… past work, current ideas, and future directions….

Apologies

Google has updated their (free) websites, and I have been slow to adapt. As far as I can tell, the main problem is that some file may not be freely accessible until I change the permissions (In such cases, google will ask if you want it to ask for permission on your behalf).

JSM 2021 Speed Presentation: Tolstoy Targets

The ASA's JSM is once again virtual. This year I did a speed presentation of Tolstoy Targets (If I can't get out of Delaware, at least they can), and a speed presentation is just the right length to hit the highlights. Here is the longer version with more explanations (I also presented this at SDSS in the spring).

https://drive.google.com/file/d/1eM_MGHO1kw_62pseoxW2zOCbXOz02TNr/view?usp=sharing

JSM 2020 Poster

The American Statistical Association's (ASA) Joint Statistical Meeting (JSM) has gone virtual this year because of COVID-19.

Early instructions limited the posters to 5 pages (!), so that is what is on the JSM site. Here is a link to a 8 page easier to read version.

https://www.slideshare.net/denswei/yet-another-randomization-system-yprimes-batch-randomization

Here is a link to the White Paper at YPrime.

resources.yprime.com/white-papers/the-randomizer-yprimes-batch-randomization-system/

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Simulation using Excel: Correlated Random Variables

I have updated the simulation package with 2 VBA functions, one for simulating finite discrete variables, and another (Cholesky()) for generating correlated random variables. The Sampler macro and example spreadsheets have been updated to generate examples of both of these (including an example 3 correlated normally distributed variables, and a way to generate arbitrary correlated random variables).

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Trial Management using Time-to-Event data

I'm glad to see people are starting to do some the of the Time-to-Event modeling & Simulation that I had done >10years ago. Cytel recently put this video on line.

https://youtu.be/H4fwAC01iHE

The presentation of my work is here: (Annual meeting of the American Statistical Association, August 2006).

Modeling Multiphase Clinical Trials: Time to Completion, Study Management

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Simulation using Excel: A Clinical Study Application

This (hypothetical) spreadsheet simulation evaluates the impacts on timeline and budget of various combinations of high, medium, and low quality sites, and compares 2 options for performing an interim analysis: either after a fixed number of patients, or after a fixed length of time. Since enrollment rates are uncertain, the former option is more of a challenge for planning timelines, as the time of the interim analysis will vary, while the later risks having too few patients in the interim analysis.

Simulated costs for multiple scenarios are compared against a conventional point estimate of costs for the study, showing a high risk of going over budget because of uncertainty in screen failure and enrollment rates.

Simple Simulation--Clinical Study Simulation comparing options for interim analysis

OR: https://sites.google.com/site/dennissweitzer/home/quick-simple-simulation-using-ms-excel

I've also been looking the SIPmath™ Modeler Tools for Excel v3.0. These tools should simplify some aspects of writing a simulation in Excel, but more importantly allow a simulation to be written that uses the results of other simulations as part of it's simulated universes, and publish it's results for use by other simulations and reports. They are found at:

http://probabilitymanagement.org/tools.html

(NB: these require later versions of MS Excel, and the code does not seem stable yet.)

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Introducing Tolstoy Targets: Motivation, Principles and Examples

Updated slides for my pet graphics project, Tolstoy Targets, including motivation, basic principles, and examples.

https://sites.google.com/site/dennissweitzer/home/tolstoy-targets

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Modeling & Simulation in Managing Studies: Some Examples

http://www.slideshare.net/denswei/clinical-study-modeling-simulation

http://www.slideshare.net/denswei/clinical-study-modeling-simulation

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Randomization Metrics:

Jointly assessing predictability and efficiency loss in covariate adaptive randomization designs

A presentation given August 8, 2013 at the Joint Statistical Meetings in Montreal

Randomization methods generally are designed to be both unpredictable and balanced between treatment allocations overall and within strata. However, when planning studies, little consideration is given to measuring these characteristics, nor are they examined jointly, and published comparisons between methods often are not useful.

In order to compare randomization performance, we simulated various covariate-adjusted randomization methods, and compared efficiency & unpredictability graphically and statistically using proposed metrics.

Publication:

DOI: 10.13140/RG.2.1.3590.6329

https://www.researchgate.net/publication/296704183_Randomization_Metrics_Jointly_Assessing_Predictability_and_Efficiency_Loss_in_Covariate_Adaptive_Randomization_Designs

Slide set:

http://www.slideshare.net/denswei/jsm2013598sweitzerrandomization-metricsv2aug08

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Randomization: Too important to gamble with

Randomization is a central part of Randomized Controlled Trials (RCTs) which are considered the 'gold standard' of clinical evidence. This talk is a basic introduction to the topic of randomization: It outlines how randomization is key to reducing various kinds of bias in a study; Explains several common randomization techniques; Proposes measurements of predictability and imbalance of a randomization method; And compares the performance of the methods in a series of simulations.

Summarized as a poster for the Society for Clinical Trials Annual meeting, May, 2013

Presented to the Delaware ASA monthly meeting in October, 2012

http://www.udel.edu/ASA/oct2012_meeting.html

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Animated explanation of Tolstoy Targets:

This simple & animated explanation starts from the very basics & includes examples from project management and chemical screening.

Tolstoy Targets Explained

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Research Study in Pancreatic Cancer (2012)

THE EFFECTS OF PREDNISONE, MONTELUKAST, CETIRIZINE, RANITIDINE, POTASSIUM PHOSPHATE, MAGNESIUM, AND VERAPAMIL ON PANCREATIC ADENOCARCINOMA STAGE IV.

..is approved and ready to enroll. This is a small proof of concept study to examine the benefits of this combination therapy in managing late stage pancreatic cancer symptoms. While there is no comparator group, I plan to do a comparison between enrolled patients and patients from the SEER database (a cancer registry) matched on baseline characteristics, and including year of diagnosis as a covariate to adjust for improvements in treatment over time. (NB: Only descriptive statistics will be compared).

For more details, see: http://www.nashedspa.com/index.html

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January 2012 update:

Simulation using Excel: Tricks, Trials, & Trends

Outline

•How to do Simulation in Excel

•Notes on using Inverse Probability Functions

(For Verification, Validation, and Sensitivity)

• Some tools: Excel Macros, Visual Basic Functions

• Examples from Clinical Trials

•Probability Management in SIPS, SLURPS, & DIST

Slides and spreadsheets at:

https://sites.google.com/site/dennissweitzer/home/quick-simple-simulation-using-ms-excel

My resume summarized as a word cloud:

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Haiku:

Question (from someone on LinkedIn): "It is difficult for me to comprehend the difference between standard deviation and variance after having good amount of reading. Can you pl help me."

Answer (mine):

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Targets, Traffic Lights, and Tolstoy

These 4 graphs plot project outcomes (for instance, Adverse Events, Efficacy goals, lab findings, schedule & budget). Which projects are on target? Which are in trouble?

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This is a tongue-in-cheek statistics talk that I gave sometime around the 2004 election cycle in the Bush vs Kerry match-up. In it, I outlined some quirky models for predicting election results, but also the approach used by Nate Silver of simulating state outcomes from models of polling data to predict the electoral college results. (This was long before Nate Silver was famous though).

Election Forecasting, 2004

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