what does it mean to pool studies

Full Answer

What is data pooling in research?

In contrast, pooling refers to combining data from multiple studies into a single dataset, so that analyses can be run on that new compiled dataset. Therefore, in a submission, pooled data represent a subset of the integrated information to be presented. Pros and cons of pooling

What is meant by pooling of results?

Pooling of results is a Meta-analysis method used to combine the results of different studies in order to get qualitative analysis. Usually used when the size of study is too small to evaluate the effect or relationship. So, pooling results will increase the power of statistical analyses. You can use a multiple imputation to complete data set.

What is the advantage of pooling results in research?

Usually used when the size of study is too small to evaluate the effect or relationship. So, pooling results will increase the power of statistical analyses. You can use a multiple imputation to complete data set.

What is the medical definition of pool?

Medical Definition of pool (Entry 2 of 2) : a readily available supply: as a : the whole quantity of a particular material present in the body and available for function or the satisfying of metabolic demands — see gene pool, metabolic pool b : a body product (as blood) collected from many donors and stored for later use

When to use pooled output?

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What is a pooling study?

A pooled analysis is a statistical technique for combining the results of multiple epidemiological studies. It is one of three types of literature reviews frequently used in epidemiology, along with meta-analysis and traditional narrative reviews. Pooled analyses may be either retrospective or prospective.

What does it mean to pool data?

Data pooling is basically what it sounds like – combining together data to improve the overall effectiveness. This is otherwise known as second party data. Given the need to develop better customer relationships, companies are now looking beyond their own customer data to create a more well-rounded view.

What is pooled analysis in clinical trials?

Pooling consists of adding the numbers of events observed in a given treatment group across the trials and dividing the results by the total number of patients included in this group.

What is pooled data with example?

Pooled data is a mixture of time series data and cross-section data. One example is GNP per capita of all European countries over ten years. Panel, longitudinal or micropanel data is a type that is pooled data of nature.

What are the benefits of pooling data?

Benefits of pooling individual subject data include enhanced statistical power, the ability to compare outcomes and validate models across sites or settings, and opportunities to develop new measures.

How does a data pool work?

A data pool is a centralized repository of data where trading partners (retailers, distributors, or suppliers) can obtain, maintain, and exchange information about products in a standard format. Suppliers can upload data to a data pool, which retailers receive through their data pool.

What do you mean by pooled?

/puːl/ us. /puːl/ to collect something such as money in order for it to be used by several different people or groups: The kids pooled their money to buy their parents a wedding anniversary gift. Collecting and amassing.

What is a pooled effect?

The pooled effect under meta-analysis is weighted average of the study level effect sizes. The only thing which differs in various synthesizing methods is the calculation of weights and how these weights incorporate between study heterogeneity.

What is a pooled rate?

Pooled Rate means the blended hourly rate for the performance of services under the Work Order process described in the Contract for service hours incurred after all Pooled Hours have been used.

What is the difference between pooled data and panel data?

Pooled data occur when we have a “time series of cross sections,” but the observations in each cross section do not necessarily refer to the same unit. Panel data refers to samples of the same cross-sectional units observed at multiple points in time.

What is the pooled model?

Pooled regression model is one type of model that has constant coefficients, referring to both intercepts and slopes. For this model researchers can pool all of the data and run an ordinary least squares regression model.

When to use pooled OLS vs fixed effects?

According to Wooldridge (2010), pooled OLS is employed when you select a different sample for each year/month/period of the panel data. Fixed effects or random effects are employed when you are going to observe the same sample of individuals/countries/states/cities/etc.

What is pooled data analysis?

Abstract. The simple pooling of data is often used to provide an overall summary of subgroup data or data from a number of related studies. In simple pooling, data are combined without being weighted. Therefore, the analysis is performed as if the data were derived from a single sample.

What do you mean by pooled?

/puːl/ us. /puːl/ to collect something such as money in order for it to be used by several different people or groups: The kids pooled their money to buy their parents a wedding anniversary gift. Collecting and amassing.

What is the difference between panel data and pooled data?

Pooled data occur when we have a “time series of cross sections,” but the observations in each cross section do not necessarily refer to the same unit. Panel data refers to samples of the same cross-sectional units observed at multiple points in time.

What is a company data pool?

Data pools, through which different companies combine and share data, are becoming increasingly important. There are manifold reasons for establishing a common data pool.

F - Test Pooled OLS vs. Fixed effect model - Statalist

Dear experts, I am running my fixed effects model and the f test results in choosing Pooled OLS instead of Fixed effect. However, most of the theories I have read supported using fixed effects in panel data because it can control for the unobserved heterogeneity and time-invariant issues in comparison with OLS.

Panel Data: Pooled OLS vs. RE vs. FE Effects - Cross Validated

We had some discussion about the usefullness of Pooled-OLS and RE Estimators compared to FE. So as far as I can tell, the Pooled OLS estimation is simply an OLS technique run on Panel data. Theref...

pooled OLS regression in Stata - Stack Overflow

I'm using Stata/MP 13.0 for Mac. I need to run a pooled OLS regression using Stata on a data set and have the cluster robust variance matrix. I know the regress command for a normal regression but how do I run a POLS regression ?. If someone knows as well a good text explaining POLS (Google wasn't my friend in that case).

What is the difference between a pooled OLS regression model and a ...

I have a given data set and I am asked to fit a pooled OLS regression model, and then a fixed effect model with specific variables. From the research I've done, I am thinking that a pooled OLS regression is just panel data regression.

What does "pool" mean?

1 : a small deep body of usually fresh water. 2 : something like a pool (as in shape or depth) The lamp cast a pool of light. 3 : a small body of standing liquid : puddle a pool of blood. 4 : swimming pool. pool.

What does "pool" mean in medical terms?

Medical Definition of pool. (Entry 1 of 2) of blood. : to accumulate or become static (as in the veins of a bodily part) blood pooled in his legs. pool. noun. Medical Definition of pool (Entry 2 of 2) : a readily available supply: as. a : the whole quantity of a particular material present in the body and available for function or the satisfying ...

What does "pool" mean in a sentence?

2 : a group of people available for some purpose — see also jury pool. pool. transitive verb. Legal Definition of pool (Entry 2 of 2) : to combine (as assets or votes) in a common form or effort especially : to combine (interests) so as not to have a merger of companies considered a purchase for accounting purposes.

What is pooling data?

In contrast, pooling refers to combining data from multiple studies into a single dataset, so that analyses can be run on that new compiled dataset. Therefore, in a submission, pooled data represent a subset of the integrated information to be presented.

How to pool data?

Remember also, that the activity of pooling is largely a programming effort. This impacts your pooling decisions in three main ways: 1 The more similar the programming of each of the studies to be pooled, the easier the pooling will be (eg, pooling data from five recent studies from your own company will require significantly less work than pooling data from three studies done by three different companies over a 10-year period) 2 It will likely take the programmers the same amount of time to pool a small subset of the data from 2 or more studies as to pool all the data from those studies (because of potential differences in variable naming and data structure) 3 To save critical time to submission after your last study, you should plan for additional programming resources to complete the pooled output in parallel with completing the output for the final clinical study.

Why is pooled data less useful?

Pooled data are least useful when the degree of differences makes the results of the pooled data less meaningful or even meaningless. This applies to safety, but is especially true for efficacy pooling. For example, no one would gain any useful information from pooling the efficacy data from two studies in different indications. Similarly, pooling safety data from a single-dose safety study with data from a 52-week safety study would likely dilute the overall AE incidence.

How to save critical time to submission after your last study?

To save critical time to submission after your last study, you should plan for additional programming resources to complete the pooled output in parallel with completing the output for the final clinical study.

Why is data pooled in pharmacokinetics?

For pharmacokinetics, data are pooled for modeling purposes. In most cases, more data is better, regardless of the source, as long as all the pertinent dosing and patient information are available. Differences in dosing, study design, and patient population can be built into and tested in pharmacokinetic models.

Why is pooled data important?

Pooled data are most useful for analyses and evaluations that benefit from a larger sample size , even if that sample is more heterogeneous (includes some differences in patient population, study design, treatment duration, etc):

Is pooling a programming effort?

Remember also, that the activity of pooling is largely a programming effort . This impacts your pooling decisions in three main ways:

What is pooling in statistics?

Whether it is statistically correct to do so depends very much on the specific case. Pooling can refer to combining data, but it can also refer to combining information rather than the raw data. One of the most common uses of pooling is in estimating a variance.

How to use pooling?

Pooling can refer to combining data, but it can also refer to combining information rather than the raw data. One of the most common uses of pooling is in estimating a variance. If we believe that 2 populations have the same variance, but not necesarily the same mean, then we can calculate the 2 estimates of the variance from samples of the 2 groups, then pool them (take a weighted average) to get a single estimate of the common variance. We do not compute a single estimate of the variance from the combined data because if the means are not equal then that will inflate the variance estimate.

Why is validation required in pooling?

As pooling often requires the derivation of common derived variables to enable the mapping of data of similar times (visits), time points etc to a common reportable way, validation may also require a formal review to ensure that data is mapped as expected and required.

Why do pharmaceutical companies pool clinical data?

Submission to Health Authorities of a new compound is one of the main reasons to pool data but there are multiple of others needs for pooling data such as ongoing safety review and publications.

Is there a right or wrong way to pool data?

There is no obvious right or wrong way to pool data but they are many points that need to be well thought in advance. The early planning and a clear planning strategy would definitely help to create a robust pool. In terms of programming the possibilities to pool from raw or derived should be considered depending on the pool objectives, XSGDWHGDWDDYDLODELOLW« Also the strategy for the programs set up should be decided with clear rationale. Some specific points such as treatment re-mapping, inclusion of interim analysis, re-FRGLQJ«KDYHWREHFOHDUOdocumented to avoid possible errors.

Abstract

Meta-analysis can be used to pool rate measures across studies, but challenges arise when follow-up duration varies. Our objective was to compare different statistical approaches for pooling count data of varying follow-up times in terms of estimates of effect, precision, and clinical interpretability.

Background

Meta-analysis has become recognized as an objective means of summarizing evidence from disparate clinical trials [ 1 ]. It is particularly useful when the trials are small and the data are conflicting. Meta-analysis incorporates statistical approaches to pool aggregate data from clinical trials into a summary effect measure [ 2 ].

Methods

Data were taken from a recently published Cochrane systematic review on the effects of asthma self-management education in children [ 11 ]. We selected the two outcomes involving continuous rate measures with the greatest number of contributing studies: days of school absence and emergency room (ER) visits.

Results

We illustrate the use of SMD, IRD, and IRR methods for pooling continuous rate measures using data from a published Cochrane systematic review and meta-analysis that examined the effect of self-management education on morbidity and health services outcomes in children and adolescents with asthma [ 11 ].

Discussion

This paper presented three statistical methods of pooling continuous rate measures in which the denominator reflects varying duration of observation. All methods were fairly easy to implement using standard statistical software.

Conclusions

In this study, we demonstrated that choice of method among the ones presented here for continuous rate measures had little effect on inference. SMD, IRD, and IRR methods all gave qualitatively similar estimates of effect and suggest that the intervention was effective for both outcomes.

Appendix

Table 5 demonstrates the need to perform analyses stratified by study when comparing event rates between treatments. A similar argument would apply to the comparison of risks.

What was the purpose of the Bethesda pool?

The pool is believed to have been used throughout history for ritualistic baths as well as a place where invalids waited to step into the pool for healing . The Bethesda Pool where Jesus healed the paralytic man believed to have been a mikveh, or ritual bath, in the time of Christ. Roman citizens in Jerusalem a century or two later had medicinal baths constructed at the Bethesda Pool. To commemorate Jesus healing the lame man at the Pool of Bethesda, Christians controlling Jerusalem in the later Byzantine and Crusader periods added a chapel and churches that now cover the Bethesda Pool complex.

What Was the Purpose of the Pool of Bethesda?

The Pool of Bethesda in Jerusalem was where sick people gathered with hopes of being cured of their illnesses. The Aramaic word Bethesda means “house of mercy” or “house of grace” in English. Lounging at one of the five porticos or porches adjacent to the pool, the ill people waited and watched for an angel to stir the water. The hopeful, diseased people at the poolside believed that the first person to step into the water after it was stirred by an angel was healed ( John 5:1-4 ).

When to use pooled output?

You can use a multiple imputation to complete data set. Pooled output can be used if there are missing values or you want more accurate results.

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Clinical Trial Data Integration Versus Pooling

Pros and Cons of Pooling

• Pooled data are most useful for analyses and evaluations that benefit from a larger sample size, even if that sample is more heterogeneous (includes some differences in patient population, study design, treatment duration, etc): 1. Assessing variability in efficacy or safety effects in subgroups, such as older patients, women, or individuals with a...

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Pooling Considerations

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