Regarding the analysis of RCT data there is a debate going on whether an adjustment for the baseline value of the outcome variable should be made. When an adjustment is made, there is a lot of misunderstanding regarding the way this should be done. Therefore, the aims of this educational paper are: 1) to explain different methods used to estimate treatment effects in RCTs, 2) to illustrate the different methods with a real life example and 3) to give an advise on how to analyse RCT data. Longitudinal analysis of covariance, repeated measures analysis in which also the baseline value is used as outcome and the analysis of changes were theoretically explained and applied to an example dataset investigating a systolic blood pressure lowering treatment. It was shown that differences at baseline should be taken into account and that regular repeated measures analysis and regular analysis of changes did not adjust for the baseline differences between the groups and therefore lead to biased estimates of the treatment effect. In the real life example, due to the differences at baseline between the treatment and control group, the different methods lead to different estimates of the treatment effect. Regarding the analysis of RCT data, it is advised to use longitudinal analysis of covariance or a repeated measures analysis without the treatment variable, but with the interaction between treatment and time in the model.

Because missing data are present in almost every study, it is important to handle missing data properly. First of all, the missing data mechanism should be considered. Missing data can be either completely at random (MCAR), at random (MAR), or not at random (MNAR). When missing data are MCAR, a complete case analysis can be valid. Also when missing data are MAR, in some situations a complete case analysis leads to valid results. However, in most situations, missing data imputation should be used. Regarding imputation methods, it is highly advised to use multiple imputations because multiple imputations lead to valid estimates including the uncertainty about the imputed values. When missing data are MNAR, also multiple imputations do not lead to valid results. A complication hereby is that it not possible to distinguish whether missing data are MAR or MNAR. Finally, it should be realized that preventing to have missing data is always better than the treatment of missing data.

According to the CONSORT statement, significance testing of baseline differences in randomized controlled trials should not be performed. In fact, this practice has been discouraged by numerous authors throughout the last forty years. During that time span, reporting of baseline differences has substantially decreased in the leading general medical journals. Our own experience in the field of nutrition behavior research however, is that co-authors, reviewers and even editors are still very persistent in their demand for these tests. The aim of this paper is therefore to negate this demand by providing clear evidence as to why testing for baseline differences between intervention groups statistically is superfluous and why such results should not be published. Testing for baseline differences is often propagated because of the belief that it shows whether randomization was successful and it identifies real or important differences between treatment arms that should be accounted for in the statistical analyses. Especially the latter argument is flawed, because it ignores the fact that the prognostic strength of a variable is also important when the interest is in adjustment for confounding. In addition, including prognostic variables as covariates can increase the precision of the effect estimate. This means that choosing covariates based on significance tests for baseline differences might lead to omissions of important covariates and, less importantly, to inclusion of irrelevant covariates in the analysis. We used data from four supermarket trials on the effects of pricing strategies on fruit and vegetables purchases, to show that results from fully adjusted analyses sometimes do appreciably differ from results from analyses adjusted for significant baseline differences only. We propose to adjust for known or anticipated important prognostic variables. These could or should be pre-specified in trial protocols. Subsequently, authors should report results from the fully adjusted as well as crude analyses, especially for dichotomous and time to event data. Based on our arguments, which were illustrated by our findings, we propose that journals in and outside the field of nutrition behavior actively adopt the CONSORT 2010 statement on this topic by not publishing significance tests for baseline differences anymore.

Muscle quantity at intensive care unit (ICU) admission has been independently associated with mortality. In addition to quantity, muscle quality may be important for survival. Muscle quality is influenced by fatty infiltration or myosteatosis, which can be assessed on computed tomography (CT) scans by analysing skeletal muscle density (SMD) and the amount of intermuscular adipose tissue (IMAT). We investigated whether CT-derived low skeletal muscle quality at ICU admission is independently associated with 6-month mortality and other clinical outcomes. This retrospective study included 491 mechanically ventilated critically ill adult patients with a CT scan of the abdomen made 1 day before to 4 days after ICU admission. Cox regression analysis was used to determine the association between SMD or IMAT and 6-month mortality, with adjustments for Acute Physiological, Age, and Chronic Health Evaluation (APACHE) II score, body mass index (BMI), and skeletal muscle area. Logistic and linear regression analyses were used for other clinical outcomes. Mean APACHE II score was 24 ± 8 and 6-month mortality was 35.6%. Non-survivors had a lower SMD (25.1 vs. 31.4 Hounsfield Units (HU); p < 0.001), and more IMAT (17.1 vs. 13.3 cm2; p = 0.004). Higher SMD was associated with a lower 6-month mortality (hazard ratio (HR) per 10 HU, 0.640; 95% confidence interval (CI), 0.552–0.742; p < 0.001), and also after correction for APACHE II score, BMI, and skeletal muscle area (HR, 0.774; 95% CI, 0.643–0.931; p = 0.006). Higher IMAT was not significantly associated with higher 6-month mortality after adjustment for confounders. A 10 HU increase in SMD was associated with a 14% shorter hospital length of stay. Low skeletal muscle quality at ICU admission, as assessed by CT-derived skeletal muscle density, is independently associated with higher 6-month mortality in mechanically ventilated patients. Thus, muscle quality as well as muscle quantity are prognostic factors in the ICU. Retrospectively registered (initial release on 06/23/2016) at ClinicalTrials.gov: NCT02817646 .