A potential new enriching trial design for selecting non-small-cell lung cancer patients with no predictive biomarker for trials based on both histology and early tumor response: further analysis of a thalidomide trial

There are few predictive biomarkers for antiangiogenic trials in lung cancer. We examine a potential treatment strategy in which a patient group is enriched using both histology and an early assessment of response during standard chemotherapy, and where a new agent is given for the remainder of chemotherapy and as maintenance. We performed a retrospective analysis of 722 stage IIIB/IV non-small-cell lung cancer patients from a double-blind placebo-controlled trial of thalidomide or placebo 100-200 mg/day, combined with gemcitabine/carboplatin (for up to four cycles), then given as single agent maintenance therapy. There was a significant statistical interaction between treatment and histology, with a possible benefit among squamous cell cancer (SCC) patients. We examined 150 SCC patients who were "nonprogressors" (stable disease or complete/partial response) after completing the second chemotherapy cycle. Endpoints were progression-free survival (PFS) and overall survival (OS). Among the 150 patients nonprogressors after cycle 2 (thalidomide, n = 72; placebo, n = 78; baseline characteristics were similar), the hazard ratios (HRs) were: OS = 0.76 (95% CI: 0.54-1.07) and PFS = 0.69 (95% CI: 0.50-0.97). In 57 patients who had a complete/partial response, the HRs were: OS = 0.63 (95% CI: 0.34-1.15) and PFS = 0.50 (95% CI: 0.28-0.88). SCC patients who were nonprogressors after 2 cycles of standard chemotherapy showed evidence of a benefit from thalidomide when taken for the remainder of chemotherapy and as maintenance. This strategy based on histology and, importantly, early assessment of tumor response, as a means of patient enrichment, could be examined in other lung cancer studies. Such an approach might be suitable for trials where there are no predictive biomarkers

Recalibrating Health Technology Assessment Methods for Cell and Gene Therapies

Recently licensed cell and gene therapies have promising but highly uncertain clinical benefits. They are entering the market at very high prices, with the latest entrants costing hundreds of thousands of dollars. The significant long-term uncertainty posed by these therapies has already complicated the use of conventional economic evaluation approaches such as cost-effectiveness and cost-utility analyses, which are widely used for assessing the value of new health interventions. Cell and gene therapies also risk jeopardising healthcare systems' financial sustainability. As a result, there is a need to recalibrate the current health technology assessment methods used to measure and compensate their value. In this paper, we outline a set of technical adaptations and methodological refinements to address key challenges in the appraisal of cell and gene therapies' value, including the assessment of efficiency and affordability. We also discuss the potential role of alternative financing mechanisms. Ultimately, uncertainties associated with cell and gene therapies can only be meaningfully addressed by improving the evidence base supporting their approval and adoption in healthcare systems

Life expectancy difference and life expectancy ratio: two measures of treatment effects in randomised trials with non-proportional hazards

The hazard ratio (HR) is the most common measure of treatment effect in clinical trials that use time-to-event outcomes such as survival. When survival curves cross over or separate only after a considerable time, the proportional hazards assumption of the Cox model is violated, and HR can be misleading. We present two measures of treatment effects for situations where the HR changes over time: the life expectancy difference (LED) and life expectancy ratio (LER). LED is the difference between mean survival times in the intervention and control arms. LER is the ratio of these two times. LED and LER can be calculated for at least two time intervals during the trial, allowing for curves where the treatment effect changes over time. The two measures are readily interpretable as absolute and relative gains or losses in life expectancy

Smaller sample sizes for phase II trials based on exact tests with actual error rates by trading-off their nominal levels of significance and power

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Publishing interim results of randomised clinical trials in peer-reviewed journals

Background: Interim analyses of randomised controlled trials are sometimes published before the final results are available. In several cases, the treatment effects were noticeably different after patient recruitment and follow-up completed. We therefore conducted a literature review of peer-reviewed journals to compare the reported treatment effects between interim and final publications and to examine the magnitude of the difference. Methods: We performed an electronic search of MEDLINE from 1990 to 2014 (keywords: ‘clinical trial’ OR ‘clinical study’ AND ‘random*’ AND ‘interim’ OR ‘preliminary’), and we manually identified the corresponding final publication. Where the electronic search produced a final report in which the abstract cited interim results, we found the interim publication. We also manually searched every randomised controlled trial in eight journals, covering a range of impact factors and general medical and specialist publications (1996–2014). All paired articles were checked to ensure that the same comparison between interventions was available in both. Results: In all, 63 studies are included in our review, and the same quantitative comparison was available in 58 of these. The final treatment effects were smaller than the interim ones in 39 (67%) trials and the same size or larger in 19 (33%). There was a marked reduction, defined as a ≥20% decrease in the size of the treatment effect from interim to final analysis, in 11 (19%) trials compared to a marked increase in 3 (5%), p = 0.057. The magnitude of percentage change was larger in trials where commercial support was reported, and increased as the proportion of final events at the interim report decreased in trials where commercial support was reported (interaction p = 0.023). There was no evidence of a difference between trials that stopped recruitment at the interim analysis where this was reported as being pre-specified versus those that were not pre-specified (interaction p = 0.87). Conclusion: Published interim trial results were more likely to be associated with larger treatment effects than those based on the final report. Publishing interim results should be discouraged, in order to have reliable estimates of treatment effects for clinical decision-making, regulatory authority reviews and health economic analyses. Our work should be expanded to include conference publications and manual searches of additional journal publications