A competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal Alzheimer's disease

Joint models inform the longitudinal assessment of patient-reported outcomes in clinical trials: a simulation study and secondary analysis of the restrictive Vs. liberal fluid therapy for major abdominal surgery (RELIEF) randomized controlled trial

OBJECTIVES

Evaluate the utility of a joint model when analysing a patient-reported endpoint as part of a randomized controlled trial (RCT) in which censoring occurs when patients die during follow-up.

STUDY DESIGN AND SETTING

The present study comprises two parts as follows: first we reanalyzed data from a previously published RCT comparing two fluid regimens in the first 24 hours of major abdomino-pelvic surgery ('Restrictive versus Liberal Fluid Therapy for Major Abdominal Surgery [RELIEF]' trial). In this trial, patient-reported disability was measured at multiple timepoints before and after surgery. Next, we conducted a simulation study to jointly emulate patient-reported disability and survival, similar to the RELIEF trial, under nine treatment-outcome scenarios. In both parts, we compared a joint model analysis to a linear mixed-effect model combined with one of the several traditional methods of handling longitudinal missingness as follows: available data analysis, complete case analysis, last observation carried forward, and worst-case assumption.

RESULTS

In part one, the joint model revealed no between-group differences in patient-reported disability at 1, 3, 6, and 12 months after surgery. The worst-case approach consistently resulted in the largest deviation from the joint model estimates, although in this particular setting none of the approaches materially changed the study's conclusions. In part two, the simulations revealed that across all treatment-outcome scenarios, the joint model expectedly produced unbiased estimates of patient-reported disability. Similarly, employing an approach based on all available data (ie, relying on the maximum likelihood estimator for handling missingness) yielded disability estimates close to the simulated values, albeit with slight bias across some scenarios. The last observation carried forward approach that mirrored the joint model's estimates except when the treatment had a nonnull effect on patient-reported disability. The worst-case analysis resulted in high bias, which was particularly evident when the treatment had a large effect on survival. The complete case analysis resulted in high bias across all scenarios.

CONCLUSION

In randomized trials that employ a patient-reported outcome as one of their endpoints, a joint model can address bias arising from informative missingness related to death. Methods for handling missingness based on all available data appear to be a reasonable alternative to joint models, with only slight bias across some simulated scenarios.

PLAIN LANGUAGE SUMMARY

'Patient-centered research' focuses on outcomes that are prioritized by patients. This approach often involves asking patients to complete questionnaires about their health experiences. However, if a patient does not finish a study, dealing with their missing answers can pose significant challenges. Joint models are a recent statistical method that may help address this issue. In this study, we used joint models in a real-world clinical trial, and in a series of simulated trials, to determine how well they handle missing questionnaire data from patients. We found that joint models offer significant benefits over most traditional methods used to analyze clinical trials.

Joint modeling of longitudinal and competing risks for assessing blood oxygen saturation and its association with survival outcomes in COVID-19 patients

BACKGROUND

The objective of the present study is to evaluate the association between longitudinal and survival outcomes in the presence of competing risk events. To illustrate the application of joint modeling in clinical research, we assessed the blood oxygen saturation (SPO2) and its association with survival outcomes in coronavirus disease (COVID-19).

MATERIALS AND METHODS

In this prospective cohort study, we followed 300 COVID-19 patients, who were diagnosed with severe COVID-19 in the Rohani Hospital in Babol, the north of Iran from October 22, 2020 to March 5, 2021, where death was the event of interest, surviving was the competing risk event and SPO2 was the longitudinal outcome. Joint modeling analyses were compared to separate analyses for these data.

RESULT

The estimation of the association parameter in the joint modeling verified the association between longitudinal outcome SPO2 with survival outcome of death (Hazard Ratio (HR) = 0.33, P = 0.001) and the competing risk outcome of surviving (HR = 4.18, P < 0.001). Based on the joint modeling, longitudinal outcome (SPO2) decreased in hypertension patients (β = -0.28, P = 0.581) and increased in those with a high level of SPO2 on admission (β = 0.75, P = 0.03). Also, in the survival submodel in the joint model, the risk of death survival outcome increased in patients with diabetes comorbidity (HR = 4.38, P = 0.026).

CONCLUSION

The association between longitudinal measurements of SPO2 and survival outcomes of COVID-19 confirms that SPO2 is an important indicator in this disease. Thus, the application of this joint model can provide useful clinical evidence in the different areas of medical sciences.

Robust estimation of dementia prevalence from two-phase surveys with non-responders via propensity score stratification

BACKGROUND

Missing diagnoses are common in cross-sectional studies of dementia, and this missingness is usually related to whether the respondent has dementia or not. Failure to properly address this issue can lead to underestimation of prevalence. To obtain accurate prevalence estimates, we propose different estimation methods within the framework of propensity score stratification (PSS), which can significantly reduce the negative impact of non-response on prevalence estimates.

METHODS

To obtain accurate estimates of dementia prevalence, we calculated the propensity score (PS) of each participant to be a non-responder using logistic regression with demographic information, cognitive tests and physical function variables as covariates. We then divided all participants into five equal-sized strata based on their PS. The stratum-specific prevalence of dementia was estimated using simple estimation (SE), regression estimation (RE), and regression estimation with multiple imputation (REMI). These stratum-specific estimates were integrated to obtain an overall estimate of dementia prevalence.

RESULTS

The estimated prevalence of dementia using SE, RE, and REMI with PSS was 12.24%, 12.28%, and 12.20%, respectively. These estimates showed higher consistency than the estimates obtained without PSS, which were 11.64%, 12.33%, and 11.98%, respectively. Furthermore, considering only the observed diagnoses, the prevalence in the same group was found to be 9.95%, which is significantly lower than the prevalence estimated by our proposed method. This suggested that prevalence estimates obtained without properly accounting for missing data might underestimate the true prevalence.

CONCLUSION

Estimating the prevalence of dementia using the PSS provides a more robust and less biased estimate.

The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence

Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.