Web-Based Lectures




Title: Advancing the Interpretation of Patient-reported Outcome Data
Presenters: Joseph C. Cappelleri, Pfizer Inc., Lisa A. Kammerman, AstraZeneca Inc., and Kathleen W. Wyrwich, Eli Lilly and Company
Date and Time: Thursday, February 22, 2018, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Tuesday, February 20, at 12:00 p.m. Eastern time

Description:
This webinar discusses approaches for interpreting patient-reported outcome (PRO) data that are intended to support labeling and promotional claims or to extend beyond them, for instance, for publications. PRO measures used for claims and publications should have interpretation guidelines that are useful and meaningful to patients in clinical studies. Two conventional ways to interpret PRO scores are anchor-based methods and distribution-based methods. Anchor-based approaches use a criterion measure that is clinically interpretable and correlated with the targeted PRO measure of interest. Examples include reference-based interpretation, content-based interpretation, and responder analysis. Distribution-based approaches use the statistical distribution of the data to gauge the meaning of PRO scores. Examples include effect size and cumulative distribution functions. In addition, two novel approaches – bookmarking and qualitative explorations – will be featured. We will also discuss the interpretation of PRO data in the presence of missing data and in the context of estimands. Moreover, some regulatory considerations will be highlighted. Illustrations and real-life applications will be given throughout.

Registration Fees:
Biopharmaceutical Section Members: $0
ASA Members: $59
Nonmembers: $74

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Registration is now closed.

Access Information
Registered persons will be sent an email the afternoon of Tuesday, February 20, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Title: Thirty years of Numbers Needed to Treat (NNT): why they don't mean what many people think they do
Presenter: Stephen Senn
Date and Time: Tuesday, March 13, 2018, 12:00 p.m. – 1:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Friday, March 9, at 12:00 p.m. Eastern time

Description:
Abstract: The Wikipedia entry (consulted on 15 February 2018) on NNTs states the following The number needed to treat (NNT) is an epidemiological measure used in communicating the effectiveness of a health-care intervention, typically a treatment with medication. The NNT is the average number of patients who need to be treated to prevent one additional bad outcome (e.g. the number of patients that need to be treated for one of them to benefit compared with a control in a clinical trial). It is defined as the inverse of the absolute risk reduction. It was described in 1988 by McMaster University's Laupacis, Sackett and Roberts. The ideal NNT is 1, where everyone improves with treatment and no one improves with control. The higher the NNT, the less effective is the treatment.

The article is generally helpful, yet in my opinion the second sentence encourages misunderstanding. NNTs seems to suffer from a problem that P-values have: the take home message for many users is simply wrong.

The problem is not necessarily inherent to NNTs but it is partly a side effect of wanting to calculate them. Many clinical trial outcome measures, for example, are not naturally binary but patients are frequently classified as ‘responders’ or ‘non-responders’ based on either, a dichotomy of a continuous outcome at a given time-point or the dichotomy of a time to event measure at a given time of follow-up. I shall show how both of these cases cause problems.

The talk is not deep but in my view statisticians should be doing more to explain the problem. I am confident that not only will some of the audience regard it as obvious but also that some will regard it as wrong. That’s excuse enough for giving it.

Registration Fees:
Biopharmaceutical Section Members: $0
ASA Members: $59
Nonmembers: $74

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register

Access Information
Registered persons will be sent an email the afternoon of Friday, March 9, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Title: PROC SQL for Data Science
Presenter: Lewis Church, SAS
Date and Time: Thursday, March 15, 2018, 12:00 p.m. – 1:00 p.m. Eastern time
Sponsor: Section for Statistical Programmers and Analysts

Registration Deadline: Tuesday, March 13, at 12:00 p.m. Eastern time

Description:
PROC SQL is a Base SAS® Procedure that implement the ANSI Standard Structured Query Language. It can combines the functionality of DATA and PROC features into a single procedure. This webinar will demonstrate the various feature of this procedure: aggregate functions with complex summary statistics, manage the datasets and create new tables/datasets. With SAS® Output delivery system (ODS) feature, you can have the flexibility to output your tables to EXCEL or other database system.

We will conclude with a Q&A session. Please note that there are no open phone lines so the audience submits their questions using a chat feature built into the webinar dashboard.

Registration Fees:
Member of the Section for Statistical Programmers and Analysts: $0
ASA Member: $59
Nonmember: $74

Each registration is allowed one connection to the webinar. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register

Access Information
Registered persons will be sent an email the afternoon of Tuesday, March 13, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Title: Mixed Models for Intensive Longitudinal Data
Presenter: Donald Hedeker
Date and Time: Wednesday, March 21, 2018, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Mental Health Statistics Section

Registration Deadline: Monday, March 19, at 12:00 p.m. Eastern time

Description:
Modern data collection procedures, such as ecological momentary assessments (EMA), experience sampling, and diary methods have been developed to record the momentary events and experiences of subjects in daily life. These procedures yield relatively large numbers of subjects and observations per subject, and data from such designs are often referred to as intensive longitudinal data. Data from such studies are inherently multilevel with, for example, (level-1) observations nested within (level-2) subjects, or observations (level-1) within days (level-2) within subjects (level-3). Thus, mixed models (aka multilevel or hierarchical linear models) are increasingly used for data analysis. In this webinar, focus will be on some of the extended uses of mixed models for analysis of intensive longitudinal data.

A primary focus area of the webinar will be on the modeling of variances from EMA data. In the standard mixed model, the error variance and the variance of the random effects are usually considered to be homogeneous. These variance terms characterize the within-subjects (error variance) and between-subjects (random-effects variance) variation in the data. In EMA studies, up to thirty or forty observations are often obtained for each subject, and there may be interest in characterizing changes in the variances, both within- and between-subjects. Thus, an extension of the standard mixed model will be described which adds a subject-level random effect to the within-subject variance specification. This permits subjects to have influence on the mean, or location, and variability, or scale, of their mood responses. These mixed-effects location scale models have useful applications in many research areas where interest centers on the joint modeling of the mean and variance structure.

Registration Fees:
Member of the Mental Health Statistics Section: $60
ASA Member: $90
Nonmember: $110

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register

Access Information
Registered persons will be sent an email the afternoon of Monday, March 19, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Title: Preparing to Meet FDA Requirements for Submission of Standardized Data and Documentation
Presenters: Steven Kirby and Mario Widel
Date and Time: Thursday, April 12, 2018, 12:00 p.m. – 1:30 p.m. Eastern time
Sponsor: Section for Statistical Programmers and Analysts

Registration Deadline: Tuesday, April 10, at 12:00 p.m. Eastern time

Description:
PFUFA V gave the FDA the authority to require electronic submission of study data in standard format. That authority was confirmed by PDUFA VI. This webinar will provide a practical overview of FDA expectations for submission of standardized study data and associated documentation for statisticians and statistical programmers.

The speakers will provide a summary of key submission deliverables for SDTM and ADaM and will touch on when and how legacy data may need to be included as part of a submission. The focus areas will be: 1) what needs to be submitted, 2) when work on each submission deliverable should start, and 3) how each deliverable should be reviewed. Areas where automated tools can be leveraged will be discussed and areas where manual review is unavoidable will be highlighted.

We will conclude with a Q&A session. Please note that there are no open phone lines so the audience submits their questions at any time during the presentation using a chat feature built into the webinar dashboard.

Registration Fees:
Member of the Section for Statistical Programmers and Analysts: $0
ASA Member: $59
Nonmember: $74

Each registration is allowed one connection to the webinar. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register

Access Information
Registered persons will be sent an email the afternoon of Tuesday, April 10, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Title: Sensitivity Analysis in Observational Research: Introducing the E-Value
Presenter: Tyler VanderWeele
Date and Time: Tuesday, April 24, 2018, 2:00 p.m. – 3:30 p.m. Eastern time
Sponsor: Mental Health Statistics Section

Registration Deadline: Friday, April 20, at 12:00 p.m. Eastern time

Description:
Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This webinar introduces a new measure called the “E-value,” which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment–outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The speaker and his collaborators propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.

Registration Fees:
Member of the Mental Health Statistics Section: $60
ASA Member: $90
Nonmember: $110

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register

Access Information
Registered persons will be sent an email the afternoon of Friday, April 20, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Title: Adaptive Enrichment Trial Designs: Statistical Methods, Trial Optimization Software, and Case Studies
Presenter: Michael Rosenblum
Date and Time: Tuesday, May 15, 2018, 12:00 p.m. – 1:30 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Friday, May 11, at 12:00 p.m. Eastern time

Description:
This webinar focuses on adaptive enrichment designs, that is, designs with preplanned rules for modifying enrollment criteria based on data accrued in an ongoing trial. For example, enrollment of a subpopulation where there is sufficient evidence of treatment efficacy, futility, or harm could be stopped, while enrollment for the complementary subpopulation is continued. Such designs may be useful when it’s suspected that a subpopulation may benefit more than the overall population. The subpopulation could be defined by a risk score or biomarker measured at baseline. Adaptive enrichment designs have potential to provide stronger evidence than standard designs about treatment benefits for the subpopulation, its complement, and the combined population. We present new statistical methods for adaptive enrichment designs, simulation-based case studies in Stroke and Heart Disease, and open-source adaptive design optimization software. The tradeoffs involved in using adaptive enrichment designs, compared to standard designs, will be presented. Our software searches over hundreds of candidate adaptive designs with the aim of finding one that satisfies the user’s requirements for power and Type I error at the minimum sample size, which is then compared to simpler designs in terms of sample size, duration, power, Type I error, and bias in an automatically generated report.

Registration Fees:
Biopharmaceutical Section Members: $0
ASA Members: $59
Nonmembers: $74

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register

Access Information
Registered persons will be sent an email the afternoon of Friday, May 11, with the access information to join the webinar and the link to download and print a copy of the presentation slides.