What a crock of dung. Deal with the filth in your profession and department first. Then bother about systematic reviews. While people like Dr. Peter Gotzche write things like The Critical Psychiatry Textbook and people like the late Bhargavi Davar used to fight against injustices arising out of Psychiatry, you have these clowns lecture people about systematic reviews while they utter rubbish to people in their departments. How about some speeches on the damaging and stigmatising nature of psychiatric labelling, the gaslighting that follows, psychiatric disease-mongering, victims of abuse/crime ending up in psych wards whilst perpetrators being free etc.? There's PLENTY of data to collect on such things in Psychiatry. But that would jeopardise your careers. Instead, you want to bother about which drug works better for which infectious disease, which has nothing to do with what you do in practice. There are enough guys in non-psychiatry medical branches to do systematic reviews and to teach others how to do them. Even if this video is 7 years old, the point still stands. It's a general message to anyone taking up MD (Psychiatry), MRPsych etc.

Why is this in a final fantasy playlist

Thanks, Helpful for me

Great lecture. It has been very helpful. Thank you.

You can't identify outcomes beforehand Ina real scientific review, especially Not in life science, i.e. neurophysics, neuroscience, biophysics...

Thanks from Germany, I learnt good points from it with easy language.

As white, male, and stale as it could ever get.

Thank you so much!

Weighing results by study quality is Absolutely NECESSARY. With all the rubbish published on mainstream-predatory journals if you're not careful uou end up including wedding lists too. That part is absolutely eron, and wrongfully taught too. If you decide by yourself that something is wrong, you MUST explain the learners why. STEM student and teacher here.

You guys need a social media person. This explanation was very hard to follow. Important info though, thank you!

Thank you so much.

Incredibly helpful and insightful talk. Thank you.

Great presentation, can i have a copy of this presentation file if possible?

प्रति, माननीय सांसद महोदय / महोदया भारत सरकार , नई दिल्ली । विषय - तत्काल छात्र चित्रेश के हित को ध्यान में रखकर कार्यवाही करने एवं सांसद में प्रश्न उठाने हेतु । महोदय, सविनय निवेदन है कि जेएनयू प्रशासन ने छात्र चित्रेश कुमार ( मैं स्वयं) के नये सेमेस्टर में प्रवेश पर प्रतिबंध लगाया है। विगत लगभग ८ माह से जेएनयू से सामाजिक बहिष्कार का सामना कर रहे छात्र चित्रेश को यौन उत्पीड़न के फर्जी मामले मे जेएनयू के डां सुनीता रेड्डी ने अपनी बेटी की ओर से आई .सी .सी. जे. एन .यू . में दिनांक १६/१०/२०१९ को लिखित शिकायत दर्ज करायी थी। जिसके तहत जे एन यू चीफ प्रराक्टर धनन्जय सिंह ने कार्यवाही करते हुए १४/०१/२०२० को लिखित आदेश के माध्यम से जेएनयू से चित्रेश का सामाजिक बहिष्कार किया , जिसके चलते महामारी और महाबंद के दौरान महिनो तक फूटपाथ पर गुजर बसर करना पडा़ जिसके कारण चित्रेश का स्वास्थ्य इतना बिगडा़ की अभी भी लगातार डाक्टर के सलाह से दवाईयों का सेवन लगातार जारी है , महाबंद के दौरान भी हजार कि. मी. दूर से यहां दिल्ली पढा़ई करने आए चित्रेश जब कोरोना का सस्पेक्ट मरीज था तब भी जेएनयू रजिस्टार के आदेश पर सुरक्ष विभाग ने किसी भी प्रकार से सहयोग करने से साफ साफ इंकार कर दिया चित्रेश को ऐसे ही मरने के लिए छोड़ दिया और अब किसी भी प्रकार से बिना मामले की स्पष्ट जानकारी दिए बगैर महामारी और महाबंद के पश्चात यह कहना कि - You Are Not Eligible To Regiter For This Semester , Please Contact Evalution Branch For More Information. Rusticated And Out Of Bounds. अब समझना मुश्किल हो रहा है कि आखिर चित्रेश के साथ इतना भेदभावपूर्ण असमानतावादी व्यवहार के द्वारा उनके भविष्य को क्यों बरबाद किया जा रहा है ??? और कौन कौन इस जातिवादी किश्म के आपराधिक षड़यंत्र में शामिल हैं ? ? पुलिस से अनेक बार शिकायत करने के बाद भी अब तब कोई कार्यवाही करना तो दूर एफ. आई . आर. तक दर्ज क्यों नही किया ? पुलिस के पीछे कौन है ? अतः आगे की कार्यवाही हेतु यह पत्र आपको सादर प्रतुत है। धन्यवाद। आवेदक चित्रेश कुमार बंजारे शोधार्थी एवं मानव अधिकार कार्यकर्ता सामूदायिक स्वास्थ्य एवं सामाजिक चिकिसा सामाजिक विञान भवन - २ जवाहरलाल नेहरु विश्वविद्यालय ,नई दिल्ली पिन नंबर - १ १ ० ० ६ ७ मोब. - ७ २ ९ १ ० ५ १ ३ ८ २ / ९ ० ३ ९ ७ ८ २ ६ ४ ९ ईमेल आई डी - [email protected] पता-................. ........................... .................................................... ..................................................... ................................................... नोट - भविष्य में चित्रेश पर (मैं स्वयं) अथवा उनके पारिवारीक सदस्यों पर किसी भी प्रकार से घटना , दुर्घटना , हत्या , आत्महया , हमले आदि गैरकानूनी कार्यो के लिए प्रत्यक्ष परोक्ष रुप से प्रयास किया जाता है तो उस स्थिति में आत्मरक्छा हेतू सुरक्छोपाय का प्रयोग किया जा सकता है ।

Things are not clear about meta Analysis yet Dr LakshmanRao Krisnapuri Chennai India [email protected]

Thank you

THANK YOU

Thanks. Engaging vid

super

Fabulous video and very instructive, thank you!

Excellent Prsentation Monisha..

Very nice Excellent design

Excellent

Excellent presentation!!

Proud of you Sir....

00:00 Presentation 01:45 the Forest Plot 05:30 Overview of the exposition 07:30 key of Stadistical significance is transparency and replicability 08:30 Background on Meta-analysis 12:12 the focus of meta-analysis, definition, ways to measure it 17:30 Computing effect sizes 19:27 Standarized mean group 20:19 How do you make different types of outcome comparable? standarization - correlation coefficient, normal distribution 21:30 what do you do if you have dichotomous outcomes reported in a study? 23:15 Nice effect size calculator 26:15 Basics of Meta-analysis 27:55 Determining the mean effect size, the precision of measuring the effect size is not equally accurate in all studies 30:20 Variance Weighted meta-analysis as a solution of effect size precision variance It helps also to find out how precise our mean is 31:15 Once we have the measures of size effect, its time to analyse it 33:01 We use Log of RR or OD just to make the measures more simetrical, hence we use inverse variance weights 34:05 All we should note about the inverse variance weights formulas are that they use amot sample sizes 35:30 Some studies have a variety of number of size effects, and in order to keep stadistical independence we need to take 1 size effect per study, if wwe take all of the some studies will be louder than others. 37:30 Check list to be ready for meta analisis: Effect Sizes, the inverted variance weight of those sizes, those effect sizes and calculus should be in subsets that answers the same question of the research. 37:45 Calculate the Mean of these inverse variances, this is not a normal average, its a weighted average 38:40 The standard error of the mean of the inverse variances is ITS INVERSE. 39:10 Once we have that we can calculate confidence intervals 40:15 Now we can calculate the z test of that 42:00 with the z test we can now calculate the p value 42:50 We need to do an homogeneity test to know if the values are really explaining something 43:45 If we get heterogeneity; this means all the studies are not telling the same story, hence it can give a hint that the values are an oversimplification of a problem 44:10 If that's the case, youhave options: you can model the difference between the studies a random effect model is an example 44:40 Homogeneity Analisis 46:40 Homogeneity Analisis Q Statistic, this is a weighted sum of squares, looks like the top of the variance formula, its distributed as chi square with k - 1 degrees of freedom why k, we dont know but it works 47:30 If you have Q stadistically significant it means that you have heterogeneity 48:00 On the other side if you get Q not stadistically, it can mean also that you have an small size sample, so you should keep in mind that a Q not stadistically significant doesnt mean automatically that its homogeneous 48:18 I square its an alternative to Q (but apparently not becouse it includes Q), it measure how much heterogeneity there is. 49:45 This meta-analysis is called fixed effects meta-analysis and ironically is not recomended to do alone, its better to do random effects meta-analysis 50:20 How Random effects meta-analiysis works? it assumes homogeneity first and when there is differences it assumes is becouse sampling errors, this means there is a distribution of random effects that produces heterogeneity, this hetereogeneity is assumed an takes the nature of imperfection process unrelated to the nature of the investigative procedure by itself, which is the heterogeneity we want to really measure. The problem with this asumtion is that the random effect meta-analysis assumes this random errors are normally distributed, which it's something that are still in debate. 55:00 You should use Random effects, if you feel isnt enought you can use fixed effects, but not fixed effects alone 55:35 How to do Random effects? you need a new set of weights 56:20 This set of weight has to have the sampling error plus the study variability (not counted in fixed effect model) 56:35 this study variability comes from Q, and then we need to calculate Tau square Tau square will be the constant where the new weight of each study will come from (once it's inversed with log) 58:20 Once we have the new set of w, we just calculate again the weight mean, the standard deviation of w, the standard error, confidence interval (bigger EE, fatter confidence intervals) 59:15 There are different ways to calculate tau square 01:01:05 Normally once you have already all calculated hetereogeneity and you have created the forest plot, it's always a good idea to ask if its possible to know where this variability comes from. 01:01:30 This explanations can come from your coding procedure, where you have identified the differences of the studies. 01:02:00 So you use moderator analysis to compare the diversity of identified differences, one example is: you can identify slight differences between procedures and compare them using categorical models (ANOVA) 01:03:15 If your study has levels of continuity f.e. quality degrees of implementation, and you want to know if that affects the size effect, you have to use a meta analytic form of regression, and not a regular (stadistic form of) regression. 01:04:15 Once you have decided to use a moderator analysis you have to then do a fixed or(/and idealy) random effects meta-analysis of each. becouse you then have to explain where the diversity comes from. Random effects = mixed effects 01:05:00 Example of categorical model ANOVA, random (mixed) effects, then you can see this compares the variability between the randomized and non randomized studies. You then should not take the stadistic significance as indicator of heterogeneity, the same way you did not do it with the study effects size themselves, becouse again, that stadistic significance can mean the samples sizes are different, so what you have to do is again use Q test that is a t test but for meta-analisis (DONT USE T TEST) so then you can measure the difference between the means. 01:06:40 This is the way you can define if the variable you took is accountable to explain the variability that let to heterogeneity, hence the reason you have noise in your data 01:07:00 In the case of meta analytic regression, you must use specialized software becouse is not regular stadistic regression, it is an (special) regression, we can call it meta analytic regresion. 01:08:20 Whit a meta analytic regression you have the regular features you can find in a regular stadistic regression analysis, the Q test is the remplacement of the F test in normal stadistics, and it helps us to determine if the regression is explaining variability. If that is the case, you then need to see if they are stadistically significant, if yes then there is variability. 01:09:15 So in summary you need to take in count; you must look at the average, its confidence interval, then homogeneity, then if there is heterogeneity then try to explain where in comes from through a Moderator model, from wich you will use average, confidence interval, Q test to figure out if the variables you choose to explain the heterogeneity actually explains it. 01:09:45 Forest plot, overview 01:11:00 Question about graphic presentation of Forest PLot 01:13:45 Comments on Software 01:16:30 Final Comments 01:17:15 Take note on publication selection bias, becouse its a big topic, important to get the proper stuff in and the improper out, becouse this gives you the size effects income at the end of the day. 01:17:30 Common errors I hope this could give a reference overview to explore, thanks for the video, amazing information.

00:00 Presentation 02:08 Coding Levels 03:56 Whats coding and why study it Data extraction - Category Straction - Double coding (two people coding) 06:30 Abstract Screening Selection 07:50 You do Double Screening/Coding when reading the full text 10:35 Software file maker to elegibility criteria, Revman 13:15 Study Content code and size code coding book technical detail - critical: metodology,quality of metodology, the findings and the quality of the findings 18:60 What to do with multiple studies same publication and viceversa 22:00 Example of how a coding looks like 23:30 Methods of Coding 25:00 what is Cochrane risk of Bias (overview) 31:15 Modification of Cochrane risk of bias 31:40 GRADE system 33:33 Method Quality Checklist (scales to qualify quality) summarizes different issues of the studies, dont use them to weight study outcomes. 35:12 Direct Method coding 38:50 a Sample of Coding Item (Group Assignment) 41:23 Recomendations about method quality 43:00 Intervention Coding (Importance of intervention, how diverse they are under the same names) Intervention is one of the hardest thing to code, a lot of different ways to report interventions 50:50 Ways to solve the new variables problem when coding interventions 51:10 Participant Coding 53:10 Study Outcome Coding, necessary to know what the called constructs are. 54:35 Structuring your Data 55:35 Coding Studies, Hierachical databases 58:16 Creating your code manual 1:01:07 Common Mistakes 1:06:00 Ways to select the right codes = the matrix correlation option, the theory option this is so diverse, there is no consense on a right way to do this. 1:09:50 There are some methods to develop interanalitic data when 2 reports comes from 1 study A study is a sample of people, if the sample changes, the study isnt the same anymore 1:10:00 Questions relating changes of control samples, inclution of changed or manipulated data . I guess that is an overview, thanks for the valuable video.

Well if that ain’t my uncle right there

Amazing lecture. Thank you so much, Dr.

Link at about 1 hour updated to campbellcollaboration.org/research-resources/effect-size-calculator.html

Thanks! This is quite useful for my "Writing research proposals" class :D You've made a grad student happy!

Good work Ashrita...keep it up

Very nice sir

Can anybody direct me to the data file location, the one presented here at 22:32 does not work.

Interesting

very nice.. more videos if possible

very nice thank yoy

SPSS add-on macros can be downloaded here sites.google.com/site/ahmaddaryanto/meta-analysis-macros

Hey, Can anyone help me to reference the above formulas, is there any citation that include all of these formulas, any help?

Thanks for your relevant and objective presentation. Could you available your presentation in PDF or another way that print?

Can you elaborate on what is an effect size ?

This is very clear and helpful. Thanks!

This was a great lecture. Really vivid way of explaining, thank you.

This is awesome I was stuck u answered many of my questions I'm doing a meta analysis...for my MSc dissertation ...thanks!!

I've been looking how to calculate my effect sizes for my meta-analysis... thanks very much.. you're the answer to my prayers..

thanks for this free videos.

great video and thanks for sharing such knowledge on calculations

Great video and overview of meta-analysis! thanks!

I would never feel comfortable using these equations in real life unless I understood every bit of detail of how they were derived and proved.

Absolutely, a wonderful lecture.