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Aim: The European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ‐C30) assesses the health‐related quality of life of patients in cancer trials. There are currently no minimally important difference (MID) guidelines for the EORTC QLQ‐C30 for colorectal cancer (CRC). This study aims to estimate MIDs for the EORTC QLQ‐C30 scales in patients with advanced CRC treated with chemotherapy and enrolled in clinical trials. Method: The data were obtained from three published EORTC trials that treated CRC patients using chemotherapy. Potential anchors were selected from clinical variables based on their correlation with EORTC QLQ‐C30 scales. Anchor‐based MIDs for within‐group change and between‐group change were estimated via the mean change method and linear regression, respectively, and summarized using weighted correlation. Distribution‐based MIDs were also examined. Results: Anchor‐based MIDs were determined for deterioration in 8 of the 14 EORTC QLQ‐C30 scales and in 9 scales for improvement, and varied by scale, direction of change and anchor. MIDs for improvement (deterioration) ranged from 6 to 18 (−11 to −5) points for within‐group change and 5 to 15 (−10 to −4) for between‐group change. Summarized MIDs (in absolute values) per scale mostly ranged from 5 to 10 points. Conclusions: These findings have clinical relevance for the interpretation of treatment efficacy and the design of clinical trials by informing sample size requirements. [ABSTRACT FROM AUTHOR]

This paper extends dynamic prediction by landmarking to recurrent event data. The motivating data comprised post-kidney transplantation records of repeated infections and repeated measurements of multiple markers. At each landmark time point ts, a Cox proportional hazards model with a frailty term was fitted using data of individuals who were at risk at landmark s. This model included the time-updated marker values at ts as time-fixed covariates. Based on a stacked data set that merged all landmark data sets, we considered supermodels that allow parameters to depend on the landmarks in a smooth fashion. We described and evaluated four ways to parameterize the supermodels for recurrent event data. With both the study data and simulated data sets, we compared supermodels that were fitted on stacked data sets that consisted of either overlapping or non-overlapping landmark periods. We observed that for recurrent event data, the supermodels may yield biased estimates when overlapping landmark periods are used for stacking. Using the best supermodel amongst the ones considered, we dynamically estimated the probability to remain infection free between ts and a prediction horizon thor, conditional on the information available at ts. [ABSTRACT FROM AUTHOR]

In medical studies we are often confronted with complex longitudinal data. During the follow-up period, which can be ended prematurely by a terminal event (e.g. death), a subject can experience recurrent events of multiple types. In addition, we collect repeated measurements from multiple markers. An adverse health status, represented by ‘bad’ marker values and an abnormal number of recurrent events, is often associated with the risk of experiencing the terminal event. In this situation, the missingness of the data is not at random and, to avoid bias, it is necessary to model all data simultaneously using a joint model. The correlations between the repeated observations of a marker or an event type within an individual are captured by normally distributed random effects. Because the joint likelihood contains an analytically intractable integral, Bayesian approaches or quadrature approximation techniques are necessary to evaluate the likelihood. However, when the number of recurrent event types and markers is large, the dimensionality of the integral is high and these methods are too computationally expensive. As an alternative, we propose a simulated maximum-likelihood approach based on quasi-Monte Carlo integration to evaluate the likelihood of joint models with multiple recurrent event types and markers. [ABSTRACT FROM PUBLISHER]