Only one enrollment form needs to be completed for all desired courses.

Deadline:

03/01/2026

Format:
Online
Term Offered:
Spring

Overview

IMS 241 is offered as a 2-unit Design course with an optional 1-unit Analysis Lab.The course provides a foundation in the main study design approaches used to evaluate interventions in real-world implementation settings. Learners examine both non-randomized quasi-experimental designs (including Pre-Post and Interrupted Time Series designs), randomized designs (including pragmatic trials, cluster-randomized trials, stepped-wedge trials, factorial designs, MOST, SMART, and Choice/Preference designs), and hybrid effectiveness-implementation designs (Types I, II, III).For each design, scholars assess core features, common pitfalls, and strategies to strengthen internal and external validity. Throughout the course, learners are challenged to apply each design to their 'real-world' implementation...

IMS 241 is offered as a 2-unit Design course with an optional 1-unit Analysis Lab.

The course provides a foundation in the main study design approaches used to evaluate interventions in real-world implementation settings. Learners examine both non-randomized quasi-experimental designs (including Pre-Post and Interrupted Time Series designs), randomized designs (including pragmatic trials, cluster-randomized trials, stepped-wedge trials, factorial designs, MOST, SMART, and Choice/Preference designs), and hybrid effectiveness-implementation designs (Types I, II, III).

For each design, scholars assess core features, common pitfalls, and strategies to strengthen internal and external validity. Throughout the course, learners are challenged to apply each design to their 'real-world' implementation research questions and settings and to select design features to maximize overall study quality.

The course also introduces analytic approaches for each study design to allow learners to engage effectively with biostatisticians to advance study ideas from concept to implementation. The course will also invite interested learners to conduct more in-depth sample size calculations and analysis of real-world data in an optional Analysis Lab.

Course Details

At the end of the course, scholars will be able to:

  1. Describe the key characteristics of - and rationale for choosing among - common non-randomized quasi-experimental study designs (e.g., pre-post, interrupted time series) and randomized study designs (e.g., pragmatic, cluster, stepped wedge, factorial, MOST, SMART, and choice/preference) used in real-world implementation research.
  2. Identify key threats to internal validity across study designs " including confounding, mediation, effect modification, selection bias, information and reporting bias, and random error - and propose design or analytic strategies to mitigate each threat.
  3. Design hybrid implementation-effectiveness studies and define appropriate implementation and effectiveness outcomes for each study design.
  4. Create clear visual diagrams for each study design for inclusion in protocols, presentations, and grant proposals.
  5. Develop a plan for discussing sampling strategy, sample size determination, and analytic approach with a biostatistician for each study design.

    Optional 1-unit Analysis Lab, in addition to the objectives above:

  6. Identify appropriate analytic methods for each study design covered in the course. Methods reviewed include regression, segmented regression, autoregressive  integrated moving average (ARIMA) models, generalized estimating equations, and mixed effects models.
  7. Calculate and interpret sample size estimates for each study design using STATA software.
  8. Conduct and interpret analyses in STATA using example datasets and code provided for each study design.

For Core Design Course, training or experience in public health, epidemiology, quality improvement or health care organization leadership.  For Analysis Lab, it is also essential that you have training or experience in regression analysis using STATA or other statistical software. Exceptions to these prerequisites may be made with the consent of the Course Director.

This course is taught fully remotely (online) with both synchronous and asynchronous components. Learners engage with content organized into 10 weekly modules. Each week, learners view recorded lectures, assigned readings, and submit written assignments in an online forum. Weekly synchronous meetings occur over Zoom. When scheduling meetings, faculty strive to accommodate the schedules of busy professionals across multiple time zones. Synchronous meeting attendance is recommended but not required. 

IMS 241 is offered as a 2-unit Design course, with an optional 1-unit Analysis Lab. 

Completing this Core Design course will take approximately 4-8 hours per module. Those in the optional Analysis Lab will have an additional 2-4 hours per module.

The Design course constitutes the core curriculum and is required for all learners. Each week, learners will be asked to watch online lectures, review case studies, apply study designs to their own research questions and settings, and provide supportive written feedback on peer assignments. 

At the start of the term, learners may elect to enroll in the optional Analysis Lab, which provides hands-on experience applying analytic methods to the study designs covered in the course. Each week, learners enrolled in the Analysis Lab compute sample size estimates and analyze real or simulated data sets, generating and interpreting results aligned with the weekly design topics.

Note for UCSF Graduate Division Students: The core Design course is worth 2 units, and the optional Analysis Lab is worth 1 additional unit. The Analysis Lab may only be taken in conjunction with the core course.  Students are responsible for managing their enrollment  (e.g., 2 vs. 3 units) with the registrar's office. 

This course requires learners to have a stable internet connection and access to the Zoom platform for synchronous meetings.

All learners who meet the minimum course requirements will receive a formal letter documenting their successful completion of the course, its objectives, and requirements. Only learners who are matriculated in a UCSF Graduate Division program (e.g., Masters, PhD) may receive a letter grade on their UCSF transcript. UCSF graduate students should complete the ImS enrollment form (see below) and enroll via the UCSF Office of the Registrar – Student Portal.

Learners are expected to view assigned video lectures, complete required readings, submit weekly homework assignments (Modules 1-9), provide constructive peer feedback via small group forums, submit the grant proposal excerpt, and compete course evaluations. 

Module 10 (pragmatic trials) does not include a weekly homework assignments and is intended to support completion of the final assignment. 

Completing this Core Design course will take an estimated 4-8 hours of work per module. Those in the optional Analysis Lab will have an additional 2-4 hours per module. 

In order to receive a letter of course completion, learners must:

  • Abide by course Ground Rules and Community Norms (see course website)
  • Turn in weekly assignments by the designated due date and time each week (Modules 1-9).
  • Provide substantive feedback on at least two peers’ assignments during each assigned week.
  • Submit the final assignment by the designated deadline. 

Pass/No Pass vs. Letter Grade

Success in this course is determined as Pass/No Pass unless a letter grade is required for a UCSF Graduate Division Program. Successful learners will receive a letter of course completion following the end of the term.

Leaners taking the course Pass/No Pass, may miss up to two weeks of weekly homework assignments and required peer feedback. 

Learners taking the course for a letter grade must submit all weekly homework and provide peer feedback for each of the nine weeks in which homework is assigned.

All learners must submit the Final Assignment.

Students who do not turn in weekly assignments on time or do not provide required feedback to peers in more than two weeks will have the option of auditing or dropping the course. In either case, course fees will not be refunded.

Academic Year 2025-2026: The fee for each course is $2,900. A reduced fee of $2,250 is available for individuals affiliated with UCSF, individuals based in countries designated as low- or lower-middle-income-economies (LMIC), and individuals based in areas of ongoing armed conflict. Applicants seeking the reduced fee will be asked to provide support for their status. UCSF affiliation is defined as concurrent enrollment in a UCSF–sponsored residency or postdoctoral fellowship program; or a registered student in one of the UCSF professional schools or graduate programs; or individuals who hold full-time salaried UCSF faculty, academic, or staff positions.

Fees do not include books, supplies, or software. (These costs are minimal as most readings are from open-access journals.)

At the end of the course, scholars will be able to:

  1. Describe the key characteristics of - and rationale for choosing among - common non-randomized quasi-experimental study designs (e.g., pre-post, interrupted time series) and randomized study designs (e.g., pragmatic, cluster, stepped wedge, factorial, MOST, SMART, and choice/preference) used in real-world implementation research.
  2. Identify key threats to internal validity across study designs " including confounding, mediation, effect modification, selection bias, information and reporting bias, and random error - and propose design or analytic strategies to mitigate each threat.
  3. Design hybrid implementation-effectiveness studies and define appropriate implementation and effectiveness outcomes for each study design.
  4. Create clear visual diagrams for each study design for inclusion in protocols, presentations, and grant proposals.
  5. Develop a plan for discussing sampling strategy, sample size determination, and analytic approach with a biostatistician for each study design.

    Optional 1-unit Analysis Lab, in addition to the objectives above:

  6. Identify appropriate analytic methods for each study design covered in the course. Methods reviewed include regression, segmented regression, autoregressive  integrated moving average (ARIMA) models, generalized estimating equations, and mixed effects models.
  7. Calculate and interpret sample size estimates for each study design using STATA software.
  8. Conduct and interpret analyses in STATA using example datasets and code provided for each study design.

For Core Design Course, training or experience in public health, epidemiology, quality improvement or health care organization leadership.  For Analysis Lab, it is also essential that you have training or experience in regression analysis using STATA or other statistical software. Exceptions to these prerequisites may be made with the consent of the Course Director.

This course is taught fully remotely (online) with both synchronous and asynchronous components. Learners engage with content organized into 10 weekly modules. Each week, learners view recorded lectures, assigned readings, and submit written assignments in an online forum. Weekly synchronous meetings occur over Zoom. When scheduling meetings, faculty strive to accommodate the schedules of busy professionals across multiple time zones. Synchronous meeting attendance is recommended but not required. 

IMS 241 is offered as a 2-unit Design course, with an optional 1-unit Analysis Lab. 

Completing this Core Design course will take approximately 4-8 hours per module. Those in the optional Analysis Lab will have an additional 2-4 hours per module.

The Design course constitutes the core curriculum and is required for all learners. Each week, learners will be asked to watch online lectures, review case studies, apply study designs to their own research questions and settings, and provide supportive written feedback on peer assignments. 

At the start of the term, learners may elect to enroll in the optional Analysis Lab, which provides hands-on experience applying analytic methods to the study designs covered in the course. Each week, learners enrolled in the Analysis Lab compute sample size estimates and analyze real or simulated data sets, generating and interpreting results aligned with the weekly design topics.

Note for UCSF Graduate Division Students: The core Design course is worth 2 units, and the optional Analysis Lab is worth 1 additional unit. The Analysis Lab may only be taken in conjunction with the core course.  Students are responsible for managing their enrollment  (e.g., 2 vs. 3 units) with the registrar's office. 

This course requires learners to have a stable internet connection and access to the Zoom platform for synchronous meetings.

All learners who meet the minimum course requirements will receive a formal letter documenting their successful completion of the course, its objectives, and requirements. Only learners who are matriculated in a UCSF Graduate Division program (e.g., Masters, PhD) may receive a letter grade on their UCSF transcript. UCSF graduate students should complete the ImS enrollment form (see below) and enroll via the UCSF Office of the Registrar – Student Portal.

Learners are expected to view assigned video lectures, complete required readings, submit weekly homework assignments (Modules 1-9), provide constructive peer feedback via small group forums, submit the grant proposal excerpt, and compete course evaluations. 

Module 10 (pragmatic trials) does not include a weekly homework assignments and is intended to support completion of the final assignment. 

Completing this Core Design course will take an estimated 4-8 hours of work per module. Those in the optional Analysis Lab will have an additional 2-4 hours per module. 

In order to receive a letter of course completion, learners must:

  • Abide by course Ground Rules and Community Norms (see course website)
  • Turn in weekly assignments by the designated due date and time each week (Modules 1-9).
  • Provide substantive feedback on at least two peers’ assignments during each assigned week.
  • Submit the final assignment by the designated deadline. 

Pass/No Pass vs. Letter Grade

Success in this course is determined as Pass/No Pass unless a letter grade is required for a UCSF Graduate Division Program. Successful learners will receive a letter of course completion following the end of the term.

Leaners taking the course Pass/No Pass, may miss up to two weeks of weekly homework assignments and required peer feedback. 

Learners taking the course for a letter grade must submit all weekly homework and provide peer feedback for each of the nine weeks in which homework is assigned.

All learners must submit the Final Assignment.

Students who do not turn in weekly assignments on time or do not provide required feedback to peers in more than two weeks will have the option of auditing or dropping the course. In either case, course fees will not be refunded.

Academic Year 2025-2026: The fee for each course is $2,900. A reduced fee of $2,250 is available for individuals affiliated with UCSF, individuals based in countries designated as low- or lower-middle-income-economies (LMIC), and individuals based in areas of ongoing armed conflict. Applicants seeking the reduced fee will be asked to provide support for their status. UCSF affiliation is defined as concurrent enrollment in a UCSF–sponsored residency or postdoctoral fellowship program; or a registered student in one of the UCSF professional schools or graduate programs; or individuals who hold full-time salaried UCSF faculty, academic, or staff positions.

Fees do not include books, supplies, or software. (These costs are minimal as most readings are from open-access journals.)

Meet the Faculty

Image
A headshot of Starley -- Starley is smiling and wearing glasses and a white shirt. She has shoulder length blonde hair.
View profile
Starley Shade, PhD, MPH

Professor, Department of Epidemiology & Biostatistics

Image
A headshot of Joelle -- she is sitting in an office, smiling and wearing a blue shirt with light polka dots. She has longer light brown hair.
View profile
Joelle Brown, PhD

Associate Professor, Department of Epidemiology & Biostatistics

How to Enroll

Whether you're exploring the field, enhancing your skills, or working toward a larger academic goal, most individual courses are open to everyone. Below, you’ll find details on course fees, how to apply and pay, and the full course schedule. Be sure you understand the enrollment policies and payment process before completing the enrollment form.

And remember: you only need to submit one form for all the new courses you want to take.

Leaners who are matriculated in a UCSF Graduate Division program (e.g., Masters, PhD) may take this course Pass/No Pass or for a letter grade. Please 

  1. Consult with your program manager about taking ImS courses
  2. Complete the ImS enrollment form (see below), and
  3. Enroll via the UCSF Office of the Registrar – Student Portal. 

Direct any enrollment questions to your program manager.

Enrollment policies and payment process (please read before completing the enrollment form)

Please complete this form and select courses. An invoice will be sent shortly. 

For returning learners or those who previously created an account, please login to your existing account to select additional courses. To find the unique link to your account, search your inbox for UCSF Implementation Science Program: Registration Link.

For help accessing your existing account, please email [email protected].

Enrollment in selected courses will be confirmed upon receipt of payment. If a course fills ahead of the registration deadline, you will be placed on a waitlist and notified.

If the registration deadline has passed, please inquire about enrollment opportunities: [email protected]

About two weeks before the course begins, enrolled learners will be sent a welcome note that includes course access instructions as well as the syllabus.

The schedule of synchronous meetings (Zoom) will be determined during the first week of the course. Please promptly respond to any inquiries about your availability. Direct any questions to the faculty leader of the small group to which you will be assigned.