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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

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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
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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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What is an Experiment?01:12

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An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
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Comparing the Survival Analysis of Two or More Groups01:20

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
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Instrumental variable estimation of early treatment effect in randomized screening trials.

Sudipta Saha1, Zhihui Liu2, Olli Saarela3

  • 1Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Ontario, M5T 3M7, Canada.

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|July 13, 2021
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Summary
This summary is machine-generated.

This study proposes new methods to estimate the causal effect of early cancer treatment on mortality using data from screening trials. These methods help quantify treatment benefits in the screening-detectable population.

Keywords:
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Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • Randomized screening trials for cancer primarily use the intention-to-screen principle.
  • Cancer mortality reductions in trials are influenced by screening, technology, and early treatment effects.
  • There's a need to separately evaluate the impact of early treatment on mortality within the screening-detectable subgroup.

Purpose of the Study:

  • To define and estimate the causal effect of early versus delayed treatment on cancer mortality among individuals detected through screening.
  • To develop novel statistical methods for analyzing screening trial data to isolate treatment effects.

Main Methods:

  • Formulation of a simplified structural multi-state model for screening trials.
  • Development of two estimators (estimating equation and likelihood-based) for cause-specific hazard ratios.
  • Extension of instrumental variable methods to handle time-dependent intermediate variables.
  • Simulation studies to evaluate estimator performance using a data-generating mechanism based on the multi-state model.

Main Results:

  • The proposed methods extend existing instrumental variable approaches for time-to-event and competing risks data.
  • Simulation studies demonstrate the performance of the new estimators.
  • The methods are illustrated using data from the US National Lung Screening Trial (NLST) for CT screening for lung cancer.

Conclusions:

  • The developed methods provide a way to estimate the causal effect of early treatment on cancer mortality in screening-detectable populations.
  • These approaches can disentangle the effects of screening from the effects of early treatment.
  • The application to lung cancer screening trial data demonstrates practical utility.