A couple of things...
Just a couple of interesting things…
The Child Health Evaluative Sciences Program at the Hospital for Sick Children in Toronto is recruiting a PhD Biostatistician to lead the execution of a CIHR funded clinical trial methodology project and the planning of upcoming trials with a focus on:
- improving and using methods of Bayesian Decision analysis and Value of Information in pediatric trial design and analysis;
- using patient and caregiver preference elicitation methods (e.g. discrete choice experiments) in pediatrics;
- developing of statistical plan and conducting the statistical analysis for pediatric clinical trials.
The Biostatistician will collaborate with the Principal Investigators (PIs) of four trials that are in the design stage, and with two senior biostatisticians and methodologists within the CHES program. The successful candidate will have protected time for independent methods development. A cross appointment with the Dalla Lana School of Public Health at the University of Toronto will be sought.
Here’s What You’ll Get To Do:
In collaboration with the trials' Principal Investigators (PIs), develop the study protocols;
Contribute in the conceptualization and development of decision analytic models;
Contribute in conducting literature reviews and keep current with study literature;
Assist with design/development and implementation of value of information methods;
Contribute to preparation of reports, presentations, and manuscripts.
Here’s What You’ll Need:
Graduate degree in Statistics, Biostatistics, Health Economics or a related discipline;
Ability to function independently yet collaboratively within a team;
_Excellent statistical programming skills predominantly using R software; _
Experience with report and manuscript writing;
Effective communication, interpersonal, facilitation and organizational skills;
Meticulous attention to detail.
_Employment Type: _
Temporary, Full-Time (3 year contract with possibilities for renewal)
2. And Manuel has an advert for a very interesting short course on Missing Data in health economic evaluations (I will do my bit on Bayesian methods to do this, which is also very much related to the talk I’ll give at the RSS conference in Glasgow, later in September $-$ this is part of Andrea’s PhD work). I’ll post more on this later.
Two-day short course: Methods for addressing missing data in health economic evaluation
Dates: 21-22 September, 2017
Venue: University College London
Missing data are ubiquitous in health economic evaluation. The major concern that arises with missing data is that individuals with missing information tend to be systematically different from those with complete data. As a result, cost-effectiveness inferences based on complete cases are often misleading. These concerns face health economic evaluation based on a single study, and studies that synthesise data from several sources in decision models. While accessible, appropriate methods for addressing the missing data are available in most software packages, their uptake in health economic evaluation has been limited.
Taught by leading experts in missing data methodology, this course offers an in-depth description of both introductory and advanced methods for addressing missing data in economic evaluation. These will include multiple imputation, hierarchical approaches, sensitivity analysis using pattern mixture models and Bayesian methods. The course will introduce the statistical concepts and underlying assumptions of each method, and provide extensive guidance on the application of the methods in practice. Participants will engage in practical sessions illustrating how to implement each technique with user-friendly software (Stata).
At the end of the course, the participants should be able to develop an entire strategy to address missing data in health economic studies, from describing the problem, to choosing an appropriate statistical approach, to conducting sensitivity analysis to standard missing data assumptions, to interpreting the cost-effectiveness results in light of those assumptions.
Who should apply?
The course is aimed at health economists, statisticians, policy advisors or other analysts with an interest in health economic evaluation, who would like to expand their toolbox. It is anticipated that participants will be interested in undertaking or interpreting cost-effectiveness analyses that use patient-level data, either from clinical trials or observational data.
Course fees: £600 (Commercial/Industry); £450 (Public sector); £200 (Students); payable by the 8th September 2017.
_To register for the course or for further information, please see here. _