Alexander Bore

Bio: Alexander Bore is an academic researcher from AstraZeneca. The author has contributed to research in topics: Clinical endpoint & Hazard ratio. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Modeling the impact of some independent parameters on the syngas characteristics during plasma gasification of municipal solid waste using artificial neural network and stepwise linear regression methods

Abstract: Thermal plasma gasification is considered as an attractive technology to produce high quality syngas from municipal solid waste (MSW). It is imperative to study the effect of operating parameters on syngas quality and find a practical way to predict syngas characteristics. This paper compiled 112 research cases to develop quantitative models for 8 kinds of syngas characteristics and explored the simultaneous effects of input parameters during the plasma gasification by applying stepwise linear regression (SLR) and artificial neural network (ANN) methods. The SLR model has a higher predictive accuracy than the ANN model for gas yield, volume fraction of CH4 and CO2, as well as mechanical gasification efficiency (MGE), with Rtesting2 = 0.659–0.916. The ANN model demonstrates better performance than the SLR model for low heating value (LHV), dry gas ratio, volume fraction of H2 and CO, with Rtesting2 = 0.807–0.939. According to the importance analysis, flow rates of the work gas-N2, feedstock type, flow rates of the work gas-steam, and input power are the most critical parameters for LHV, gas yield, and volume fraction of CH4 and H2, respectively. Input power and specific energy requirements (SER) are the most important factors affecting volume fractions of H2 (25.7–57.3 vol%) and input power plays a dominant role. The models developed in this study could enhance understanding of plasma gasification and are unique to considering multiple input parameters together.

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

Abstract: 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.

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

Abstract: 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.

Properties of the weighted log-rank test in the design of confirmatory studies with delayed effects

Abstract: Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. This assumption not only affects the analysis part but also the sample size calculation. The presence of delayed effects causes a change in the hazard ratio while the trial is ongoing since at the beginning we do not observe any difference between treatment arms and after some unknown time point, the differences between treatment arms will start to appear. Hence, the proportional hazards assumption no longer holds and both sample size calculation and analysis methods to be used should be reconsidered. The weighted log-rank test allows a weighting for early, middle and late differences through the Fleming and Harrington class of weights, and is proven to be more efficient when the proportional hazards assumption does not hold. The Fleming and Harrington class of weights, along with the estimated delay, can be incorporated into the sample size calculation in order to maintain the desired power once the treatment arm differences start to appear. In this article, we explore the impact of delayed effects in group sequential and adaptive group sequential designs, and make an empirical evaluation in terms of power and type-I error rate of the of the weighted log-rank test in a simulated scenario with fixed values of the Fleming and Harrington class of weights. We also give some practical recommendations regarding which methodology should be used in the presence of delayed effects depending on certain characteristics of the trial.

Quantifying treatment differences in confirmatory trials under non-proportional hazards

Abstract: Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. With the emergence of immunotherapy and novel targeted therapies, departure from the proportional hazard assumption is not rare in nowadays clinical research. Under non-proportional hazards, the hazard ratio does not have a straightforward clinical interpretation, and the log-rank test is no longer the most powerful statistical test even though it is still valid. Nevertheless, the log-rank test and the hazard ratio are still the primary analysis tools, and traditional approaches such as sample size increase are still proposed to account for the impact of non-proportional hazards. The weighed log-rank test and the test based on the restricted mean survival time (RMST) are receiving a lot of attention as a potential alternative to the log-rank test. We conduct a simulation study comparing the performance and operating characteristics of the log-rank test, the weighted log-rank test and the test based on the RMST, including a treatment effect estimation, under different non-proportional hazards patterns. Results show that, under non-proportional hazards, the hazard ratio and weighted hazard ratio have no straightforward clinical interpretation whereas the RMST ratio can be interpreted regardless of the proportional hazards assumption. In terms of power, the RMST achieves a similar performance when compared to the log-rank test.