Separating gains and losses in health when calculating the minimum important difference for mapped utility measures

Michael B Nichol1, Joshua D Epstein

  • 1University of Southern California, Los Angeles, CA, USA. mnichol@usc.edu

Abstract

Related Concept Videos

Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
Midrange01:07

Midrange

A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to outliers and...
Regression Toward the Mean01:52

Regression Toward the Mean

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 researchers try to extrapolate results...
Minor Losses in Pipes01:25

Minor Losses in Pipes

In pipe systems, minor losses refer to energy losses arising from components such as valves, bends, fittings, expansions, and other features that disrupt the steady flow of fluid. These disturbances cause energy dissipation through turbulence and resistance, which engineers quantify to manage system efficiency effectively.
Valves play a significant role in generating minor losses by obstructing or redirecting the fluid flow. When a valve is closed or partially closed, it restricts the flow...
Properties of Limits in Multivariable Calculus01:27

Properties of Limits in Multivariable Calculus

In multivariable calculus, the laws of limits provide systematic rules for evaluating limits of functions involving several variables. These laws allow complex expressions to be broken into simpler components whose limits are known. Suppose that\begin{equation*}\lim_{(x,y)\to(a,b)} f(x,y) = L\end{equation*}and\begin{equation*}\lim_{(x,y)\to(a,b)} g(x,y) = M\end{equation*}where both limits exist, the principal laws are stated as follows.Sum LawThe limit of a sum equals the sum of the...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...