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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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...
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...

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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

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Published on: January 8, 2020

Using common random numbers in health care cost-effectiveness simulation modeling.

Daniel R Murphy1, Robert W Klein, Lee J Smolen

  • 1Medical Decision Modeling Inc., Indianapolis, IN 46268, USA. drm@mdm-inc.com

Health Services Research
|February 14, 2013
PubMed
Summary
This summary is machine-generated.

Common random numbers (CRNs) significantly reduce statistical noise in health economic models. This variance reduction improves the assessment of treatment costs and effectiveness, leading to clearer insights into therapeutic benefits.

Keywords:
Variance reductioncommon random numberscost-effectiveness modelingosteoporosis

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Area of Science:

  • Health economics
  • Biostatistics
  • Pharmacoeconomics

Background:

  • Health outcomes modeling often struggles to differentiate statistical noise from true treatment effects.
  • Accurate cost-effectiveness analysis is crucial for healthcare decision-making, especially for treatments like osteoporosis medication.

Purpose of the Study:

  • To address the challenge of separating statistical noise from treatment effects in health outcome models.
  • To demonstrate the utility of common random numbers (CRNs) in health economic evaluations.
  • To assess the impact of CRNs on evaluating costs and outcomes under uncertainty.

Main Methods:

  • A microsimulation model was developed for osteoporosis treatment evaluation.
  • Incremental cost-effectiveness ratios (ICERs) were calculated using full CRNs, partial CRNs, and no CRNs.
  • A modified probabilistic sensitivity analysis (PSA) was employed to assess variance reduction's impact.

Main Results:

  • Full implementation of CRNs achieved a 93.6% variance reduction compared to simulations without CRNs.
  • Partial CRN implementation resulted in a 5.6% variance reduction.
  • PSA with full CRNs showed a significantly narrower range of cost-benefit outcomes for teriparatide compared to analyses without CRNs.

Conclusions:

  • Common random numbers (CRNs) offer substantial variance reduction in cost-effectiveness studies.
  • By minimizing variability unrelated to the treatment, CRNs enhance the understanding of treatment effects and associated risks.