If your understanding is as great as you think it is, how come you don't work in that field?
Surely if you believe in your knowledge that much, you should do everything possible to let everyone know that?
Jab Fans, any comments ?
- Thread starter bnet
- Start date
Surely if you believe in your knowledge that much, you should do everything possible to let everyone know that?
RegiRoka
Roofer
I'm assuming then that you do work in fields that you often pontificate on. Great!
Thatâs quite a big jump.I'm assuming then that you do work in fields that you often pontificate on. Great!
Whatever I âpontificateâ on, they are merely my own views, because I also have a life like you do. And if someone disagrees, thatâs fine.
What I donât do is post stuff, challenge people to read it (but âbet that they wonât, because theyâre too narrow-mindedâ), & if I am then challenged, have a fucking meltdown everytime about it.
TBH, Iâm as stupid as everyone else around here.
bnet
Roofer
That's a bit of a random one to pick but, yeah, fair enough. It is a scientific study. The variables involved aren't set out sufficiently and there is little presented about obvious alternative factors that could be as influential, but it's mildly interesting.
I'm going to attempt to understand one of your sort of theories in return. Here it is: https://www.audubon.org/news/are-bi...d-drones-so-says-new-conspiracy-theory-making
bnet
Roofer
you're blocked as l told you, but l have a look every now and then just to see how bitter you are. Its funny.
Ah, just making stuff up again to make yourself feel better.
I just have some popcorn at the ready for when you get angry again. And it will happen again.
Blocked
Not blocked
Blocked
RegiRoka
Roofer
I'm glad that we can agree that we're all a bit daft.
I don't think it's a big jump though.
It's just a different way of going about things.
You're still confident that one view is right and another is wrong. Without being an expert.
bnet
Roofer
Watch this you piece of shit !
It's fucking idiots like you why these people/kids are dying. You and your kind are cunts and l fucking hope the jab does it to you rather than any poor kid who as listened to utter wankers like you.
You're a fucking dog.
Yet again, you use another lunatic to argue your point. Please try harder. Share some evidence from someone with an ounce of credibility.
https://doctors.cpso.on.ca/DoctorDetails/Christopher-Alan-Shoemaker/0024010-28832
"It is alleged that Dr. Shoemaker engaged in disgraceful, dishonourable or unprofessional conduct and/or failed to maintain the standard of practice of the profession"
bnet
Roofer
This study has a number of flaws that limits its usefulness in drawing any firm conclusions:
1. Immortal Time Bias (ITB) Correction
roblem: The authorsâ method of correcting ITB by aligning all subjects on a single index date might not adequately address the bias. The division of follow-up periods into distinct vaccination status categories (e.g., unvaccinated, 1-dose, 2-dose) may still leave residual bias, as the time spent unvaccinated or partially vaccinated can overlap with different risk periods.A more nuanced approach using time-dependent Cox models might better adjust for changes in risk over different periods.
2. Confounding Factors:While the authors adjusted for several confounders (e.g., age, sex, comorbidities), there is a possibility of residual confounding. The study may not account for all relevant factors, such as socioeconomic status or healthcare access, which could influence both vaccination status and mortality risk.Including more comprehensive data on social determinants of health and performing sensitivity analyses could help address this issue.
3. Outcome Misclassification:The authors excluded âCOVID-19-related deathsâ from their primary analysis due to perceived unreliability in death attribution. This exclusion may introduce bias, as it disregards a significant portion of relevant mortality data.Re-evaluating the criteria for COVID-19-related deaths and including them with appropriate adjustments could provide a more accurate assessment of vaccine impact on mortality.
4. Selection Bias:The study may suffer from selection bias, particularly in how individuals were categorized into different vaccination groups. For example, healthier individuals might be more likely to receive additional vaccine doses, skewing the results.Implementing propensity score matching or other statistical techniques to balance the comparison groups could mitigate this bias.
5. Statistical Analysis and Interpretation:The use of Cox proportional hazard models and the assumptions underlying them may not be fully justified. The Schoenfeld test indicated violations of proportionality assumptions for some comparisons.Exploring alternative survival analysis methods, such as time-varying covariate models, and thoroughly validating model assumptions would strengthen the analysis.
6. Use of Restricted Mean Survival Time (RMST) and Restricted Mean Time Lost (RMTL):The interpretation of RMST and RMTL may be oversimplified, and the extrapolation of these metrics to lifetime expectations could be misleading.Clarifying the limitations of these metrics and avoiding over-extrapolation would provide a more realistic representation of the findings.
7. Potential Biases Not Adequately Addressed:The paper mentions potential biases such as harvesting effect, calendar-time bias, case-counting window bias, and healthy-vaccinee bias, but does not adequately quantify or correct for these biases.Implementing robust methods to quantify and adjust for these biases, such as sensitivity analyses and alternative study designs (e.g., RCTâs), would enhance the credibility of the findings.
8. Generalisability:The study is based on data from a single Italian province, which may limit the generalisability of the results to other regions or populations with different healthcare systems, vaccination strategies, or demographic characteristics.Conducting similar studies in diverse settings and populations would help validate the findings and improve their applicability.
Conclusion:The study presents an effort to critically analyze the impact of C-19 vaccination on all-cause mortality, but several methodological flaws and biases limit the reliability of its conclusions. Addressing these issues through more rigorous statistical methods, comprehensive data inclusion, and transparent reporting would strengthen the studyâs validity and contribute more robust evidence to the ongoing evaluation of C-19 vaccine effectiveness.
1. Immortal Time Bias (ITB) Correction
roblem: The authorsâ method of correcting ITB by aligning all subjects on a single index date might not adequately address the bias. The division of follow-up periods into distinct vaccination status categories (e.g., unvaccinated, 1-dose, 2-dose) may still leave residual bias, as the time spent unvaccinated or partially vaccinated can overlap with different risk periods.A more nuanced approach using time-dependent Cox models might better adjust for changes in risk over different periods.2. Confounding Factors:While the authors adjusted for several confounders (e.g., age, sex, comorbidities), there is a possibility of residual confounding. The study may not account for all relevant factors, such as socioeconomic status or healthcare access, which could influence both vaccination status and mortality risk.Including more comprehensive data on social determinants of health and performing sensitivity analyses could help address this issue.
3. Outcome Misclassification:The authors excluded âCOVID-19-related deathsâ from their primary analysis due to perceived unreliability in death attribution. This exclusion may introduce bias, as it disregards a significant portion of relevant mortality data.Re-evaluating the criteria for COVID-19-related deaths and including them with appropriate adjustments could provide a more accurate assessment of vaccine impact on mortality.
4. Selection Bias:The study may suffer from selection bias, particularly in how individuals were categorized into different vaccination groups. For example, healthier individuals might be more likely to receive additional vaccine doses, skewing the results.Implementing propensity score matching or other statistical techniques to balance the comparison groups could mitigate this bias.
5. Statistical Analysis and Interpretation:The use of Cox proportional hazard models and the assumptions underlying them may not be fully justified. The Schoenfeld test indicated violations of proportionality assumptions for some comparisons.Exploring alternative survival analysis methods, such as time-varying covariate models, and thoroughly validating model assumptions would strengthen the analysis.
6. Use of Restricted Mean Survival Time (RMST) and Restricted Mean Time Lost (RMTL):The interpretation of RMST and RMTL may be oversimplified, and the extrapolation of these metrics to lifetime expectations could be misleading.Clarifying the limitations of these metrics and avoiding over-extrapolation would provide a more realistic representation of the findings.
7. Potential Biases Not Adequately Addressed:The paper mentions potential biases such as harvesting effect, calendar-time bias, case-counting window bias, and healthy-vaccinee bias, but does not adequately quantify or correct for these biases.Implementing robust methods to quantify and adjust for these biases, such as sensitivity analyses and alternative study designs (e.g., RCTâs), would enhance the credibility of the findings.
8. Generalisability:The study is based on data from a single Italian province, which may limit the generalisability of the results to other regions or populations with different healthcare systems, vaccination strategies, or demographic characteristics.Conducting similar studies in diverse settings and populations would help validate the findings and improve their applicability.
Conclusion:The study presents an effort to critically analyze the impact of C-19 vaccination on all-cause mortality, but several methodological flaws and biases limit the reliability of its conclusions. Addressing these issues through more rigorous statistical methods, comprehensive data inclusion, and transparent reporting would strengthen the studyâs validity and contribute more robust evidence to the ongoing evaluation of C-19 vaccine effectiveness.
Club_B00k_Crutchless
Registered
Too many big words, responds with abuseThis study has a number of flaws that limits its usefulness in drawing any firm conclusions:
1. Immortal Time Bias (ITB) Correction
2. Confounding Factors:While the authors adjusted for several confounders (e.g., age, sex, comorbidities), there is a possibility of residual confounding. The study may not account for all relevant factors, such as socioeconomic status or healthcare access, which could influence both vaccination status and mortality risk.Including more comprehensive data on social determinants of health and performing sensitivity analyses could help address this issue.
3. Outcome Misclassification:The authors excluded âCOVID-19-related deathsâ from their primary analysis due to perceived unreliability in death attribution. This exclusion may introduce bias, as it disregards a significant portion of relevant mortality data.Re-evaluating the criteria for COVID-19-related deaths and including them with appropriate adjustments could provide a more accurate assessment of vaccine impact on mortality.
4. Selection Bias:The study may suffer from selection bias, particularly in how individuals were categorized into different vaccination groups. For example, healthier individuals might be more likely to receive additional vaccine doses, skewing the results.Implementing propensity score matching or other statistical techniques to balance the comparison groups could mitigate this bias.
5. Statistical Analysis and Interpretation:The use of Cox proportional hazard models and the assumptions underlying them may not be fully justified. The Schoenfeld test indicated violations of proportionality assumptions for some comparisons.Exploring alternative survival analysis methods, such as time-varying covariate models, and thoroughly validating model assumptions would strengthen the analysis.
6. Use of Restricted Mean Survival Time (RMST) and Restricted Mean Time Lost (RMTL):The interpretation of RMST and RMTL may be oversimplified, and the extrapolation of these metrics to lifetime expectations could be misleading.Clarifying the limitations of these metrics and avoiding over-extrapolation would provide a more realistic representation of the findings.
7. Potential Biases Not Adequately Addressed:The paper mentions potential biases such as harvesting effect, calendar-time bias, case-counting window bias, and healthy-vaccinee bias, but does not adequately quantify or correct for these biases.Implementing robust methods to quantify and adjust for these biases, such as sensitivity analyses and alternative study designs (e.g., RCTâs), would enhance the credibility of the findings.
8. Generalisability:The study is based on data from a single Italian province, which may limit the generalisability of the results to other regions or populations with different healthcare systems, vaccination strategies, or demographic characteristics.Conducting similar studies in diverse settings and populations would help validate the findings and improve their applicability.
Conclusion:The study presents an effort to critically analyze the impact of C-19 vaccination on all-cause mortality, but several methodological flaws and biases limit the reliability of its conclusions. Addressing these issues through more rigorous statistical methods, comprehensive data inclusion, and transparent reporting would strengthen the studyâs validity and contribute more robust evidence to the ongoing evaluation of C-19 vaccine effectiveness.
Neuro should've sent it as a video where the text was in capitals with lots of exclamations, & the person in the freeze frame looks a bit frenzied & bug-eyed.Too many big words, responds with abuse
Not a criticism, but I think we can agree that's how most YouTube links appear here.
bnet
Roofer
Here you go Jab Fans, wake up you stupid fuckers.
Seriously WAKE THE FUCK UP