baseline characteristics included sex and age, whereas biomarkers measurements, during hospital stay, included log-ferritin, log-lymphocytes, log-neutrophil granulocytes, log-C-reactive protein, glucose and LDH. A Bayesian approach using Markov chain Monte Carlo algorithm were used for fitting JM. Independent and non-informative priors for the fixed effects (age and sex) and for shared parameters were used. Hazard ratios (HR) from a (biased) time-dependent Cox and joint models for log-ferritin levels were 2.10 (1.67-2.64) and 1.73 (1.38-2.20), respectively. In multivariable JM, doubling of biomarker levels resulted in a significantly increase of mortality risk for log-neutrophil granulocytes, HR=1.78 (1.16-2.69); for log-C-reactive protein, HR=1.44 (1.13-1.83); and for LDH, HR=1.28 (1.09-1.49). Increasing of 100 mg/dl of glucose resulted in a HR=2.44 (1.28-4.26). Age, however, showed the strongest effect with mortality risk starting to rise from 60 years.

scholarly journals Hydroxychloroquine and the risk of respiratory infections among RA patients

RMD Open ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e001389
Author(s):  
Joel M Kremer ◽  
George Reed ◽  
Dimitrios A Pappas ◽  
LR Harold ◽  
Kevin Kane ◽  
...  
Keyword(s):  
Respiratory Infections ◽  
Smoking Status ◽  
Secondary Analysis ◽  
Time Varying ◽  
Biologic Dmards ◽  
Cox Models ◽  
Serious Infections ◽  
History Of ◽  
Upper Respiratory ◽  
Time Varying Covariates

ObjectivesTo determine the effect of hydroxychloroquine on the incidence of new respiratory infections in a large registry of rheumatoid arthritis (RA) patients compared with a matched cohort receiving other conventional disease-modifying antirheumatic drugs (csDMARDs).MethodsWe reviewed physician-reported infections including upper respiratory infections (URI), bronchitis and pneumonia in the Corrona RA registry from June 2008 to February 2020 with the goal of comparing infections in biologic/targeted synthetic (b/ts) DMARDs naive HCQ starts compared with starts of other csDMARDs and no HCQ. Patients on different interventions were compared using time-varying adjusted Cox models adjusting for age, sex, duration of RA, BMI, disease activity, smoking status, concurrent medications, season of the year, year of onset and history of serious infections, diabetes or cardiovascular disease (CVD). A secondary analysis in a set of propensity-matched starts were also compared adjusting for time-varying covariates. The analysis was repeated including URI and bronchitis only and also for serious respiratory infections only.ResultsNo evidence of differences was found in the incidence of any respiratory infection (URI, bronchitis, pneumonia) in patients receiving HCQ compared with other csDMARDs: HR=0.87 (0.70 to1.07) in adjusted analyses and HR=0.90 (0.70 to 1.17) in adjusted matched analysis. Similar results were found in the analysis of URI and bronchitis only and for serious respiratory infections only.ConclusionsIn patients with RA, the risk for respiratory infections was similar among patients using HCQ as compared to other non-biologic DMARDs.

scholarly journals A competing risks model with binary time varying covariates for estimation of breast cancer risks in BRCA1 families

2021 ◽  
pp. 096228022110089
Author(s):  
Yun-Hee Choi ◽  
Hae Jung ◽  
Saundra Buys ◽  
Mary Daly ◽  
Esther M John ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Competing Risks ◽  
Brca1 Mutation ◽  
Prophylactic Surgery ◽  
Time Varying ◽  
Cancer Risks ◽  
Competing Risks Model ◽  
Time Varying Covariates ◽  
Risk Reducing

Mammographic screening and prophylactic surgery such as risk-reducing salpingo oophorectomy can potentially reduce breast cancer risks among mutation carriers of BRCA families. The evaluation of these interventions is usually complicated by the fact that their effects on breast cancer may change over time and by the presence of competing risks. We introduce a correlated competing risks model to model breast and ovarian cancer risks within BRCA1 families that accounts for time-varying covariates. Different parametric forms for the effects of time-varying covariates are proposed for more flexibility and a correlated gamma frailty model is specified to account for the correlated competing events.We also introduce a new ascertainment correction approach that accounts for the selection of families through probands affected with either breast or ovarian cancer, or unaffected. Our simulation studies demonstrate the good performances of our proposed approach in terms of bias and precision of the estimators of model parameters and cause-specific penetrances over different levels of familial correlations. We applied our new approach to 498 BRCA1 mutation carrier families recruited through the Breast Cancer Family Registry. Our results demonstrate the importance of the functional form of the time-varying covariate effect when assessing the role of risk-reducing salpingo oophorectomy on breast cancer. In particular, under the best fitting time-varying covariate model, the overall effect of risk-reducing salpingo oophorectomy on breast cancer risk was statistically significant in women with BRCA1 mutation.

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