Estimating and comparing adverse event probabilities in the presence of varying follow-up times and competing events

Safety analyses in terms of adverse events (AEs) are an important aspect of benefit-risk assessments of therapies. Compared to efficacy analyses AE analyses are often rather simplistic. The probability of an AE of a specific type is typically estimated by the incidence proportion, sometimes the incidence density or the Kaplan-Meier estimator are proposed. But these analyses either do not account for censoring, rely on a too restrictive parametric model, or ignore competing events. With the non-parametric Aalen-Johansen estimator as the gold-standard, these potential sources of bias are investigated in a data example from oncology and in simulations, both in the one-sample and in the two-sample case. As the estimators may have large variances at the end of follow-up, the estimators are not only compared at the maximal event time but also at two quantiles of the observed times. To date, consequences for safety comparisons have hardly been investigated in the literature. The impact of using different estimators for group comparisons is unclear, as, for example, the ratio of two both underestimating or overestimating estimators may or may not be comparable to the ratio of the gold-standard estimator. Therefore, the ratio of the AE probabilities is also calculated based on different approaches. By simulations investigating constant and non-constant hazards, different censoring mechanisms and event frequencies, we show that ignoring competing events is more of a problem than falsely assuming constant hazards by use of the incidence density and that the choice of the AE probability estimator is crucial for group comparisons.Comment: 27 pages, 5 figure

Survival analysis for AdVerse events with VarYing follow-up times (SAVVY): summary of findings and a roadmap for the future of safety analyses in clinical trials

The SAVVY project aims to improve the analyses of adverse events (AEs) in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events (CEs). This paper summarizes key features and conclusions from the various SAVVY papers. Through theoretical investigations using simulations and in an empirical study including randomized clinical trials from several sponsor organisations, biases from ignoring varying follow-up times or CEs are investigated. The bias of commonly used estimators of the absolute and relative AE risk is quantified. Furthermore, we provide a cursory assessment of how pertinent guidelines for the analysis of safety data deal with the features of varying follow-up time and CEs. SAVVY finds that for both, avoiding bias and categorization of evidence with respect to treatment effect on AE risk into categories, the choice of the estimator is key and more important than features of the underlying data such as percentage of censoring, CEs, amount of follow-up, or value of the gold-standard. The choice of the estimator of the cumulative AE probability and the definition of CEs are crucial. SAVVY recommends using the Aalen-Johansen estimator (AJE) with an appropriate definition of CEs whenever the risk for AEs is to be quantified. There is an urgent need to improve the guidelines of reporting AEs so that incidence proportions or one minus Kaplan-Meier estimators are finally replaced by the AJE with appropriate definition of CEs.Comment: 17 pages, 1 Figure, 4 Tables. arXiv admin note: text overlap with arXiv:2008.0788

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

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, i.e. 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

Burden of disease in Lambert-Eaton myasthenic syndrome: taking the patient’s perspective

Background: Lambert-Eaton myasthenic syndrome (LEMS) is an autoimmune-mediated neuromuscular disorder leading to muscle weakness, autonomic dysregulation and hyporeflexia. Psychosocial well-being is affected. Previously, we assessed burden of disease for Myasthenia gravis (MG). Here, we aim to elucidate burden of disease by comparing health-related quality of life (HRQoL) of patients with LEMS to the general population (genP) as well as MG patients. Methods: A questionnaire-based survey included sociodemographic and clinical data along with standardized questionnaires, e.g. the Short Form Health (SF-36). HRQoL was evaluated through matched-pairs analyses. Participants from a general health survey served as control group. Results: 46 LEMS patients matched by age and gender were compared to 92 controls from the genP and a matched cohort of 92 MG patients. LEMS participants showed lower levels of physical functioning (SF-36 mean 34.2 SD 28.6) compared to genP (mean 78.6 SD 21.1) and MG patients (mean 61.3 SD 31.8). LEMS patients showed lower mental health sub-scores compared to genP (SF-36 mean 62.7 SD 20.2, vs. 75.7 SD 15.1) and MG patients (SF-36 mean 62.7 SD 20.2, vs. 66.0 SD 18.). Depression, anxiety and fatigue were prevalent. Female gender, low income, lower activities of daily living, symptoms of depression, anxiety and fatigue were associated with a lower HRQoL in LEMS. Discussion: HRQoL is lower in patients with LEMS compared to genP and MG in a matched pair-analysis. The burden of LEMS includes economic and social aspects as well as emotional well-being. Trial Registration Information: drks.de: DRKS00024527, submitted: February 02, 2021, https://drks.de/search/en/trial/DRKS00024527

The burden of myasthenia gravis – highlighting the impact on family planning and the role of social support

Background: Myasthenia gravis (MG) is a rare autoimmune disease and chronic condition that necessitates specialized care. Patients experience a significant burden of disease affecting various aspects of their lives. The aim of this study was to investigate the impact of MG on family planning, challenges associated with pregnancy, childcare responsibilities and the extent to which MG patients perceive and utilize social support. Methods: This analysis used data from our main data of a large cross-sectional study built on a questionnaire-based survey encompassing 1,660 MG patients and members of the German Myasthenia Association (Deutsche Myasthenie Gesellschaft), and focused on sociodemographic, clinical and family planning relevant data points. Results: Decisions regarding family planning were significantly impacted for individuals with MG when MG symptoms started either before or during their family planning (men: n = 19 and 29.7%; women: n = 156 and 58.4%). In this subgroup a substantial proportion opted against parenthood due to MG (men: n = 8 and 50.0%; women: n = 54 and 38.0% and/or another n = 12 and 8.4% of female participants encountered partner-related refusals). In the subgroup of female SP with MG starting before or during family planning who have reported ever been pregnant the self-reported miscarriage rate was 29.0% (n = 51). MG patients with medium incomes or moderate disease severity reported lower levels of perceived social support. 42.7% (n = 606) of participants needed assistance in negotiations with health insurers and 28.0% (n = 459) needed support for transportation to medical appointments. Conclusion: This study shows a significant impact of MG on family planning decisions, affecting both women and men, and often resulting in life-altering decisions such as voluntary childlessness due to MG. The significance of social support becomes evident as a vital factor, especially when navigating through the healthcare system. Tailored healthcare approaches, organized guidance and comprehensive support is needed to enable informed decision-making and offer assistance for MG patients. Clinical trial registration: https://clinicaltrials.gov/study/NCT03979521, Registered 7 June 2019 (retrospectively registered)

Survival analysis for AdVerse events with VarYing follow-up times (SAVVY) -- estimation of adverse event risks

The SAVVY project aims to improve the analyses of adverse event (AE) data in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events (CEs). Although statistical methodologies have advanced, in AE analyses often the incidence proportion, the incidence density, or a non-parametric Kaplan-Meier estimator (KME) are used, which either ignore censoring or CEs. In an empirical study including randomized clinical trials from several sponsor organisations, these potential sources of bias are investigated. The main aim is to compare the estimators that are typically used in AE analysis to the Aalen-Johansen estimator (AJE) as the gold-standard. Here, one-sample findings are reported, while a companion paper considers consequences when comparing treatment groups. Estimators are compared with descriptive statistics, graphical displays and with a random effects meta-analysis. The influence of different factors on the size of the bias is investigated in a meta-regression. Comparisons are conducted at the maximum follow-up time and at earlier evaluation time points. CEs definition does not only include death before AE but also end of follow-up for AEs due to events possibly related to the disease course or the treatment. Ten sponsor organisations provided 17 trials including 186 types of AEs. The one minus KME was on average about 1.2-fold larger than the AJE. Leading forces influencing bias were the amount of censoring and of CEs. As a consequence, the average bias using the incidence proportion was less than 5%. Assuming constant hazards using incidence densities was hardly an issue provided that CEs were accounted for. There is a need to improve the guidelines of reporting risks of AEs so that the KME and the incidence proportion are replaced by the AJE with an appropriate definition of CEs

Survival analysis for AdVerse events with VarYing follow-up times (SAVVY) -- comparison of adverse event risks in randomized controlled trials

Analyses of adverse events (AEs) are an important aspect of the evaluation of experimental therapies. The SAVVY (Survival analysis for AdVerse events with Varying follow-up times) project aims to improve the analyses of AE data in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times, censoring, and competing events (CE). In an empirical study including seventeen randomized clinical trials the effect of varying follow-up times, censoring, and competing events on comparisons of two treatment arms with respect to AE risks is investigated. The comparisons of relative risks (RR) of standard probability-based estimators to the gold-standard Aalen-Johansen estimator or hazard-based estimators to an estimated hazard ratio (HR) from Cox regression are done descriptively, with graphical displays, and using a random effects meta-analysis on AE level. The influence of different factors on the size of the bias is investigated in a meta-regression. We find that for both, avoiding bias and categorization of evidence with respect to treatment effect on AE risk into categories, the choice of the estimator is key and more important than features of the underlying data such as percentage of censoring, CEs, amount of follow-up, or value of the gold-standard RR. There is an urgent need to improve the guidelines of reporting AEs so that incidence proportions are finally replaced by the Aalen-Johansen estimator - rather than by Kaplan-Meier - with appropriate definition of CEs. For RRs based on hazards, the HR based on Cox regression has better properties than the ratio of incidence densities

Estimating cumulative incidence functions in competing risks data with dependent left‐truncation

Both delayed study entry (left‐truncation) and competing risks are common phenomena in observational time‐to‐event studies. For example, in studies conducted by Teratology Information Services (TIS) on adverse drug reactions during pregnancy, the natural time scale is gestational age, but women enter the study after time origin and upon contact with the service. Competing risks are present, because an elective termination may be precluded by a spontaneous abortion. If left‐truncation is entirely random, the Aalen‐Johansen estimator is the canonical estimator of the cumulative incidence functions of the competing events. If the assumption of random left‐truncation is in doubt, we propose a new semiparametric estimator of the cumulative incidence function. The dependence between entry time and time‐to‐event is modeled using a cause‐specific Cox proportional hazards model and the marginal (unconditional) estimates are derived via inverse probability weighting arguments. We apply the new estimator to data about coumarin usage during pregnancy. Here, the concern is that the cause‐specific hazard of experiencing an induced abortion may depend on the time when seeking advice by a TIS, which also is the time of left‐truncation or study entry. While the aims of counseling by a TIS are to reduce the rate of elective terminations based on irrational overestimation of drug risks and to lead to better and safer medical treatment of maternal disease, it is conceivable that women considering an induced abortion are more likely to seek counseling. The new estimator is also evaluated in extensive simulation studies and found preferable compared to the Aalen‐Johansen estimator in non–misspecified scenarios and to at least provide for a sensitivity analysis otherwise