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Related Concept Videos

Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

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...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Blind Procedures02:07

Blind Procedures

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 child was...
Cochran's Q Test01:17

Cochran's Q Test

Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square distribution,...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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 value between...
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...

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Related Experiment Video

Updated: Jun 18, 2026

Strategies for Assessing Autistic-Like Behaviors in Mice
07:38

Strategies for Assessing Autistic-Like Behaviors in Mice

Published on: September 20, 2024

Facilitating consensus by examining patterns of treatment effects.

Richard D Gelber1, Shari Gelber,

  • 1International Breast Cancer Study Group Statistical Center, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA. gelber@jimmy.harvard.edu

Breast (Edinburgh, Scotland)
|November 17, 2009
PubMed
Summary

Randomized trials show adjuvant therapies for breast cancer are effective, but analyzing patient subgroups and recurrence patterns is key to personalizing treatment for better outcomes.

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Last Updated: Jun 18, 2026

Strategies for Assessing Autistic-Like Behaviors in Mice
07:38

Strategies for Assessing Autistic-Like Behaviors in Mice

Published on: September 20, 2024

Area of Science:

  • Oncology
  • Clinical Trials
  • Breast Cancer Research

Background:

  • Randomized clinical trials (RCTs) are crucial for evaluating adjuvant therapies in breast cancer.
  • However, RCTs often provide average results for diverse patient populations, limiting individualized treatment insights.
  • Tailoring therapy requires understanding patient-specific disease characteristics and treatment responses.

Purpose of the Study:

  • To highlight the necessity of analyzing treatment response patterns within patient subpopulations.
  • To emphasize the importance of considering individual disease characteristics (e.g., estrogen receptor status, HER2 status) and patient factors (e.g., menopausal status) for personalized adjuvant therapy.
  • To facilitate consensus on improving breast cancer care by examining recurrence patterns and treatment responsiveness.

Main Methods:

  • Analysis of patterns of recurrence risk over time based on disease characteristics (ER-negative vs. ER-positive, HER2-positive).
  • Examination of treatment effectiveness in relation to early versus late relapse risks.
  • Review of how patient factors (e.g., premenopausal status) influence late relapse patterns.

Main Results:

  • Estrogen receptor (ER)-negative breast cancer is associated with early relapse risk, addressed by specific treatments.
  • ER-positive breast cancer shows later relapse patterns, benefiting from different therapeutic strategies.
  • HER2-positive disease often presents with early relapse, effectively managed by targeted therapies like trastuzumab.
  • Premenopausal patients with ER-positive disease benefit from ovarian function suppression for late relapse reduction.

Conclusions:

  • Understanding recurrence patterns in relation to specific biomarkers and patient demographics is vital for personalized adjuvant therapy in breast cancer.
  • Integrating data from multiple RCTs on subpopulation responses can refine treatment strategies.
  • Further research into patient and disease factors influencing treatment response will advance breast cancer care.