612 research outputs found
Endpoints In Intensive Care Unit Based Randomized Clinical Trials
With few exceptions, intensive care unit (ICU)-based randomized clinical trials (RCTs) have failed to demonstrate hypothesized treatment effects. Undoubtedly, some of these failures are attributable to interventions that truly do not provide hoped-for benefits. However, this dissertation pursues the thesis that many null findings represent “false negatives” that are due not to ineffective therapies but to flawed study designs or analytic approaches. We examine the design and statistical methods traditionally employed in ICU-based RCTs, and their potential impacts on the efficient measurement and interpretation of treatment effects. Paper one presents a systematic review of 146 contemporary ICU-based RCTs in which we find that most trials were underpowered to detect small but potentially important mortality differences between treatment arms. We also find that the majority of RCTs (73%) specified primary outcomes other than mortality, that trials employing nonmortal primary outcomes more frequently identified significant treatment effects, and that both mortal and nonmortal endpoints were heterogeneously defined, measured and analyzed across RCTs. Thus, papers two and three focus on nonmortal endpoints, using ICU length of stay (LOS) as a case study to evaluate how best to measure and analyze duration-based nonmortal endpoints. In paper two, we conduct a statistical simulation study, demonstrating that nonmortal endpoints are interlinked with and confounded by mortality, and that the manner in which investigators choose to account for deaths in LOS analyses may influence their conclusions. In paper three, we examine another potential source of error in LOS analyses, namely the measurement error attributable to the additional ICU time that patients commonly accrue after they are clinically ready for ICU discharge. Using simulated data informed by our own ICU-based RCT, we demonstrate that this “immutable time” (which cannot plausibly be altered by the interventions under study) combines with clinically necessary ICU time to produce overall LOS distributions that may either mask true treatment effects or suggest false treatment effects. Our work provides evidence of the potential benefits and pitfalls when employing nonmortal outcomes in ICU-based RCTs, and also identifies a clear need for standardized methods for defining and analyzing such outcomes
Understanding the Impact of Critical Illness on Families: A Call for Standardization of Outcomes and Longitudinal Research.
No abstract available
Temporal Trends in Incidence, Sepsis-Related Mortality, and Hospital-Based Acute Care After Sepsis.
OBJECTIVES: A growing number of patients survive sepsis hospitalizations each year and are at high risk for readmission. However, little is known about temporal trends in hospital-based acute care (emergency department treat-and-release visits and hospital readmission) after sepsis. Our primary objective was to measure temporal trends in sepsis survivorship and hospital-based acute care use in sepsis survivors. In addition, because readmissions after pneumonia are subject to penalty under the national readmission reduction program, we examined whether readmission rates declined after sepsis hospitalizations related to pneumonia.
DESIGN AND SETTING: Retrospective, observational cohort study conducted within an academic healthcare system from 2010 to 2015.
PATIENTS: We used three validated, claims-based approaches to identify 17,256 sepsis or severe sepsis hospitalizations to examine trends in hospital-based acute care after sepsis.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: From 2010 to 2015, sepsis as a proportion of medical and surgical admissions increased from 3.9% to 9.4%, whereas in-hospital mortality rate for sepsis hospitalizations declined from 24.1% to 14.8%. As a result, the proportion of medical and surgical discharges at-risk for hospital readmission after sepsis increased from 2.7% to 7.8%. Over 6 years, 30-day hospital readmission rates declined modestly, from 26.4% in 2010 to 23.1% in 2015, driven largely by a decline in readmission rates among survivors of nonsevere sepsis, and nonpneumonia sepsis specifically, as the readmission rate of severe sepsis survivors was stable. The modest decline in 30-day readmission rates was offset by an increase in emergency department treat-and-release visits, from 2.8% in 2010 to a peak of 5.4% in 2014.
CONCLUSIONS: Owing to increasing incidence and declining mortality, the number of sepsis survivors at risk for hospital readmission rose significantly between 2010 and 2015. The 30-day hospital readmission rates for sepsis declined modestly but were offset by a rise in emergency department treat-and-release visits
Eliminating ambiguous treatment effects using estimands
Most reported treatment effects in medical research studies are ambiguously defined, which can lead to misinterpretation of study results. This is because most studies do not attempt to describe what the treatment effect represents, and instead require readers to deduce this based on the reported statistical methods. However, this approach is fraught, as many methods provide counterintuitive results. For example, some methods include data from all patients, yet the resulting treatment effect applies only to a subset of patients, whereas other methods will exclude certain patients while results will apply to everyone. Additionally, some analyses provide estimates pertaining to hypothetical settings where patients never die or discontinue treatment. Herein we introduce estimands as a solution to the aforementioned problem. An estimand is a clear description of what the treatment effect represents, thus saving readers the necessity of trying to infer this from study methods and potentially getting it wrong. We provide examples of how estimands can remove ambiguity from reported treatment effects and describe their current use in practice. The crux of our argument is that readers should not have to infer what investigators are estimating; they should be told explicitly
Adipokines and the Right Ventricle: The MESA-RV Study.
ObjectiveObesity is associated with changes in both right (RV) and left (LV) ventricular morphology, but the biological basis of this finding is not well established. We examined whether adipokine levels were associated with RV morphology and function in a population-based multiethnic sample free of clinical cardiovascular disease.MethodsWe examined relationships of leptin, resistin, TNF-α, and adiponectin with RV morphology and function (from cardiac MRI) in participants (n = 1,267) free of clinical cardiovascular disease from the Multi-Ethnic Study of Atherosclerosis (MESA)-RV study. Multivariable regressions (linear, quantile [25th and 75th] and generalized additive models [GAM]) were used to examine the independent association of each adipokine with RV mass, RV end-diastolic volume (RVEDV), RV end-systolic volume (RVESV), RV stroke volume (RVSV) and RV ejection fraction (RVEF).ResultsHigher leptin levels were associated with significantly lower levels of RV mass, RVEDV, RVESV and stroke volume, but not RVEF, after adjustment for age, gender, race, height and weight. These associations were somewhat attenuated but still significant after adjustment for traditional risk factors and covariates, and were completely attenuated when correcting for the respective LV measures. There were no significant interactions of age, gender, or race/ethnicity on the relationship between the four adipokines and RV structure or function.ConclusionsLeptin levels are associated with favorable RV morphology in a multi-ethnic population free of cardiovascular disease, however these associations may be explained by a yet to be understood bi-ventricular process as this association was no longer present after adjustment for LV values. These findings complement the associations previously shown between adipokines and LV structure and function in both healthy and diseased patients. The mechanisms linking adipokines to healthy cardiovascular function require further investigation
A simple principal stratum estimator for failure to initiate treatment
A common intercurrent event affecting many trials is when some participants
do not begin their assigned treatment. For example, in a trial comparing two
different methods for fluid delivery during surgery, some participants may have
their surgery cancelled. Similarly, in a double-blind drug trial, some
participants may not receive any dose of study medication. The commonly used
intention-to-treat analysis preserves the randomisation structure, thus
protecting against biases from post-randomisation exclusions. However, it
estimates a treatment policy effect (i.e. addresses the question "what is the
effect of the intervention, regardless of whether the participant actually
begins treatment?"), which may not be the most clinically relevant estimand. A
principal stratum approach, estimating the treatment effect in the
subpopulation of participants who would initiate treatment (regardless of
treatment arm), may be a more clinically relevant estimand for many trials. We
show that a simple principal stratum estimator based on a "modified
intention-to-treat" population, where participants who experience the
intercurrent event are excluded, is unbiased for the principal stratum estimand
under certain assumptions that are likely to be plausible in many trials,
namely that participants who initiate the intervention under one treatment
condition would also do so under the other treatment condition. We provide
several examples of trials where this assumption is plausible, and several
instances where it is not. We conclude that this simple principal stratum
estimator can be a useful strategy for handling failure to initiate treatment
Using modified intention-to-treat as a principal stratum estimator for failure to initiate treatment
BACKGROUND: A common intercurrent event affecting many trials is when some participants do not begin their assigned treatment. For example, in a double-blind drug trial, some participants may not receive any dose of study medication. Many trials use a 'modified intention-to-treat' approach, whereby participants who do not initiate treatment are excluded from the analysis. However, it is not clear (a) the estimand being targeted by such an approach and (b) the assumptions necessary for such an approach to be unbiased. METHODS: Using potential outcome notation, we demonstrate that a modified intention-to-treat analysis which excludes participants who do not begin treatment is estimating a principal stratum estimand (i.e. the treatment effect in the subpopulation of participants who would begin treatment, regardless of which arm they were assigned to). The modified intention-to-treat estimator is unbiased for the principal stratum estimand under the assumption that the intercurrent event is not affected by the assigned treatment arm, that is, participants who initiate treatment in one arm would also do so in the other arm (i.e. if someone began the intervention, they would also have begun the control, and vice versa). RESULTS: We identify two key criteria in determining whether the modified intention-to-treat estimator is likely to be unbiased: first, we must be able to measure the participants in each treatment arm who experience the intercurrent event, and second, the assumption that treatment allocation will not affect whether the participant begins treatment must be reasonable. Most double-blind trials will satisfy these criteria, as the decision to start treatment cannot be influenced by the allocation, and we provide an example of an open-label trial where these criteria are likely to be satisfied as well, implying that a modified intention-to-treat analysis which excludes participants who do not begin treatment is an unbiased estimator for the principal stratum effect in these settings. We also give two examples where these criteria will not be satisfied (one comparing an active intervention vs usual care, where we cannot identify which usual care participants would have initiated the active intervention, and another comparing two active interventions in an unblinded manner, where knowledge of the assigned treatment arm may affect the participant's choice to begin or not), implying that a modified intention-to-treat estimator will be biased in these settings. CONCLUSION: A modified intention-to-treat analysis which excludes participants who do not begin treatment can be an unbiased estimator for the principal stratum estimand. Our framework can help identify when the assumptions for unbiasedness are likely to hold, and thus whether modified intention-to-treat is appropriate or not
Eliminating ambiguous treatment effects using estimands
Most reported treatment effects in medical research studies are ambiguously defined, which can lead to misinterpretation of study results. This is because most studies do not attempt to describe what the treatment effect represents, and instead require readers to deduce this based on the reported statistical methods. However, this approach is fraught, as many methods provide counterintuitive results. For example, some methods include data from all patients, yet the resulting treatment effect applies only to a subset of patients, whereas other methods will exclude certain patients while results will apply to everyone. Additionally, some analyses provide estimates pertaining to hypothetical settings where patients never die or discontinue treatment. Herein we introduce estimands as a solution to the aforementioned problem. An estimand is a clear description of what the treatment effect represents, thus saving readers the necessity of trying to infer this from study methods and potentially getting it wrong. We provide examples of how estimands can remove ambiguity from reported treatment effects and describe their current use in practice. The crux of our argument is that readers should not have to infer what investigators are estimating; they should be told explicitly
Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials
Understanding whether and how treatment effects vary across subgroups is
crucial to inform clinical practice and recommendations. Accordingly, the
assessment of heterogeneous treatment effects (HTE) based on pre-specified
potential effect modifiers has become a common goal in modern randomized
trials. However, when one or more potential effect modifiers are missing,
complete-case analysis may lead to bias and under-coverage. While statistical
methods for handling missing data have been proposed and compared for
individually randomized trials with missing effect modifier data, few
guidelines exist for the cluster-randomized setting, where intracluster
correlations in the effect modifiers, outcomes, or even missingness mechanisms
may introduce further threats to accurate assessment of HTE. In this article,
the performance of several missing data methods are compared through a
simulation study of cluster-randomized trials with continuous outcome and
missing binary effect modifier data, and further illustrated using real data
from the Work, Family, and Health Study. Our results suggest that multilevel
multiple imputation (MMI) and Bayesian MMI have better performance than other
available methods, and that Bayesian MMI has lower bias and closer to nominal
coverage than standard MMI when there are model specification or compatibility
issues
Using modified intention-to-treat as a principal stratum estimator for failure to initiate treatment
Background: A common intercurrent event affecting many trials is when some
participants do not begin their assigned treatment. Many trials use a modified
intention-to-treat (mITT) approach, whereby participants who do not initiate
treatment are excluded from the analysis. However, it is not clear the estimand
being targeted by such an approach or the assumptions necessary for it to be
unbiased.
Methods: We demonstrate that a mITT analysis which excludes participants who
do not begin treatment is estimating a principal stratum estimand (i.e. the
treatment effect in the subpopulation of participants who would begin
treatment, regardless of which arm they were assigned to). The mITT estimator
is unbiased for the principal stratum estimand under the assumption that the
intercurrent event is not affected by the assigned treatment arm, that is,
participants who initiate treatment in one arm would also do so in the other
arm.
Results: We identify two key criteria in determining whether the mITT
estimator is likely to be unbiased: first, we must be able to measure the
participants in each treatment arm who experience the intercurrent event, and
second, the assumption that treatment allocation will not affect whether the
participant begins treatment must be reasonable. Most double-blind trials will
satisfy these criteria, and we provide an example of an open-label trial where
these criteria are likely to be satisfied as well.
Conclusions: A modified intention-to-treat analysis which excludes
participants who do not begin treatment can be an unbiased estimator for the
principal stratum estimand. Our framework can help identify when the
assumptions for unbiasedness are likely to hold, and thus whether modified
intention-to-treat is appropriate or not.Comment: Changes to Introduction and Abstract, minor changes to Method
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