Education > Continuing Education

Web-Based Lectures

Previously recorded webinars are available through the LearnSTAT OnDemand program.

Current Webinar Offerings:


March 5, 2015 Graphical Approaches to Multiple Testing
March 19th, 2015 Design, Weighting and Variance Estimation for Population-based Evaluation Studies
April 16, 2015 Statistical Methods Used in Pre-Clinical Drug Combination Studies
May 14, 2015 Interpretation of Patient-Reported Outcomes
June 24, 2015 Propensity Score Methods for Estimating Causal Effects in Pharmaceutical Research




Back to top

Title: Graphical Approaches to Multiple Testing
Presenter: Frank Bretz, Novartis Pharma AG, and Dong Xi, Novartis Pharmaceutical Company
Date and Time: Thursday, March 5, 2015, 10:00 a.m. - 12:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Tuesday, March 3, at 12:00 p.m. Eastern time

Description:
Methods for addressing multiplicity are becoming increasingly more important in clinical trials and other applications. In the recent past, several multiple test procedures have been developed that allow one to map the relative importance of different study objectives as well as their relation onto an appropriately tailored multiple test procedure, such as fixed-sequence, fallback, and gatekeeping procedures. In this webinar we focus on graphical approaches that can be applied to common multiple test problems, such as comparing several treatments with a control, assessing the benefit of a new treatment for more than one outcome variable, and combined non-inferiority and superiority testing. Using graphical approaches, one can easily construct and explore different test strategies and thus tailor the test procedure to the given study objectives. The resulting multiple test procedures are represented by directed, weighted graphs, where each node corresponds to an elementary hypothesis, together with a simple algorithm to generate such graphs while sequentially testing the individual hypotheses. We also present several case studies to illustrate how the approach can be used in practice. In addition, we briefly consider power and sample size consideration to optimize a multiple test procedure for given study objectives. The presented methods will be illustrated using the graphical user interface from the gMCP package in R, which is freely available on CRAN.

Reference: Bretz, Maurer, Maca (2014) Graphical approaches to multiple testing. In: Young and Chen (eds.), Clinical Trial Biostatistics and Biopharmaceutical Applications, Taylor & Francis, pp. 349-394.

Registration Fees:
Biopharmaceutical Section Members: $44
ASA Members: $59
Nonmembers: $74

Each registration is allowed one web connection and one audio connection. 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 3, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Back to top

Title: Design, Weighting and Variance Estimation for Population-based Evaluation Studies
Presenter: David Judkins, Principal Scientist, Abt Associates
Date and Time: Thursday, March 19th, 12:30 p.m. - 2:30 p.m. Eastern time
Sponsor: Survey Research Methods Section

Registration Deadline: Tuesday, March 17, at 12:00 p.m. Eastern time

Description:
Most years, there are a few really large population-based evaluation studies going on of federal programs designed to improve the economic well-being and health of disadvantaged domestic populations. They are typically sponsored by evaluation divisions of the Departments of Labor, Agriculture, Education, and Health and Human Services. One of the largest in U.S. history is now being conducted by the Social Security Administration on ways of encouraging disabled adults to return to the labor force. These evaluations often involve true experimental designs, but may also involve quasi-experimental designs and regression discontinuity designs. Sometimes the studies rely on only either administrative data or follow-up survey data to measure outcomes, but often both follow-up survey and administrative data are used to measure outcomes. Usually some degree of clustering is employed in the design - possibly to make collection of outcome data more efficient, but more often because of resource constraints for treatment delivery or monitoring of treatment delivery. Probabilities of treatment assignment often drift over time in response to local treatment capacities. The combination of clustering, differential treatment assignment probabilities, follow-up survey nonresponse, and linked administrative data make for an interesting set of challenges very similar to those encountered in the design and analysis of descriptive population surveys. In addition, if only administrative data are used, sample sizes can be very large, approaching survey sample sizes otherwise seen only in the American Community Survey. These sample sizes imply data processing challenges for resampling-based variance estimation and multiple-comparison adjustment procedures. This course will present solutions to many of these more interesting challenges that are aligned with survey methods issues.

Prerequisites:
Basic knowledge of survey sampling methods and variance estimation

Outline:

  • The controversy over baseline balance testing
  • The controversy over imbalanced response rates on followup surveys for treatment and control groups
  • Using post-randomization administrative data on parallel outcomes to reduce the risk of nonresponse bias in the followup survey
  • Using post-randomization administrative data on service take-up to improve precision
  • The controversy over whether to reflect clustering in variance estimates for multi-centre trials
  • Software choices: Likelihood-based or survey-sensitive modeling software?
  • Data reduction techniques on massive experiments
  • Empirically-driven covariate selection
  • Multiple comparison adjustment

Registration Fees:
Members of the Survey Research Methods Section: $60
AAPOR members: $60
ASA members: $75
Nonmembers: $95

Each registration is allowed one web connection and one audio connection. 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 17, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Back to top

Title: Statistical Methods Used in Pre-Clinical Drug Combination Studies
Presenter: Wei Zhao, Medimmune
Date and Time: Thursday, April 16, 2015, 12:00 p.m. - 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Tuesday, April 14, at 12:00 p.m. Eastern time

Description:
Various oncogenic cell signaling pathways are known to provide cross-talk and redundancy within tumors. Thus inhibition of such pathways individually by a single targeted therapy has been shown to lead to compensation by other pathways. This, in turn, results in a loss of sensitivity to the original targeted therapeutic agent at the cellular level. In the clinic, this type of compensation leads to tumor resistance and relapse. Because advanced tumors are often resistant to single agents, there is an increasing trend to combine drugs to achieve better treatment effect and reduce safety issues. The growing interest in using combination drugs has spawned the development of many novel statistical methodologies. In this webinar presentation, I will demonstrate the various statistical methods used in designing and analyzing pre-clinical drug combination studies.

Registration Fees:
Biopharmaceutical Section Members: $44
ASA Members: $59
Nonmembers: $74

Each registration is allowed one web connection and one audio connection. 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 14, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Back to top

Title: Interpretation of Patient-Reported Outcomes
Presenter: Joseph Cappelleri, Pfizer, Inc.
Date and Time: Thursday, May 14, 2015, 12:00 p.m. - 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section and Health Policy Statistics Section

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

Description:
A patient-reported outcome is any report on the status of a patient's health condition that comes directly from the patient. Clear and meaningful interpretation of patient-reported outcome scores are fundamental to their use as they can be valuable in designing studies, evaluating interventions, educating consumers, and informing health policy makers involved with regulatory, reimbursement, and advisory agencies. Interpretation of patient-reported outcome scores, however, is often not well understood because of insufficient data or lack of experience or clinical understanding to draw from.

This presentation provides an update review on two broad approaches - anchor-based and distributed-based - aimed at enhancing the understanding and meaning of patient-reported outcome scores. Anchor-based approaches include percentages based on thresholds, criterion-group interpretation, content-based interpretation, and clinical important difference. Distributed-based approaches include effect size, probability of relative benefit, and responder analysis and cumulative proportions.

A third strategy called mediation analysis, which can elucidate a health condition measured by a patient-reported outcome in the context of an intervention's mechanism of action, is also highlighted and illustrated. Mediation analysis in the context of interpretation of patient-reported outcome scores is a relatively new development.

The logic and rationale of the three methods are expressed generally. While the three approaches themselves are not new, some applications of them taken from their examples published in the past several years are original and coalesced in this presentation.

Registration Fees:
Biopharmaceutical Section Members: $44
Health Policy Statistics Section Members: $44
ASA Members: $59
Nonmembers: $74

Each registration is allowed one web connection and one audio connection. 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, May 12, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Back to top

Title: Propensity Score Methods for Estimating Causal Effects in Pharmaceutical Research
Presenter: Elizabeth Stuart, Johns Hopkins Bloomberg School of Public Health
Date and Time: Wednesday, June 24, 2015, 12:00 p.m. - 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Monday, June 22, at 12:00 p.m. Eastern time

Description:
Propensity scores are an increasingly common tool for estimating the effects of interventions in observational ("non-experimental") settings and for answering complex questions in randomized controlled trials. They can be of great use in pharmaceutical and health services research, for example helping assess broad population effects of drugs, devices, or biologics already on the market, especially investigating post-marketing safety outcomes, or for answering questions regarding the outcomes of long-term use using claims data. This webinar will discuss the importance of the careful design of observational studies, and the role of propensity scores in that design, with the main goal of providing practical guidance on the use of propensity scores to estimate causal effects. The webinar will briefly cover the primary ways of using propensity scores to adjust for confounders when estimating the effect of a particular "cause" or "intervention," including weighting, subclassification, and matching. Topics covered will include how to specify and estimate the propensity score model, selecting covariates to include in the model, diagnostics, and common challenges and solutions. Software for implementing analyses using propensity scores will also be briefly discussed. The webinar will also highlight recent advances in the propensity score literature, with a focus on topics particularly relevant for pharmaceutical contexts, including prognostic scores, covariate balancing propensity scores, methods for non-binary treatments (such as dosage levels of a drug or when comparing multiple drugs, devices, or biologics simultaneously), and approaches to be used when there are large numbers of covariates available (as in claims data).

Registration Fees:
Biopharmaceutical Section Members: $44
ASA Members: $59
Nonmembers: $74

Each registration is allowed one web connection and one audio connection. 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, June 22, with the access information to join the webinar and the link to download and print a copy of the presentation slides.