The effects of misclassification of the actual cause of death in competing risks analysis

Misclassification of the actual causes of death and its impact on analysis: A case study in non-small cell lung cancer

OBJECTIVES

Cumulative incidence of lung cancer deaths (LC-CID) is an important metric to understand cancer prognosis and to determine treatment options. However, credible estimates of LC-CID rely on accurate cause-of-death coding in death certificates. Results from lung cancer screening trials estimated 15% under-reporting and 1% over-reporting of lung cancer deaths due to misclassification. This study investigated the impact of cause-of-death misclassification on the estimation of LC-CID.

MATERIALS AND METHODS

Patients with stage I/II non-small cell lung cancer (NSCLC) from the Surveillance, Epidemiology, and End Results registry were included. LC-CID was estimated using the competing-risk approach in two ways: (1) reporting observed estimates that ignore potential cause-of-death misclassification and (2) correcting for plausible misclassification rates reported in the literature (15% under-reporting and 1% over-reporting). Bias was quantified as the difference between observed and corrected 10-year LC-CIDs: positive values indicated that observed LC-CID overestimated true LC-CID, whereas negative values indicated the opposite.

RESULTS

Among 66,179 patients, the impact of over-reporting on 10-year LC-CID was negligible across all age groups. In contrast, under-reporting resulted in substantial underestimation of 10-year LC-CID. The biases increased as age increased due to higher LC-CIDs: 10-year LC-CIDs among stage I patients 18-44, 45-59, 60-74 and ≥75 years were 25%, 32%, 41%, and 50%, respectively, and the corresponding biases given the plausible misclassification rates were -4.4%, -5.6%, -7.1%, and -8.6%. Because the observed LC-CIDs among patients with stage II disease were higher than those with stage I disease, the biases were greater among stage II patients, up to -12.5% in the oldest age group.

CONCLUSIONS

In lung cancer, LC-CID may be severely underestimated due to under-reporting of lung cancer deaths, particularly among older patients or those with late-stage disease. Future studies that involve such subpopulations should present the corrected LC-CIDs based on plausible misclassification rates alongside the observed LC-CIDs.

A fatal review: Exploring how children's deaths are reported in the United States

Child death reports are the leading data source used to orchestrate child fatality prevention policy. Therefore, the way in which child death is reported is crucial to how we sustain life. We sought to assess the systematic ways in which death is reported for children. Based on a qualitative analysis of medico-legal investigation reports collected from a medical examiner's office and a coroner's office, we examined several indicators of data completeness, quality, site organizational structure, and consistency. We found stark differences between the two sites, as well as issues regarding death diagnosis certainty, and a general lack in consistency in the reports' content, as well as procedures performed post-mortem. We conclude that there are some flaws in our death reporting system for child populations, which have the potential to hinder reliability and accuracy of these death reports, as well as thwart their overall usefulness in prevention policies.

The impact of under and over-recording of cancer on death certificates in a competing risks analysis: a simulation study

BACKGROUND

With linked register and cause of death data becoming more accessible than ever, competing risks methodology is being increasingly used as a way of obtaining "real world" probabilities of death broken down by specific causes. It is important, in terms of the validity of these studies, to have accurate cause of death information. However, it is well documented that cause of death information taken from death certificates is often lacking in accuracy and completeness.

METHODS

We assess through use of a simulation study the effect of under and over-recording of cancer on death certificates in a competing risks analysis consisting of three competing causes of death: cancer, heart disease and other causes. Using realistic levels of misclassification, we consider 24 scenarios and examine the bias in the cause-specific hazard ratios and the cumulative incidence function.

RESULTS

The bias in the cumulative incidence function was highest in the oldest age group reaching values as high as 2.6 percentage units for the "good" cancer prognosis scenario and 9.7 percentage units for the "poor" prognosis scenario.

CONCLUSION

The bias resulting from the chosen levels of misclassification in this study accentuate concerns that unreliable cause of death information may be providing misleading results. The results of this simulation study convey an important message to applied epidemiological researchers.

Estimation with Cox models: cause-specific survival analysis with misclassified cause of failure

While epidemiologic and clinical research often aims to analyze predictors of specific endpoints, time-to-the-specific-event analysis can be hampered by problems with cause ascertainment. Under typical assumptions of competing risks analysis (and missing-data settings), we correct the cause-specific proportional hazards analysis when information on the reliability of diagnosis is available. Our method avoids bias in effect estimates at low cost in variance, thus offering a perspective for better-informed decision making. The ratio of different cause-specific hazards can be estimated flexibly for this purpose. It thus complements an all-cause analysis. In a sensitivity analysis, this approach can reveal the likely extent and direction of the bias of a standard cause-specific analysis when the diagnosis is suspect. These 2 uses are illustrated in a randomized vaccine trial and an epidemiologic cohort study, respectively.

Design and testing for clinical trials faced with misclassified causes of death

With clinical trials under pressure to produce more convincing results faster, we reexamine relative efficiencies for the semiparametric comparison of cause-specific rather than all-cause mortality events, observing that in many settings misclassification of cause of failure is not negligible. By incorporating known misclassification rates, we derive an adapted logrank test that optimizes power when the alternative treatment effect is confined to the cause-specific hazard. We derive sample size calculations for this test as well as for the corresponding all-cause mortality and naive cause-specific logrank test which ignores the misclassification. This may lead to new options at the design stage which we discuss. We reexamine a recently closed vaccine trial in this light and find the sample size needed for the new test to be 32% smaller than for the equivalent all-cause analysis, leading to a reduction of 41 224 participants.

Bayesian statistics in medicine: a 25 year review

This review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

Polyhazard models for lifetime data

We propose a polyhazard model to deal with lifetime data associated with latent competing risks. The causes of failure are assumed unobserved and affecting individuals independently. The general framework allows a broad class of hazard models that includes the most common hazard-based models. The model accommodates bathtub and multimodal hazards, keeping enough flexibility for common lifetime data that cannot be accommodated by usual hazard-based models. Maximum likelihood estimation is discussed, and parametric simulation is used for hypothesis testing.

[Competing death risks]

The death of an individual is not a repetitive event, so if a cause of death overtakes another cause in producing death, mortality rates from the overtaken cause decrease. This phenomenon is known as competing risks of death and it must be taken into account in any cause-specific mortality analysis. In this work the competing risks concept is formalized and some historical data are described. The main parametric tools to analyze competing risks are displayed, with a special look at the Gompertz and Weibull functions. Regarding non-parametric models, the Chiang method is shown and its applicability on both dependent and independent causes of death is discussed. Finally, other tools specially useful in clinical epidemiology are enumerated, including Cox regression, Kaplan-Meier and log-rank methods, as well as the interactions between competing risks and misclassification and selection biases.