scholarly journals Frailty modelling for survival data from multi-centre clinical trials

2011 ◽  
Vol 30 (17) ◽  
pp. 2144-2159 ◽  
Author(s):  
Il Do Ha ◽  
Richard Sylvester ◽  
Catherine Legrand ◽  
Gilbert MacKenzie
Keyword(s):  
Clinical Trials ◽  
Survival Data

The Effect of Anti-Shock Garments on Prehospital Survival: The Need for Controlled Clinical Trials

1987 ◽  
Vol 3 (1) ◽  
pp. 40-45 ◽  
Author(s):  
Paul E. Pepe ◽  
William H. Bickell ◽  
Kenneth L. Mattox
Keyword(s):  
Clinical Trials ◽  
Survival Data ◽  
Trauma Center ◽  
Present Report ◽  
Early Report ◽  
Medical Personnel ◽  
The United States ◽  
Controlled Clinical Trials ◽  
Initial Field ◽  
Significant Difference

AbstractThere exists strong sentiment, among emergency medical personnel and physicians alike, that the pneumatic anti-shock garment (PASG) “saves lives.” As a result, controlled studies have been criticized as the “withholding of important therapy.” The purpose of this presentation is to confirm the need for controlled clinical trials of the PASG. Despite an early report that the PASG offered no advantage in terms of the presenting emergency center Trauma Score (TS), similar disparagements have continued, particularly because survival data were not discussed. The present report is a pilot analysis of the effect of the PASG on the prehospital survival of patients arriving at an urban trauma center in the United States. In the study, sixty-eight patients were assigned randomly to control and PASG groups in a prospective investigation involving injured patients with systemic hypotension. The 32 control patients, whose mean initial systolic blood pressure (BP) was 59 ± 32 mm Hg, and the 36 PASG-treated patients, whose mean initial BP was 55 ± 31 mm Hg, were found to be well matched for age, sex, type and location of injuries, initial field TS; response, field management, and transport times; and the total amount of intravenous crystalloid infused. The results demonstrated no significant difference between the control and PASG-treated groups in terms of those pronounced dead on arrival at the trauma center (9/32 vs. 10/36). Further studies are therefore justified to determine how the PASG affects the long-term morbidity and mortality of injury victims, particularly those within certain sub-groups such as penetrating abdominal versus those with penetrating thoracic injuries. This report reaffirms the need for early responsible, scientific scrutiny of prehospital interventions.

Two-Stage Adaptive Design for Clinical Trials with Survival Data

2005 ◽  
Vol 15 (4) ◽  
pp. 707-718 ◽  
Author(s):  
Gang Li ◽  
Weichung J. Shih ◽  
Yining Wang
Keyword(s):  
Clinical Trials ◽  
Survival Data ◽  
Adaptive Design ◽  
Two Stage

Analysing Survival Data from Clinical Trials and Observational Studies

Technometrics ◽  
1996 ◽  
Vol 38 (3) ◽  
pp. 299
Author(s):  
Eric R. Ziegel ◽  
E. Marubini ◽  
M. Valsecchi
Keyword(s):  
Clinical Trials ◽  
Survival Data ◽  
Observational Studies

scholarly journals Bayesian Adaptive Randomized Trial Design for Patients With Recurrent Glioblastoma

2012 ◽  
Vol 30 (26) ◽  
pp. 3258-3263 ◽  
Author(s):  
Lorenzo Trippa ◽  
Eudocia Q. Lee ◽  
Patrick Y. Wen ◽  
Tracy T. Batchelor ◽  
Timothy Cloughesy ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Phase Ii ◽  
Survival Data ◽  
Trial Design ◽  
Statistical Power ◽  
Power Level ◽  
Recurrent Glioblastoma ◽  
Phase Ii Trials ◽  
Number Of Patients ◽  
Short Time

Purpose To evaluate whether the use of Bayesian adaptive randomized (AR) designs in clinical trials for glioblastoma is feasible and would allow for more efficient trials. Patients and Methods We generated an adaptive randomization procedure that was retrospectively applied to primary patient data from four separate phase II clinical trials in patients with recurrent glioblastoma. We then compared AR designs with more conventional trial designs by using realistic hypothetical scenarios consistent with survival data reported in the literature. Our primary end point was the number of patients needed to achieve a desired statistical power. Results If our phase II trials had been a single, multiarm trial using AR design, 30 fewer patients would have been needed compared with a multiarm balanced randomized (BR) design to attain the same power level. More generally, Bayesian AR trial design for patients with glioblastoma would result in trials with fewer overall patients with no loss in statistical power and in more patients being randomly assigned to effective treatment arms. For a 140-patient trial with a control arm, two ineffective arms, and one effective arm with a hazard ratio of 0.6, a median of 47 patients would be randomly assigned to the effective arm compared with 35 in a BR trial design. Conclusion Given the desire for control arms in phase II trials, an increasing number of experimental therapeutics, and a relatively short time for events, Bayesian AR designs are attractive for clinical trials in glioblastoma.

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.

Survival data and toxicity profile in advanced non-small cell lung cancer (NSCLC) patients (PTS) treated within or outside controlled clinical trials.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e18103-e18103
Author(s):  
Tiziana Vavala ◽  
Giulia Rovere ◽  
Simonetta Rapetti ◽  
Enrica Capelletto ◽  
Marina Longo ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Retrospective Analysis ◽  
Survival Data ◽  
Standard Therapy ◽  
Statistical Significance ◽  
Stage Iv ◽  
Line Treatment ◽  
Controlled Clinical Trials ◽  
Single Institution ◽  
Third Line

e18103 Background: Even without well defined data, a common perception is that clinical trials inclusion improve outcomes in pts with cancer. We conducted a retrospective analysis, in a single institution, evaluating survival data and tolerability in advanced NSCLC pts treated with standard therapy or within phase II/III clinical trials. Methods: We analysed 300 consecutive pts treated at the Thoracic Oncology Unit at San Luigi Gonzaga Hospital from 2004, January the 1st to 2011, December 31st having these characteristics: histo/cytological diagnosis of NCSLC, stage IV who received at least 3 chemotherapy cycles, minimum follow-up of 6 months. In this pts population 180 (60%) were enrolled in controlled clinical trials (T), while 120 (40%) were treated with standard therapy (S). Results: The most important differences between the two groups were about median age at the time of starting first line treatment (61.2 in group T vs 64 years in group S), pts older than 70 years (19% vs 27%, respectively) and ECOG/PS distribution (PS=2: 0 vs 9 pts, PS=1: 33 vs 53 pts, PS=0: 145 vs 60 pts in T and S group, respectively). Evaluating T and S population: 106 (59%) vs 43 (36%) pts were eligible for second line treatment and 45 (25%) vs 19 (16%) pts for third line treatment. Median OS was 16,3 in T population vs 14,4 months in S population and PFS was 7.1 vs 6.3 months respectively. Any relevant differences was seen between group T and S in terms of toxicity. Conclusions: This single institution retrospective analysis documented a trend of better outcome for pts enrolled in clinical trials compared to those treated with standard therapy (even without a statistical significance) with an higher percentage of pts reaching second and third line therapies. A larger multicenter prospective dedicated trial is needed to have further, more cleaned, data about this item.

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