Application of methods for central statistical monitoring in clinical trials

Background On-site source data verification is a common and expensive activity, with little evidence that it is worthwhile. Central statistical monitoring (CSM) is a cheaper alternative, where data checks are performed by the coordinating centre, avoiding the need to visit all sites. Several publications have suggested methods for CSM; however, few have described their use in real trials. Methods R-programs were created to check data at either the subject level (7 tests within 3 programs) or site level (9 tests within 8 programs) using previously described methods or new ones we developed. These aimed to find possible data errors such as outliers, incorrect dates, or anomalous data patterns; digit preference, values too close or too far from the means, unusual correlation structures, extreme variances which may indicate fraud or procedural errors and under-reporting of adverse events. The methods were applied to three trials, one of which had closed and has been published, one in follow-up, and a third to which fabricated data were added. We examined how well the methods work, discussing their strengths and limitations. Results The R-programs produced simple tables or easy-to-read figures. Few data errors were found in the first two trials, and those added to the third were easily detected. The programs were able to identify patients with outliers based on single or multiple variables. They also detected (1) fabricated patients, generated to have values too close to the multivariate mean, or with too low variances in repeated measurements, and (2) sites which had unusual correlation structures or too few adverse events. Some methods were unreliable if applied to centres with few patients or if data were fabricated in a way which did not fit the assumptions used to create the programs. Outputs from the R-programs are interpreted using examples. Limitations Detecting data errors is relatively straightforward; however, there are several limitations in the detection of fraud: some programs cannot be applied to small trials or to centres with few patients (<10) and data falsified in a manner which does not fit the program’s assumptions may not be detected. In addition, many tests require a visual assessment of the output (showing flagged participants or sites), before data queries are made or on-site visits performed. Conclusions CSM is a worthwhile alternative to on-site data checking and may be used to limit the number of site visits by targeting only sites which are picked up by the programs. We summarise the methods, show how they are implemented and that they can be easy to interpret. The methods can identify incorrect or unusual data for a trial subject, or centres where the data considered together are too different to other centres and therefore should be reviewed, possibly through an on-site visit

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

Research in progress—LungSEARCH: a randomised controlled trial of surveillance for the early detection of lung cancer in a high-risk group

Abstract Low-dose CT screening for lung cancer is effective but expensive. Therefore, cheaper or more focused screening strategies may be required. LungSEARCH is a randomised prospective trial of 1568 high-risk individuals (ie, current or former moderate to heavy smokers with mild/moderate COPD) who undergo either annual sputum cytology/cytometry testing or no screening. Those with abnormal sputum then receive annual CT and fluorescent bronchoscopy for the remainder of 5 years, to identify early stage lung cancer. It is hoped that these simple initial tests could identify those requiring expensive CT scans, and the aim is to demonstrate a stage shift towards early stage cancers. Trial registration numbers ISRCTN: ISRCTN80745975, clinicaltrials.gov: NCT00512746

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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