A multistate modelling approach for pancreatic cancer development in genetically high-risk families

Early detection of pancreatic cancer

Pancreatic ductal adenocarcinoma is most frequently detected at an advanced stage. Such late detection restricts treatment options and contributes to a dismal 5-year survival rate of 3-15%. Pancreatic ductal adenocarcinoma is relatively uncommon and screening of the asymptomatic adult population is not feasible or recommended with current modalities. However, screening of individuals in high-risk groups is recommended. Here, we review groups at high risk for pancreatic ductal adenocarcinoma, including individuals with inherited predisposition and patients with pancreatic cystic lesions. We discuss studies aimed at finding ways of identifying pancreatic ductal adenocarcinoma in high-risk groups, such as among individuals with new-onset diabetes mellitus and people attending primary and secondary care practices with symptoms that suggest this cancer. We review early detection biomarkers, explore the potential of using social media for detection, appraise prediction models developed using electronic health records and research data, and examine the application of artificial intelligence to medical imaging for the purposes of early detection.

Modelling variable dropout in randomised controlled trials with longitudinal outcomes: application to the MAGNETIC study

BACKGROUND

Clinical trials with longitudinally measured outcomes are often plagued by missing data due to patients withdrawing or dropping out from the trial before completing the measurement schedule. The reasons for dropout are sometimes clearly known and recorded during the trial, but in many instances these reasons are unknown or unclear. Often such reasons for dropout are non-ignorable. However, the standard methods for analysing longitudinal outcome data assume that missingness is non-informative and ignore the reasons for dropout, which could result in a biased comparison between the treatment groups.

METHODS

In this article, as a post hoc analysis, we explore the impact of informative dropout due to competing reasons on the evaluation of treatment effect in the MAGNETIC trial, the largest randomised placebo-controlled study to date comparing the addition of nebulised magnesium sulphate to standard treatment in acute severe asthma in children. We jointly model longitudinal outcome and informative dropout process to incorporate the information regarding the reasons for dropout by treatment group.

RESULTS

The effect of nebulised magnesium sulphate compared with standard treatment is evaluated more accurately using a joint longitudinal-competing risk model by taking account of such complexities. The corresponding estimates indicate that the rate of dropout due to good prognosis is about twice as high in the magnesium group compared with standard treatment.

CONCLUSIONS

We emphasise the importance of identifying reasons for dropout and undertaking an appropriate statistical analysis accounting for such dropout. The joint modelling approach accounting for competing reasons for dropout is proposed as a general approach for evaluating the sensitivity of conclusions to assumptions regarding missing data in clinical trials with longitudinal outcomes.

TRIAL REGISTRATION

EudraCT number 2007-006227-12 . Registration date 18 Mar 2008.