Identifying heterogeneity is central to making informed personalized healthcare decisions. Treatment effect heterogeneity can be investigated using subpopulation treatment effect pattern plot (STEPP), a non-parametric graphical approach that constructs overlapping patient subpopulations with varying values of a characteristic.
Why do we need a test for treatment effect heterogeneity?
Patient populations within a research study are heterogeneous. That is, they embody characteristics that vary between individuals, such as age, sex, disease etiology and severity, presence of comorbidities, concomitant exposures, and genetic variants. These varying patient characteristics can potentially modify the effect of a treatment on outcomes. Despite the …
Does treatment effect heterogeneity exist in regression discontinuity?
2 Testing for Heterogeneity. 3 Conditional Average Treatment Effects (CATEs) 4 Interaction Effects: Treatment-by-Covariate versus Treatment-by-Treatment. 5 Estimating CATEs and Interaction Effects. 6 Hypothesis Testing for Interaction Effects. 7 Multiple Comparisons. 8 Use a Pre-Analysis Plan To Reduce the Number of Hypothesis Tests.
How common is risk heterogeneity in clinical trials?
The appropriate statistical method for studying the HTE is to test for the statistical interaction between treatment and the covariate of interest, such as patient's gender. ... Duan N, Braslow J. Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages. Milbank Q. 2004; 82 (4):661–687. Erratum in: Milbank Q ...
Can Stepp be used to study treatment effect heterogeneity?
Dec 14, 2021 · The factors that might affect treatment effectiveness, such as the genetic subtype of disease or other factors, may be quite similarly distributed among men and women. 4 Even if the treatment effect varies considerably across individuals (eg, from harm to very large benefit), the subgroup-specific treatment effects for subgroups that vary by ...
How do you test the treatment effect?
What is treatment response heterogeneity?
What is homogeneous treatment effect?
What is heterogeneity in RCT?
What do you mean by heterogeneity?
: the quality or state of consisting of dissimilar or diverse elements : the quality or state of being heterogeneous cultural heterogeneity.
What is heterogeneous medicine?
How do you test for heterogeneous?
What is heterogeneity systematic review?
What is the difference between ATT and ATE?
What is an example of heterogeneity?
How do you interpret heterogeneity i2?
- 0% to 40%: might not be important;
- 30% to 60%: may represent moderate heterogeneity*;
- 50% to 90%: may represent substantial heterogeneity*;
- 75% to 100%: considerable heterogeneity*.
What is heterogeneity issue?
How to test whether an interaction effect could have occurred by chance?
To test whether the estimated interaction effect could have occurred by chance, one can use randomization inference: First generate a full schedule of potential outcomes under the null hypothesis that the true treatment effect is constant and equal to the estimated ATE. Then simulate random assignment a large number of times and calculate how often the simulated estimate of the interaction effect is at least as large (in absolute value) as the actual estimate.
What is a cate in a treatment effect?
A CATE is an average treatment effect specific to a subgroup of subjects, where the subgroup is defined by subjects’ attributes (e.g., the ATE among female subjects) or attributes of the context in which the experiment occurs ( e.g., the ATE among subjects at a specific site in a multi-site field experiment).
What is FWER in statistics?
Familywise error rate (FWER) control methods limit the probability of making at least one type I error given the number of tests conducted. Suppose one is testing K hypotheses, H 1, H 2, …, H K, and K 0 of the K hypotheses are true, where K 0 ≤ K. The familywise error rate is the probability that at least one of the K 0 true hypotheses is falsely rejected. The FWER increases in the number of hypotheses tested. FWER control methods adjust the p -values so that, for example, if we reject a hypothesis only when the adjusted p -value is less than 0.05, the FWER will not exceed 5 percent.
Why is machine learning useful?
Machine learning methods are useful to automate the search for systematic variation in treatment effects. These automated approaches are attractive because they minimize researchers’ use of ad hoc discretion in selecting and testing interactions, and are useful for conducting exploratory analyses.
What is a cate?
A CATE is an average treatment effect specific to a subgroup of subjects, where the subgroup is defined by subjects’ attributes (e.g., the ATE among female subjects) or attributes of the context in which the experiment occurs (e.g., the ATE among subjects at a specific site in a multi-site field experiment).
What was the reaction of the medical profession to the idea of randomized clinical trials?
When the Scottish epidemiologist Archie Cochrane suggested that clinical practice should principally be guided by rigorously designed evaluations, in particular randomized clinical trials (RCTs), the reaction of the medical profession was largely negative . Critics suggested that relying on impersonal statistically-derived "evidence" based on averages to determine clinical decision-making was antithetical to the practice of medicine, which should rather be based on a physician's expertise, acumen and clinical experience, and on knowing the individual patient and considering what is best for each person given their individual circumstances and needs [ 1 – 3 ].
What is primary subgroup analysis?
Here we define primary subgroup analysis as those subgroup comparisons that are well justified (hypothesis-testing, not hypothesis-generating) so as to yield potentially actionable results appropriate for guiding clinical care. Therefore, all primary subgroup comparisons must be fully specified and justified a priori.
What is the purpose of Table 1 in a clinical trial?
Finally, including this information in "Table 1 " of a clinical trial allows the reader to assess whether there are important baseline differences between treatment arms on the most important baseline attribute (i.e., differences in overall risk for the study's main outcome). It is common to note multiple modest deviations between treatment arms when baseline patient factors are listed one at a time. These differences typically have little influence on trial results, particularly when they combine so as to cancel each other out. However, similar differences in overall baseline risk may influence the trial result, such that comparing the risk distribution between the treatment groups using a composite risk model can be informative and facilitate risk adjustment.
Who wrote the initial draft of the manuscript?
All authors contributed to the conceptual framework presented in the manuscript. DMK and RAH co-wrote the initial draft. All authors revised the manuscript for important content and approved the final manuscript.
What is heterogeneity of treatment effect?
Individuals differ in their response to therapies. This can be called heterogeneity of treatment effect (HTE). Traditional trials aimed at understanding the efficacy of an intervention seek to answer the question as to whether an intervention works under optimal circumstances in a carefully chosen, treatment-adherent patient population. Variation in outcomes is reduced by excluding people with characteristics that may cause variations in responses to treatment, and sometimes even by analyzing only treatment adherent individuals. These trials typically report a single summary measure of treatment effect, the average treatment effect. Pragmatic trials, in contrast, are typically more inclusive and more closely replicate practice in a usual care setting. Our aims in this white paper are three-fold: (1) to characterize HTE, (2) to explore how HTE is particularly prominent in pragmatic trials due to their design, and (3) to explore how this heterogeneity can be useful. To illustrate, we use an example of a suggested design for a pragmatic trial investigating the effectiveness of a drug for the treatment of osteoporosis. Our premise is that trialists should not be aiming to eliminate HTE from trials; they might welcome HTE in pragmatic trials as a source of important data. We recommend that pragmatic trials always have a goal of informing the users of the trial about HTE so that multiple stakeholders including patients, clinicians and policy-makers can all benefit from the evidence. We recommend that some hypotheses be specified as the confirmatory hypotheses. Other hypotheses will be specified as being exploratory and provide important information for testing in future studies, although not for decision-making. There should be attention to ascertainment of multiple outcomes (including harms) as heterogeneity in responses may differ by outcome.
Is HTE a variable?
HTE is not simply variability in outcomes. Variability is part of any study. Random variability is uncorrelated with explanatory variables and can be handled well with basic statistical approaches for bounding uncertainty. We focus, here, on the non-random variability in treatment effects that can be attributed to patient, treatment, provider or environmental factors. Therefore, we define HTE as non-random variability in the direction or magnitude of a treatment effect, where the effect is measured using clinical outcomes.
Do beta blockers help with depression?
Individuals differ in their response to therapies. Clinicians know that prescription of a beta-blocker for hypertension control may or may not provide the desired response in blood pressure in an individual patient; the prescription of a serotonin reuptake blocker for an individual with depression may or may or may not relieve the depressive symptoms. In contrast, most individuals treated with a HMG-CoA reductase inhibitor have a reduction in low density lipoproteins, and most individuals vaccinated against the varicella virus avoid chicken pox. Complex interactions between an individual’s genes, diet, environment, stressors, concurrent medical conditions, other medications, and behaviors, including adherence to treatment, influence the response to an intervention. While this is well-appreciated clinically, traditional randomized controlled trials, upon which most practice recommendations are based, are designed to minimize these differences between enrolled individuals rather than to learn from them.
What is treatment effect heterogeneity?
Treatment effect heterogeneity is frequently studied in regression discontinuity (RD) applications. This paper proposes, under the RD setup, formal tests for treatment effect heterogeneity among individuals with different observed pre-treatment characteristics. The proposed tests study whether a policy treatment (1) is beneficial for at least some subpopulations defined by pre-treatment covariate values, (2) has any impact on at least some subpopulations, and (3) has a heterogeneous impact across subpopulations. The empirical section applies the tests to study the impact of attending a better high school and discovers interesting patterns of treatment effect heterogeneity neglected by previous studies.
Where is the running variable Z i distributed?
The running variable Z i is continuously distributed in a neighborhood around the threshold value c, where c is an interior point of its support. Also, assume that for some δ > 0,