Handling informative dropout in longitudinal analysis of health-related quality of life: application of three approaches to data from the esophageal cancer clinical trial PRODIGE 5/ACCORD 17

When a joint model should be preferred over a linear mixed model for analysis of longitudinal health-related quality of life data in cancer clinical trials

BACKGROUND

Patient-reported outcomes such as health-related quality of life (HRQoL) are increasingly used as endpoints in randomized cancer clinical trials. However, the patients often drop out so that observation of the HRQoL longitudinal outcome ends prematurely, leading to monotone missing data. The patients may drop out for various reasons including occurrence of toxicities, disease progression, or may die. In case of informative dropout, the usual linear mixed model analysis will produce biased estimates. Unbiased estimates cannot be obtained unless the dropout is jointly modeled with the longitudinal outcome, for instance by using a joint model composed of a linear mixed (sub)model linked to a survival (sub)model. Our objective was to investigate in a clinical trial context the consequences of using the most frequently used linear mixed model, the random intercept and slope model, rather than its corresponding joint model.

METHODS

We first illustrate and compare the models on data of patients with metastatic pancreatic cancer. We then perform a more formal comparison through a simulation study.

RESULTS

From the application, we derived hypotheses on the situations in which biases arise and on their nature. Through the simulation study, we confirmed and complemented these hypotheses and provided general explanations of the bias mechanisms.

CONCLUSIONS

In particular, this article reveals how the linear mixed model fails in the typical situation where poor HRQoL is associated with an increased risk of dropout and the experimental treatment improves survival. Unlike the joint model, in this situation the linear mixed model will overestimate the HRQoL in both arms, but not equally, misestimating the difference between the HRQoL trajectories of the two arms to the disadvantage of the experimental arm.

Temporal Profiles of Symptom Scores After Palliative Radiotherapy for Bleeding Gastric Cancer With Adjustment for the Palliative Prognostic Index: An Exploratory Analysis of a Multicentre Prospective Observational Study (JROSG 17-3)

AIMS

Although palliative radiotherapy for gastric cancer may improve some symptoms, it may also have a negative impact due to its toxicity. We investigated whether symptoms improved after radiotherapy with adjustment for the Palliative Prognostic Index (PPI) considering that patients with limited survival tend to experience deterioration of symptoms.

MATERIALS AND METHODS

This study was an exploratory analysis of the Japanese Radiation Oncology Study Group study (JROSG 17-3). We assessed six symptom scores (nausea, anorexia, fatigue, shortness of breath, pain at the irradiated area and distress) at registration and 2, 4 and 8 weeks thereafter. We tested whether symptoms linearly improved after adjusting for the baseline PPI. Shared parameter models were used to adjust for potential bias in missing data.

RESULTS

The present study analysed all 55 patients enrolled in JROSG 17-3. With time from registration as the only explanatory variable in the model, a significant linear decrease was observed in shortness of breath, pain and distress (slopes, -0.26, -0.22 and -0.19, respectively). Given that the interaction terms (i.e. PPI × time) were not significantly associated with symptom scores in any of the six symptoms, only PPI was included as the main effect in the final multivariable models. After adjusting for the PPI, shortness of breath, pain and distress significantly improved (slope, -0.25, -0.19 and -0.17; P < 0.001, 0.002 and 0.047, respectively). An improvement in fatigue and distress was observed only in patients treated with a biologically effective dose ≤14.4 Gy.

CONCLUSION

Shortness of breath, pain and distress improved after radiotherapy. Moreover, a higher PPI was significantly associated with higher symptom scores at all time points, including baseline. In contrast, PPI did not seem to influence the improvement of these symptoms. Regardless of the expected survival, patients receiving radiotherapy for gastric cancer can expect an improvement in shortness of breath, pain and distress over 8 weeks. Multiple-fraction radiotherapy might hamper the improvement in fatigue and distress by its toxicity or treatment burden.

Multiply robust subgroup analysis based on a single-index threshold linear marginal model for longitudinal data with dropouts

Identifying subpopulations that may be sensitive to the specific treatment is an important step toward precision medicine. On the other hand, longitudinal data with dropouts is common in medical research, and subgroup analysis for this data type is still limited. In this paper, we consider a single-index threshold linear marginal model, which can be used simultaneously to identify subgroups with differential treatment effects based on linear combination of the selected biomarkers, estimate the treatment effects in different subgroups based on regression coefficients, and test the significance of the difference in treatment effects based on treatment-subgroup interaction. The regression parameters are estimated by solving a penalized smoothed generalized estimating equation and the selection bias caused by missingness is corrected by a multiply robust weighting matrix, which allows multiple missingness models to be taken account into estimation. The proposed estimator remains consistent when any model for missingness is correctly specified. Under regularity conditions, the asymptotic normality of the estimator is established. Simulation studies confirm the desirable finite-sample performance of the proposed method. As an application, we analyze the data from a clinical trial on pancreatic cancer.

Joint modelling with competing risks of dropout for longitudinal analysis of health-related quality of life in cancer clinical trials

PURPOSE

Health-related quality of life (HRQoL) is an important endpoint in cancer clinical trials. Analysis of HRQoL longitudinal data is plagued by missing data, notably due to dropout. Joint models are increasingly receiving attention for modelling longitudinal outcomes and the time-to-dropout. However, dropout can be informative or non-informative depending on the cause.

METHODS

We propose using a joint model that includes a competing risks sub-model for the cause-specific time-to-dropout. We compared a competing risks joint model (CR JM) that distinguishes between two causes of dropout with a standard joint model (SJM) that treats all the dropouts equally. First, we applied the CR JM and SJM to data from 267 patients with advanced oesophageal cancer from the randomized clinical trial PRODIGE 5/ACCORD 17 to analyse HRQoL data in the presence of dropouts unrelated and related to a clinical event. Then, we compared the models using a simulation study.

RESULTS

We showed that the CR JM performed as well as the SJM in situations where the risk of dropout was the same whatever the cause. In the presence of both informative and non-informative dropouts, only the SJM estimations were biased, impacting the HRQoL estimated parameters.

CONCLUSION

The systematic collection of the reasons for dropout in clinical trials would facilitate the use of CR JMs, which could be a satisfactory approach to analysing HRQoL data in presence of both informative and non-informative dropout.

TRIAL REGISTRATION

This study is registered with ClinicalTrials.gov, number NCT00861094.