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Related Concept Videos

What are Estimates?01:06

What are Estimates?

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
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Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
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Blind Procedures02:07

Blind Procedures

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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Blinding01:11

Blinding

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Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
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Eliminating Ambiguous Treatment Effects Using Estimands.

Brennan C Kahan, Suzie Cro, Fan Li

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    Ambiguous treatment effects in medical studies cause misinterpretation. Introducing estimands provides clear definitions of treatment effects, improving clarity and accuracy in research reporting.

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    Area of Science:

    • Medical Research
    • Biostatistics
    • Clinical Trials

    Background:

    • Treatment effects in medical research are often ambiguously defined.
    • Readers must infer the meaning of treatment effects from statistical methods, leading to potential misinterpretation.
    • Existing methods can yield counterintuitive results, such as effects applying to subsets of patients or hypothetical scenarios.

    Purpose of the Study:

    • To introduce estimands as a solution to the ambiguity in defining treatment effects.
    • To provide a clear description of what a treatment effect represents, removing the need for reader inference.
    • To illustrate how estimands enhance clarity and accuracy in reporting study results.

    Main Methods:

    • Defining estimands as explicit descriptions of treatment effects.
    • Providing examples of how estimands clarify ambiguous reporting.
    • Discussing the current practical application of estimands in medical research.

    Main Results:

    • Estimands remove the necessity for readers to infer treatment effect definitions from statistical methods.
    • Clear estimand definitions prevent misinterpretation of study results.
    • The use of estimands improves the transparency and understanding of clinical trial outcomes.

    Conclusions:

    • Estimands are essential for unambiguous reporting of treatment effects in medical research.
    • Implementing estimands ensures that readers are explicitly informed about what is being estimated.
    • Adoption of estimands will lead to more accurate interpretation of medical study findings.