Need a good solid example of selection bias and a good example of sampling bias.

[whatdidigetinto]

I need a good example of a selection bias and a good example of a sampling bias to write down in first aid.
Thanks.

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[Jamiu22]

I need a good example of a selection bias and a good example of a sampling bias to write down in first aid.
Thanks.

of all the bias choices... this one really confuses me too.... but my take on them depends on the actual bias (how you "select" the sample, lol)...

Sampling bias is a lot easier for me to relate to - I just say the study population doesn't represent the actual population of interest.. kinda like studying the rates of pregnancy in inner city youths.... and your sample includes 80% rich kids living in the outskirts of the inner city, while including only 20% of the poor kids (While in fact the poor kids represented 90% of the population). That's sampling bias.

The way I look at selection bias on the other hand is if the researcher isn't selecting study participants accordingly i.e. you are studying the effects of a drug on patients with CHF.. but your selected participants that weren't even that sick to begin with...

It's still somewhat confusing to me, but I look at sampling bias as a subset of selection bias... but sampling is more specific - because it tells you specifically how the selection bias is wrong (not representing the population well)... hope it helps lol

 

[withrye]

Let's say I want to study the effect of a new drug on reducing prostate cancer. I gather a sample of men and women for my study, separate them into two groups, and give one group the drug and the other group the placebo. You can already see that it doesn't matter if I randomize my groups or whatever, I've already committed a sampling error: none of the women in this trial are going to get prostate cancer anyway. You can see that sampling errors undermine the external validity of my study. I picked the wrong sample to properly model my target population.

Let's say I ignore this obvious problem with sampling my population. Now, when I go to make my trial groups, I select all the women to be in the drug trial, and all the men in the placebo trial. Over time, some of the men (all on placebo) get prostate cancer. None of the women (who are all taking drug) get prostate cancer. Does my drug prevent all incidence of prostate cancer? Obviously, I've committed a selection bias in terms of deciding who got drug vs. placebo, and it's affected the internal validity of my trial. I was not careful in selecting my patients evenly and randomly.

I hope that helps.

 

[11MD2B]

There are three main categories of bias; Selection bias, information bias and confounding. sampling error is a form of selection bias. Selection bias and information bias can be most easily understood if you consider any 2x2 table of exposure and outcome. Information bias is a systematic error in the study design that inappropriately counts exposed as unexposed (and vice versa) or diseased as not diseased (and vice versa). This means that some subjects in the study are put into the wrong box in the 2x2 table. An example is recall bias since cases are more likely to accurately describe themselves as exposed compared to controls without disease so more controls end up, wrongly, in the unexposed box, thereby biasing results away from the null or strengthening the observed association. Selection bias is a systematic error in selection of study subjects. The problem here is who (or if) subjects were appropriately or inappropriately included in the study at all. Selection bias can take many forms. In a prospective cohort study or clinical trial, loss to follow up is a common form of selection bias. Consider if subjects are lost to follow up, we do not include them in our final 2x2 table. If the loss to follow up was related to disease and exposure, a bias is present.
Sampling error occurs in case control studies. A classic example is as follows. Consider a study exploring the protective effect of pap smears (exposure) on cervical cancer (outcome). Cases of confirmed cervical cancer are enrolled from the nearby hospital and information on their exposure status is obtained (ie, were they getting pap smears). The investigators decide to generate a control group by going to door to door (or random digit dialing)in the community that the hospital serves and randomly enrolling women. BUT, the recruitment occurs only during business hours (9-5). Whats the bias? well, those women selected as controls (a sampling of the population that gave rise to cases) were less likely to have medical insurance and therefore less likely to have regular pap smears since those enrolled were home during business hours and therefore not employed ergo, no health insurance. This would bias results since those included (in the 2x2 table) as controls was tied exposure status, ie controls were less likely to be exposed than the true source population that generated the cases due to the study design (recruiting subjects during business hours). Specifically, the protective effect of pap smears would be underestimated (biased towards the null) because the selected control population (who do not have cancer) are less likely than the the true source population to have gotten their pap smears. Other types of selection bias include the healthy worker effect (comparing an occupational cohort and there rate of some disease compared to the general population is biased towards the null because in general, occupational cohorts are inherently healthier than the general population because they can work where as the general population includes people too sick to work).

Sorry that was long, hope it helps

 

[stormchao]

This is how I think about it and it's short and sweet.

Sample bias = you sample people into your entire study. Sample bias include recruiting people for your study by enrolling people at Walmart(low socioeconomic class) or whole foods(high socioeconomic class) or McDonald(they don't care about their health) or at a liquor store(probably less healthy). The idea is that your study population does not represent the population as a whole. It'd be like enrolling 90% black people in your study, but the general population is only 10% black people.

Selection bias = You select people to go into your 2 groups in the study. You select sick people to go into the drug group, and healthy people to go into the placebo group. That is a classic example.

 

[Armadillos]

selection bias- polling SDN to determine the average stepI score of medical students

 

[MLT2MT2DO]

This is how I think about it and it's short and sweet.

Sample bias = you sample people into your entire study. Sample bias include recruiting people for your study by enrolling people at Walmart(low socioeconomic class) or whole foods(high socioeconomic class) or McDonald(they don't care about their health) or at a liquor store(probably less healthy). The idea is that your study population does not represent the population as a whole. It'd be like enrolling 90% black people in your study, but the general population is only 10% black people.

Selection bias = You select people to go into your 2 groups in the study. You select sick people to go into the drug group, and healthy people to go into the placebo group. That is a classic example.

So sample bias can never be a bias with say a cohort study?

 

[Jamiu22]

So sample bias can never be a bias with say a cohort study?

It definitely can imho... correct me if I'm wrong, but cohort simply just means you follow a group of study participants and see what happens to them on an exposed risk factor....

... since sampling bias just means the study participants are not representative of the population in question, then you could have sampling bias...if you followed the wrong subjects in your prospective or retrospective study.

 

[11MD2B]

This is how I think about it and it's short and sweet.

Sample bias = you sample people into your entire study. Sample bias include recruiting people for your study by enrolling people at Walmart(low socioeconomic class) or whole foods(high socioeconomic class) or McDonald(they don't care about their health) or at a liquor store(probably less healthy). The idea is that your study population does not represent the population as a whole. It'd be like enrolling 90% black people in your study, but the general population is only 10% black people.

Selection bias = You select people to go into your 2 groups in the study. You select sick people to go into the drug group, and healthy people to go into the placebo group. That is a classic example.

Almost. You are confusing sampling bias with generalizability.
Again, sampling bias is type of selection bias specific to case-control studies. In sampling bias, the control group does NOT reflect the distribution of exposure in the source population that gave rise to cases. Enrolling 90% black people into a case-control may limit the generalizability of your results to your study (to predominantly black population) but is not a source of bias. It is also something you can control for and is therefore more a potential for confounding.
The selection bias example you have is indeed classic, in this case it is bias by indication (a group receiving a new treatment appears to do worse because that group was sicker to begin with).

 

[11MD2B]

It definitely can imho... correct me if I'm wrong, but cohort simply just means you follow a group of study participants and see what happens to them on an exposed risk factor....

... since sampling bias just means the study participants are not representative of the population in question, then you could have sampling bias...if you followed the wrong subjects in your prospective or retrospective study.

Sampling bias occurs in case-conrtrol studies. There is no sampling in cohort studies. You just follow a cohort through time and evaluate outcomes among those exposed and those unexposed, In case-control studies you start with cases that arose from a source population. you then need to come up with a sample of that source population that are not diseased ie controls, and compare the exposure distribtion in teh two groups. The purpose of the control group is to reflect the baseline exposure in the source population, ie the population that gave rise to the cases. If you source population does not do that, meaning there was some flaw in your study design such that controls were more or less likely to be exposed, then your result would be biased. If your sampling method selected controls (again who dont have disease by definition) that are less likely to be exposed, then you would assume a stronger association between exposure and outcome, ie biased away from the null hypothesis.

It is confusing and feels like semantics but the differences are real.
Just remember, selection bias is more general and can occur in all types of studies. It arises when your study design includes subjects that shouldn't be included (put the wrong people in your 2x2 table . Sampling bias is a type of selection bias that occurs in case-control studies. Sampling is only done in case-control studies.

Feel free to PM me if i need to do a better job

 

[Jamiu22]

Sampling bias occurs in case-conrtrol studies. There is no sampling in cohort studies. You just follow a cohort through time and evaluate outcomes among those exposed and those unexposed, In case-control studies you start with cases that arose from a source population. you then need to come up with a sample of that source population that are not diseased ie controls, and compare the exposure distribtion in teh two groups. The purpose of the control group is to reflect the baseline exposure in the source population, ie the population that gave rise to the cases. If you source population does not do that, meaning there was some flaw in your study design such that controls were more or less likely to be exposed, then your result would be biased. If your sampling method selected controls (again who dont have disease by definition) that are less likely to be exposed, then you would assume a stronger association between exposure and outcome, ie biased away from the null hypothesis.

It is confusing and feels like semantics but the differences are real.
Just remember, selection bias is more general and can occur in all types of studies. It arises when your study design includes subjects that shouldn't be included (put the wrong people in your 2x2 table . Sampling bias is a type of selection bias that occurs in case-control studies. Sampling is only done in case-control studies.

Feel free to PM me if i need to do a better job

I stand corrected...Thanks for the detailed explanation

 

[MLT2MT2DO]

Sampling bias occurs in case-conrtrol studies. There is no sampling in cohort studies. You just follow a cohort through time and evaluate outcomes among those exposed and those unexposed, In case-control studies you start with cases that arose from a source population. you then need to come up with a sample of that source population that are not diseased ie controls, and compare the exposure distribtion in teh two groups. The purpose of the control group is to reflect the baseline exposure in the source population, ie the population that gave rise to the cases. If you source population does not do that, meaning there was some flaw in your study design such that controls were more or less likely to be exposed, then your result would be biased. If your sampling method selected controls (again who dont have disease by definition) that are less likely to be exposed, then you would assume a stronger association between exposure and outcome, ie biased away from the null hypothesis.

It is confusing and feels like semantics but the differences are real.
Just remember, selection bias is more general and can occur in all types of studies. It arises when your study design includes subjects that shouldn't be included (put the wrong people in your 2x2 table . Sampling bias is a type of selection bias that occurs in case-control studies. Sampling is only done in case-control studies.

Feel free to PM me if i need to do a better job

If I omit data from a cohort study, is that a form of sampling bias? Or is that still selection bias? Because the data would have already been ran and I would be choosing afterwards.

 

[11MD2B]

If I omit data from a cohort study, is that a form of sampling bias? Or is that still selection bias? Because the data would have already been ran and I would be choosing afterwards.

If you omit data, it's unethical. Not really an issue of study design or systematic error in the way you gather data so no, not technically a bias but it could behave like a form of selection bias, though it doesn't have to. If you arbitrarily decided to ignore data equally from both axis, then there would be no bias but your study would loose power due to smaller sample size.