Derek Hazard

Background: Children with profound combined immunodeficiency (P-CID) are at high risk of severe infections, immune dysregulation, organ damage, and death. Clinical factors that may inform hematopoetic-stem-cell-transplantation (HSCT) decisions remain poorly defined because evidence is limited to retrospective studies.Methods: We conducted a prospective, multicenter cohort study of 139 children aged 1–16 years with impaired T-cell immunity and at least one disease-related manifestation. Patients were recruited from 31 centers in 14 countries between 2011 and 2018 and followed for 5–12 years regardless of subsequent HSCT. Disease severity at study entry was classified pragmatically as severe or non-severe according to the treating physician’s integrated assessment during HSCT decision-making. Outcomes included death, recurrent severe events, and chronic organ damage. We used joint frailty models to identify predictors of outcome and marginal structural models to explore the association between HSCT and mortality across cumulative disease burden. As treatment was not randomized, results may be affected by residual confounding.Findings: Most patients continued accumulating events during follow-up. Poor outcomes were more frequent among patients classified as severe. In multivariable analyses, severe infections predicted recurrent disease-related events and death, while chronic organ damage and malignancy predicted death. Autoimmune cytopenia, lymphoproliferation, and organ-specific immune dysregulation were not independently associated with outcome. Genetic diagnosis, CD4 counts, and naïve CD4 percentages were not informative for risk stratification. In univariable analyses, low post-neonatal TREC/KREC levels and impaired T-cell proliferation were associated with poor outcome. In observational analyses, HSCT was associated with lower mortality at higher pre-transplant event burden, with wide confidence intervals.Interpretation: In P-CID, selected clinical parameters are more informative than genotype or T-cell counts for risk assessment. Our findings support repeated reassessment of disease burden and organ damage when considering HSCT rather than reliance on a fixed transplant threshold. Whether earlier transplantation improves outcome remains uncertain.

Introduction: This study aims to discuss and assess the impact of three prevalent methodological biases: competing risks, immortal-time bias, and confounding bias in real-world observational studies evaluating treatment effectiveness. We use a demonstrative observational data example of COVID-19 patients to assess the impact of these biases and propose potential solutions.Methods: We describe competing risks, immortal-time bias, and time-fixed confounding bias by evaluating treatment effectiveness in hospitalized patients with COVID-19. For our demonstrative analysis, we use observational data from the registry of patients with COVID-19 who were admitted to the Bellvitge University Hospital in Spain from March 2020 to February 2021 and met our predefined inclusion criteria. We compare estimates of a single-dose, time-dependent treatment with the standard of care. We analyze the treatment effectiveness using common statistical approaches, either by ignoring or only partially accounting for the methodological biases. To address these challenges, we emulate a target trial through the clone-censor-weight approach.Results: Overlooking competing risk bias and employing the naïve Kaplan-Meier estimator led to increased in-hospital death probabilities in patients with COVID-19. Specifically, in the treatment effectiveness analysis, the Kaplan-Meier estimator resulted in an in-hospital mortality of 45.6% for treated patients and 59.0% for untreated patients. In contrast, employing an emulated trial framework with the weighted Aalen-Johansen estimator, we observed that in-hospital death probabilities were reduced to 27.9% in the “X”-treated arm and 40.1% in the non-“X”-treated arm. Immortal-time bias led to an underestimated hazard ratio of treatment.Conclusion: Overlooking competing risks, immortal-time bias, and confounding bias leads to shifted estimates of treatment effects. Applying the naïve Kaplan-Meier method resulted in the most biased results and overestimated probabilities for the primary outcome in analyses of hospital data from COVID-19 patients. This overestimation could mislead clinical decision-making. Both immortal-time bias and confounding bias must be addressed in assessments of treatment effectiveness. The trial emulation framework offers a potential solution to address all three methodological biases.

Background: Real-world observational data are an important source of evidence on the treatment effectiveness for patients hospitalized with coronavirus disease 2019 (COVID-19). However, observational studies evaluating treatment effectiveness based on longitudinal data are often prone to methodological biases such as immortal time bias, confounding bias, and competing risks. Methods: For exemplary target trial emulation, we used a cohort of patients hospitalized with COVID-19 (n = 501) in a single centre. We described the methodology for evaluating the effectiveness of a single-dose treatment, emulated a trial using real-world data, and drafted a hypothetical study protocol describing the main components. To avoid immortal time and time-fixed confounding biases, we applied the clone-censor-weight technique. We set a 5-day grace period as a period of time when treatment could be initiated. We used the inverse probability of censoring weights to account for the selection bias introduced by artificial censoring. To estimate the treatment effects, we took the multi-state model approach. We considered a multi-state model with five states. The primary endpoint was defined as clinical severity status, assessed by a 5-point ordinal scale on day 30. Differences between the treatment group and standard of care treatment group were calculated using a proportional odds model and shown as odds ratios. Additionally, the weighted cause-specific hazards and transition probabilities for each treatment arm were presented. Results: Our study demonstrates that trial emulation with a multi-state model analysis is a suitable approach to address observational data limitations, evaluate treatment effects on clinically heterogeneous in-hospital death and discharge alive endpoints, and consider the intermediate state of admission to ICU. The multi-state model analysis allows us to summarize results using stacked probability plots that make it easier to interpret results. Conclusions: Extending the emulated target trial approach to multi-state model analysis complements treatment effectiveness analysis by gaining information on competing events. Combining two methodologies offers an option to address immortal time bias, confounding bias, and competing risk events. This methodological approach can provide additional insight for decision-making, particularly when data from randomized controlled trials (RCTs) are unavailable.

Multistate methodology proves effective in analyzing hospitalized coronavirus disease 2019 (COVID-19) patients with emerging variants in real time. An analysis of 2,548 admissions in Freiburg, Germany, showed reduced severity over time in terms of shorter hospital stays and higher discharge rates when comparing more recent phases with earlier phases of the pandemic.

Methodological biases are common in observational studies evaluating treatment effectiveness. The objective of this study is to emulate a target trial in a competing risks setting using hospital-based observational data. We extend established methodology accounting for immortal time bias and time-fixed confounding biases to a setting where no survival information beyond hospital discharge is available: a condition common to coronavirus disease 2019 (COVID-19) research data. This exemplary study includes a cohort of 618 hospitalized patients with COVID-19. We describe methodological opportunities and challenges that cannot be overcome applying traditional statistical methods. We demonstrate the practical implementation of this trial emulation approach via clone–censor–weight techniques. We undertake a competing risk analysis, reporting the cause-specific cumulative hazards and cumulative incidence probabilities. Our analysis demonstrates that a target trial emulation framework can be extended to account for competing risks in COVID-19 hospital studies. In our analysis, we avoid immortal time bias, time-fixed confounding bias, and competing risks bias simultaneously. Choosing the length of the grace period is justified from a clinical perspective and has an important advantage in ensuring reliable results. This extended trial emulation with the competing risk analysis enables an unbiased estimation of treatment effects, along with the ability to interpret the effectiveness of treatment on all clinically important outcomes.

Polyether ether ketone (PEEK) has been introduced into implant dentistry as a viable alternative to current implant abutment materials. However, data on its physico-mechanical properties are still scarce. The present study sought to shed light on this topic utilizing an ex vivo chewing simulator model. A total of 48 titanium two-piece implants were allocated into three groups (n = 16 per group): (1) implants with PEEK abutments and an internal butt-joint connection (PBJ), (2) implants with PEEK abutments and an internal conical implant–abutment connection (PC), and (3) implants with zirconia abutments and an internal butt-joint connection (ZA). All abutments were restored with a non-precious metal alloy crown mimicking the upper right central incisor. A dynamic chewing simulation of half (n = 8) of the specimens per group was performed with 5 × 106 cycles and a load of 49 N at a frequency of 1.7 Hz with thermocycling between 5 and 55 °C. The other eight specimens served as unloaded controls. Surface roughness, implant–abutment connection microgaps (IACMs), and the titanium base–abutment interface microgaps (TAIMs) in the loaded groups were evaluated. Finally, a quasi-static loading test was performed in a universal testing machine with all samples to evaluate fracture resistance. Overall, 23 samples survived the artificial chewing process. One abutment screw fracture was observed in the PC group. The ZA group showed higher surface roughness values than PEEK abutments. Furthermore, ZA revealed lower TAIM values compared to PEEK abutments. Similarly, ZA was associated with lower IACM values compared to PBJ. Fracture loads/bending moments were 1018 N/704 N cm for PBJ, 966 N/676 N cm for PC, and 738 N/508 N cm for ZA, with no significant differences compared to the unloaded references. Artificial loading did not significantly affect fracture resistance of the examined materials. PEEK abutments were associated with better load-bearing properties than zirconia abutments, although they showed higher microgap values. PEEK abutments could, therefore, be feasible alternatives to zirconia abutments based on the present ex vivo findings resembling 20 years of clinical service.

Purpose: When studying nosocomial infections, resource-efficient sampling designs such as nested case-control, case-cohort, and point prevalence studies are preferred. However, standard analyses of these study designs can introduce selection bias, especially when interested in absolute rates and risks. Moreover, nosocomial infection studies are often subject to competing risks. We aim to demonstrate in this tutorial how to address these challenges for all three study designs using simple weighting techniques. Patients and Methods: We discuss the study designs and explain how inverse probability weights (IPW) are applied to obtain unbiased hazard ratios (HR), odds ratios and cumulative incidences. We illustrate these methods in a multi-state framework using a dataset from a nosocomial infections study (n = 2286) in Moscow, Russia. Results: Including IPW in the analysis corrects the unweighted naïve analyses and enables the estimation of absolute risks. Resulting estimates are close to the full cohort estimates using substantially smaller numbers of patients. Conclusion: IPW is a powerful tool to account for the unequal selection of controls in case-cohort, nested case-control and point prevalence studies. Findings can be generalized to the full population and absolute risks can be estimated. When applied to a multi-state model, competing risks are also taken into account. Graphical Abstract: