How to account for early overly small risk sets in the analysis of pregnancy outcome data?-Comparison of different methods for stabilizing the Aalen-Johansen estimator.

PURPOSE: In analyzing pregnancy data concerning drug exposure in the first trimester, the risk of spontaneous abortions is of primary interest. For estimating the cumulative incidence function, the Aalen-Johansen estimator is typically used, and competing risks such as induced abortion and livebirth are considered. However, the delayed study entry can lead to overly small risk sets for the first events. This results in large jumps in the estimated cumulative incidence function of spontaneous abortions or induced abortions using the Aalen-Johansen estimator, and consequently in an overestimation of the probability. METHODS: Several approaches account for early overly small risk sets. The first approach is conditioning on the event time being greater than the event time causing the large jump. Second, the events can be ignored by censoring them. Third, the events can be postponed until a large enough number is at risk. These three approaches are compared. RESULTS: All approaches are applied using data of 54 lacosamide-exposed pregnancies. The Aalen-Johansen estimate of the probability of spontaneous abortion is 22.64%, which is relatively large for only three spontaneous abortions in the dataset. The conditional approach and the ignore approach have an estimated probability of 7.17%. In contrast, the estimate of the postpone approach is 16.45%. In this small sample, bootstrapped confidence intervals seem more accurate. CONCLUSIONS: In the analyses of pregnancy data with rare events, the postpone approach is favorable as no events are excluded. However, the approach that ignores early events has the narrowest confidence interval.

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

Safety analyses of adverse events (AEs) are important in assessing benefit-risk of therapies but are often rather simplistic compared to efficacy analyses. AE probabilities are typically estimated by incidence proportions, sometimes incidence densities or Kaplan-Meier estimation are proposed. 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", that is, reference estimator, potential sources of bias are investigated in an example from oncology and in simulations, for both one-sample and two-sample scenarios. The Aalen-Johansen estimator serves as a reference, because it is the proper non-parametric generalization of the Kaplan-Meier estimator to multiple outcomes. Because of potential large variances at the end of follow-up, comparisons also consider further quantiles of the observed times. To date, consequences for safety comparisons have hardly been investigated, the impact of using different estimators for group comparisons being unclear. For example, the ratio of two both underestimating or overestimating estimators may not be comparable to the ratio of the reference, and our investigation also considers the ratio of AE probabilities. We find that ignoring competing events is more of a problem than falsely assuming constant hazards by the use of the incidence density and that the choice of the AE probability estimator is crucial for group comparisons.

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, 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.

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.

Multistate methodology improves risk assessment under time-varying drug intake-a new view on pregnancy outcomes following coumarin exposure.

PURPOSE: Observational cohort studies are essential to evaluate the risk of adverse pregnancy outcomes associated with drug intake. Besides left truncation and competing events, it is crucial to account for the time-dynamic pattern of drug exposure. In fact, potentially harmful medications are often discontinued, which might affect the outcome. Ignoring these challenges may lead to biased estimation of drug-related risks highlighting the need for adequate statistical techniques. METHODS: We reanalyze updated data of a recently published study provided by the German Embryotox pharmacovigilance institute. The aim of the study was to quantify the effect of discontinuation of vitamin K antagonist phenprocoumon on the risk of spontaneous abortion. RESULTS: We outline multistate methodology as a powerful method removing bias in probability estimation inherent to commonly used crude proportions. We incorporate time-dependent discontinuation and competing pregnancy outcomes as separate states in a multistate model, which enables the formulation of hazard-based Cox proportional hazard models and the application of so-called landmark techniques. Results show that early discontinuation of phenprocoumon substantially reduces the risk of spontaneous abortion, which is of great importance for both pregnant women and treating physicians. CONCLUSIONS: An adequate handling of discontinuation times is essential when analyzing the risk of spontaneous abortion. The proposed concepts are not restricted to pregnancy outcome studies but have broad usage in other fields of epidemiology. Our nontechnical report may provide guidance for the design and analysis of future studies. Example code is provided.

Survival analysis for AdVerse events with VarYing follow-up times (SAVVY): summary of findings and assessment of existing guidelines.

BACKGROUND: 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. METHODS: Summarizing several papers reporting theoretical investigations using simulations and an empirical study including randomized clinical trials from several sponsor organizations, biases from ignoring varying follow-up times or CEs are investigated. The bias of commonly used estimators of the absolute (incidence proportion and one minus Kaplan-Meier) and relative (risk and hazard ratio) 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. RESULTS: 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. CONCLUSIONS: The choice of the estimator of the cumulative AE probability and the definition of CEs are crucial. Whenever varying follow-up times and/or CEs are present in the assessment of AEs, SAVVY recommends using the Aalen-Johansen estimator (AJE) with an appropriate definition of CEs to quantify AE risk. There is an urgent need to improve pertinent clinical trial reporting guidelines for reporting AEs so that incidence proportions or one minus Kaplan-Meier estimators are finally replaced by the AJE with appropriate definition of CEs.

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 .

Modeling unmeasured baseline information in observational time-to-event data subject to delayed study entry.

Unmeasured baseline information in left-truncated data situations frequently occurs in observational time-to-event analyses. For instance, a typical timescale in trials of antidiabetic treatment is "time since treatment initiation", but individuals may have initiated treatment before the start of longitudinal data collection. When the focus is on baseline effects, one widespread approach is to fit a Cox proportional hazards model incorporating the measurements at delayed study entry. This has been criticized because of the potential time dependency of covariates. We tackle this problem by using a Bayesian joint model that combines a mixed-effects model for the longitudinal trajectory with a proportional hazards model for the event of interest incorporating the baseline covariate, possibly unmeasured in the presence of left truncation. The novelty is that our procedure is not used to account for non-continuously monitored longitudinal covariates in right-censored time-to-event studies, but to utilize these trajectories to make inferences about missing baseline measurements in left-truncated data. Simulating times-to-event depending on baseline covariates we also compared our proposal to a simpler two-stage approach which performed favorably. Our approach is illustrated by investigating the impact of baseline blood glucose levels on antidiabetic treatment failure using data from a German diabetes register.

Increasing use of newer antiseizure medication during pregnancy: An observational study with special focus on lacosamide.

INTRODUCTION: Epilepsy is a common neurological disease requiring long-term therapy also during pregnancy. Most studies on pregnancy outcomes in women with epilepsy are based on antiseizure medication (ASM) in monotherapy. However, about 20-30% of epilepsy patients require polytherapy and newer ASMs are an option, when seizure control is not achieved with first line ASMs. METHODS: Observational study evaluating the use of newer ASMs with marketing authorization since 2005 reported to the Embryotox Center of Clinical Teratology and Drug Safety in Pregnancy between 2004 and 2019. In addition, course and outcome of lacosamide exposed pregnancies were analysed. RESULTS: Our study confirms the increasing use of newer ASMs also in pregnant women. This is especially true for lacosamide, eslicarbazepine and brivaracetam with rising numbers of exposed pregnancies soon after marketing authorization. Analysis of 55 prospectively and 10 retrospectively ascertained lacosamide exposed pregnancies does not indicate increased risks of major birth defects or spontaneous abortion. However, bradycardia observed in 3 neonates might be related to prenatal lacosamide exposure. CONCLUSION: Available data do not support the assumption of lacosamide being a major teratogen. The increasing use of newer ASMs during pregnancy underscores the need for more studies to guide preconception counselling, especially for lacosamide, eslicarbazepine and brivaracetam.

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).

Can non-responding mask or mimic drug effects on pregnancy outcome? Evaluation of case characteristics based on the national Embryotox cohort.

Women and their health care provider (HCP) often seek advice for drug safety in pregnancy at Teratology Information Services (TIS). In turn, TIS ask for details of drug exposure and pregnancy outcome. These data constitute a valuable basis for research on prenatal drug risks in many countries. Non-response to follow-up questionnaires, however, may cause biased study results. To assess the potential of non-response bias, this study based on the German Embryotox cohort compares maternal and HCP characteristics of responders and non-responders. Change in loss of follow-up rates over time is investigated using logistic regression. From 2010 until the end of 2020, 48,410 pregnant women and/or their HCP consented to participation in follow-up. Of these, 25.0 % did not return follow-up questionnaires. Loss rates were similar for patients and HCP but increased over time. Participants from semi-dense populated areas had a smaller loss rate (20.4 %) than those from rural (28.4 %) or urban areas (25.6 %). Responding women were older than non-responders, had a lower BMI, a more positive attitude towards pregnancy, a higher educational level, a lower number of previous pregnancies, smoked less, and indicated alcohol consumption more but social drugs less often. Non-response bias cannot be ruled out in studies based on observational data on drug use in pregnancy as those collected by TIS. However, differences between the complete and lost-to follow-up cohort do not suggest a particularly high or low risk profile for one of the cohorts that might substantially confound study results or even mask or mimic potential drug toxicity.

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

BACKGROUND: The SAVVY project aims to improve the analyses of adverse events (AEs), whether prespecified or emerging, 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 are used, which ignore either censoring or CEs. In an empirical study including randomized clinical trials from several sponsor organizations, these potential sources of bias are investigated. The main purpose is to compare the estimators that are typically used to quantify AE risk within trial arms to the non-parametric Aalen-Johansen estimator as the gold-standard for estimating cumulative AE probabilities. A follow-up paper will consider consequences when comparing safety between treatment groups. METHODS: Estimators are compared with descriptive statistics, graphical displays, and a more formal assessment using a random effects meta-analysis. The influence of different factors on the size of deviations from the gold-standard is investigated in a meta-regression. Comparisons are conducted at the maximum follow-up time and at earlier evaluation times. CEs definition does not only include death before AE but also end of follow-up for AEs due to events related to the disease course or safety of the treatment. RESULTS: Ten sponsor organizations provided 17 clinical trials including 186 types of investigated AEs. The one minus Kaplan-Meier estimator was on average about 1.2-fold larger than the Aalen-Johansen estimator and the probability transform of the incidence density ignoring CEs was even 2-fold larger. 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. The meta-regression showed that the bias depended mainly on the amount of censoring and on the amount of CEs. CONCLUSIONS: The choice of the estimator of the cumulative AE probability and the definition of CEs are crucial. We recommend using the Aalen-Johansen estimator with an appropriate definition of CEs whenever the risk for AEs is to be quantified and to change the guidelines accordingly.