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Biostatistics & Public Health Research
เข้าร่วมเมื่อ 12 ส.ค. 2021
Statistical plots such as Histogram, box plot, Q-Q plot, n-p plot in Stata: Part-3
The main topics of this channel are given below:
Sample size calculation
Data management in STATA and SPSS
Advance data analysis
Epidemiology
Sample size calculation
Data management in STATA and SPSS
Advance data analysis
Epidemiology
มุมมอง: 40
วีดีโอ
Bi-variate analysis for one quantitative and one binary variable in Stata- Part 4
Bi-variate analysis for one quantitative and one binary variable in Stata- Part 4 ttest in Stata Mean comparison Mean difference between two groups Data Source: github.com/ahshanulhaque/MyData/raw/main/MyData.dta The main topics of this channel are given below: Sample size calculation Data management in STATA and SPSS Advance data analysis Epidemiology
Bi-variate analysis in Stata for beginners: Part-2 | Cross tabulation, Fisher Exact Test
มุมมอง 31วันที่ผ่านมา
Bi-variate analysis in Stata for beginners: Part-2 | Cross tabulation, Fisher Exact Test Data Source: github.com/ahshanulhaque/MyData/raw/main/MyData.dta tab v024 v025 tab v024 v025, col tab v024 v025, col tab v024 v025, row tab anc2 v025, ro tab anc2 v025, ch tab anc2 v025, col ch tab anc2 v025, ro ch tab anc2 v025, exp tab anc2 v025, exp exa The main topics of this channel are given below: Sa...
Univariate analysis in Stata for beginners: Part-1 | mean, SD, frequency, proportion/percentage
มุมมอง 6814 วันที่ผ่านมา
Univariate analysis in Stata for beginners: Part-1 | mean, SD, frequency, proportion/percentage Data Source: github.com/ahshanulhaque/MyData/raw/main/MyData.dta codebook v024 tab v024 tab v025 tab v190 tab1 v024 v025 v190 b4 bmiCAT toilet2 edu DV anc2 stunting sum v012 sum v012 mH mW ChildAge sum v012, detail ameans mH ameans v012 mH mW Welcome to this beginner-friendly tutorial on performing u...
Stata learning for beginners: “for var” command in Stata
มุมมอง 4414 วันที่ผ่านมา
“for var” command in Stata Data Source: github.com/ahshanulhaque/MyData/raw/main/MyData.dta In this video, we explore the powerful "for var" command in Stata, a versatile tool that allows you to efficiently loop through multiple variables and apply the same operation to each one. Whether you're managing large datasets, generating repetitive outputs, or streamlining your data analysis process, t...
Stata Learning for beginners: How to rename variables in Stata
มุมมอง 4621 วันที่ผ่านมา
How to rename variables in Stata Data Source: github.com/ahshanulhaque/MyData/raw/main/MyData.dta The main topics of this channel are given below: Sample size calculation Data management in STATA and SPSS Advance data analysis Epidemiology
What is a 95% confidence interval #95%CI
มุมมอง 821 วันที่ผ่านมา
A 95% confidence interval is an interval estimate that has a probability of 0.95 of containing the true value of the population The main topics of this channel are given below: Sample size calculation Data management in STATA and SPSS Advance data analysis Epidemiology
Stata learning for beginners: 'Set More Off' || How to View all Stata results/outputs at Once
มุมมอง 4728 วันที่ผ่านมา
'Set More Off' in Stata | How to View all Stata results/outputs at Once In Stata, the command set more off is a handy feature that allows you to view all of your output without interruption. Normally, Stata pauses the display of results after a certain number of lines, prompting you to press a key to continue. This can be useful for step-by-step analysis but can also slow you down when you're r...
How open SPSS data in Stata || convert SPSS data into Stata | Episode - 10
มุมมอง 139หลายเดือนก่อน
The main topics of this channel are given below: Sample size calculation Data management in STATA and SPSS Advance data analysis Epidemiology
How to make asset index /wealth index in Stata using polychoricpca Command
มุมมอง 247หลายเดือนก่อน
How to make asset index /wealth index in Stata using polychoricpca Command
String to date or numeric, encode, split variable in Stata || Episode-9
มุมมอง 76หลายเดือนก่อน
String to date or numeric, encode, split variable in Stata || Episode-9
How to calculate Minimum dietary diversity for women in Stata
มุมมอง 83หลายเดือนก่อน
How to calculate Minimum dietary diversity for women in Stata
Variable generate, composite variable from categorical/quantitative variables in Stata || Episode-8
มุมมอง 191หลายเดือนก่อน
Variable generate, composite variable from categorical/quantitative variables in Stata || Episode-8
Data replace or values transform, find missing values in Stata || Episode-7
มุมมอง 43หลายเดือนก่อน
Data replace or values transform, find missing values in Stata || Episode-7
What forest plot | How to interpret the forest plot | 95% confidence interval plot
มุมมอง 67หลายเดือนก่อน
What forest plot | How to interpret the forest plot | 95% confidence interval plot
Meta-analysis stratified by another variable in Stata #metaanalysis
มุมมอง 902 หลายเดือนก่อน
Meta-analysis stratified by another variable in Stata #metaanalysis
Meta-analysis in Stata || Funnel Plot || Egger’s Test
มุมมอง 3112 หลายเดือนก่อน
Meta-analysis in Stata || Funnel Plot || Egger’s Test
Meta-Analysis in Stata || English Language #MetaAnalysis #ForestPlot
มุมมอง 1662 หลายเดือนก่อน
Meta-Analysis in Stata || English Language #MetaAnalysis #ForestPlot
Data merge and append in Stata for beginners || Episode-6
มุมมอง 482 หลายเดือนก่อน
Data merge and append in Stata for beginners || Episode-6
DHS data analysis: composite index of anthropometric failure in Stata #CIAF
มุมมอง 1072 หลายเดือนก่อน
DHS data analysis: composite index of anthropometric failure in Stata #CIAF
Logical operator in Stata for beginners || Episode-5
มุมมอง 512 หลายเดือนก่อน
Logical operator in Stata for beginners || Episode-5
Nepal Demographic and Health Survey Data Analysis: stunting, wasting and underweight in Stata
มุมมอง 2142 หลายเดือนก่อน
Nepal Demographic and Health Survey Data Analysis: stunting, wasting and underweight in Stata
How to open Stata, Save data, Data view, variable view, DO file for beginners || Episode-1
มุมมอง 1952 หลายเดือนก่อน
How to open Stata, Save data, Data view, variable view, DO file for beginners || Episode-1
How to calculate Household Food Insecurity Access Scale Calculation in Stata
มุมมอง 8902 หลายเดือนก่อน
How to calculate Household Food Insecurity Access Scale Calculation in Stata
Variable order, drop, keep, sort ascending or descending, browse in Stata for beginners || Episode-4
มุมมอง 532 หลายเดือนก่อน
Variable order, drop, keep, sort ascending or descending, browse in Stata for beginners || Episode-4
How to make a customized table in Stata-18
มุมมอง 1622 หลายเดือนก่อน
How to make a customized table in Stata-18
Variable label and value label in Stata for beginners || Episode-3
มุมมอง 652 หลายเดือนก่อน
Variable label and value label in Stata for beginners || Episode-3
How to download Stata-18 || Free for only 7 days
มุมมอง 9582 หลายเดือนก่อน
How to download Stata-18 || Free for only 7 days
Data Read in Stata from Excel file for beginners || Episode-2
มุมมอง 1182 หลายเดือนก่อน
Data Read in Stata from Excel file for beginners || Episode-2
Case-Control data analysis in R using gtsummary || Logistic regression analysis
มุมมอง 9397 หลายเดือนก่อน
Case-Control data analysis in R using gtsummary || Logistic regression analysis
Amazing vidéo Thanks
After comp1, is the procedure the same for all other components?
@@aklimakhatun1530 Yes, if you need 2nd or 3rd, then type predict comp2 comp3
For clear understanding I have a few questions? Let's say, I have 3 groups of intervention and one group is control. 1. Since the sample size is 117 here, do I have to have 117 participants in each group or it is 117/4 ? 2. And is the calculation always comes like 117 or it depends on power of the study? 3. To get a larger sample size what would be the formula?
Thank you. Using this method, we will get sample size for one group. Then we have to multiply by number of groups (n=n1*3)
Think Keu Sir
Ma Shallah
salam sir can i find full video in the youtub channel?
@@user-jb3fk6xd5x Walaikumussalam I will make as soon as possible
think Keu Sir
think Keu Sir
মাশাআল্লাহ
Dear sir, would you mind share us a video of HIFAS questionnaire found in excel and how to import it on STATA? Thanks
Think Keu Sir
Think Keu Sir
ভাই খুজতে খুজতে দেখছি fies নিয়ে আপনি ভিডিও বানিয়েছেন। কয়েকদিন আগে বিবিএস থেকে ডেটাসেট পেলাম।
This is example dataset.
Mash alla think Keu Sir
Positivity is contagious. Spread it! 🌈
Excellent
think Keu Sir
ভাইয়া, Epidemiology ও এর স্টাডি ডিজাইনের উপর যদি টিউটোরিয়াল দিতেন তাহলে উপকৃত হতাম।
Sure bhaia
Great video. Thanks so much. My question is why "predict comp1" only? What if comp2 and comp3 have eigen values greater than 1, do we also run the "predict comp2" and "predict comp2" command.
please i have this message : estat kmo correlation matrix is singular
Thanks! This is actually simpler than I anticipated it to be. Great work! I will try it out. However, I was wondering, do you not think that we are losing a nuance in the data by converting it into a binary variable? For example, maybe there's some important information between "having an item (1) and not having that item (0), which is most likely lost when categorising into a binary variable?
First of all, thank you so much for this video!!!, it really helped that it wasn´t edited so I could understand all the process of analyzing case-control data. Im new in the field of data analysis and biostatistics, but I have rather a silly rookie question why OR doesn't appear in No smoking, Rural, Normal BMI, Female and Service holder? Also would you mind showing how to do a OR Forest Plot for this data? It would be really helpful
I love it especially for education purposes
#------------------------------------------------------R Package-------------- library(readxl) #----Import 'Excel' Files library(tidyverse) #--- Several package -- library(expss) # ---Var Label library(gtsummary) #--Descriptive Statistics using Psych Package epid # # Data Source:# github.com/ahshanulhaque/MyData/raw/main/MyData.xlsx # mydata<-read_excel("D:/abc/MyData.xlsx", sheet = "Data1") # MyStat <- list(all_continuous() ~ "{mean} ± {sd}", all_categorical() ~ "{n} ({p})") MyDigit <- list( all_categorical() ~ c(0, 2), all_continuous() ~ c(2,2) ) # A11<- mutate(mydata, bmiCAT=factor(bmiCAT, levels = c(0,1), labels = c("Non-underweight", "Underweight"), exclude=NA), edu = factor(edu, levels = c(0, 1), labels = c("Below secondary", "Secondary and above")), DV = factor(DV, levels = c(0, 1), labels = c("Non-violent", "Violent")), anc2 = factor(anc2, levels = c(0, 1), labels = c("Less than 4", "At least 4")), b4 = factor(b4, levels = c(1, 2), labels = c("Male", "Female")), stunting = factor(stunting, levels = c(0, 1), labels = c("Non-stunted", "Stunted")), v024 = factor(v024, levels = 1:8, labels = c("Barisal", "Chittagong", "Dhaka", "Khulna", "Mymensingh", "Rajshahi", "Rangpur", "Sylhet")), # v025 = factor(v025, levels = c(1, 2), labels = c("Urban", "Rural")), v190 = factor(v190, levels = 1:5, labels = c("Poorest", "Poorer", "Middle", "Richer", "Richest")), toilet2 = factor(toilet2, levels = c(0, 1), labels = c("Improved", "Unimproved")) )%>% apply_labels( main_id="Study ID by SRL", v012="Respondent's current age", mH = "Maternal Height in cm", mW = "Maternal Weight in kg", bmiCAT = "Maternal underweight(BMI<18.5)", edu = "Education", DV = "Attitudes to domestic Violence", anc2 = "At least 4 ANC from Medically trained", b4 = "Sex of child", ChildAge = "Child's Age in Months", stunting = "Childhood stunting", v024 = "Division", v025 = "Type of place of residence", v025 = c("Urban" = 1, "Rural"=2), v190 = "Wealth index", toilet2 = "Type of toilet facility", v021 = "Primary sampling unit" ) A11%>% filter(!is.na(v025))%>% select(-main_id)%>% tbl_summary(by = v025, missing = "no",statistic = MyStat, digits = MyDigit)%>% bold_labels()
#------------------------------------------------------R Package-------------- library(readxl) #----Import 'Excel' Files library(tidyverse) #--- Several package -- library(expss) # ---Var Label library(gtsummary) #--Descriptive Statistics using Psych Package epid # # Data Source:# github.com/ahshanulhaque/MyData/raw/main/MyData.xlsx # mydata<-read_excel("D:/abc/MyData.xlsx", sheet = "Data1") # MyStat <- list(all_continuous() ~ "{mean} ± {sd}", all_categorical() ~ "{n} ({p})") MyDigit <- list( all_categorical() ~ c(0, 2), all_continuous() ~ c(2,2) ) # A11<- mutate(mydata, bmiCAT=factor(bmiCAT, levels = c(0,1), labels = c("Non-underweight", "Underweight"), exclude=NA), edu = factor(edu, levels = c(0, 1), labels = c("Below secondary", "Secondary and above")), DV = factor(DV, levels = c(0, 1), labels = c("Non-violent", "Violent")), anc2 = factor(anc2, levels = c(0, 1), labels = c("Less than 4", "At least 4")), b4 = factor(b4, levels = c(1, 2), labels = c("Male", "Female")), stunting = factor(stunting, levels = c(0, 1), labels = c("Non-stunted", "Stunted")), v024 = factor(v024, levels = 1:8, labels = c("Barisal", "Chittagong", "Dhaka", "Khulna", "Mymensingh", "Rajshahi", "Rangpur", "Sylhet")), # v025 = factor(v025, levels = c(1, 2), labels = c("Urban", "Rural")), v190 = factor(v190, levels = 1:5, labels = c("Poorest", "Poorer", "Middle", "Richer", "Richest")), toilet2 = factor(toilet2, levels = c(0, 1), labels = c("Improved", "Unimproved")) )%>% apply_labels( main_id="Study ID by SRL", v012="Respondent's current age", mH = "Maternal Height in cm", mW = "Maternal Weight in kg", bmiCAT = "Maternal underweight(BMI<18.5)", edu = "Education", DV = "Attitudes to domestic Violence", anc2 = "At least 4 ANC from Medically trained", b4 = "Sex of child", ChildAge = "Child's Age in Months", stunting = "Childhood stunting", v024 = "Division", v025 = "Type of place of residence", v025 = c("Urban" = 1, "Rural"=2), v190 = "Wealth index", toilet2 = "Type of toilet facility", v021 = "Primary sampling unit" ) A11%>% filter(!is.na(v025))%>% select(-main_id)%>% tbl_summary(by = v025, missing = "no",statistic = MyStat, digits = MyDigit)%>% bold_labels()
Good Morning Sir @amalsedekah
* Set variable labels label variable main_id "Study ID by SRL" label variable v012 "Respondent's current age" label variable mH "Maternal Height in cm" label variable mW "Maternal Weight in kg" label variable bmiCAT "Maternal underweight (BMI<18.5)" label variable edu "Education" label variable DV "Attitudes to domestic Violence" label variable anc2 "At least 4 ANC from Medically trained" label variable b4 "Sex of child" label variable ChildAge "Child's Age in Months" label variable stunting "Childhood stunting" label variable v024 "Division" label variable v025 "Type of place of residence" label variable v190 "Wealth index" label variable toilet2 "Type of toilet facility" label variable v021 "Primary sampling unit" * Set value labels label define bmiCAT_lbl 0 "Non-underweight" 1 "Underweight" label define edu_lbl 0 "Below secondary" 1 "Secondary and above" label define DV_lbl 0 "Non-violent" 1 "Violent" label define anc2_lbl 0 "Less than 4" 1 "At least 4" label define b4_lbl 1 "Male" 2 "Female" label define stunting_lbl 0 "Non-stunted" 1 "Stunted" label define v024_lbl 1 "Barisal" 2 "Chittagong" 3 "Dhaka" 4 "Khulna" 5 "Mymensingh" 6 "Rajshahi" 7 "Rangpur" 8 "Sylhet" label define v025_lbl 1 "Urban" 2 "Rural" label define v190_lbl 1 "Poorest" 2 "Poorer" 3 "Middle" 4 "Richer" 5 "Richest" label define toilet2_lbl 0 "Improved" 1 "Unimproved" * Apply value labels label values bmiCAT bmiCAT_lbl label values edu edu_lbl label values DV DV_lbl label values anc2 anc2_lbl label values b4 b4_lbl label values stunting stunting_lbl label values v024 v024_lbl label values v025 v025_lbl label values v190 v190_lbl label values toilet2 toilet2_lbl
does coefplot work also with HR from cox regression?
Yes, it be worked
Saved me a lot of stress. thank you so much
Very helpful. thanks
thank you
Sir,I need the reference source of the formula for my research study..can you pls help?
woow this was perfect
Learn to speak English first then make videos.. Waste of my time
There is no link in the description
Sorry, Now the link has been given. Thank you.
@@biostatbd Many thanks.
this is gold mine for me! thanks
Thank you very much for your video. It was very helpful. Can i get the do file? Thanks