{'fraction_non_alphanumeric': 0.015739484396200813, 'fraction_numerical': 0.0, 'mean_word_length': 5.311643835616438, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Commentary on ‘Joint modeling of survival and longitudinal non‐survival data: current methods and issues’

Many clinical trials and observational studies include outcomes of interest that may be classified as longitudinal or event time. In the regulatory setting where both types of data may be informative, it is not uncommon for longitudinal and event time or survival data to be analyzed separately using wellestablished methods. However, the appropriateness of analyzing correlated longitudinal and survival data separately has been a topic of interest within the statistical community for many years. Critics of separate analyses of correlated survival and longitudinal outcomes argue that the analyses are often inefficient or biased. The current article encourages and endorses the use of joint models as a tool to improve clinical trial efficiency and to provide additional insight into the underlying relationship between survival and longitudinal data. The article acknowledges the plethora of joint modeling approaches that have become prolific in the literature. However, the intent of the article is not to provide an extensive review of the approaches because many such reviews exist. Instead, the authors’ aim is to motivate the use of joint modeling methods. As stated in the article, ‘The key message is that joint modeling methods can be used, perhaps even routinely, to link survival-type outcomes with longitudinal measurements to get better insights into both.’ In keeping with the key message, we focus on the motivation and utility of the models in a regulatory setting as opposed to a critique of the various approaches. We initially visit the inferential goals of various joint modeling approaches because the utility of the models depends on the research objectives. One research goal may be to evaluate the longitudinal outcome where the event time information is an important component accounting for informative censoring. Another goal may be to evaluate the time to an event of interest, and the repeated measures are used to further inform the underlying process. A final goal may be to evaluate the treatment effect on both the longitudinal and time-to-event outcomes. For the former objective, the absence of longitudinal observations beyond the event time creates a form of non-ignorable missingness. In a regulatory environment where the prevention and treatment of missing data in clinical trials continues to be at the forefront of discussions, joint models, such as selection models or pattern mixture models, may be useful analytic methods to estimate the treatment effect in the presence of outcome-related dropout. We view this as a use of joint models that is ripe for additional research and application. For the latter two objectives, we do not disagree that many joint modeling approaches provide efficient estimates of the treatment effect and reduce bias in the estimates. We additionally concur with the authors’ assessment that joint modeling approaches are likely to provide additional insights into the relationship between the longitudinal response and event times. Specifically, joint models may further elucidate the within-subject patterns of change over time and/or the association between a longitudinal response and the event times. However, in a regulatory setting, these insights and benefits may best be realized as important exploratory analyses because of numerous challenges. As alluded to in the article, challenges include the computational demand, strong assumptions associated with some joint modeling approaches, and potentially complex implementation; nevertheless, strides have been made in the literature that may