A new perspective on loss to follow‐up in failure time and life history studies

Lawless, J. F.; Cook, Richard J.

doi:10.1002/sim.8318

Cited by 14 publications

(18 citation statements)

References 41 publications

(132 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally in related work we have discussed the idea of tracing studies as a means of strengthening inferences for joint models (Lawless and Cook, 2019). In the LTF setting this would involve use of extraordinary resources to track down individuals who are identified as LTF or perhaps use of external administrative databases to obtain partial information on the status of the individuals LTF.…”

Section: Discussionmentioning

confidence: 99%

“…In Figure 1a,b, the intensity for LTF from States 1 and 2 is allowed to be different. In Figure 1b, we also allow the failure intensity to differ for persons before and after LTF; Lawless and Cook (2019) consider this for the case where individuals are continuously rather than intermittently observed.…”

Section: Intermittent Observation Schemes and Loss To Follow‐upmentioning

confidence: 99%

“…This type of dependent or nonignorable LTF has been studied extensively in the case where individuals can be continuously monitored (see Fisher & Kanarek, 1974;Scharfstein & Robins, 2002;Siannis, 2011;Slud & Rubinstein, 1983). Lawless and Cook (2019) review this area and discuss the use of tracing studies (e.g., Baker, Wax, & Patterson, 1993;Farewell, Lawless, & Gladman, 2003;Frangakis & Rubin, 2001) that collect failure information on persons after LTF. However, aside from aforementioned studies on how to define LTF times, there has been very little study of settings with intermittent observation.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Failure time studies with intermittent observation and losses to follow‐up

Cook

Lawless

2020

Scandinavian J Statistics

Self Cite

View full text Add to dashboard Cite

In health research interest often lies in modeling a failure time process but in many cohort studies failure status is only determined at scheduled assessment times. While the assessment times may be fixed upon study entry, individuals may become lost to follow-up and miss visits subsequent to the time of loss to follow-up. We consider a three-state model to characterize a joint failure and loss to follow-up process, and use it to investigate the impact of dependent loss to follow-up on standard parametric, nonparametric, and semiparametric analysis. The effect of dependent loss to follow-up is mitigated by fitting the joint model. The performance of standard methods is studied using the asymptotic theory of misspecified models, and the finite sample performance is examined for the standard and joint analyses through simulation studies. An application to data from a youth smoking prevention study is presented for illustration. K E Y W O R D S dependent censoring, failure time analysis, joint modeling, loss to follow-up, model misspecification 1 INTRODUCTION Longitudinal studies of event occurrences or failure time outcomes typically involve intermittent observation of study individuals at scheduled times; for convenience we refer to the occasions at Richard J. Cook and Jerald F. Lawless contributed equally to this study.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Intermittent Observation Schemes and Loss To Follow‐upmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Failure time studies with intermittent observation and losses to follow‐up

Cook

Lawless

2020

Scandinavian J Statistics

Self Cite

View full text Add to dashboard Cite

show abstract

“…If the random censoring process is conditionally independent of the recurrent event process then we can write

\overline{λ} (t | \overline{ℋ} (t)) = Y (t) λ (t | ℋ (t)),

and the intensities governing the j → j + 1 transitions in Figure 1B are aligned with those in Figure 1A. This is essential for standard analysis of the data obtained under right‐censoring scheme to yield information about the recurrent event process in the absence of censoring 28 …”

Section: Notation and Model Formulationmentioning

confidence: 99%

Semiparametric recurrent event vs time‐to‐first‐event analyses in randomized trials: Estimands and model misspecification

Zhong

Cook

2021

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

Insights regarding the merits of recurrent event and time‐to‐first‐event analyses are needed to provide guidance on strategies for analyzing intervention effects in randomized trials involving recurrent event responses. Using established asymptotic results we introduce a framework for studying the large sample properties of estimators arising from semiparametric proportional rate function models and Cox regression under model misspecification. The asymptotic biases and power implications are investigated for different data generating models, and we study the impact of dependent censoring on these findings. Illustrative applications are given involving data from a cystic fibrosis trial and a carcinogenicity experiment, following which we summarize findings and discuss implications for clinical trial design.

show abstract

“…Hence, if end of follow‐up for AEs, withdrawal of consent or discontinuation is disease or treatment related, this would be handled as a competing event. See Lawless and Cook, 2019, for related considerations.…”

Section: Definition Of Eventsmentioning

confidence: 99%

Survival analysis for AdVerse events with VarYing follow‐up times (SAVVY): Rationale and statistical concept of a meta‐analytic study

et al. 2020

View full text Add to dashboard Cite

The assessment of safety is an important aspect of the evaluation of new therapies in clinical trials, with analyses of adverse events being an essential part of this. Standard methods for the analysis of adverse events such as the incidence proportion, that is the number of patients with a specific adverse event out of all patients in the treatment groups, do not account for both varying follow‐up times and competing risks. Alternative approaches such as the Aalen–Johansen estimator of the cumulative incidence function have been suggested. Theoretical arguments and numerical evaluations support the application of these more advanced methodology, but as yet there is to our knowledge only insufficient empirical evidence whether these methods would lead to different conclusions in safety evaluations. The Survival analysis for AdVerse events with VarYing follow‐up times (SAVVY) project strives to close this gap in evidence by conducting a meta‐analytical study to assess the impact of the methodology on the conclusion of the safety assessment empirically. Here we present the rationale and statistical concept of the empirical study conducted as part of the SAVVY project. The statistical methods are presented in unified notation, and examples of their implementation in R and SAS are provided.

show abstract

A new perspective on loss to follow‐up in failure time and life history studies

Cited by 14 publications

References 41 publications

Failure time studies with intermittent observation and losses to follow‐up

Failure time studies with intermittent observation and losses to follow‐up

Semiparametric recurrent event vs time‐to‐first‐event analyses in randomized trials: Estimands and model misspecification

Survival analysis for AdVerse events with VarYing follow‐up times (SAVVY): Rationale and statistical concept of a meta‐analytic study

Contact Info

Product

Resources

About