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Administrative claims databases often do not capture date or fact of death, so studies using these data may inappropriately treat death as a censoring event—equivalent to other withdrawal reasons—rather than a competing event. We examined 1-, 3-, and 5-year inverse-probability-of-treatment weighted cumulative risks of a composite cardiovascular outcome among 34 527 initiators of telmisartan (exposure) and ramipril (referent), who were aged ≥55 years, in Optum (United States) claims data from 2003 to 2020. Differences in cumulative risks of the cardiovascular endpoint due to censoring of death (cause-specific), as compared with treating death as a competing event (subdistribution), increased with greater follow-up time and older age, where event and mortality risks were higher. Among ramipril users, 5-year cause-specific and subdistribution cumulative risk estimates per 100, respectively, were 16.4 (95% CI, 15.3-17.5) and 16.2 (95% CI, 15.1-17.3) among ages 55-64 (difference = 0.2) and were 43.2 (95% CI, 41.3-45.2) and 39.7 (95% CI, 37.9-41.4) among ages ≥75 (difference = 3.6). Plasmode simulation results demonstrated the differences in cause-specific versus subdistribution cumulative risks to increase with increasing mortality rate. We suggest researchers consider the cohort's baseline mortality risk when deciding whether real-world data with incomplete death information can be used without concern. This article is part of a Special Collection on Pharmacoepidemiology. [ABSTRACT FROM AUTHOR]

Mortality assessment in cohorts with high numbers of persons lost to follow-up (LTFU) is challenging in settings with limited civil registration systems. We aimed to assess mortality in a clinical cohort (the Kilombero and Ulanga Antiretroviral Cohort (KIULARCO)) of human immunodeficiency virus (HIV)-infected persons in rural Tanzania, accounting for unseen deaths among participants LTFU. We included adults enrolled in 2005–2015 and traced a nonrandom sample of those LTFU. We estimated mortality using Kaplan-Meier methods 1) with routinely captured data (method A), 2) crudely incorporating tracing data (method B), 3) weighting using tracing data to crudely correct for unobserved deaths among participants LTFU (method C), and 4) weighting using tracing data accounting for participant characteristics (method D). We investigated associated factors using proportional hazards models. Among 7,460 adults, 646 (9%) died, 883 (12%) transferred to other clinics, and 2,911 (39%) were LTFU. Of 2,010 (69%) traced participants, 325 (16%) were found: 131 (40%) had died and 130 (40%) had transferred. Five-year mortality estimates derived using the 4 methods were 13.1% (A), 16.2% (B), 36.8% (C), and 35.1% (D), respectively. Higher mortality was associated with male sex, referral as a hospital inpatient, living close to the index clinic, lower body mass index, more advanced World Health Organization HIV clinical stage, lower CD4 cell count, and less time since initiation of antiretroviral therapy. Adjusting for unseen deaths among participants LTFU approximately doubled the 5-year mortality estimates. Our approach is applicable to other cohort studies adopting targeted tracing. [ABSTRACT FROM AUTHOR]

The Kaplan-Meier (KM) estimator of the survival function imputes event times for right-censored and lefttruncated observations, but these imputations are hidden and therefore sometimes unrecognized by applied health scientists. Using a simple example data set and the redistribution algorithm, we illustrate how imputations are made by the KM estimator. We also discuss the assumptions necessary for valid analyses of survival data. Illustrating imputations hidden by the KM estimator helps to clarify these assumptions and therefore may reduce inappropriate inferences. [ABSTRACT FROM AUTHOR]

Accurate survival estimates are needed for guiding cancer control efforts in sub-Saharan Africa, but previous studies have been hampered by unknown biases due to excessive loss to follow-up (LTFU). In the African Breast Cancer—Disparities in Outcomes Study, a prospective breast cancer cohort study, we implemented active mobile health follow-up, telephoning each woman or her next-of-kin (NOK) trimonthly on her mobile phone to update information on her vital status. Dates of every contact with women/NOK were analyzed from diagnosis in 2014–2017 to the earliest of September 1, 2018, death, or 3 years postdiagnosis. The cumulative incidence of being LTFU was calculated considering deaths as competing events. In all, 1,490 women were followed for a median of 24.2 (interquartile range (IQR), 14.2–34.5) months, corresponding to 8,529 successful contacts (77% of total contacts) with the women/NOK. Median time between successful contacts was 3.0 (IQR, 3.0–3.7) months. In all, 71 women (5.3%) were LTFU at 3 years: 0.8% in Nigeria, 2.2% in Namibia, and 5.6% in Uganda. Because of temporary discontinuity of active follow-up, 20.3% of women were LTFU after 2 years in Zambia. The median time to study notification of a death was 9.1 (IQR, 3.9–14.0) weeks. Although the present study was not a randomized controlled trial, in this cancer cohort with active mobile health follow-up, LTFU was much lower than in previous studies and enabled estimation of up-to-date and reliable cancer survival. [ABSTRACT FROM AUTHOR]

Loss to follow-up is an endemic feature of time-to-event analyses that precludes observation of the event of interest. To our knowledge, in typical cohort studies with encounters occurring at regular or irregular intervals, there is no consensus on how to handle person-time between participants' last study encounter and the point at which they meet a definition of loss to follow-up. We demonstrate, using simulation and an example, that when the event of interest is captured outside of a study encounter (e.g., in a registry), person-time should be censored when the study-defined criterion for loss to follow-up is met (e.g., 1 year after last encounter), rather than at the last study encounter. Conversely, when the event of interest must be measured within the context of a study encounter (e.g., a biomarker value), person-time should be censored at the last study encounter. An inappropriate censoring scheme has the potential to result in substantial bias that may not be easily corrected. [ABSTRACT FROM AUTHOR]

Prospective cohort studies are important tools for identifying causes of disease. However, these studies are susceptible to attrition. When information collected after enrollment is through interview or exam, attrition leads to missing information for nonrespondents. The Agricultural Health Study enrolled 52,394 farmers in 1993-1997 and collected additional information during subsequent interviews. Forty-six percent of enrolled farmers responded to the 2005-2010 interview; 7% of farmers died prior to the interview. We examined whether response was related to attributes measured at enrollment. To characterize potential bias from attrition, we evaluated differences in associations between smoking and incidence of 3 cancer types between the enrolled cohort and the subcohort of 2005-2010 respondents, using cancer registry information. In the subcohort we evaluated the ability of inverse probability weighting (IPW) to reduce bias. Response was related to age, state, race/ethnicity, education, marital status, smoking, and alcohol consumption. When exposure and outcome were associated and case response was differential by exposure, some bias was observed; IPW conditional on exposure and covariates failed to correct estimates. When response was nondifferential, subcohort and full-cohort estimates were similar, making IPW unnecessary. This example provides a demonstration of investigating the influence of attrition in cohort studies using information that has been self-reported after enrollment. [ABSTRACT FROM AUTHOR]

In several studies of antiretroviral treatment (ART) programs for persons with human immunodeficiency virus infection, investigators have reported that there has been a higher rate of loss to follow-up (LTFU) among patients initiating ART in recent years than among patients who initiated ART during earlier time periods. This finding is frequently interpreted as reflecting deterioration of patient retention in the face of increasing patient loads. However, in this paper we demonstrate by simulation that transient gaps in follow-up could lead to bias when standard survival analysis techniques are applied. We created a simulated cohort of patients with different dates of ART initiation. Rates of ART interruption, ART resumption, and mortality were assumed to remain constant over time, but when we applied a standard definition of LTFU, the simulated probability of being classified LTFU at a particular ART duration was substantially higher in recently enrolled cohorts. This suggests that much of the apparent trend towards increased LTFU may be attributed to bias caused by transient interruptions in care. Alternative statistical techniques need to be used when analyzing predictors of LTFU—for example, using “prospective” definitions of LTFU in place of “retrospective” definitions. Similar considerations may apply when analyzing predictors of LTFU from treatment programs for other chronic diseases. [ABSTRACT FROM PUBLISHER]

In causal analyses, conditioning on a collider generally results in selection bias. Conditioning on a prognostic factor that is independent of the exposure--and therefore is not a collider--can also result in selection bias when 1) the exposure has a non-null effect on the outcome and 2) the association between the noncollider and the outcome is heterogenous across levels of the exposure. This result was empirically demonstrated by Greenland in 1977 (Am J Epidemiol. 1977;106(3):184-187). [ABSTRACT FROM AUTHOR]

Although clinic-based cohorts are most representative of the “real world,” they are susceptible to loss to follow-up. Strategies for managing the impact of loss to follow-up are therefore needed to maximize the value of studies conducted in these cohorts. The authors evaluated adult patients starting antiretroviral therapy at an HIV/AIDS clinic in Uganda, where 29% of patients were lost to follow-up after 2 years (January 1, 2004–September 30, 2007). Unweighted, inverse probability of censoring weighted (IPCW), and sampling-based approaches (using supplemental data from a sample of lost patients subsequently tracked in the community) were used to identify the predictive value of sex on mortality. Directed acyclic graphs (DAGs) were used to explore the structural basis for bias in each approach. Among 3,628 patients, unweighted and IPCW analyses found men to have higher mortality than women, whereas the sampling-based approach did not. DAGs encoding knowledge about the data-generating process, including the fact that death is a cause of being classified as lost to follow-up in this setting, revealed “collider” bias in the unweighted and IPCW approaches. In a clinic-based cohort in Africa, unweighted and IPCW approaches—which rely on the “missing at random” assumption—yielded biased estimates. A sampling-based approach can in general strengthen epidemiologic analyses conducted in many clinic-based cohorts, including those examining other diseases. [ABSTRACT FROM PUBLISHER]

Although missing outcome data are an important problem in randomized trials and observational studies, methods to address this issue can be difficult to apply. Using simulated data, the authors compared 3 methods to handle missing outcome data: 1) complete case analysis; 2) single imputation; and 3) multiple imputation (all 3 with and without covariate adjustment). Simulated scenarios focused on continuous or dichotomous missing outcome data from randomized trials or observational studies. When outcomes were missing at random, single and multiple imputations yielded unbiased estimates after covariate adjustment. Estimates obtained by complete case analysis with covariate adjustment were unbiased as well, with coverage close to 95%. When outcome data were missing not at random, all methods gave biased estimates, but handling missing outcome data by means of 1 of the 3 methods reduced bias compared with a complete case analysis without covariate adjustment. Complete case analysis with covariate adjustment and multiple imputation yield similar estimates in the event of missing outcome data, as long as the same predictors of missingness are included. Hence, complete case analysis with covariate adjustment can and should be used as the analysis of choice more often. Multiple imputation, in addition, can accommodate the missing-not-at-random scenario more flexibly, making it especially suited for sensitivity analyses. [ABSTRACT FROM PUBLISHER]