2010 JSM - Meta Stat Issue Medical Devices
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This document summarizes statistical issues that commonly arise in meta-analyses of drug-eluting stent data. It discusses key topics like using fixed effect versus random effects models, strategies for handling zero event rates, and approaches for incorporating time-to-event data like Kaplan-Meier curves. The document provides examples and references to illustrate important considerations for conducting meta-analyses and addressing heterogeneity between studies.
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v When to Choose a Statistical Tests OR When NOT to Choose? v Parametric vs. Non-Parametric Tests (Comparison)
v Parameters to check when Choosing a Statistical Test:
- Distribution of Data
- Type of data/Variable
- Types of Analysis (What’s the hypothesis)
- No of groups or data-sets
- Data Group Design
v Snapshot of all statistical test and “How” to Choose using above parameters v Explanation using Examples:
- Mann Whitney U Test
- Wilcoxon Sign Rank Test
- Spearman’s co-relation
- Chi-Square Test
v Conclusion
2014-10-22 EUGM | WEI | Moving Beyond the Comfort Zone in Practicing Translat...
1. The document discusses moving beyond conventional practices in translational statistics to obtain more robust and clinically meaningful results from clinical studies.
2. Several methodology issues are discussed, including how to define primary endpoints when there are multiple outcomes, how to handle dropouts and competing risks, and how to quantify treatment contrasts in a model-free way.
3. Alternative approaches are proposed for various types of studies, such as using restricted mean survival times instead of hazard ratios for survival analyses and performing meta-analyses for evaluating safety issues using large amounts of data.
Measurement and Modeling Issues with Adherence to Pharmacotherapy
The document discusses issues related to measuring and modeling medication adherence using claims data. It covers calculating adherence measures like MPR and PDC, defining adherence thresholds, handling primary non-compliance, and addressing endogeneity and selection bias when modeling adherence as an independent variable to estimate its impact on outcomes. Regression adjustment, propensity score matching, and instrumental variables are some methods discussed to address biases in observational studies of adherence.
Clinical trial bms clinical trials methodology 17012018
Clinical trials are studies that compare the effectiveness of two or more treatments. They are important for determining if a new treatment is better than no treatment, an old treatment, or a placebo. Key features of clinical trials include randomization of patients, use of controls, appropriate sample size, blinded assessment, and intention-to-treat analysis. Proper design and conduct of clinical trials can limit bias, but biased interpretation of results remains a risk.
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More from Terry Liao
2011 JSM - Meta Rare Event Performance Goal in Medical Device Studies
The document discusses challenges in using meta-analysis to set performance goals for rare event medical device studies. It explores issues with small sample sizes, small numbers of studies, and rare event rates in historical data. The author presents simulations examining the impact of these challenges on developing performance goals based on meta-analysis of historical rates. They find minimal impact from removing or imputing zero event studies and from using fixed effect versus random effects models. The author emphasizes that meta-analysis, margins, and performance goals should involve clinical experts in addition to statisticians.
2011 JSM - Good Statistical Practices
The document discusses best practices for statistical analysis in medical device clinical trials, including optimizing sample size calculations, handling missing data through various sensitivity analyses, pre-specifying interim analyses and adjustments for multiplicity, and justifying the pooling of data across sites. It emphasizes the importance of protocolizing these statistical elements to ensure like-to-like treatment group comparisons and distinguish treatment effects from other influences.
2010 FDA/Industry Statistics Workshop
The document discusses statistical issues that arise in meta-analyses of drug-eluting stent data. Specifically, it notes that meta-analyses in this field face common challenges like publication bias due to a lack of qualified publications and study heterogeneity stemming from how zero events are handled. It aims to address universal issues related to these concerns, such as how to measure effects, ensure study comparability, select fixed or random effects models, adjust for imbalanced baselines, handle multiple outcomes/timepoints, and conduct sensitivity and power analyses.
2010 ACC i2 Summit
Jashdeep S. Dhoot, Michael S. Lee, Hsini Liao, Tae Yang, Jonathan Tobis, Gregg Fonarow, Ehtisham Mahmud, “Comparison of Bivalirudin Versus Heparin Plus Glycoprotein IIB/IIIA Inhibitors in Patients Undergoing An Invasive Strategy: A Meta-Analysis of Randomized Clinical Trials”, Poster Hall B5, American College of Cardiology, i2 Summit, No 2504-420, Atlanta, GA, March 2010
2009 JSM - Meta Time to Event Data
Hsini (Terry) Liao, Ph.D., Yun Lu, Hong Wang, “Meta-Analysis of Time-to-Event Survival Curves in Drug Eluting Stent Data”, Abstract No 304048, Joint Statistical Meetings, Session No 205, Washington D.C., August 2009
2008 JSM - Meta Study Data vs Patient Data
Hsini (Terry) Liao, Ph.D., Yun Lu, Hong Wang, “Comparison of Individual Patient-Level and Study-Level Meta-Analyses Using time to Event Analysis in Drug-Eluting Stent Data”, Abstract No 301037, Joint Statistical Meetings, Session No 90, Denver, CO, August 2008
More from Terry Liao (6)
2011 JSM - Meta Rare Event Performance Goal in Medical Device Studies
2011 JSM - Meta Rare Event Performance Goal in Medical Device Studies
2010 JSM - Meta Stat Issue Medical Devices
- 1. Statistical Issues of Meta-Analysis Practices in Drug-Eluting Stent Data Hsini Terry Liao, PhD Boston Scientific Corporation Contact: terry.liao@bsci.com Presented to Joint Statistical Meetings August 5, 2010
- 9. RCT vs. Registry * H2H: PES vs. Olimus by RCTs and Registries (Paper in Progress)
- 15. Comparison of Odds vs. Risk 5.0 4.0 3.0 2.0 1.0 0.0
- 18. Biasness
- 19. True Treatment Effect Coverage