Dataset Information

Mixed effects models for recurrent events data with partially observed time-varying covariates: Ecological momentary assessment of smoking.

ABSTRACT: Cigarette smoking is a prototypical example of a recurrent event. The pattern of recurrent smoking events may depend on time-varying covariates including mood and environmental variables. Fixed effects and frailty models for recurrent events data assume that smokers have a common association with time-varying covariates. We develop a mixed effects version of a recurrent events model that may be used to describe variation among smokers in how they respond to those covariates, potentially leading to the development of individual-based smoking cessation therapies. Our method extends the modified EM algorithm of Steele (1996) for generalized mixed models to recurrent events data with partially observed time-varying covariates. It is offered as an alternative to the method of Rizopoulos, Verbeke, and Lesaffre (2009) who extended Steele's (1996) algorithm to a joint-model for the recurrent events data and time-varying covariates. Our approach does not require a model for the time-varying covariates, but instead assumes that the time-varying covariates are sampled according to a Poisson point process with known intensity. Our methods are well suited to data collected using Ecological Momentary Assessment (EMA), a method of data collection widely used in the behavioral sciences to collect data on emotional state and recurrent events in the every-day environments of study subjects using electronic devices such as Personal Digital Assistants (PDA) or smart phones.

SUBMITTER: Rathbun SL

PROVIDER: S-EPMC4808622 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Mixed effects models for recurrent events data with partially observed time-varying covariates: Ecological momentary assessment of smoking.

Rathbun Stephen L SL Shiffman Saul S

Biometrics 20150927 1

Cigarette smoking is a prototypical example of a recurrent event. The pattern of recurrent smoking events may depend on time-varying covariates including mood and environmental variables. Fixed effects and frailty models for recurrent events data assume that smokers have a common association with time-varying covariates. We develop a mixed effects version of a recurrent events model that may be used to describe variation among smokers in how they respond to those covariates, potentially leading ...[more]

PMID: 26410189

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Project description:Inverse probability of treatment weighting is a popular propensity score-based approach to estimate marginal treatment effects in observational studies at risk of confounding bias. A major issue when estimating the propensity score is the presence of partially observed covariates. Multiple imputation is a natural approach to handle missing data on covariates: covariates are imputed and a propensity score analysis is performed in each imputed dataset to estimate the treatment effect. The treatment effect estimates from each imputed dataset are then combined to obtain an overall estimate. We call this method MIte. However, an alternative approach has been proposed, in which the propensity scores are combined across the imputed datasets (MIps). Therefore, there are remaining uncertainties about how to implement multiple imputation for propensity score analysis: (a) should we apply Rubin's rules to the inverse probability of treatment weighting treatment effect estimates or to the propensity score estimates themselves? (b) does the outcome have to be included in the imputation model? (c) how should we estimate the variance of the inverse probability of treatment weighting estimator after multiple imputation? We studied the consistency and balancing properties of the MIte and MIps estimators and performed a simulation study to empirically assess their performance for the analysis of a binary outcome. We also compared the performance of these methods to complete case analysis and the missingness pattern approach, which uses a different propensity score model for each pattern of missingness, and a third multiple imputation approach in which the propensity score parameters are combined rather than the propensity scores themselves (MIpar). Under a missing at random mechanism, complete case and missingness pattern analyses were biased in most cases for estimating the marginal treatment effect, whereas multiple imputation approaches were approximately unbiased as long as the outcome was included in the imputation model. Only MIte was unbiased in all the studied scenarios and Rubin's rules provided good variance estimates for MIte. The propensity score estimated in the MIte approach showed good balancing properties. In conclusion, when using multiple imputation in the inverse probability of treatment weighting context, MIte with the outcome included in the imputation model is the preferred approach.

Project description:AIMS:To assess whether individuals trying to quit smoking who have high depressive symptoms (HD), compared with low depressive symptoms (LD): (1) report more frequent stressful events (SEs), (2) are more likely to smoke after SEs, (3) experience greater acute or persistent changes in affect after an SE, and (4) are at greater risk of smoking following affective changes. DESIGN:Smoking cessation data were analyzed using multi-level path modeling to examine the moderating effects of depressive symptoms on relations among SEs, subsequent affect, and smoking. SETTING:An academic research center in Central New Jersey, USA. PARTICIPANTS:Seventy-one adult treatment-seeking daily smokers recruited from 2010 to 2012. MEASUREMENTS:Baseline depressive symptoms [HD: Center for Epidemiological Studies Depression Scale (CES-D) ? 16 versus LD: CES-D < 16]; and real-time ecological momentary assessment (EMA) reports of SEs, affect, and smoking assessed during 21 days post-quit. FINDINGS:Multi-level models indicated that HD smokers were more likely than LD smokers to report stressful events [odds ratio (OR) = 2.323, P = 0.009], but had similar post-stress acute affective changes (negative affect: b = -0.117, P = 0.137, positive affect: b = 0.020, P = 0.805). Only HD smokers reported increased negative affect (NA) (b = 0.199, P = 0.030) and decreased positive affect (PA) up to 12 hours later (b = -0.217, P = 0.021), and greater lapse risk up to 24 hours after an SE (OR = 3.213, P = 0.017). The persistence of elevated NA and suppressed PA was partially explained by increased odds of subsequent SEs among HD smokers. However, the heightened stress-lapse association over 24 hours found in HD smokers was not fully explained by sustained aversive affect or subsequent SEs. CONCLUSIONS:Depressed and non-depressed smokers trying to quit appear to experience similar acute affective changes following stress: however, depressed smokers experience higher rates of exposure to stress, longer-lasting post-stress affective disturbance and greater risk of smoking lapse 12-24 hours after a stressful event than non-depressed smokers.

Dataset Information

Mixed effects models for recurrent events data with partially observed time-varying covariates: Ecological momentary assessment of smoking.

Publications

Mixed effects models for recurrent events data with partially observed time-varying covariates: Ecological momentary assessment of smoking.

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