scholarly journals Improved two-stage estimation to adjust for treatment switching in randomised trials: g-estimation to address time-dependent confounding

2020 ◽  
Vol 29 (10) ◽  
pp. 2900-2918
Author(s):  
NR Latimer ◽  
IR White ◽  
K Tilling ◽  
U Siebert
Keyword(s):  
Disease Progression ◽  
Failure Time ◽  
Experimental Treatment ◽  
Time Dependent ◽  
Control Group ◽  
Two Stage ◽  
Inverse Probability ◽  
Nested Models ◽  
Treatment Switching ◽  
Stage Estimation

In oncology trials, control group patients often switch onto the experimental treatment during follow-up, usually after disease progression. In this case, an intention-to-treat analysis will not address the policy question of interest – that of whether the new treatment represents an effective and cost-effective use of health care resources, compared to the standard treatment. Rank preserving structural failure time models (RPSFTM), inverse probability of censoring weights (IPCW) and two-stage estimation (TSE) have often been used to adjust for switching to inform treatment reimbursement policy decisions. TSE has been applied using a simple approach (TSEsimp), assuming no time-dependent confounding between the time of disease progression and the time of switch. This is problematic if there is a delay between progression and switch. In this paper we introduce TSEgest, which uses structural nested models and g-estimation to account for time-dependent confounding, and compare it to TSEsimp, RPSFTM and IPCW. We simulated scenarios where control group patients could switch onto the experimental treatment with and without time-dependent confounding being present. We varied switching proportions, treatment effects and censoring proportions. We assessed adjustment methods according to their estimation of control group restricted mean survival times that would have been observed in the absence of switching. All methods performed well in scenarios with no time-dependent confounding. TSEgest and RPSFTM continued to perform well in scenarios with time-dependent confounding, but TSEsimp resulted in substantial bias. IPCW also performed well in scenarios with time-dependent confounding, except when inverse probability weights were high in relation to the size of the group being subjected to weighting, which occurred when there was a combination of modest sample size and high switching proportions. TSEgest represents a useful addition to the collection of methods that may be used to adjust for treatment switching in trials in order to address policy-relevant questions.

scholarly journals TREATMENT SWITCHING: STATISTICAL AND DECISION-MAKING CHALLENGES AND APPROACHES

2016 ◽  
Vol 32 (3) ◽  
pp. 160-166 ◽  
Author(s):  
Nicholas R. Latimer ◽  
Chris Henshall ◽  
Uwe Siebert ◽  
Helen Bell
Keyword(s):  
Statistical Methods ◽  
Failure Time ◽  
Controlled Trial ◽  
International Workshop ◽  
Experimental Treatment ◽  
Decision Makers ◽  
Control Group ◽  
Intention To Treat ◽  
Treatment Switching ◽  
New Treatment

Objectives: Treatment switching refers to the situation in a randomized controlled trial where patients switch from their randomly assigned treatment onto an alternative. Often, switching is from the control group onto the experimental treatment. In this instance, a standard intention-to-treat analysis does not identify the true comparative effectiveness of the treatments under investigation. We aim to describe statistical methods for adjusting for treatment switching in a comprehensible way for nonstatisticians, and to summarize views on these methods expressed by stakeholders at the 2014 Adelaide International Workshop on Treatment Switching in Clinical Trials.Methods: We describe three statistical methods used to adjust for treatment switching: marginal structural models, two-stage adjustment, and rank preserving structural failure time models. We draw upon discussion heard at the Adelaide International Workshop to explore the views of stakeholders on the acceptability of these methods.Results: Stakeholders noted that adjustment methods are based on assumptions, the validity of which may often be questionable. There was disagreement on the acceptability of adjustment methods, but consensus that when these are used, they should be justified rigorously. The utility of adjustment methods depends upon the decision being made and the processes used by the decision-maker.Conclusions: Treatment switching makes estimating the true comparative effect of a new treatment challenging. However, many decision-makers have reservations with adjustment methods. These, and how they affect the utility of adjustment methods, require further exploration. Further technical work is required to develop adjustment methods to meet real world needs, to enhance their acceptability to decision-makers.

scholarly journals Causal inference for long-term survival in randomised trials with treatment switching: Should re-censoring be applied when estimating counterfactual survival times?

2018 ◽  
Vol 28 (8) ◽  
pp. 2475-2493 ◽  
Author(s):  
NR Latimer ◽  
IR White ◽  
KR Abrams ◽  
U Siebert
Keyword(s):  
Treatment Effect ◽  
Failure Time ◽  
Survival Function ◽  
Experimental Treatment ◽  
Control Group ◽  
Survival Times ◽  
Term Survival ◽  
Long Term Survival ◽  
Treatment Switching

Treatment switching often has a crucial impact on estimates of effectiveness and cost-effectiveness of new oncology treatments. Rank preserving structural failure time models (RPSFTM) and two-stage estimation (TSE) methods estimate ‘counterfactual’ (i.e. had there been no switching) survival times and incorporate re-censoring to guard against informative censoring in the counterfactual dataset. However, re-censoring causes a loss of longer term survival information which is problematic when estimates of long-term survival effects are required, as is often the case for health technology assessment decision making. We present a simulation study designed to investigate applications of the RPSFTM and TSE with and without re-censoring, to determine whether re-censoring should always be recommended within adjustment analyses. We investigate a context where switching is from the control group onto the experimental treatment in scenarios with varying switch proportions, treatment effect sizes, treatment effect changes over time, survival function shapes, disease severity and switcher prognosis. Methods were assessed according to their estimation of control group restricted mean survival that would be observed in the absence of switching, up to the end of trial follow-up. We found that analyses which re-censored usually produced negative bias (i.e. underestimating control group restricted mean survival and overestimating the treatment effect), whereas analyses that did not re-censor consistently produced positive bias which was often smaller in magnitude than the bias associated with re-censored analyses, particularly when the treatment effect was high and the switching proportion was low. The RPSFTM with re-censoring generally resulted in increased bias compared to the other methods. We believe that analyses should be conducted with and without re-censoring, as this may provide decision-makers with useful information on where the true treatment effect is likely to lie. Incorporating re-censoring should not always represent the default approach when the objective is to estimate long-term survival times and treatment effects.

scholarly journals Assessing methods for dealing with treatment switching in clinical trials: A follow-up simulation study

2016 ◽  
Vol 27 (3) ◽  
pp. 765-784 ◽  
Author(s):  
Nicholas R Latimer ◽  
Keith R Abrams ◽  
Paul C Lambert ◽  
James P Morden ◽  
Michael J Crowther
Keyword(s):  
Sample Size ◽  
Treatment Effect ◽  
Failure Time ◽  
Control Group ◽  
Structural Failure ◽  
Inverse Probability ◽  
Intention To Treat ◽  
Treatment Switching ◽  
Randomised Controlled ◽  
The Impact

When patients randomised to the control group of a randomised controlled trial are allowed to switch onto the experimental treatment, intention-to-treat analyses of the treatment effect are confounded because the separation of randomised groups is lost. Previous research has investigated statistical methods that aim to estimate the treatment effect that would have been observed had this treatment switching not occurred and has demonstrated their performance in a limited set of scenarios. Here, we investigate these methods in a new range of realistic scenarios, allowing conclusions to be made based upon a broader evidence base. We simulated randomised controlled trials incorporating prognosis-related treatment switching and investigated the impact of sample size, reduced switching proportions, disease severity, and alternative data-generating models on the performance of adjustment methods, assessed through a comparison of bias, mean squared error, and coverage, related to the estimation of true restricted mean survival in the absence of switching in the control group. Rank preserving structural failure time models, inverse probability of censoring weights, and two-stage methods consistently produced less bias than the intention-to-treat analysis. The switching proportion was confirmed to be a key determinant of bias: sample size and censoring proportion were relatively less important. It is critical to determine the size of the treatment effect in terms of an acceleration factor (rather than a hazard ratio) to provide information on the likely bias associated with rank-preserving structural failure time model adjustments. In general, inverse probability of censoring weight methods are more volatile than other adjustment methods.

Adjusting for treatment switching in randomised controlled trials – A simulation study and a simplified two-stage method

2014 ◽  
Vol 26 (2) ◽  
pp. 724-751 ◽  
Author(s):  
Nicholas R Latimer ◽  
KR Abrams ◽  
PC Lambert ◽  
MJ Crowther ◽  
AJ Wailoo ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Survival Benefit ◽  
Randomised Controlled Trials ◽  
Health Technology ◽  
Control Group ◽  
Controlled Trials ◽  
Inverse Probability ◽  
Treatment Switching ◽  
Overall Survival Benefit ◽  
Randomised Controlled

Estimates of the overall survival benefit of new cancer treatments are often confounded by treatment switching in randomised controlled trials (RCTs) – whereby patients randomised to the control group are permitted to switch onto the experimental treatment upon disease progression. In health technology assessment, estimates of the unconfounded overall survival benefit associated with the new treatment are needed. Several switching adjustment methods have been advocated in the literature, some of which have been used in health technology assessment. However, it is unclear which methods are likely to produce least bias in realistic RCT-based scenarios. We simulated RCTs in which switching, associated with patient prognosis, was permitted. Treatment effect size and time dependency, switching proportions and disease severity were varied across scenarios. We assessed the performance of alternative adjustment methods based upon bias, coverage and mean squared error, related to the estimation of true restricted mean survival in the absence of switching in the control group. We found that when the treatment effect was not time-dependent, rank preserving structural failure time models (RPSFTM) and iterative parameter estimation methods produced low levels of bias. However, in the presence of a time-dependent treatment effect, these methods produced higher levels of bias, similar to those produced by an inverse probability of censoring weights method. The inverse probability of censoring weights and structural nested models produced high levels of bias when switching proportions exceeded 85%. A simplified two-stage Weibull method produced low bias across all scenarios and provided the treatment switching mechanism is suitable, represents an appropriate adjustment method.

scholarly journals A modified weighted log-rank test for confirmatory trials with a high proportion of treatment switching

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259178
Author(s):  
José L. Jiménez ◽  
Julia Niewczas ◽  
Alexander Bore ◽  
Carl-Fredrik Burman
Keyword(s):  
Statistical Power ◽  
Experimental Treatment ◽  
Hazard Ratio ◽  
Rank Test ◽  
Control Group ◽  
Potential Hazard ◽  
Intention To Treat ◽  
Treatment Switching ◽  
Log Rank Test ◽  
The Impact

In confirmatory cancer clinical trials, overall survival (OS) is normally a primary endpoint in the intention-to-treat (ITT) analysis under regulatory standards. After the tumor progresses, it is common that patients allocated to the control group switch to the experimental treatment, or another drug in the same class. Such treatment switching may dilute the relative efficacy of the new drug compared to the control group, leading to lower statistical power. It would be possible to decrease the estimation bias by shortening the follow-up period but this may lead to a loss of information and power. Instead we propose a modified weighted log-rank test (mWLR) that aims at balancing these factors by down-weighting events occurring when many patients have switched treatment. As the weighting should be pre-specified and the impact of treatment switching is unknown, we predict the hazard ratio function and use it to compute the weights of the mWLR. The method may incorporate information from previous trials regarding the potential hazard ratio function over time. We are motivated by the RECORD-1 trial of everolimus against placebo in patients with metastatic renal-cell carcinoma where almost 80% of the patients in the placebo group received everolimus after disease progression. Extensive simulations show that the new test gives considerably higher efficiency than the standard log-rank test in realistic scenarios.

scholarly journals Experimental Study on Delaying the Failure Time of In-Service Cables in Trays by Using Fire-Retardant Coatings

2021 ◽  
Vol 11 (6) ◽  
pp. 2521
Author(s):  
Feng Jiang ◽  
Jianyong Liu ◽  
Wei Yuan ◽  
Jianbo Yan ◽  
Lin Wang ◽  
...  
Keyword(s):  
Fire Resistance ◽  
Failure Time ◽  
Fire Retardant ◽  
Resistance Test ◽  
Control Group ◽  
Engineering Practice ◽  
Standard Fire ◽  
Oil Storage ◽  
Fire Resistance Test ◽  
Test Furnace

Improving the fire resistance of the key cables connected to firefighting and safety equipment is of great importance. Based on the engineering practice of an oil storage company, this study proposes a modification scheme that entails spraying fire-retardant coatings on the outer surface of a cable tray to delay the failure times of the cables in the tray. To verify the effect, 12 specimens were processed using five kinds of fire-retardant coatings and two kinds of fire-resistant cotton to coat the cable tray. The specimens were installed in the vertical fire resistance test furnace. For the ISO 834 standard fire condition, a fire resistance test was carried out on the specimens. The data for the surface temperature and the insulation resistance of the cables in trays were collected, and the fireproof effect was analyzed. The results showed that compared with the control group, the failure time of the cable could be delayed by 1.57–14.86 times, and the thicker the fire-retardant coatings were, the better the fireproof effect was. In general, the fire protection effect of the fire-retardant coating was better than that of the fire-resistant cotton.

scholarly journals NF-κB and FosB mediate inflammation and oxidative stress in the blast lung injury of rats exposed to shock waves

2021 ◽  
Author(s):  
Hong Wang ◽  
Wenjuan Zhang ◽  
Jinren Liu ◽  
Junhong Gao ◽  
Le Fang ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Lung Injury ◽  
Shock Waves ◽  
Time Dependent ◽  
Inflammatory Factors ◽  
Control Group ◽  
Dependent Manner ◽  
Total Antioxidant ◽  
Blast Lung Injury ◽  
And Oxidative Stress

Abstract Blast lung injury (BLI) is the major cause of death in explosion-derived shock waves; however, the mechanisms of BLI are not well understood. To identify the time-dependent manner of BLI, a model of lung injury of rats induced by shock waves was established by a fuel air explosive. The model was evaluated by hematoxylin and eosin staining and pathological score. The inflammation and oxidative stress of lung injury were also investigated. The pathological scores of rats’ lung injury at 2 h, 24 h, 3 days, and 7 days post-blast were 9.75±2.96, 13.00±1.85, 8.50±1.51, and 4.00±1.41, respectively, which were significantly increased compared with those in the control group (1.13±0.64; P<0.05). The respiratory frequency and pause were increased significantly, while minute expiratory volume, inspiratory time, and inspiratory peak flow rate were decreased in a time-dependent manner at 2 and 24 h post-blast compared with those in the control group. In addition, the expressions of inflammatory factors such as interleukin (IL)-6, IL-8, FosB, and NF-κB were increased significantly at 2 h and peaked at 24 h, which gradually decreased after 3 days and returned to normal in 2 weeks. The levels of total antioxidant capacity, total superoxide dismutase, and glutathione peroxidase were significantly decreased 24 h after the shock wave blast. Conversely, the malondialdehyde level reached the peak at 24 h. These results indicated that inflammatory and oxidative stress induced by shock waves changed significantly in a time-dependent manner, which may be the important factors and novel therapeutic targets for the treatment of BLI.

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