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JAMAevidence JAMA Guide to Statistics and Methods

Latest episodes

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Dec 7, 2023 • 18min

Adjustment for Baseline Characteristics in Randomized Clinical Trials

Lars W. Andersen, a medical doctor and researcher, discusses adjusting for baseline characteristics in randomized clinical trials. They explore the concept of confounders, the importance of adjusting for baseline characteristics, and different approaches to adjustment. They also discuss the impact of adjusting baseline characteristics on treatment effect estimates and highlight the significance of this adjustment for accurate interpretation of trial results.
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Nov 2, 2023 • 12min

Odds Ratios—Current Best Practice and Use With Dr Norton

Dr. Edward C. Norton discusses the best practices and applications of odds ratios in different fields, from gambling to medical research. They also explore the impact of controlling for factors in regression and demonstrate how odds ratios can be altered by various models. Additionally, they discuss the significance of controlling for specialty when examining the effect of gender on industry payments.
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Aug 3, 2023 • 22min

Estimands, Estimators, and Estimates With Dr Little

Dr. Roderick J. Little, PhD, discusses estimates, estimators, and their importance in capturing the benefit or risk of a treatment. They explore estimation in clinical practice, intention to treat vs per protocol estimates, benefits and challenges of randomization, and alternative methods for estimating trial products.
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May 4, 2023 • 18min

Interpreting the Results of Intention-to-Treat, Per-Protocol, and As-Treated Analyses With Dr Smith

Roger J. Lewis, MD, PhD, discusses Interpreting the Results of Intention-to-Treat, Per-Protocol, and As-Treated Analyses with Valerie A. Smith, DrPH. Related Content: Interpreting the Results of Intention-to-Treat, Per-Protocol, and As-Treated Analyses of Clinical Trials
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Mar 3, 2023 • 11min

Time-to-Event Analysis With Dr Tolles

In this podcast, Dr Juliana Tolles discusses time-to-event analysis in clinical research. She explores the concept of survival curves, Kaplan-Meier curves, and statistical tests to compare survival rates. She also explains the differences between the log-rank test and the Cox proportional hazards model. Additionally, she analyzes the proportional hazard assumption and provides an example study comparing major adverse cardiac events.
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Jan 5, 2023 • 18min

Adjusting for Nonadherence or Stopping Treatments With Drs Adler and Latimer

JAMA Statistical Editor Roger J. Lewis, MD, PhD, discusses Adjusting for Nonadherence or Stopping Treatments with Amanda I. Adler, MD, PhD, and Nicholas Latimer, PhD. Related Content: Adjusting for Nonadherence or Stopping Treatments in Randomized Clinical Trials
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Nov 3, 2022 • 17min

Worst-Rank Score Methods—A Nonparametric Approach to Informatively Missing Data With Dr Lachin

JAMA Statistical Editor Roger J. Lewis, MD, PhD, discusses Worst-Rank Score Methods—A Nonparametric Approach to Informatively Missing Data with John M. Lachin, ScD. Related Content: Worst-Rank Score Methods—A Nonparametric Approach to Informatively Missing Data
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Sep 1, 2022 • 18min

Using Latent Class Analysis to Identify Hidden Clinical Phenotypes With Dr Heather G. Allore

JAMA Statistical Editor Roger Lewis, MD, PhD, discusses Using Latent Class Analysis to Identify Hidden Clinical Phenotypes with Heather G. Allore, PhD. Related Content:
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May 5, 2022 • 19min

Use of Run-in Periods in Randomized Trials With Dr Armitage

JAMA Statistical Editor Roger Lewis, MD, PhD, discusses Use of Run-in Periods in Randomized Trials with Jane M. Armitage, MBBS. Related Content: Use of Run-in Periods in Randomized Trials With Dr Armitage Short- and Long-term Effects of a Mobile Phone App in Conjunction With Brief In-Person Counseling on Physical Activity Among Physically Inactive Women: The mPED Randomized Clinical Trial
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Apr 7, 2022 • 18min

Regression Discontinuity Study Design to Estimate Observational Differences With Dr Maciejewski

JAMA Statistical Editor Roger Lewis, MD, discusses Regression Discontinuity Design with Matthew L. Maciejewski, PhD. Related Content: Regression Discontinuity Design Using Instrumental Variables to Address Bias From Unobserved Confounders

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