The risk-adjusted sequential probability test is simple to implement, can be applied in a variety of contexts, and might have been useful to detect specific instances of past divergent performance. The use of this and related techniques deserves further attention in the context of prospectively monitoring adverse clinical outcomes.

BackgroundLung metastasectomy in the treatment of advanced colorectal cancer has been widely adopted without good evidence of survival or palliative benefit. We aimed to test its effectiveness in a randomised controlled trial (RCT).MethodsMultidisciplinary teams in 13 hospitals recruited participants with potentially resectable lung metastases to a multicentre, two-arm RCT comparing active monitoring with or without metastasectomy. Other local or systemic treatments were decided by the local team. Randomisation was remote and stratified by site with minimisation for age, sex, primary cancer stage, interval since primary resection, prior liver involvement, the number of metastases, and carcinoembryonic antigen level. The central Trial Management Group were blind to patient allocation until completion of the analysis. Analysis was on intention to treat with a margin for non-inferiority of 10%.ResultsBetween December 2010 and December 2016, 65 participants were randomised. Characteristics were well-matched in the two arms and similar to those in reported studies: age 35 to 86 years (interquartile range (IQR) 60 to 74); primary resection IQR 16 to 35 months previously; stage at resection T1, 2 or 3 in 3, 8 and 46; N1 or N2 in 31 and 26; unknown in 8. Lung metastases 1 to 5 (median 2); 16/65 had previous liver metastases; carcinoembryonic antigen normal in 55/65. There were no other interventions in the first 6 months, no crossovers from control to treatment, and no treatment-related deaths or major adverse events. The Hazard ratio for death within 5 years, comparing metastasectomy with control, was 0.82 (95%CI 0.43, 1.56).ConclusionsBecause of poor and worsening recruitment, the study was stopped. The small number of participants in the trial (N = 65) precludes a conclusive answer to the research question given the large overlap in the confidence intervals in the proportions still alive at all time points. A widely held belief is that the 5-year absolute survival benefit with metastasectomy is about 35%: 40% after metastasectomy compared to < 5% in controls. The estimated survival in this study was 38% (23–62%) for metastasectomy patients and 29% (16–52%) in the well-matched controls. That is the new and important finding of this RCT.Trial registrationClinicalTrials.gov, ID: NCT01106261. Registered on 19 April 2010

Summary. Current demand for accountability and efficiency of healthcare organizations, combined with the greater availability of routine data on clinical care and outcomes, has led to an increased focus on statistical methods in healthcare regulation. We consider three different regulatory functions in which statistical analysis plays a vital role: rating organizations, deciding whom to inspect and continuous surveillance for arising problems. A common approach to data standardization based on (possibly overdispersed) Z -scores is proposed, although specific tools are used for assessing performance against a target, combining indicators when screening for inspection, and continuous monitoring using risk-adjusted sequential testing procedures. We pay particular attention to the problem of simultaneously monitoring over 200000 indicators for excess mortality, both with respect to the statistical issues surrounding massive multiplicity, and the organizational aspects of dealing with such a complex but high profile process.

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Background: Lung metastasectomy in the treatment of advanced colorectal cancer has been adopted and established without control data. Our aim was to test its effectiveness in a randomised trial. Methods: Multidisciplinary teams in 13 hospitals recruited participants with potentially resectable lung metastases to a multicentre 2-arm randomised trial comparing active monitoring with or without metastasectomy. Other treatments were as decided by the local team. Randomization was centralised with stratification by site and minimisation for age, sex, primary cancer stage, interval since primary resection, prior liver involvement, the number of metastases, and carcinoembryonic antigen. The assigned arm was not disclosed to the trial management group until completion of analysis. Analysis was on intention to treat with a margin for non-inferiority of 10%. Findings: Between December 2010 and December 2016, 65 participants were randomised. Characteristics were well-matched in the two arms and similar to those in reported studies: age 35 to 86 (IQR 60 to 74); primary resection IQR 16 to 35 months previously; stage at resection T1, 2 or 3 in 3, 8 and 46; N1 or N2 in 31 and 26; unknown in 8. Lung metastases 1 to 5 (median 2); 16/65 had previous liver metastases; carcinoembryonic antigen normal in 55/65. There were no other interventions in the first 6 months, no cross overs from control to treatment, and no treatment related deaths or major adverse events. Hazard ratio for death within 5 years, comparing metastasectomy with control, was 0.82 (95%CI 0.43, 1.56). Interpretation: The small number (N=65) precludes a conclusive answer to the research question but the similar survival in metastasectomy and control arms challenges beliefs that there is a 35% survival difference that can be attributed to lung metastasectomy. Funding: Cancer Research UK funding Grant No. C7678/A11393 Name of the registry: Clintrial.gov Registration number: NCT01106261 Date 19th April 2010 https://clinicaltrials.gov/ct2/show/NCT01106261

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The STRAND Chart (Survival Time, Risk‐Adjusted, N‐Division Chart) is a new tool for online risk‐adjusted (RA) monitoring of survival outcomes. The chart is drawn in continuous time, making it responsive to change in the process of interest, for example, performance over time of a surgical unit and the procedures that they employ. Though it is difficult to achieve with charts designed for the purpose described, we show that our suggested chart keeps patient ordering intact. We discuss the difficulties maintaining patient ordering poses, making reference to other charts in the literature. Our conclusion is that the best approach to preserving patient ordering on any chart of this nature involves compromising on the fullness of presentation of the recorded data. The chart is divided into N strands, each strand relating to a benchmark patient's survival information at t n days following treatment, n = 1,2,…,N. We present a simple version of the chart where the strands consist of Bernoulli RA exponentially weighted moving averages, recording RA failure rates at t n days. It can detect recent process change and historical change. We illustrate the STRAND Chart using a well‐known UK post cardiac surgery survival dataset, where the nature of a certain cluster in the data can be seen.