This is a really nice detailed course with tons of examples, if you can afford, please pay them or purchase of the course instead of auditing. I hope I will be able to purchase it.

The lecturers were excellent and learning outcomes were met. Highly recommended for all public health specialists.

learned a lot. Good example to help to understand. very clear

interesting and well organised course

Excellent course for beginners

讲解细致、全面、通俗，对于非英语母语的学生也很友好。

Highly Recommended.

It was a good learning

thank you very much

More on peto maybe

Need updates...

very useful

Excellent

Great

Good

I feel rather the course should additionally include the following: 1) the detailed analysis techniques performed in meta analysis- calculation of pooled OR/ SMD, generation of forest plots, funnel plots for publication bias, meta-regression or sub group analysis. 2) the different tools used to perform meta-analysis like RevMan, Stata, etc. 3) conducting meta-analysis in different type of datasets- discrete data, continuous data. 3) network meta-analysis.

The courses are great but some of them requires prior general understanding. Therefore, as a beginner it's a bit hard for me to get the whole picture.

The slides about PubMed aren't uptodate, the design of the website and some functionalities have changed which could be confusing for students.

i would love to see how to extract data in software and use it for metaanalysis. which is more practical

A good course that lacked practical information on performing a meta-analysis.

The audio quality of a lot of the lectures are quite bad.

A number of the slides seem to be un-organised at times, with repeating information, making them confusing.

While all the information seems to be presented, it feels like it could be done better with more time spent preparing and recording the videos.

This is an awfully designed course. How could coursera have such a junk content? Waste of 13+ hours, could have been done in 1 hour. There was no hands on or worked examples, only so boring outdated textbook contents. Not even one example of how to do systematic review and meta analysis (some other courses in coursera are far better designed (though not best) with a lot of practical teaching: see for example: Introduction to Statistics & Data Analysis in Public Health and related three courses from Imperial College London). Many of the contents are technically wrong. Someone says "to see if P-values were statistically significant or not"! The videos are too lengthy and highly repetitive. And the music in them are awful and off putting. Those from the field videos are meaningless and useless. I could have written more, but In fact, this course does not deserve my lengthy review. I was not expecting this low grade content from the Johns Hopkins.

I recently completed the "Introduction to Systematic Review and Meta-Analysis" course offered by Johns Hopkins University through Coursera, and it has been an incredibly enriching experience. The course provided a clear, step-by-step guide to understanding and conducting systematic reviews and meta-analyses. We began with the fundamentals—defining systematic reviews and meta-analyses, followed by learning how to formulate a research question using the PICO framework (Population, Intervention, Comparator, Outcome). From there, I gained insights into performing comprehensive literature searches, collecting and managing data from eligible studies, and assessing the risk of bias in clinical trials. These steps are essential in ensuring the reliability and validity of findings. One of the most critical lessons was how to synthesize collected evidence qualitatively and quantitatively through meta-analysis. The course demonstrated how to interpret forest plots, confidence intervals, and heterogeneity statistics—core elements for analyzing pooled study results. We also explored the importance of minimizing bias and errors throughout each stage of the review process, ensuring that conclusions are grounded in robust and transparent methodologies. Beyond theoretical knowledge, the course emphasized practical steps to initiate and manage a systematic review project: Assemble a Research Team – Collaborate with content and methods experts. Develop a Protocol – Clearly outline your research question, eligibility criteria, and review methods. Data Collection and Screening – Systematically search and screen studies. Data Abstraction and Risk of Bias Assessment – Extract meaningful data and evaluate study quality. Synthesize Findings – Qualitatively or quantitatively combine data and interpret results. Reporting and Updating Reviews – Present findings transparently and update reviews as new evidence emerges. One of the most engaging aspects was analyzing a published systematic review on early vs. late initiation of epidural analgesia during labor. We explored how researchers framed their question, performed searches, assessed study quality, and interpreted meta-analysis results. The course also highlighted advanced topics such as meta-regression, network meta-analysis, and multivariate meta-analysis, pointing us toward essential references like the Cochrane Handbook for Systematic Reviews of Interventions and the Institute of Medicine's Standards for Systematic Reviews. Overall, this course has significantly enhanced my ability to critically appraise published reviews and laid the groundwork for me to contribute to systematic reviews in healthcare. For anyone interested in evidence-based research, I highly recommend this course—it’s an invaluable resource for both beginners and experienced researchers. #LifelongLearning #SystematicReview #MetaAnalysis #HealthcareResearch #EvidenceBasedMedicine

The course was very well-designed. The pace, content and materials were very good. Being the fellow from the related field of Epidemiology and Biostatistics, thought of sharing my views on some points. The last two lectures were comparatively too fast and some participants may not at all have prior exposure to the domain of epidemiology and biostatistics. Hence, the pace for these too needs to be adjusted for more comprehensive assessment. Secondly, the rubrics for the assignments, has scope for more structured presentation either in the form of tables or diagram. As such though they were very helpful, we needed to scroll down too much to find out the elaborate explanation for the same. Hence, a more structured format may be in excel, with vlookup functions for scoring system may be very handy at the back hand. This will facilitate the peer reviewer to have quick eye ball scrolling and in fact maximize the participation.

Am looking forward for more such courses on Meta-Regression and Network-Meta Analysis in future.

Thanks a lot to all the faculties who helped us in completing the course sucessfully. Moreover the peer reviewer help, and their precious time is also duly acknowledged.

Warm Regards and Thanks

Dr Ashwani Kumar Mishra

Additional Professor of Biostatistics

National Drug Dependence Treatment Centre (NDDTC)

Natioal Investigator-National Investigator for

National Survey on Drug Use, India 2019

All India Institute of Medical Sciences (AIIMS)

New Delhi-110029

INDIA

Email: akmaiims@gmail.com