CONCLUSIONS:In a retrospective study of patients with AIH in Europe, we found that the dose of predniso(lo) ne to induce remission in patients with AIH is less relevant than assumed. An initial predniso(lo)ne dose below 0.50 mg/kg/day substantially decreases unnecessary exposure to predniso(lo)ne in patients with AIH.
Background Individual participant data meta-analysis (IPD-MA) is considered the gold standard for investigating subgroup effects. Frequently used regression-based approaches to detect subgroups in IPD-MA are: meta-regression, per-subgroup meta-analysis (PS-MA), meta-analysis of interaction terms (MA-IT), naive one-stage IPD-MA (ignoring potential study-level confounding), and centred one-stage IPD-MA (accounting for potential study-level confounding). Clear guidance on the analyses is lacking and clinical researchers may use approaches with suboptimal efficiency to investigate subgroup effects in an IPD setting. Therefore, our aim is to overview and compare the aforementioned methods, and provide recommendations over which should be preferred. Methods We conducted a simulation study where we generated IPD of randomised trials and varied the magnitude of subgroup effect (0, 25, 50% relative reduction), between-study treatment effect heterogeneity (none, medium, large), ecological bias (none, quantitative, qualitative), sample size (50,100,200), and number of trials (5,10) for binary, continuous and time-to-event outcomes. For each scenario, we assessed the power, false positive rate (FPR) and bias of aforementioned five approaches. Results Naive and centred IPD-MA yielded the highest power, whilst preserving acceptable FPR around the nominal 5% in all scenarios. Centred IPD-MA showed slightly less biased estimates than naïve IPD-MA. Similar results were obtained for MA-IT, except when analysing binary outcomes (where it yielded less power and FPR < 5%). PS-MA showed similar power as MA-IT in non-heterogeneous scenarios, but power collapsed as heterogeneity increased, and decreased even more in the presence of ecological bias. PS-MA suffered from too high FPRs in non-heterogeneous settings and showed biased estimates in all scenarios. Meta-regression showed poor power (< 20%) in all scenarios and completely biased results in settings with qualitative ecological bias. Conclusions Our results indicate that subgroup detection in IPD-MA requires careful modelling. Naive and centred IPD-MA performed equally well, but due to less bias of the estimates in the presence of ecological bias, we recommend the latter. Electronic supplementary material The online version of this article (10.1186/s12874-019-0817-6) contains supplementary material, which is available to authorized users.
Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (ie, varying with patient characteristics). In this article, we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression‐based methods that are both well‐known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrate the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data.
Objectives There are data to suggest that obesity is associated with local and systemic complications as well as mortality in acute pancreatitis (AP). Cohort studies to date, however, have shown conflicting results from mostly unadjusted analyses. Therefore, we performed an individual patient data meta-analysis with the primary aim to investigate the association between obesity and mortality in AP. Our secondary aim was to investigate the association between obesity and necrosis, organ failure, multiple organ failure, and invasive intervention. Patients and methods We systematically searched four electronic databases for prospective studies on obesity and outcomes in AP. Researchers of eligible studies were invited to share individual patient data using a standardized data collection form. All end points were investigated with a one-stage mixed effects Poisson model with random intercepts and forced entry of relevant confounders. Results We included five databases with 1302 patients, of whom 418 (32%) were obese. In total, 466 (36%) patients had necrosis, 328 (25%) had organ failure, 188 (14%) had multiple organ failure, 210 (16%) had an intervention, and 84 (7%) patients died. We found no significant association between obesity and mortality [relative risk (RR) 1.40, 95% confidence interval (CI): 0.89–2.20], necrosis (RR: 1.08, 95% CI: 0.90–1.31) or invasive intervention (RR: 1.10, 95% CI: 0.83–1.47) after adjustment for confounders. However, obesity was independently associated with the development of organ failure (RR: 1.38, 95% CI: 1.11–1.73) and multiple organ failure (RR: 1.81, 95% CI: 1.35–2.42). Conclusion Obesity is independently associated with the development of organ failure and multiple organ failure in AP. However, there is no association between obesity and mortality, necrosis, and an intervention.
ObjectivesWith the increasing interest in personalised medicine, the use of subgroup analyses is likely to increase. Subgroup analyses are challenging and often misused, possibly leading to false interpretations of the effect. It remains unclear to what extent key organisations warn for such pitfalls and translate current methodological research to detect these effects into research guidelines. The aim of this scoping review is to determine and evaluate the current guidance used by organisations for exploring, confirming and interpreting subgroup effects.DesignScoping review.Eligibility criteriaWe identified four types of key stakeholder organisations: industry, health technology assessment organisations (HTA), academic/non-profit research organisations and regulatory bodies. After literature search and expert consultation, we identified international and national organisations of each type. For each organisation that was identified, we searched for official research guidance documents and contacted the organisation for additional guidance.ResultsTwenty-seven (45%) of the 60 organisations that we included had relevant research guidance documents. We observed large differences between organisation types: 18% (n=2) of the industry organisations, 64% (n=9) of the HTA organisations, 38% (n=8) of academic/non-profit research organisations and 57% (n=8) of regulatory bodies provided guidance documents. The majority of the documents (n=33, 63%) mentioned one or more challenges in subgroup analyses, such as false positive findings or ecological bias with variations across the organisation types. Statistical recommendations were less common (n=19, 37%) and often limited to a formal test of interaction.ConclusionsAlmost half of the organisations included in this scoping review provided guidance on subgroup effect research in their guidelines. However, there were large differences between organisations in the amount and level of detail of their guidance. Effort is required to translate and integrate research findings on subgroup analysis to practical guidelines for decision making and to reduce the differences between organisations and organisation types.
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