Especially when facing reliability data with limited information (e.g., a small number of failures), there are strong motivations for using Bayesian inference methods. These include the option to use information from physics‐of‐failure or previous experience with a failure mode in a particular material to specify an informative prior distribution. Another advantage is the ability to make statistical inferences without having to rely on specious (when the number of failures is small) asymptotic theory needed to justify non‐Bayesian methods. Users of non‐Bayesian methods are faced with multiple methods of constructing uncertainty intervals (Wald, likelihood, and various bootstrap methods) that can give substantially different answers when there is little information in the data. For Bayesian inference, there is only one method of constructing equal‐tail credible intervals—but it is necessary to provide a prior distribution to fully specify the model. Much work has been done to find default prior distributions that will provide inference methods with good (and in some cases exact) frequentist coverage properties. This paper reviews some of this work and provides, evaluates, and illustrates principled extensions and adaptations of these methods to the practical realities of reliability data (e.g., non‐trivial censoring).

When analyzing field data on consumer products, model-based approaches to inference require a model with sufficient flexibility to account for multiple kinds of failures. The causes of failure, while not interesting to the consumer per se, can lead to various observed lifetime distributions. Because of this, standard lifetime models, such as using a single Weibull or lognormal distribution, may be inadequate. Usually cause-of-failure information will not be available to the consumer and thus traditional competing risk analyses cannot be performed. Furthermore, when the information carried by lifetime data are limited by sample size, censoring, and truncation, estimates can be unstable and suffer from imprecision. These limitations are typical, for example, lifetime data for high-reliability products will naturally tend to be right-censored. In this article, we present a method for joint estimation of multiple lifetime distributions based on the generalized limited failure population (GLFP) model. This five-parameter model for lifetime data accommodates lifetime distributions with multiple failure modes: early failures (sometimes referred to in the literature as "infant mortality") and failures due to wearout. We fit the GLFP model to a heterogenous population of devices using a hierarchical modeling approach. Borrowing strength across subpopulations, our method enables estimation with uncertainty of lifetime distributions even in cases where the number of model parameters is larger than the number of observed failures. Moreover, using this Bayesian method, comparison of different product brands across the heterogenous population is straightforward because estimation of arbitrary functionals is easy using draws from the joint posterior distribution of the model parameters. Potential applications include assessment and comparison of reliability to inform purchasing decisions. Supplementary materials for this article are available online.

BackgroundPlaque psoriasis is a chronic disease characterized by scaly plaques on the skin that can itch and bleed. Psoriasis covering over 10% of the body is classified as moderate to severe, and can impact patient quality of life.ObjectivesTo assess the relationship between plaque psoriasis self-reported severity symptoms and health-related quality of life, work productivity, and activity impairment among patients with moderate-to-severe psoriasis.MethodsThe study sample included 199 patients recruited from internet panels, of which 179 respondents had plaque psoriasis and 20 had plaque and inverse psoriasis. Itching, pain, and scaling symptoms were studied. A structural equation modeling framework was used to estimate the effect of these symptoms on patient outcomes. First, each severity variable was regressed on a set of covariates to generate a predicted severity score. These predicted values were placed in a second-stage model with patient mental and physical scores (Short-Form 12 questionnaire), work productivity, and activity impairment indicators as dependent variables.ResultsItching severity had a marginal negative effect (P < 0.06) on patients’ Short-Form 12 physical and mental component scores. Pain severity also negatively affected physical and mental health scores (P < 0.02). Patients were more likely to miss work because of itching (odds ratio [OR]: 2.31, 95% confidence interval [CI]: 1.30, 4.10), pain (OR: 1.78, 95% CI: 1.25, 2.52), and scaling (OR: 2.15, 95% CI: 1.31, 3.52) symptoms. These symptoms also lowered self-reported productivity. As itching (OR: 1.74, 95% CI: 1.03, 2.95), scaling (OR: 1.84, 95% CI: 1.16, 2.90), and pain symptoms (OR: 1.53, 95% CI: 1.12, 2.09) increased, so did the odds that a patient would be less productive at work.ConclusionPlaque psoriasis significantly affects patient quality of life. In addition to greater mental and physical pain, patients are more likely to miss work and have diminished productivity as symptom severity increases.

Quantifying the timing and content of policy changes affecting international travel and immigration is key to ongoing research on the spread of SARS-CoV-2 and the socioeconomic impacts of border closures. The COVID Border Accountability Project (COBAP) provides a hand-coded dataset of >1000 policies systematized to reflect a complete timeline of country-level restrictions on movement across international borders during 2020. Trained research assistants used pre-set definitions to source, categorize and verify for each new border policy: start and end dates, whether the closure is “complete” or “partial”, which exceptions are made, which countries are banned, and which air/land/sea borders were closed. COBAP verified the database through internal and external audits from public health experts. For purposes of further verification and future data mining efforts of pandemic research, the full text of each policy was archived. The structure of the COBAP dataset is designed for use by social and biomedical scientists. For broad accessibility to policymakers and the public, our website depicts the data in an interactive, user-friendly, time-based map.