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Objectives To assess whether there are identifiable subgroups of disease activity trajectory in a population of JDM patients—followed throughout childhood and into adulthood—and determine factors that predict those trajectory groupings. Methods This is a retrospective, longitudinal inception cohort of patients with idiopathic inflammatory myopathies, largely JDM. We sought to identify baseline factors that predict membership into different groups (latent classes) of disease activity trajectory. Results A total of 172 patients (64% females), with median age at diagnosis of 7.7 years, were analysed. We studied 4725 visits (1471 patient-years). We identified three latent classes of longitudinal disease activity, as measured by the modified DAS (DASm), with distinct class trajectories predicted by DASm at baseline, and by the changes of DASm from either baseline to 3 months or baseline to 6 months (early response to therapy). In the analysis in which DASm at baseline and the changes of DASm from baseline to 6 months are included as predictors, Class 1 (10%) has persistently high disease activity, Class 2 (34%) is characterized by moderate disease activity and Class 3 (56%) is characterized by individuals with a high early disease activity but an apparently good response to treatment and long-term low disease activity. Conclusion High early disease activity, and treatment resistance in the first few months, predict a more chronic longitudinal course of JDM. [ABSTRACT FROM AUTHOR]

Summary: Data collected in the context of usual care present a rich source of longitudinal data for research, but often require analyses that simultaneously enable causal inferences from observational data while handling irregular and informative assessment times. An inverse‐weighting approach to this was recently proposed, and handles the case where the assessment times are at random (ie, conditionally independent of the outcome process given the observed history). In this paper, we extend the inverse‐weighting approach to handle a special case of assessment not at random, where assessment and outcome processes are conditionally independent given past observed covariates and random effects. We use multiple outputation to accomplish the same purpose as inverse‐weighting, and apply it to the Liang semi‐parametric joint model. Moreover, we develop an alternative joint model that does not require covariates for the outcome model to be known at times where there is no assessment of the outcome. We examine the performance of these methods through simulation and illustrate them through a study of the causal effect of wheezing on time spent playing outdoors among children aged 2–9 years and enrolled in the TargetKids! study. [ABSTRACT FROM AUTHOR]

Graduate students in the health sciences who hope to become independent researchers must be able to write up their results at a standard suitable for submission to peer-reviewed journals. Bayesian analyses are still rare in the medical literature, and students are often unclear on what should be included in a manuscript. Whilst there are published guidelines on reporting of Bayesian analyses, students should also be encouraged to think about "why" some items need to be reported whereas others do not. We describe a classroom activity in which students develop their own reporting guideline. The guideline that the students produce is not intended to replace existing guidelines, rather we have found that the process of developing the guideline is helpful in encouraging students to think through the "why?" as well as the "what?" of reporting. (Contains 1 table.)

Despite the appeal of Bayesian methods in health research, they are not widely used. This is partly due to a lack of courses in Bayesian methods at an appropriate level for non-statisticians in health research. Teaching such a course can be challenging because most statisticians have been taught Bayesian methods using a mathematical approach, and this must be adapted in order to communicate with non-statisticians. We describe some of the examples we used whilst teaching a course in Bayesian methods to a group of health research methodologists. (Contains 5 tables and 11 figures.)

The timings of visits in observational longitudinal data may depend on the study outcome, and this can result in bias if ignored. Assessing the extent of visit irregularity is important because it can help determine whether visits can be treated as repeated measures or as irregular data. We propose plotting the mean proportions of individuals with 0 visits per bin against the mean proportions of individuals with >1 visit per bin as bin width is varied and using the area under the curve (AUC) to assess the extent of irregularity. The AUC is a single score which can be used to quantify the extent of irregularity and assess how closely visits resemble repeated measures. Simulation results confirm that the AUC increases with increasing irregularity while being invariant to sample size and the number of scheduled measurement occasions. A demonstration of the AUC was performed on the TARGet Kids! study which enrolls healthy children aged 0–5 years with the aim of investigating the relationship between early life exposures and later health problems. The quality of statistical analyses can be improved by using the AUC as a guide to select the appropriate analytic outcome approach and minimize the potential for biased results. [ABSTRACT FROM AUTHOR]

Abstract Objectives To quantify the improvement in health outcomes in patients after total knee replacement (TKR). Methods This was a two-year non-randomized prospective observational study in knee osteoarthritis (OA) patients undergone TKR. Patients were interviewed one week before, six months after, and two years after surgery using a standardized questionnaire including the SF-36, the Oxford Knee Score (OKS), and the Knee Society Clinical Rating Scale (KSS). A generalized estimating equation (GEE) model was used to estimate the magnitudes of the changes with and without the adjustment of age, ethnicity, BMI, and years with OA. Results A total of 298 (at baseline), 176 (at six-months), and 111 (at two-years) eligible patients were included in the analyses. All the scores changed significantly over time, with the exception of SF-36 social functioning, vitality, and mental health. With the adjustment of covariates, the magnitude of changes in these scores was similar to those without the adjustment. Conclusions Both general and knee-specific physical functioning had been significantly improved after TKR, while other health domains have not been substantially improved after the surgery.

Abstract Background Informing health care decision making may necessitate the synthesis of evidence from different study designs (e.g., randomised controlled trials, non-randomised/observational studies). Methods for synthesising different types of studies have been proposed, but their routine use requires development of approaches to adjust for potential biases, especially among non-randomised studies. The objective of this study was to extend a published Bayesian hierarchical model to adjust for bias due to confounding in synthesising evidence from studies with different designs. Methods In this new methodological approach, study estimates were adjusted for potential confounders using differences in patient characteristics (e.g., age) between study arms. The new model was applied to synthesise evidence from randomised and non-randomised studies from a published review comparing treatments for abdominal aortic aneurysms. We compared the results of the Bayesian hierarchical model adjusted for differences in study arms with: 1) unadjusted results, 2) results adjusted using aggregate study values and 3) two methods for downweighting the potentially biased non-randomised studies. Sensitivity of the results to alternative prior distributions and the inclusion of additional covariates were also assessed. Results In the base case analysis, the estimated odds ratio was 0.32 (0.13,0.76) for the randomised studies alone and 0.57 (0.41,0.82) for the non-randomised studies alone. The unadjusted result for the two types combined was 0.49 (0.21,0.98). Adjusted for differences between study arms, the estimated odds ratio was 0.37 (0.17,0.77), representing a shift towards the estimate for the randomised studies alone. Adjustment for aggregate values resulted in an estimate of 0.60 (0.28,1.20). The two methods used for downweighting gave odd ratios of 0.43 (0.18,0.89) and 0.35 (0.16,0.76), respectively. Point estimates were robust but credible intervals were wider when using vaguer priors. Conclusions Covariate adjustment using aggregate study values does not account for covariate imbalances between treatment arms and downweighting may not eliminate bias. Adjustment using differences in patient characteristics between arms provides a systematic way of adjusting for bias due to confounding. Within the context of a Bayesian hierarchical model, such an approach could facilitate the use of all available evidence to inform health policy decisions.

Data collected longitudinally as part of usual health care is becoming increasingly available for research, and is often available across several centres. Because the frequency of follow-up is typically determined by the patient's health, the timing of measurements may be related to the outcome of interest. Failure to account for the informative nature of the observation process can result in biased inferences. While methods for accounting for the association between observation frequency and outcome are available, they do not currently account for clustering within centres. We formulate a semi-parametric joint model to include random effects for centres as well as subjects. We also show how inverse-intensity weighted GEEs can be adapted to account for clustering, comparing stratification, frailty models, and covariate adjustment to account for clustering in the observation process. The finite-sample performance of the proposed methods is evaluated through simulation and the methods illustrated using a study of the relationship between outdoor play and air quality in children aged 2–9 living in the Greater Toronto Area. [ABSTRACT FROM AUTHOR]

Lyme disease (LD) is the most commonly reported vector-borne disease, but its clinical consequences remain uncertain. We conducted a systematic review of the long-term sequelae and health-related quality of life (HRQoL) associated with LD in North America and Europe. We performed searches in 6 electronic databases up to December 2018 following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, including observational studies reporting long-term sequelae, HRQoL, and prognostic factors. We included 46 studies, published between 1994 and 2019. Based on 21 studies reporting attributable outcomes, higher proportions of sequelae reported from exposed patients were: neck pain, myalgia, arthralgia, paresthesia, sleep disorder, poor appetite, and concentration difficulties. Patients with PTLDS reported impaired HRQoL compared to the general US population. Included studies were highly heterogeneous in terms of study design, settings, patient characteristics, and quality. Patients with LD are more likely to report nonspecific long-term sequelae, especially those experiencing persistent symptoms posttreatment. Opportunities exist for prospective longitudinal studies to better understand LD outcomes. [ABSTRACT FROM AUTHOR]

Background: Observational longitudinal data often feature irregular, informative visit times. We propose descriptive measures to quantify the extent of irregularity to select an appropriate analytic outcome approach.Methods: We divided the study period into bins and calculated the mean proportions of individuals with 0, 1, and > 1 visits per bin. Perfect repeated measures features everyone with 1 visit per bin. Missingness leads to individuals with 0 visits per bin while irregularity leads to individuals with > 1 visit per bin. We applied these methods to: 1) the TARGet Kids! study, which invites participation at ages 2, 4, 6, 9, 12, 15, 18, 24 months, and 2) the childhood-onset Systemic Lupus Erythematosus (cSLE) study which recommended at least 1 visit every 6 months.Results: The mean proportions of 0 and > 1 visits per bin were above 0.67 and below 0.03 respectively in the TARGet Kids! study, suggesting repeated measures with missingness. For the cSLE study, bin widths of 6 months yielded mean proportions of 1 and > 1 visits per bin of 0.39, suggesting irregular visits.Conclusions: Our methods describe the extent of irregularity and help distinguish between protocol-driven visits and irregular visits. This is an important step in choosing an analytic strategy for the outcome. [ABSTRACT FROM AUTHOR]

Background. Resource-constrained countries have difficulty conducting large EQ-5D valuation studies, which limits their ability to conduct cost-utility analyses using a value set specific to their own population. When estimates of similar but related parameters are available, shrinkage estimators reduce uncertainty and yield estimators with smaller mean square error (MSE). We hypothesized that health utilities based on shrinkage estimators can reduce MSE and mean absolute error (MAE) when compared to country-specific health utilities. Methods. We conducted a simulation study (1,000 iterations) based on the observed means and standard deviations (or standard errors) of the EQ-5D-3L valuation studies from 14 counties. In each iteration, the simulated data were fitted with the model based on the country-specific functional form of the scoring algorithm to create country-specific health utilities (“naïve” estimators). Shrinkage estimators were calculated based on the empirical Bayes estimation methods. The performance of shrinkage estimators was compared with those of the naïve estimators over a range of different sample sizes based on MSE, MAE, mean bias, standard errors and the width of confidence intervals. Results. The MSE of the shrinkage estimators was smaller than the MSE of the naïve estimators on average, as theoretically predicted. Importantly, the MAE of the shrinkage estimators was also smaller than the MAE of the naïve estimators on average. In addition, the reduction in MSE with the use of shrinkage estimators did not substantially increase bias. The degree of reduction in uncertainty by shrinkage estimators is most apparent in valuation studies with small sample size. Conclusion. Health utilities derived from shrinkage estimation allow valuation studies with small sample size to “borrow strength” from other valuation studies to reduce uncertainty. [ABSTRACT FROM AUTHOR]