TARGET Protein: the effect of augmented administration of enteral protein to critically ill adults on clinical outcomes—statistical analysis plan for a cluster randomized, cross-sectional, double cross-over, clinical trial
Approximately one in four critically ill patients who receive enteral nutrition (EN) in the intensive care unit (ICU) does not survive to hospital discharge . For those that do survive, rapid and substantial muscle wasting occurs , which is strongly associated with adverse patient-centered outcomes . International critical care nutrition guidelines recommend protein delivery of at least 1.2 g/kg body weight/day . Critically ill patients typically receive dietary protein at rates < 1.0 g/kg body weight/day  and it has been proposed that augmenting dietary protein in critical illness could improve outcomes . However, it is unclear from existing data whether augmenting dietary protein in the ICU improves patient outcomes, with existing meta-analysis indicating that increased protein could reduce mortality by up to 12% or increasing mortality by up to 11% . The TARGET Protein trial is a cluster randomized, cross-sectional, double cross-over, registry-embedded, pragmatic open-label clinical trial with the aim of evaluating the effect of higher enteral protein delivery (augmented protein) on clinical outcomes of critically ill adult patients when compared to usual care . This manuscript describes the statistical analysis plan for the TARGET Protein trial and supersedes the plan provided in the previously published protocol  and trial registration (Australian New Zealand Clinical Trials Registry, ACTRN12621001484831). At least three relevant clinical trials have emerged since the commencement of the TARGET Protein trial. During the recruitment for the TARGET Protein trial, the EFFORT Protein trial was published (25 January 2023) . That trial evaluated the effect of increased protein (≥ 2.2 g/kg/day) compared to usual care (protein ≤ 1.2 g/kg/day) and was ceased with less than one third of the planned cohort recruited at 1301 patients due to slow recruitment consequent upon the COVID-19 pandemic. The revised primary outcome of the EFFORT Protein trial was “time to discharge alive from hospital up to 60 days after ICU admission” and there was no evidence of a between group difference . The authors reported potentially worse outcomes in the sub-group of patients with acute kidney injury  and high SOFA score (≥ 9) on admission; however, the authors acknowledged multiplicity of testing with accompanying inflation of type 1 errors. Further, the sub-group with acute kidney injury was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine criteria, which include post-randomization biochemical changes up to day 7. The PRotEin Provision in Critical IllneSs (PRECISe) trial was an investigator-initiated, multi-center, parallel group, blinded, randomized controlled trial evaluating functional recovery following ICU admission . The investigators randomized 935 patients to either higher dose enteral protein or usual dose enteral protein (the same trial enteral formulae used in TARGET Protein). The primary endpoint was health-related quality of life as measured by the Euro-QoL-5 dimension-5-level questionnaire (EQ-5D-5L) Health Utility Score with between-group differences assessed over three time points using linear mixed-effects models. Trial results have not been published as of July 2024; however, a protocol for a Bayesian re-analysis of the trial data was published in late 2023 . The Replacing Protein Via Enteral Nutrition in Critically Ill Patients (REPLENISH) trial is an open-label, multi-center parallel-group randomized clinical trial to evaluate whether supplemental enteral protein, in addition to standard enteral nutrition to achieve a high enteral protein dose of 2–2.4 g/kg/day, administered from ICU day 5, when compared to usual enteral nutrition to achieve a moderate enteral protein dose of 0.8–1.2 g/kg/day, will reduce all-cause 90-day mortality in critically ill patients . The statistical analysis plan for the REPLENISH trial was recently published , and the trial registration site estimates a completion date in mid-2025 (ClinicalTrials.gov ID NCT04475666). The TARGET Protein trial will be a pragmatic trial that will enroll over 3000 patients, the largest protein interventional study conducted in critically ill patients to date, which will provide greater certainty of the effect size protein dosage has on clinical outcomes. 

TARGET Protein is a cluster randomized, cross-sectional double cross-over open-label trial conducted within eight ICUs across Australia and New Zealand. The study was prospectively registered at the Australian New Zealand Clinical Trials Registry on 1 November 2021 (ACTRN12621001484831). Clusters Four of eight ICUs were randomly allocated to the sequence augmented protein enteral formula alternating after each 3-month period with usual care enteral protein formula; with the other four ICUs following the opposite sequence. Further details regarding treatment allocation generation and randomization are provided in the published study protocol . In the overall 12-month trial duration, this resulted in four periods with alternate enteral nutrition formulae within each of the eight ICU clusters. Patients admitted to each ICU during each 3-month cluster-period received the same trial enteral formula until either the patient reached day 90, was discharged from ICU, or died. In the cluster double cross-over design, each cluster is randomized to receive each intervention at separate periods of time and individuals are included only once. The randomization of the clusters to the order that the intervention is received enables us to control for period effects. The inclusion of the crossover element increases the power of the study and reduces the required number of participants when compared to a parallel cluster design . Participants Inclusion criteria All patients admitted to ICU are eligible if they are: ≥ 16 years of age Receiving/about to commence EN during their index admission to ICU or receiving/about to commence EN for the first time in ICU during subsequent admissions Exclusion criteria At the commencement of EN, the treating clinician considers the trial enteral formula to be contraindicated. The patient has received greater than 12 h of non-trial enteral formula. The patient has previously participated in the TARGET Protein trial. Patient flow through the trial will be summarized in a Consolidated Standards of Reporting Trials (CONSORT) diagram (Fig. ). We will report cluster sequence allocation, number of patients assessed for eligibility, number of patients who met trial inclusion and exclusion criteria, number of patients enrolled, number of patients withdrawn, and number of patients included in the primary outcome analysis. Intervention and comparator During the intervention cluster periods (augmented dietary protein), all eligible patients received the enteral formula “Nutrison Protein Intense®” (Nutricia Australia Pty Limited) which contains 100 g protein per 1000 ml and 1260 kcal per 1000 ml. During the comparator cluster periods (usual dietary protein), all patients received the enteral formula “Nutrison Protein Plus®” (Nutricia Australia Pty Limited), which contains 63 g protein per 1000 ml and 1250 kcal per 1000 ml. Outcomes Primary outcome The primary outcome is the number of days free of the index hospital and alive at day 90. This is a patient-centered outcome measure that captures both death and morbidity . This will be calculated as 90 days minus all days admitted to the index hospital after commencement of trial enteral formula, minus any days readmitted to the index hospital within 90 days. As patients may be readmitted for part of a day (e.g., for dialysis), a readmission will only be counted when the patient remains in hospital at midnight. Patients will be considered alive if they survive the index-hospital episode, and there is no evidence of death before day 90. In Australia, evidence of death after hospital discharge will be ascertained from the local health record by site investigators and the Australian National Death Index. Similarly, at the New Zealand site, The Australian New Zealand Intensive Care Society Adult Patient Database (ANZICS APD) is linked to the New Zealand national death registry to record death after hospital discharge. Patients who die during the 90-day post-trial recruitment period will be assigned a days free of the index hospital value of 0. Examples of this calculation are provided below: A patient who survived and is discharged from hospital on day 30 after commencement of trial enteral formula is assigned a value of 60. A patient who survived and is discharged from hospital on day 92 after commencement of trial enteral formula would be censored at day 90 and assigned a value of 0. A patient who dies on any day up to and including day 90 is assigned a value of 0. Secondary outcomes 1. Days free of the index hospital and alive at day 90 in survivors. Derived as described for the primary outcome, but the analysis will only be conducted in those patients who survive to day 90 after commencement of trial enteral formula. 2. Alive at day 90. Patients will be considered alive if they are alive at discharge from the index hospital and there is no evidence of death before day 90, either from the local health record or via linkage to the relevant National Death Index. Health service records will be used to provide evidence of patient status, including if the patient was discharged alive from hospital but known to have a health event prior to day 90. Health events include rehabilitation facility admission, outpatient appointment attended, pathology test completed, other medical investigation, or day admission, e.g., dialysis and chemotherapy. 3. Duration of invasive ventilation (hours). Defined as the total hours of invasive mechanical ventilation during the index hospital admission, this includes any re-ventilation periods in ICU following initial weaning during the index hospital admission but does not include ventilation in operating rooms for a procedure. Ventilation during subsequent hospital admissions will not count towards this outcome. Only invasive ventilation will count, and non-invasive ventilation, continuous positive end-expiratory pressure or high-flow oxygen will not contribute to time. The duration of invasive ventilation reported in the Adult Patient Database will be used within this pragmatic registry-based trial noting it will include any periods of time prior to the commencement of study EN. However, the time from ICU admission to commencement of trial enteral nutrition is anticipated to be very short, based on the TARGET trial with similar inclusion criteria . 4. Duration of admission to ICU (days). Defined as the time after commencement of trial enteral formula until ICU discharge for the index ICU admission, plus any days that the patient is readmitted prior to hospital discharge, but does not include subsequent hospital admissions. 5. Duration of admission to hospital (days). Defined as the time after commencement of trial enteral formula until hospital discharge for index hospital admission (i.e., it excludes any time spent in hospital if readmitted, which will be counted for the primary outcome). 6. Incidence of tracheostomy insertion. Defined as the proportion of participants requiring new tracheostomy insertion during their study ICU admission. 7. Incidence of new renal replacement therapy. Defined as the proportion of participants commencing new renal replacement therapy during their study ICU admission, subsequent to commencement of trial nutrition. 8. Discharge destination. Discharge destinations are collected in the Adult Patient Database Case Report Form (CRF) as home, other hospital ICU, other acute hospital, rehabilitation, long-term care, and other (a free text field) . Data source Data will be sourced from The Australian New Zealand Intensive Care Society Adult Patient Database (ANZICS APD) . In Australia, death after hospital discharge will be ascertained using  the local health record where outcome at day 90 is known and  via data linkage with the Australian National Death Index. At the New Zealand site, the Adult Patient Database is linked to the New Zealand national death registry to record death after hospital discharge. Study day 1 commences on the day TARGET Protein trial enteral formula is commenced and concludes at the end of that calendar day (2400 h). Sample size The sample size was calculated using The Shiny Cluster Randomized Trial Calculator and using data from TARGET Calories . In TARGET Calories, the standard deviation for the primary outcome being used in TARGET Protein (days free of the index hospital and alive at day 90) was 11.3 days. Assuming an exchangeable correlation structure, a coefficient of variation for cluster size of 0.5—[based on 8 ICUs (i.e., 8 clusters) each enrolling a mean of 160 patients per 3-month period (95% confidence interval, 80 to 240 patients per cluster per period)]—and the default settings for cluster trials of an intracluster correlation coefficients of 0.02 (with a lower extreme of 0.001 and upper extreme of 0.1), 60 to 65 patients per period per cluster would provide 80% power, and ~ 80 patients per cluster per period would provide 90% power to find a mean difference of 1 day for the primary outcome. Assuming a normal distribution, a sample size of 2560 from the 8 clusters would provide 90% power to detect a mean difference of 1 day free of the index hospital and alive at day 90. However, the primary outcome (days free of the index hospital and alive at day 90) is unlikely to be normally distributed and parametric calculations are optimistic. For this reason, the goal sample size was inflated by 15% to allow for the likely skewed distribution of data, such that ~ 3000 patients from the 8 clusters should provide 90% power . After the trial was completed, it was noted that the sample size estimates specified eight clusters based on eight ICUs being included in the trial. However, the design utilizes four clusters in each of the two treatment sequences. With only four clusters and cluster period size of ≥ 60 patients, there was still > 80% power to detect a 2-day difference in the number of days free of the index hospital and alive at day 90 under a range of plausible assumptions. Statistical principles The statistical analysis will be completed by biostatisticians from the Methods and Implementation Support for Clinical and Health (MISCH) Research Hub (University of Melbourne, Australia). The analyses will be independently checked by a senior biostatistician at MISCH (AK) and any discrepancies between the two analyses will be discussed and resolved by consensus. All analyses will account for the cluster-randomized cross-over design to ensure correct estimation of the 95% confidence intervals (further details provided for the primary and secondary outcomes below). For the primary outcome, estimates, two-sided 95% CIs, and p values will be presented. For the secondary outcomes, we will present effect sizes and two-sided 95% CIs only, because the trial was not powered for the secondary outcomes and, thus, have no planned multiplicity adjustment for the secondary outcomes. All statistical tests will be performed in Stata/SE version 18.0 (Stata Corporation, College Station, TX, USA), or R version 4.3.1 or later. Analysis sets Because of the cluster-randomized cross-over design of this trial, the intention-to-treat principle applies at both the cluster level and the participant level. At the cluster level, the intention-to-treat population includes all randomized ICUs that recruited subjects, regardless of the degree of adherence to the study interventions. At the participant level, the intention-to-treat population means that all patients will be analyzed according to the randomized treatment of their ICU at the time of their index ICU admission, regardless of treatment actually received. Statistical methods Descriptive statistics at cluster level will be presented by allocated treatment sequence, and patient characteristics will be summarized by allocated treatment sequence and observation period. Descriptive statistics of variables concerning trial enteral formula delivery (e.g., duration of trial nutrition (hours), mean volume of trial enteral formula delivery (ml/day), calories delivered (kcal/day), and protein delivered (g/day)) will be summarized by allocated treatment sequence. The median (25th and 75th percentiles) of protein (g/day) and calories (kcal/day) from trial nutrition on days 1–5, 10, 20, and 30 of the study will be presented visually by treatment group. The statistical methods for the primary and secondary outcomes are described below. Details regarding the main analysis plan, sensitivity analyses, and subgroup analyses are located in Table . Primary outcome: number of days free of the index hospital and alive at day 90 Main analysis Comparisons of treatment groups will use individual patient-level data and a quantile mixed effects model will be fitted to compare the median (0.5 quantile) response of the treatment groups. The quantile mixed effects model will include treatment group (augmented protein versus usual care), period (1, 2, 3, and 4), and the stratification variable used in randomization, delayed start (“group 1—commencing May 2022” and “group 2—commencing August 2022”), as fixed effects. Cluster (ICU) will be included as a random effect and assumed to be normally distributed with mean zero and variance component [12pt]{minimal}
				
				 
				 
				 
				
				
				
				
				$${ }_{C}^{2}$$ σ C 2 . The effect of treatment comparisons will be presented as a difference in medians (95% confidence interval (CI)); the 95% CI will be calculated using the block-bootstrap method . If model convergence issues occur, then the analysis will be performed on the scaled primary outcome (zero mean and unit variance). The treatment effect will then compare the median of the scaled primary outcome between treatment groups. Secondary analyses A Bayesian quantile mixed model for continuous outcomes will be used to model the median (0.5 quantile) response . The same fixed and random effects described in the main analysis will be included in the Bayesian quantile mixed model. The effect of treatment comparisons will be presented as the posterior mean of the regression coefficient for treatment (difference in medians) and 95% credible interval. Bayesian quantile mixed modeling will be performed using brms package  in R. The default hyperprior distributions in the brms packaged will be specified for the fixed effects (improper flat hyperprior) and standard deviation of the cluster random effect (weakly informative half student-t hyperprior). The following analysis will be performed to align with the sample size calculation and in view of the recent findings from Granholm et al.  that linear models adequately estimated group means despite not fitting the data well enough to mimic the “days alive without life support” calculation. Treatment group comparisons will use individual patient-level data and be estimated using a linear mixed effects model. The linear mixed effects model will include treatment group (augmented protein versus usual care), period (1, 2, 3, and 4), and the stratification variable used in randomizsation, delayed start (“group 1—commencing May 2022” and “group 2—commencing August 2022”), as fixed effects. Cluster (ICU) will be included as a random effect and assumed to be normally distributed with mean zero and variance component [12pt]{minimal}
				
				 
				 
				 
				
				
				
				
				$${ }_{C}^{2}$$ σ C 2 . The effect of treatment comparisons will be presented as a mean difference (95% CI) from the linear mixed effects model. Secondary outcomes Days free of the index hospital at day 90 in survivors This will follow the same approach as for the primary outcome. Alive at day 90, incidence of tracheostomy insertion, and incidence of renal replacement therapy Comparison of randomized treatment groups will use individual patient-level data and generalized estimating equations (GEE) with a logarithmic link function, binomial distribution, an exchangeable working correlation matrix, and Fay and Graubard’s small sample bias corrected standard errors using the ICU as the clustering unit . Indicator variables for treatment and period will be included in the marginal model, as well as the stratification variable used in randomization, i.e., delayed start. The effect of treatment comparisons will be presented as risk ratio and 95% CI from the GEE analysis . Should the risk ratio model fail to converge, GEE with Poisson distribution, a logarithmic link, exchangeable working correlation matrix, and Fay and Graubard’s small sample bias corrected standard errors will be employed . Duration of invasive ventilation, admission to ICU, and admission to hospital Time to discharge alive from index ICU and index hospital admission, and liberation from invasive mechanical ventilation will be summarized with medians and interquartile ranges obtained from cumulative incidence functions regarding mortality as a competing risk . Treatment group comparisons will use Cox regression with covariate adjustment for period and delayed start, with stratification by ICU, and robust standard errors clustered at ICU level (for any residual within-ICU correlation) to estimate cause-specific hazard ratios and confidence intervals, with patients dying prior to discharge (or extubation) censored at their time of death . Assessment of the proportionality of hazards assumption in these models will be made using Schoenfeld residuals, with resultant covariate stratification or modeling of time-dependent treatment effects, where necessary. For the duration of invasive mechanical ventilation outcome, if the recorded duration of mechanical ventilation for a patient is within 48 h of their duration of hospital stay resulting in death, then it will be assumed that such patients were extubated at that time with palliative intent and, hence, these patients’ data will be censored at time of tracheal extubation in the analyses. Discharge destination A descriptive analysis will be performed, where the frequency of each discharge destination will be presented by allocated treatment sequence and observation period. Subgroup analyses Planned subgroup analyses will assess heterogeneity of the treatment effect for the primary outcome across the following baseline variables: mechanical ventilation, age groups (70 years and older vs younger than 70), body mass index (categorized as 35 kg/m 2 and greater vs less than 35 kg/m 2 ), and those with acute kidney injury (AKI; yes vs no), defined as receiving new renal replacement therapy between hospital admission and commencement of trial enteral formula on ICU admission. Subgroup analyses will include treatment × subgroup interaction terms in respective regression models. We will present subgroup specific estimates of the treatment effect (95% CI), which will be derived from the treatment main effect estimate and interaction term estimate and the p value from a likelihood ratio test of an interaction between treatment and the subgroup variable. Sensitivity analyses The following sensitivity analyses of the primary outcome and main analysis will be performed: Exclusion of patients if they are known to have received non-trial enteral formula Exclusion of patients admitted for palliative care or organ donation Imputation of missing data using worst-best and best–worst scenarios (further details provided in section “Handling missing data”) Handling missing data Given that this trial will be using routinely collected data from the ANZICS APD for the primary and secondary outcomes, we anticipate minimal missing data. However, a sensitivity analysis assessing the impact of missing primary outcome data for the main analysis will be performed and involve imputing outcomes under “worst-best” and “best–worst” case scenarios  (see below). Primary outcome In the “worst-best” scenario, a “worst” outcome event (e.g., 0 days free of the index hospital and alive) will be assigned to all patients missing the outcome in one treatment group, and a “best” outcome event (e.g., 90 days free of the index hospital and alive) will be assigned to all patients missing the outcome in the other treatment group. The “best–worst” scenario is the exact opposite assignment of outcomes. If substantively different conclusions do not arise from these two analyses, then no further missing data assessments will be performed for that outcome. If a substantively different conclusion does arise, then a more refined analysis will employ a complete case analysis adjusting for baseline covariates predictive of missingness of the specific outcome. These analyses use data from all patients who have complete outcome data and are valid under the “covariate missing at random assumption” that missingness depends on the baseline covariates only and not on the value of the missing outcome itself or of other outcomes . If more than 5% of the data for a primary outcome is missing , and when one outcome is missing for a patient but other outcomes are present, further analyses will use multiple imputation methods that take into account the outcomes that are available and the clustered data structure . Secondary outcomes Secondary outcomes will be considered hypothesis generating and will only be analyzed on complete cases. No further analyses or investigation of missing data for secondary outcomes will be conducted. Current status of the trial The TARGET Protein trial commenced recruitment at four sites (in South Australia, New South Wales, and New Zealand) on 23 May 2022, with four sites in Victoria commencing on 23 August 2022. Each site participated in the trial for 12 months and recruitment ceased on 23 May 2023 and 23 August 2023 respectively. At the time of submission, data linkage was being performed by the Australian Institute of Health and Welfare . 

Four of eight ICUs were randomly allocated to the sequence augmented protein enteral formula alternating after each 3-month period with usual care enteral protein formula; with the other four ICUs following the opposite sequence. Further details regarding treatment allocation generation and randomization are provided in the published study protocol . In the overall 12-month trial duration, this resulted in four periods with alternate enteral nutrition formulae within each of the eight ICU clusters. Patients admitted to each ICU during each 3-month cluster-period received the same trial enteral formula until either the patient reached day 90, was discharged from ICU, or died. In the cluster double cross-over design, each cluster is randomized to receive each intervention at separate periods of time and individuals are included only once. The randomization of the clusters to the order that the intervention is received enables us to control for period effects. The inclusion of the crossover element increases the power of the study and reduces the required number of participants when compared to a parallel cluster design . 

Inclusion criteria All patients admitted to ICU are eligible if they are: ≥ 16 years of age Receiving/about to commence EN during their index admission to ICU or receiving/about to commence EN for the first time in ICU during subsequent admissions Exclusion criteria At the commencement of EN, the treating clinician considers the trial enteral formula to be contraindicated. The patient has received greater than 12 h of non-trial enteral formula. The patient has previously participated in the TARGET Protein trial. Patient flow through the trial will be summarized in a Consolidated Standards of Reporting Trials (CONSORT) diagram (Fig. ). We will report cluster sequence allocation, number of patients assessed for eligibility, number of patients who met trial inclusion and exclusion criteria, number of patients enrolled, number of patients withdrawn, and number of patients included in the primary outcome analysis. 

All patients admitted to ICU are eligible if they are: ≥ 16 years of age Receiving/about to commence EN during their index admission to ICU or receiving/about to commence EN for the first time in ICU during subsequent admissions 

At the commencement of EN, the treating clinician considers the trial enteral formula to be contraindicated. The patient has received greater than 12 h of non-trial enteral formula. The patient has previously participated in the TARGET Protein trial. Patient flow through the trial will be summarized in a Consolidated Standards of Reporting Trials (CONSORT) diagram (Fig. ). We will report cluster sequence allocation, number of patients assessed for eligibility, number of patients who met trial inclusion and exclusion criteria, number of patients enrolled, number of patients withdrawn, and number of patients included in the primary outcome analysis. 

During the intervention cluster periods (augmented dietary protein), all eligible patients received the enteral formula “Nutrison Protein Intense®” (Nutricia Australia Pty Limited) which contains 100 g protein per 1000 ml and 1260 kcal per 1000 ml. During the comparator cluster periods (usual dietary protein), all patients received the enteral formula “Nutrison Protein Plus®” (Nutricia Australia Pty Limited), which contains 63 g protein per 1000 ml and 1250 kcal per 1000 ml. 

Primary outcome The primary outcome is the number of days free of the index hospital and alive at day 90. This is a patient-centered outcome measure that captures both death and morbidity . This will be calculated as 90 days minus all days admitted to the index hospital after commencement of trial enteral formula, minus any days readmitted to the index hospital within 90 days. As patients may be readmitted for part of a day (e.g., for dialysis), a readmission will only be counted when the patient remains in hospital at midnight. Patients will be considered alive if they survive the index-hospital episode, and there is no evidence of death before day 90. In Australia, evidence of death after hospital discharge will be ascertained from the local health record by site investigators and the Australian National Death Index. Similarly, at the New Zealand site, The Australian New Zealand Intensive Care Society Adult Patient Database (ANZICS APD) is linked to the New Zealand national death registry to record death after hospital discharge. Patients who die during the 90-day post-trial recruitment period will be assigned a days free of the index hospital value of 0. Examples of this calculation are provided below: A patient who survived and is discharged from hospital on day 30 after commencement of trial enteral formula is assigned a value of 60. A patient who survived and is discharged from hospital on day 92 after commencement of trial enteral formula would be censored at day 90 and assigned a value of 0. A patient who dies on any day up to and including day 90 is assigned a value of 0. Secondary outcomes 1. Days free of the index hospital and alive at day 90 in survivors. Derived as described for the primary outcome, but the analysis will only be conducted in those patients who survive to day 90 after commencement of trial enteral formula. 2. Alive at day 90. Patients will be considered alive if they are alive at discharge from the index hospital and there is no evidence of death before day 90, either from the local health record or via linkage to the relevant National Death Index. Health service records will be used to provide evidence of patient status, including if the patient was discharged alive from hospital but known to have a health event prior to day 90. Health events include rehabilitation facility admission, outpatient appointment attended, pathology test completed, other medical investigation, or day admission, e.g., dialysis and chemotherapy. 3. Duration of invasive ventilation (hours). Defined as the total hours of invasive mechanical ventilation during the index hospital admission, this includes any re-ventilation periods in ICU following initial weaning during the index hospital admission but does not include ventilation in operating rooms for a procedure. Ventilation during subsequent hospital admissions will not count towards this outcome. Only invasive ventilation will count, and non-invasive ventilation, continuous positive end-expiratory pressure or high-flow oxygen will not contribute to time. The duration of invasive ventilation reported in the Adult Patient Database will be used within this pragmatic registry-based trial noting it will include any periods of time prior to the commencement of study EN. However, the time from ICU admission to commencement of trial enteral nutrition is anticipated to be very short, based on the TARGET trial with similar inclusion criteria . 4. Duration of admission to ICU (days). Defined as the time after commencement of trial enteral formula until ICU discharge for the index ICU admission, plus any days that the patient is readmitted prior to hospital discharge, but does not include subsequent hospital admissions. 5. Duration of admission to hospital (days). Defined as the time after commencement of trial enteral formula until hospital discharge for index hospital admission (i.e., it excludes any time spent in hospital if readmitted, which will be counted for the primary outcome). 6. Incidence of tracheostomy insertion. Defined as the proportion of participants requiring new tracheostomy insertion during their study ICU admission. 7. Incidence of new renal replacement therapy. Defined as the proportion of participants commencing new renal replacement therapy during their study ICU admission, subsequent to commencement of trial nutrition. 8. Discharge destination. Discharge destinations are collected in the Adult Patient Database Case Report Form (CRF) as home, other hospital ICU, other acute hospital, rehabilitation, long-term care, and other (a free text field) . 

The primary outcome is the number of days free of the index hospital and alive at day 90. This is a patient-centered outcome measure that captures both death and morbidity . This will be calculated as 90 days minus all days admitted to the index hospital after commencement of trial enteral formula, minus any days readmitted to the index hospital within 90 days. As patients may be readmitted for part of a day (e.g., for dialysis), a readmission will only be counted when the patient remains in hospital at midnight. Patients will be considered alive if they survive the index-hospital episode, and there is no evidence of death before day 90. In Australia, evidence of death after hospital discharge will be ascertained from the local health record by site investigators and the Australian National Death Index. Similarly, at the New Zealand site, The Australian New Zealand Intensive Care Society Adult Patient Database (ANZICS APD) is linked to the New Zealand national death registry to record death after hospital discharge. Patients who die during the 90-day post-trial recruitment period will be assigned a days free of the index hospital value of 0. Examples of this calculation are provided below: A patient who survived and is discharged from hospital on day 30 after commencement of trial enteral formula is assigned a value of 60. A patient who survived and is discharged from hospital on day 92 after commencement of trial enteral formula would be censored at day 90 and assigned a value of 0. A patient who dies on any day up to and including day 90 is assigned a value of 0. 

1. Days free of the index hospital and alive at day 90 in survivors. Derived as described for the primary outcome, but the analysis will only be conducted in those patients who survive to day 90 after commencement of trial enteral formula. 2. Alive at day 90. Patients will be considered alive if they are alive at discharge from the index hospital and there is no evidence of death before day 90, either from the local health record or via linkage to the relevant National Death Index. Health service records will be used to provide evidence of patient status, including if the patient was discharged alive from hospital but known to have a health event prior to day 90. Health events include rehabilitation facility admission, outpatient appointment attended, pathology test completed, other medical investigation, or day admission, e.g., dialysis and chemotherapy. 3. Duration of invasive ventilation (hours). Defined as the total hours of invasive mechanical ventilation during the index hospital admission, this includes any re-ventilation periods in ICU following initial weaning during the index hospital admission but does not include ventilation in operating rooms for a procedure. Ventilation during subsequent hospital admissions will not count towards this outcome. Only invasive ventilation will count, and non-invasive ventilation, continuous positive end-expiratory pressure or high-flow oxygen will not contribute to time. The duration of invasive ventilation reported in the Adult Patient Database will be used within this pragmatic registry-based trial noting it will include any periods of time prior to the commencement of study EN. However, the time from ICU admission to commencement of trial enteral nutrition is anticipated to be very short, based on the TARGET trial with similar inclusion criteria . 4. Duration of admission to ICU (days). Defined as the time after commencement of trial enteral formula until ICU discharge for the index ICU admission, plus any days that the patient is readmitted prior to hospital discharge, but does not include subsequent hospital admissions. 5. Duration of admission to hospital (days). Defined as the time after commencement of trial enteral formula until hospital discharge for index hospital admission (i.e., it excludes any time spent in hospital if readmitted, which will be counted for the primary outcome). 6. Incidence of tracheostomy insertion. Defined as the proportion of participants requiring new tracheostomy insertion during their study ICU admission. 7. Incidence of new renal replacement therapy. Defined as the proportion of participants commencing new renal replacement therapy during their study ICU admission, subsequent to commencement of trial nutrition. 8. Discharge destination. Discharge destinations are collected in the Adult Patient Database Case Report Form (CRF) as home, other hospital ICU, other acute hospital, rehabilitation, long-term care, and other (a free text field) . 

Data will be sourced from The Australian New Zealand Intensive Care Society Adult Patient Database (ANZICS APD) . In Australia, death after hospital discharge will be ascertained using  the local health record where outcome at day 90 is known and  via data linkage with the Australian National Death Index. At the New Zealand site, the Adult Patient Database is linked to the New Zealand national death registry to record death after hospital discharge. Study day 1 commences on the day TARGET Protein trial enteral formula is commenced and concludes at the end of that calendar day (2400 h). 

The sample size was calculated using The Shiny Cluster Randomized Trial Calculator and using data from TARGET Calories . In TARGET Calories, the standard deviation for the primary outcome being used in TARGET Protein (days free of the index hospital and alive at day 90) was 11.3 days. Assuming an exchangeable correlation structure, a coefficient of variation for cluster size of 0.5—[based on 8 ICUs (i.e., 8 clusters) each enrolling a mean of 160 patients per 3-month period (95% confidence interval, 80 to 240 patients per cluster per period)]—and the default settings for cluster trials of an intracluster correlation coefficients of 0.02 (with a lower extreme of 0.001 and upper extreme of 0.1), 60 to 65 patients per period per cluster would provide 80% power, and ~ 80 patients per cluster per period would provide 90% power to find a mean difference of 1 day for the primary outcome. Assuming a normal distribution, a sample size of 2560 from the 8 clusters would provide 90% power to detect a mean difference of 1 day free of the index hospital and alive at day 90. However, the primary outcome (days free of the index hospital and alive at day 90) is unlikely to be normally distributed and parametric calculations are optimistic. For this reason, the goal sample size was inflated by 15% to allow for the likely skewed distribution of data, such that ~ 3000 patients from the 8 clusters should provide 90% power . After the trial was completed, it was noted that the sample size estimates specified eight clusters based on eight ICUs being included in the trial. However, the design utilizes four clusters in each of the two treatment sequences. With only four clusters and cluster period size of ≥ 60 patients, there was still > 80% power to detect a 2-day difference in the number of days free of the index hospital and alive at day 90 under a range of plausible assumptions. 

The statistical analysis will be completed by biostatisticians from the Methods and Implementation Support for Clinical and Health (MISCH) Research Hub (University of Melbourne, Australia). The analyses will be independently checked by a senior biostatistician at MISCH (AK) and any discrepancies between the two analyses will be discussed and resolved by consensus. All analyses will account for the cluster-randomized cross-over design to ensure correct estimation of the 95% confidence intervals (further details provided for the primary and secondary outcomes below). For the primary outcome, estimates, two-sided 95% CIs, and p values will be presented. For the secondary outcomes, we will present effect sizes and two-sided 95% CIs only, because the trial was not powered for the secondary outcomes and, thus, have no planned multiplicity adjustment for the secondary outcomes. All statistical tests will be performed in Stata/SE version 18.0 (Stata Corporation, College Station, TX, USA), or R version 4.3.1 or later. 

Because of the cluster-randomized cross-over design of this trial, the intention-to-treat principle applies at both the cluster level and the participant level. At the cluster level, the intention-to-treat population includes all randomized ICUs that recruited subjects, regardless of the degree of adherence to the study interventions. At the participant level, the intention-to-treat population means that all patients will be analyzed according to the randomized treatment of their ICU at the time of their index ICU admission, regardless of treatment actually received. 

Descriptive statistics at cluster level will be presented by allocated treatment sequence, and patient characteristics will be summarized by allocated treatment sequence and observation period. Descriptive statistics of variables concerning trial enteral formula delivery (e.g., duration of trial nutrition (hours), mean volume of trial enteral formula delivery (ml/day), calories delivered (kcal/day), and protein delivered (g/day)) will be summarized by allocated treatment sequence. The median (25th and 75th percentiles) of protein (g/day) and calories (kcal/day) from trial nutrition on days 1–5, 10, 20, and 30 of the study will be presented visually by treatment group. The statistical methods for the primary and secondary outcomes are described below. Details regarding the main analysis plan, sensitivity analyses, and subgroup analyses are located in Table . Primary outcome: number of days free of the index hospital and alive at day 90 Main analysis Comparisons of treatment groups will use individual patient-level data and a quantile mixed effects model will be fitted to compare the median (0.5 quantile) response of the treatment groups. The quantile mixed effects model will include treatment group (augmented protein versus usual care), period (1, 2, 3, and 4), and the stratification variable used in randomization, delayed start (“group 1—commencing May 2022” and “group 2—commencing August 2022”), as fixed effects. Cluster (ICU) will be included as a random effect and assumed to be normally distributed with mean zero and variance component [12pt]{minimal}
				
				 
				 
				 
				
				
				
				
				$${ }_{C}^{2}$$ σ C 2 . The effect of treatment comparisons will be presented as a difference in medians (95% confidence interval (CI)); the 95% CI will be calculated using the block-bootstrap method . If model convergence issues occur, then the analysis will be performed on the scaled primary outcome (zero mean and unit variance). The treatment effect will then compare the median of the scaled primary outcome between treatment groups. Secondary analyses A Bayesian quantile mixed model for continuous outcomes will be used to model the median (0.5 quantile) response . The same fixed and random effects described in the main analysis will be included in the Bayesian quantile mixed model. The effect of treatment comparisons will be presented as the posterior mean of the regression coefficient for treatment (difference in medians) and 95% credible interval. Bayesian quantile mixed modeling will be performed using brms package  in R. The default hyperprior distributions in the brms packaged will be specified for the fixed effects (improper flat hyperprior) and standard deviation of the cluster random effect (weakly informative half student-t hyperprior). The following analysis will be performed to align with the sample size calculation and in view of the recent findings from Granholm et al.  that linear models adequately estimated group means despite not fitting the data well enough to mimic the “days alive without life support” calculation. Treatment group comparisons will use individual patient-level data and be estimated using a linear mixed effects model. The linear mixed effects model will include treatment group (augmented protein versus usual care), period (1, 2, 3, and 4), and the stratification variable used in randomizsation, delayed start (“group 1—commencing May 2022” and “group 2—commencing August 2022”), as fixed effects. Cluster (ICU) will be included as a random effect and assumed to be normally distributed with mean zero and variance component [12pt]{minimal}
				
				 
				 
				 
				
				
				
				
				$${ }_{C}^{2}$$ σ C 2 . The effect of treatment comparisons will be presented as a mean difference (95% CI) from the linear mixed effects model. Secondary outcomes Days free of the index hospital at day 90 in survivors This will follow the same approach as for the primary outcome. Alive at day 90, incidence of tracheostomy insertion, and incidence of renal replacement therapy Comparison of randomized treatment groups will use individual patient-level data and generalized estimating equations (GEE) with a logarithmic link function, binomial distribution, an exchangeable working correlation matrix, and Fay and Graubard’s small sample bias corrected standard errors using the ICU as the clustering unit . Indicator variables for treatment and period will be included in the marginal model, as well as the stratification variable used in randomization, i.e., delayed start. The effect of treatment comparisons will be presented as risk ratio and 95% CI from the GEE analysis . Should the risk ratio model fail to converge, GEE with Poisson distribution, a logarithmic link, exchangeable working correlation matrix, and Fay and Graubard’s small sample bias corrected standard errors will be employed . Duration of invasive ventilation, admission to ICU, and admission to hospital Time to discharge alive from index ICU and index hospital admission, and liberation from invasive mechanical ventilation will be summarized with medians and interquartile ranges obtained from cumulative incidence functions regarding mortality as a competing risk . Treatment group comparisons will use Cox regression with covariate adjustment for period and delayed start, with stratification by ICU, and robust standard errors clustered at ICU level (for any residual within-ICU correlation) to estimate cause-specific hazard ratios and confidence intervals, with patients dying prior to discharge (or extubation) censored at their time of death . Assessment of the proportionality of hazards assumption in these models will be made using Schoenfeld residuals, with resultant covariate stratification or modeling of time-dependent treatment effects, where necessary. For the duration of invasive mechanical ventilation outcome, if the recorded duration of mechanical ventilation for a patient is within 48 h of their duration of hospital stay resulting in death, then it will be assumed that such patients were extubated at that time with palliative intent and, hence, these patients’ data will be censored at time of tracheal extubation in the analyses. Discharge destination A descriptive analysis will be performed, where the frequency of each discharge destination will be presented by allocated treatment sequence and observation period. Subgroup analyses Planned subgroup analyses will assess heterogeneity of the treatment effect for the primary outcome across the following baseline variables: mechanical ventilation, age groups (70 years and older vs younger than 70), body mass index (categorized as 35 kg/m 2 and greater vs less than 35 kg/m 2 ), and those with acute kidney injury (AKI; yes vs no), defined as receiving new renal replacement therapy between hospital admission and commencement of trial enteral formula on ICU admission. Subgroup analyses will include treatment × subgroup interaction terms in respective regression models. We will present subgroup specific estimates of the treatment effect (95% CI), which will be derived from the treatment main effect estimate and interaction term estimate and the p value from a likelihood ratio test of an interaction between treatment and the subgroup variable. Sensitivity analyses The following sensitivity analyses of the primary outcome and main analysis will be performed: Exclusion of patients if they are known to have received non-trial enteral formula Exclusion of patients admitted for palliative care or organ donation Imputation of missing data using worst-best and best–worst scenarios (further details provided in section “Handling missing data”) Handling missing data Given that this trial will be using routinely collected data from the ANZICS APD for the primary and secondary outcomes, we anticipate minimal missing data. However, a sensitivity analysis assessing the impact of missing primary outcome data for the main analysis will be performed and involve imputing outcomes under “worst-best” and “best–worst” case scenarios  (see below). Primary outcome In the “worst-best” scenario, a “worst” outcome event (e.g., 0 days free of the index hospital and alive) will be assigned to all patients missing the outcome in one treatment group, and a “best” outcome event (e.g., 90 days free of the index hospital and alive) will be assigned to all patients missing the outcome in the other treatment group. The “best–worst” scenario is the exact opposite assignment of outcomes. If substantively different conclusions do not arise from these two analyses, then no further missing data assessments will be performed for that outcome. If a substantively different conclusion does arise, then a more refined analysis will employ a complete case analysis adjusting for baseline covariates predictive of missingness of the specific outcome. These analyses use data from all patients who have complete outcome data and are valid under the “covariate missing at random assumption” that missingness depends on the baseline covariates only and not on the value of the missing outcome itself or of other outcomes . If more than 5% of the data for a primary outcome is missing , and when one outcome is missing for a patient but other outcomes are present, further analyses will use multiple imputation methods that take into account the outcomes that are available and the clustered data structure . Secondary outcomes Secondary outcomes will be considered hypothesis generating and will only be analyzed on complete cases. No further analyses or investigation of missing data for secondary outcomes will be conducted. Current status of the trial The TARGET Protein trial commenced recruitment at four sites (in South Australia, New South Wales, and New Zealand) on 23 May 2022, with four sites in Victoria commencing on 23 August 2022. Each site participated in the trial for 12 months and recruitment ceased on 23 May 2023 and 23 August 2023 respectively. At the time of submission, data linkage was being performed by the Australian Institute of Health and Welfare . 

Main analysis Comparisons of treatment groups will use individual patient-level data and a quantile mixed effects model will be fitted to compare the median (0.5 quantile) response of the treatment groups. The quantile mixed effects model will include treatment group (augmented protein versus usual care), period (1, 2, 3, and 4), and the stratification variable used in randomization, delayed start (“group 1—commencing May 2022” and “group 2—commencing August 2022”), as fixed effects. Cluster (ICU) will be included as a random effect and assumed to be normally distributed with mean zero and variance component [12pt]{minimal}
				
				 
				 
				 
				
				
				
				
				$${ }_{C}^{2}$$ σ C 2 . The effect of treatment comparisons will be presented as a difference in medians (95% confidence interval (CI)); the 95% CI will be calculated using the block-bootstrap method . If model convergence issues occur, then the analysis will be performed on the scaled primary outcome (zero mean and unit variance). The treatment effect will then compare the median of the scaled primary outcome between treatment groups. Secondary analyses A Bayesian quantile mixed model for continuous outcomes will be used to model the median (0.5 quantile) response . The same fixed and random effects described in the main analysis will be included in the Bayesian quantile mixed model. The effect of treatment comparisons will be presented as the posterior mean of the regression coefficient for treatment (difference in medians) and 95% credible interval. Bayesian quantile mixed modeling will be performed using brms package  in R. The default hyperprior distributions in the brms packaged will be specified for the fixed effects (improper flat hyperprior) and standard deviation of the cluster random effect (weakly informative half student-t hyperprior). The following analysis will be performed to align with the sample size calculation and in view of the recent findings from Granholm et al.  that linear models adequately estimated group means despite not fitting the data well enough to mimic the “days alive without life support” calculation. Treatment group comparisons will use individual patient-level data and be estimated using a linear mixed effects model. The linear mixed effects model will include treatment group (augmented protein versus usual care), period (1, 2, 3, and 4), and the stratification variable used in randomizsation, delayed start (“group 1—commencing May 2022” and “group 2—commencing August 2022”), as fixed effects. Cluster (ICU) will be included as a random effect and assumed to be normally distributed with mean zero and variance component [12pt]{minimal}
				
				 
				 
				 
				
				
				
				
				$${ }_{C}^{2}$$ σ C 2 . The effect of treatment comparisons will be presented as a mean difference (95% CI) from the linear mixed effects model. Secondary outcomes Days free of the index hospital at day 90 in survivors This will follow the same approach as for the primary outcome. Alive at day 90, incidence of tracheostomy insertion, and incidence of renal replacement therapy Comparison of randomized treatment groups will use individual patient-level data and generalized estimating equations (GEE) with a logarithmic link function, binomial distribution, an exchangeable working correlation matrix, and Fay and Graubard’s small sample bias corrected standard errors using the ICU as the clustering unit . Indicator variables for treatment and period will be included in the marginal model, as well as the stratification variable used in randomization, i.e., delayed start. The effect of treatment comparisons will be presented as risk ratio and 95% CI from the GEE analysis . Should the risk ratio model fail to converge, GEE with Poisson distribution, a logarithmic link, exchangeable working correlation matrix, and Fay and Graubard’s small sample bias corrected standard errors will be employed . Duration of invasive ventilation, admission to ICU, and admission to hospital Time to discharge alive from index ICU and index hospital admission, and liberation from invasive mechanical ventilation will be summarized with medians and interquartile ranges obtained from cumulative incidence functions regarding mortality as a competing risk . Treatment group comparisons will use Cox regression with covariate adjustment for period and delayed start, with stratification by ICU, and robust standard errors clustered at ICU level (for any residual within-ICU correlation) to estimate cause-specific hazard ratios and confidence intervals, with patients dying prior to discharge (or extubation) censored at their time of death . Assessment of the proportionality of hazards assumption in these models will be made using Schoenfeld residuals, with resultant covariate stratification or modeling of time-dependent treatment effects, where necessary. For the duration of invasive mechanical ventilation outcome, if the recorded duration of mechanical ventilation for a patient is within 48 h of their duration of hospital stay resulting in death, then it will be assumed that such patients were extubated at that time with palliative intent and, hence, these patients’ data will be censored at time of tracheal extubation in the analyses. Discharge destination A descriptive analysis will be performed, where the frequency of each discharge destination will be presented by allocated treatment sequence and observation period. 

Comparisons of treatment groups will use individual patient-level data and a quantile mixed effects model will be fitted to compare the median (0.5 quantile) response of the treatment groups. The quantile mixed effects model will include treatment group (augmented protein versus usual care), period (1, 2, 3, and 4), and the stratification variable used in randomization, delayed start (“group 1—commencing May 2022” and “group 2—commencing August 2022”), as fixed effects. Cluster (ICU) will be included as a random effect and assumed to be normally distributed with mean zero and variance component [12pt]{minimal}
				
				 
				 
				 
				
				
				
				
				$${ }_{C}^{2}$$ σ C 2 . The effect of treatment comparisons will be presented as a difference in medians (95% confidence interval (CI)); the 95% CI will be calculated using the block-bootstrap method . If model convergence issues occur, then the analysis will be performed on the scaled primary outcome (zero mean and unit variance). The treatment effect will then compare the median of the scaled primary outcome between treatment groups. 

A Bayesian quantile mixed model for continuous outcomes will be used to model the median (0.5 quantile) response . The same fixed and random effects described in the main analysis will be included in the Bayesian quantile mixed model. The effect of treatment comparisons will be presented as the posterior mean of the regression coefficient for treatment (difference in medians) and 95% credible interval. Bayesian quantile mixed modeling will be performed using brms package  in R. The default hyperprior distributions in the brms packaged will be specified for the fixed effects (improper flat hyperprior) and standard deviation of the cluster random effect (weakly informative half student-t hyperprior). The following analysis will be performed to align with the sample size calculation and in view of the recent findings from Granholm et al.  that linear models adequately estimated group means despite not fitting the data well enough to mimic the “days alive without life support” calculation. Treatment group comparisons will use individual patient-level data and be estimated using a linear mixed effects model. The linear mixed effects model will include treatment group (augmented protein versus usual care), period (1, 2, 3, and 4), and the stratification variable used in randomizsation, delayed start (“group 1—commencing May 2022” and “group 2—commencing August 2022”), as fixed effects. Cluster (ICU) will be included as a random effect and assumed to be normally distributed with mean zero and variance component [12pt]{minimal}
				
				 
				 
				 
				
				
				
				
				$${ }_{C}^{2}$$ σ C 2 . The effect of treatment comparisons will be presented as a mean difference (95% CI) from the linear mixed effects model. Secondary outcomes 

This will follow the same approach as for the primary outcome. 

Comparison of randomized treatment groups will use individual patient-level data and generalized estimating equations (GEE) with a logarithmic link function, binomial distribution, an exchangeable working correlation matrix, and Fay and Graubard’s small sample bias corrected standard errors using the ICU as the clustering unit . Indicator variables for treatment and period will be included in the marginal model, as well as the stratification variable used in randomization, i.e., delayed start. The effect of treatment comparisons will be presented as risk ratio and 95% CI from the GEE analysis . Should the risk ratio model fail to converge, GEE with Poisson distribution, a logarithmic link, exchangeable working correlation matrix, and Fay and Graubard’s small sample bias corrected standard errors will be employed . 

Time to discharge alive from index ICU and index hospital admission, and liberation from invasive mechanical ventilation will be summarized with medians and interquartile ranges obtained from cumulative incidence functions regarding mortality as a competing risk . Treatment group comparisons will use Cox regression with covariate adjustment for period and delayed start, with stratification by ICU, and robust standard errors clustered at ICU level (for any residual within-ICU correlation) to estimate cause-specific hazard ratios and confidence intervals, with patients dying prior to discharge (or extubation) censored at their time of death . Assessment of the proportionality of hazards assumption in these models will be made using Schoenfeld residuals, with resultant covariate stratification or modeling of time-dependent treatment effects, where necessary. For the duration of invasive mechanical ventilation outcome, if the recorded duration of mechanical ventilation for a patient is within 48 h of their duration of hospital stay resulting in death, then it will be assumed that such patients were extubated at that time with palliative intent and, hence, these patients’ data will be censored at time of tracheal extubation in the analyses. 

A descriptive analysis will be performed, where the frequency of each discharge destination will be presented by allocated treatment sequence and observation period. 

Planned subgroup analyses will assess heterogeneity of the treatment effect for the primary outcome across the following baseline variables: mechanical ventilation, age groups (70 years and older vs younger than 70), body mass index (categorized as 35 kg/m 2 and greater vs less than 35 kg/m 2 ), and those with acute kidney injury (AKI; yes vs no), defined as receiving new renal replacement therapy between hospital admission and commencement of trial enteral formula on ICU admission. Subgroup analyses will include treatment × subgroup interaction terms in respective regression models. We will present subgroup specific estimates of the treatment effect (95% CI), which will be derived from the treatment main effect estimate and interaction term estimate and the p value from a likelihood ratio test of an interaction between treatment and the subgroup variable. 

The following sensitivity analyses of the primary outcome and main analysis will be performed: Exclusion of patients if they are known to have received non-trial enteral formula Exclusion of patients admitted for palliative care or organ donation Imputation of missing data using worst-best and best–worst scenarios (further details provided in section “Handling missing data”) 

Given that this trial will be using routinely collected data from the ANZICS APD for the primary and secondary outcomes, we anticipate minimal missing data. However, a sensitivity analysis assessing the impact of missing primary outcome data for the main analysis will be performed and involve imputing outcomes under “worst-best” and “best–worst” case scenarios  (see below). 

In the “worst-best” scenario, a “worst” outcome event (e.g., 0 days free of the index hospital and alive) will be assigned to all patients missing the outcome in one treatment group, and a “best” outcome event (e.g., 90 days free of the index hospital and alive) will be assigned to all patients missing the outcome in the other treatment group. The “best–worst” scenario is the exact opposite assignment of outcomes. If substantively different conclusions do not arise from these two analyses, then no further missing data assessments will be performed for that outcome. If a substantively different conclusion does arise, then a more refined analysis will employ a complete case analysis adjusting for baseline covariates predictive of missingness of the specific outcome. These analyses use data from all patients who have complete outcome data and are valid under the “covariate missing at random assumption” that missingness depends on the baseline covariates only and not on the value of the missing outcome itself or of other outcomes . If more than 5% of the data for a primary outcome is missing , and when one outcome is missing for a patient but other outcomes are present, further analyses will use multiple imputation methods that take into account the outcomes that are available and the clustered data structure . 

Secondary outcomes will be considered hypothesis generating and will only be analyzed on complete cases. No further analyses or investigation of missing data for secondary outcomes will be conducted. 

The TARGET Protein trial commenced recruitment at four sites (in South Australia, New South Wales, and New Zealand) on 23 May 2022, with four sites in Victoria commencing on 23 August 2022. Each site participated in the trial for 12 months and recruitment ceased on 23 May 2023 and 23 August 2023 respectively. At the time of submission, data linkage was being performed by the Australian Institute of Health and Welfare . 

