Bayesian and heterogeneity of treatment effect analyses of the HOT‐ICU trial—A secondary analysis protocol

Klitgaard, Thomas; Schjørring, Olav Lilleholt; Lange, Theis; Møller, Morten Hylander; Perner, Anders; Rasmussen, Bodil Steen; Granholm, Anders

doi:10.1111/aas.13669

Cited by 7 publications

(23 citation statements)

References 28 publications

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“…5 Also, the protocol for this study was published before randomisation of the last patient in the HOT-ICU trial. 16 Further, our results were consistent in the sensitivity analyses using different priors, and we evaluated the presence of Posterior probability distribution for the adjusted relative risk (RR) for 90-day all-cause mortality in the primary analysis using weakly informative priors. Upper part: cumulative posterior probability distribution for the adjusted RR.…”

Section: Variablesupporting

confidence: 54%

Lower versus higher oxygenation targets in critically ill patients with severe hypoxaemia: secondary Bayesian analysis to explore heterogeneous treatment effects in the Handling Oxygenation Targets in the Intensive Care Unit (HOT-ICU) trial

Klitgaard

Schjørring

Lange

et al. 2022

British Journal of Anaesthesia

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Background: In the Handling Oxygenation Targets in the Intensive Care Unit (HOT-ICU) trial, a lower (8 kPa) vs a higher (12 kPa) PaO 2 target did not affect mortality amongst critically ill adult patients. We used Bayesian statistics to evaluate any heterogeneity in the effect of oxygenation targets on mortality between different patient groups within the HOT-ICU trial. Methods: We analysed 90-day all-cause mortality using adjusted Bayesian logistic regression models, and assessed heterogeneous treatment effects according to four selected baseline variables using both hierarchical models of subgroups and models with interactions on the continuous scales. Results are presented as mortality probability (%) and relative risk (RR) with 95% credibility intervals (CrI). Results: All 2888 patients in the intention-to-treat cohort of the HOT-ICU trial were included. The adjusted 90-day mortality rates were 43.0% (CrI: 38.3e47.8%) and 42.3% (CrI: 37.7e47.1%) in the lower and higher oxygenation groups, respectively (RR 1.02 [CrI: 0.93e1.11]), with 36.5% probability of an RR <1.00. Analyses of heterogeneous treatment effects suggested a doseeresponse relationship between baseline norepinephrine dose and increased mortality with the lower oxygenation target, with 95% probability of increased mortality associated with the lower oxygenation target as norepinephrine doses increased. Conclusions: A lower oxygenation target was unlikely to affect overall mortality amongst critically ill adult patients with acute hypoxaemic respiratory failure. However, our results suggest an increasing mortality risk for patients with a lower oxygen target as the baseline norepinephrine dose increases. These findings warrant additional investigation. Clinical trial registration: NCT03174002.

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Section: Variablesupporting

confidence: 54%

Lower versus higher oxygenation targets in critically ill patients with severe hypoxaemia: secondary Bayesian analysis to explore heterogeneous treatment effects in the Handling Oxygenation Targets in the Intensive Care Unit (HOT-ICU) trial

Klitgaard

Schjørring

Lange

et al. 2022

British Journal of Anaesthesia

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“…Models will generally be assessed as previously described 11,15,32 . We will use Stan's default dynamic Hamiltonian Monte Carlo sampler with 4 chains with at least 50,000 post–warm‐up samples in total, and with bulk/tail effective sample sizes of at least 10,000 for the parameters of interest.…”

Section: Methodsmentioning

confidence: 99%

“…9,31 2.7.7 | Model settings and diagnostics Models will generally be assessed as previously described. 11,15,32 We will use Stan's default dynamic Hamiltonian Monte Carlo sampler with 4 chains with at least 50,000 post-warm-up samples in total, and with bulk/tail effective sample sizes of at least 10,000 for the parameters of interest. We will tune sampler settings to avoid divergent transitions and assess chain convergence by visual inspection of overlain density and trace plots, and by requiring Rhat statistics ≤ 1.01 for all parameters.…”

Section: Missing Data Handlingmentioning

confidence: 99%

“…9 Bayesian statistical methods are increasingly used and recommended in clinical trials in critical care as they may offer advantages or supplementary information in addition to the conventional analyses. [10][11][12][13][14][15][16] This protocol and statistical analysis plan outline the rationale and methodology for a secondary, pre-planned Bayesian analysis of the COVID STEROID 2 trial.…”

Section: Introductionmentioning

confidence: 99%

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Higher vs Lower Doses of Dexamethasone in Patients with COVID‐19 and Severe Hypoxia (COVID STEROID 2) trial: Protocol for a secondary Bayesian analysis

Granholm

Munch

Myatra

et al. 2021

Acta Anaesthesiol Scand

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Background Coronavirus disease 2019 (COVID‐19) can lead to severe hypoxic respiratory failure and death. Corticosteroids decrease mortality in severely or critically ill patients with COVID‐19. However, the optimal dose remains unresolved. The ongoing randomised COVID STEROID 2 trial investigates the effects of higher vs lower doses of dexamethasone (12 vs 6 mg intravenously daily for up to 10 days) in 1,000 adult patients with COVID‐19 and severe hypoxia. Methods This protocol outlines the rationale and statistical methods for a secondary, pre‐planned Bayesian analysis of the primary outcome (days alive without life support at day 28) and all secondary outcomes registered up to day 90. We will use hurdle‐negative binomial models to estimate the mean number of days alive without life support in each group and present results as mean differences and incidence rate ratios with 95% credibility intervals (CrIs). Additional count outcomes will be analysed similarly and binary outcomes will be analysed using logistic regression models with results presented as probabilities, relative risks and risk differences with 95% CrIs. We will present probabilities of any benefit/harm, clinically important benefit/harm and probabilities of effects smaller than pre‐defined clinically minimally important differences for all outcomes analysed. Analyses will be adjusted for stratification variables and conducted using weakly informative priors supplemented by sensitivity analyses using sceptic priors. Discussion This secondary, pre‐planned Bayesian analysis will supplement the primary, conventional analysis and may help clinicians, researchers and policymakers interpret the results of the COVID STEROID 2 trial while avoiding arbitrarily dichotomised interpretations of the results. Trial registration ClinicalTrials.gov: NCT04509973; EudraCT: 2020‐003363‐25.

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“…If less than 5% of patients have missing data for variables included in a given analysis, we will perform a complete case analysis without imputation. If more than 5% of the data is missing for at least one variable used in an analysis, multiple imputation with chained equations will be performed as specified in the HOT‐ICU protocol 27 …”

Section: Missing Datamentioning

confidence: 99%

Higher versus lower oxygenation targets in COVID‐19 patients with severe hypoxaemia (HOT‐COVID) trial: Protocol for a secondary Bayesian analysis

Nielsen

Klitgaard

Granholm

et al. 2021

Acta Anaesthesiol Scand

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Background Respiratory failure is the main cause of mortality and morbidity among ICU patients with coronavirus disease 2019 (COVID‐19). In these patients, supplemental oxygen therapy is essential, but there is limited evidence the optimal target. To address this, the ongoing handling oxygenation targets in COVID‐19 (HOT‐COVID) trial was initiated to investigate the effect of a lower oxygenation target (partial pressure of arterial oxygen (PaO2) of 8 kPa) versus a higher oxygenation target (PaO2 of 12 kPa) in the ICU on clinical outcome in patients with COVID‐19 and hypoxaemia. Methods The HOT‐COVID is planned to enrol 780 patients. This paper presents the protocol and statistical analysis plan for the conduct of a secondary Bayesian analysis of the primary outcome of HOT‐COVID being days alive without life‐support at 90 days and the secondary outcome 90‐day all‐cause mortality. Furthermore, both outcomes will be investigated for the presence heterogeneity of treatment effects based on four baseline parameters being sequential organ failure assessment score, PaO2/fraction of inspired oxygen ratio, highest dose of norepinephrine during the 24 h before randomisation, and plasma concentration of lactate at randomisation. Conclusion The results of this pre‐planned secondary Bayesian analysis will complement the primary frequentist analysis of the HOT‐COVID trial and may facilitate a more nuanced interpretation of the trial results.

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Bayesian and heterogeneity of treatment effect analyses of the HOT‐ICU trial—A secondary analysis protocol

Cited by 7 publications

References 28 publications

Lower versus higher oxygenation targets in critically ill patients with severe hypoxaemia: secondary Bayesian analysis to explore heterogeneous treatment effects in the Handling Oxygenation Targets in the Intensive Care Unit (HOT-ICU) trial

Lower versus higher oxygenation targets in critically ill patients with severe hypoxaemia: secondary Bayesian analysis to explore heterogeneous treatment effects in the Handling Oxygenation Targets in the Intensive Care Unit (HOT-ICU) trial

Higher vs Lower Doses of Dexamethasone in Patients with COVID‐19 and Severe Hypoxia (COVID STEROID 2) trial: Protocol for a secondary Bayesian analysis

Higher versus lower oxygenation targets in COVID‐19 patients with severe hypoxaemia (HOT‐COVID) trial: Protocol for a secondary Bayesian analysis

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