What is the purpose of using the matching technique in case-control studies?
Matching is often used in case-control control studies to ensure that the cases and controls are similar in certain characteristics, and it is a useful technique to increase the efficiency of the study. Case-Control studies can usually be conducted relatively faster and are inexpensive – particularly when compared with cohort studies (prospective).
What are the advantages of a case control study?
Because of these advantages, case-control studies are commonly used as one of the first studies to build evidence of an association between exposure and an event or disease. In a case-control study, the investigator can include unequal numbers of cases with controls such as 2:1 or 4:1 to increase the power of the study.
What is an individually matched case study?
Individual Matching in Case-Control Studies In an individually matched case-control study, the population of interest is identified, and cases are randomly sampled or selected based on particular inclusion criteria.
Is the two-way table useful for matching case-control studies?
Holland and Rubin (1988)note that the traditional two-way table and its extensions generally provide no causal insight for matched case-control studies.
How does matched design affect efficiency?
What is a case-control study?
What is the causal version of the effect parameters?
What is the purpose of matched case control studies?
What are two covariates used for?
What software is used for case control weighted maximum likelihood estimation?
What is independent case control sampling?
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What is the advantage of using matching?
Advantages of matching Matching is a useful method to optimize resources in a case control study. Matching on a factor linked to other factors may automatically control for the confounding role of those factors (e.g. matching on neighborhood may control for socio-economic factors).
What is the purpose of matching in epidemiology?
Matching is not uncommon in epidemiological studies and refers to the selection of unexposed subjects' i.e., controls that in certain important characteristics are identical to cases. Most frequently matching is used in case-control studies but it can also be used in cohort studies.
What is the most appropriate reason to utilize matching as a design option in a case-control study?
Matching is often used in case-control control studies to ensure that the cases and controls are similar in certain characteristics. For example, in the smoking and lung cancer study, the authors selected controls that were similar in age and sex to carcinoma cases.
What does matching mean in research?
The term matching refers to the procedure of finding for a sample unit other units in the sample that are closest in terms of observable characteristics.
What is matching in case-control?
The Matched Pair Case-Control Study calculates the statistical relationship between exposures and the likelihood of becoming ill in a given patient population. This study is used to investigate a cause of an illness by selecting a non-ill person as the control and matching the control to a case.
What is frequency matching in case-control studies?
In frequency matching, controls are selected such that cases and controls have similar distributions of matching variables. In individual matching, matching is performed for cases individually assuming the majority in the population are controls.
What are the two types of matching used in case-control studies?
The two types of matching in case controls studies are individual and frequency.
What is a non matched case-control study?
The first is a non-matched case-control study in which we enroll controls without regard to the number, or characteristics of the cases. In this study design, the number of controls does not necessarily equal the number of cases. For example, we may enroll 105 cases and 178 controls.
What are essential considerations when selecting controls for a case-control study?
Selection of the Controls The comparison group ("controls") should be representative of the source population that produced the cases. The "controls" must be sampled in a way that is independent of the exposure, meaning that their selection should not be more (or less) likely if they have the exposure of interest.
Why would we want to use a matching method instead of a simple regression?
In contrast to traditional regression models, which do not examine the joint distribution of the predictors (and in particular of treatment assignment and the covariates), matching methods will make it clear when it is not possible to separate the effect of the treatment from other differences between the groups.
Why do researchers sometimes use matching in a difference in differences design?
As the popularity of difference‐in‐differences has risen, so has the application of matching methods to this study design. The objective of matching is to reduce potential confounding by improving the comparability of units in the treatment and control groups.
What is matching in a cohort study?
A matched cohort study involves pairs (or clusters in case several untreated subjects are matched with each of the treated individuals) formed to include individuals who differ with respect to treatment but may be matched on certain baseline characteristics.
Does matching introduce selection bias?
Matching on a non-confounder associated with the exposure leads to selection bias. Adjusting for such a variable is necessary to control the induced selection bias, which usually results in reduced efficiency relative to an unmatched study in which no adjustment for the variable would have been needed.
What is a matched cohort analysis?
A matched cohort study involves pairs (or clusters in case several untreated subjects are matched with each of the treated individuals) formed to include individuals who differ with respect to treatment but may be matched on certain baseline characteristics.
What is an unmatched case control study?
The Unmatched Case-Control study calculates the sample size recommended for a study given a set of parameters and the desired confidence level.
What is confounding in epidemiology?
Confounding is the distortion of the association between an exposure and health outcome by an extraneous, third variable called a confounder.
Matching in case-control studies | The BMJ
EDITOR,—J Martin Bland and Douglas G Altman's review of the advantages and disadvantages of matching in case-control studies omits two important problems.1 Firstly, matching in case-control studies ensures that the matching factors, such as age or sex, are equally distributed between cases and controls. …
Analysis of matched case-control studies | The BMJ
There are two common misconceptions about case-control studies: that matching in itself eliminates (controls) confounding by the matching factors, and that if matching has been performed, then a “matched analysis” is required. However, matching in a case-control study does not control for confounding by the matching factors; in fact it can introduce confounding by the matching factors even ...
MATCHING IN CASE CONTROL STUDIES Matching addresses issues of ...
Individual Matching Controls are matched to cases on one or more attributes (i.e. age, gender, smoking status, etc). Each case/control pair then has identical values on the matching factors.
1:1 Matched Case-Control Studies: Conditional Logistic Regression - IBM
Resolving The Problem. Yes, using SPSS Statistics Multinomial Logistic Regression (NOMREG), which is found in the Regression Models module. Only 1:1 matches can be analyzed using NOMREG.
Case control matching in R (or spss), based on age, sex ... - ResearchGate
I have a data frame that includes people who developed end stage renal disease (ESRD) and those who didn't (controls). I want to match my cases with controls based on age, sex and ethnicity. my ...
3 - Matched case-control studies - Cambridge Core
Case-Control Studies - March 2014. We use cookies to distinguish you from other users and to provide you with a better experience on our websites.
How were controls matched?
For each case recruited, three controls of the same age (born within 30 days of the case) who lived in the same neighbourhood had to be found. The researchers visited homes to the left and right of the case’s home until three controls were identified, and recruitment was limited to one control per household. The purpose of matching was to minimise confounding by reducing systematic differences between the groups of cases and controls (answer b ). Any differences between cases and controls would therefore not result from differences in age or neighbourhood, but rather differences in rotavirus vaccination status and other potential risk factors.
Why do parents recall past behaviour more accurately than controls?
However, parents of cases may have recalled past behaviour more accurately than parents of controls because of the worry of having a child admitted to hospital with severe rotavirus diarrhoea.
What is ecological study?
In an ecological study data are aggregated for groups of people— for example, towns, cities, or countries. Associations between risk factors and diseases are then investigated for the groups of people, allowing an initial examination of the health status and needs of communities.
Is rotavirus effective against diarrhoea?
The researchers concluded that the monovalent rotavirus vaccine was highly effective against admissions for rotavirus diarrhoea in children aged under 2 years in El Salvador. No differences were reported between cases and controls in breastfeeding patterns, premature birth, maternal education, or socioeconomic variables.
Can matching cases and controls reduce recall bias?
Matching cases and controls will not have reduced recall bias (answer d ). Information recall may be inaccurate when past behaviour or exposure to potential risk factors is being reported. Recall bias is the systematic difference between cases and controls in the accuracy of reported information. All parents were interviewed about history of breast feeding, day care attendance, birth weight, and socioeconomic status. However, parents of cases may have recalled past behaviour more accurately than parents of controls because of the worry of having a child admitted to hospital with severe rotavirus diarrhoea.
What is Pitché et al (2015)?
Pitché et al(2015) conducted a case-control study to assess the factors associated with leg erysipelas in sub-Saharan Africa. This was a multi-centre study; the cases and controls were recruited from eight countries in sub-Saharan Africa.
What is the predisposing factor for tinea pedis?
Some studies have suggested that diabetes mellitus and obesity are predisposing factors for tinea pedis. As we know, fasting plasma glucose of >100 mg/dl and raised trigylcerides (>=150 mg/dl) are criteria for diagnosis of metabolic syndrome.
Why do we use random sampling?
We may have to use sampling methods (such as random digit dialing or multistage sampling methods) to recruit controls from the population. A main advantage is that these controls are likely to satisfy the ‘study-base’ principle (described above) as suggested by Wacholder and colleagues. However, they can be expensive and time consuming. Furthermore, many of these controls will not be inclined to participate in the study; thus, the response rate may be very low.
What is the term for a control that was used in the past?
If we use controls from the past (time period when cases did not occur), then the controls are sometimes referred to historic controls . Such controls may be recruited from past hospital records.
Why are relative controls important?
They can be particularly useful when we are interested in trying to ensure that some of the measurable and non-measurable confounders are relatively equally distributed in cases and controls (such as home environment, socio-economic status, or genetic factors).
Why is it important to have a source of controls?
An important source of controls is patients attending the hospital for diseases other than the outcome of interest. These controls are easy to recruit and are more likely to have similar quality of medical records.
How was the data collected in the study of tanning devices?
The investigators assessed the use of tanning devices (using photographs), number of years, and frequency of use of these devices. They also collected information on other variables (such as sun exposure; presence of freckles and moles; and colour of skin, hair, among other exposures.
What are the risks of Kaposi's sarcoma?
If people with Kaposi's sarcoma are asked about exposure and history (e.g., HIV, asbestos, smoking, lead, sunburn, aniline dye, alcohol, herpes, human papillomavirus), the individuals with the disease are more likely to think harder about these exposures and recall having some of the exposures that the healthy controls.
What is the potential for failing to identify confounding variables or exposures?
Similarly, the researcher must recognize the potential for failing to identify confounding variables or exposures, introducing the possibility of confounding bias, which occurs when a variable that is not being accounted for that has a relationship with both the exposure and outcome.
Why do investigators need to create a control group?
The investigator must put a great deal of effort into creating a proper control group to bolster the strength of the case-control study as well as enhance their ability to find true and valid potential correlations between exposures and disease states.
Why are case control studies used?
Case-control studies, due to their typically retrospective nature, can be used to establish a correlationbetween exposures and outcomes, but cannot establish causation. These studies simply attempt to find correlations between past events and the current state.
What are the characteristics of a case group and a control group?
Ideally, the case group (those with the outcome) and the control group (those without the outcome) will have almost the same characteristics, such as age, gender, overall health status, and other factors. The two groups should have similar histories and live in similar environments.
What are the advantages of case control?
Advantages. There are many advantages to case-control studies. First, the case-control approach allows for the study of rare diseases. If a disease occurs very infrequently, one would have to follow a large group of people for a long period of time to accrue enough incident cases to study. Such use of resources may be impractical, so ...
What is case control study?
A case-control study is a type of observational study commonly used to look at factors associated with diseases or outcomes. The case-control study starts with a group of cases, which are the individuals who have the outcome of interest.
What is the purpose of matching a sample?
Matching allows to use a smaller sample size, by preparing the stratified analysis "a priori" (before the study, at the time of cases and control selection), with smaller sample sizes as compared to an unmatched sample with stratified analysis made "a posteriori".
Why is matching important in case control?
Matching is a useful method to optimize resources in a case control study . Matching on a factor linked to other factors may automatically control for the confounding role of those factors (e.g. matching on neighborhood may control for socio-economic factors). Matching allows to use a smaller sample size, by preparing the stratified analysis "a ...
What are the disadvantages of matching?
The greatest disadvantage of matching is that the effect of matching factor on the occurrence of the disease of interest cannot be studied anymore.
What is match in case control?
Matching is a useful method to optimize resources in a case control study.
Can you control for confounding factors in logistic regression?
If statistical softwares with logistic regression are available, it is possible to control for many confounding factors during the analysis of the study, and therefore preventing confounding by matching during the design of the study might not be needed, especially if the study is including a large population and there are few chances that we will end up with empty strata.
Is the matching factor a modifier?
However the study of the matching factor as an effect modifier is still possible if doing a stratified analysis over several categories of the matching factor. For example when matching on age, analysis is still feasible within each age stratum created. However to use different age categories than those used for matching would require a multivariable analysis. Trying to identify a dose response involving a matching factor would also require a multivariable model of analysis.
Is matching a confounding factor?
In this situation the matching factor is not a confounding factor and matching would bring the OR towards 1.
How does matched design affect efficiency?
Kupper et al. (1981)performed a variety of simulations to demonstrate the impact of matching on efficiency. They found that in situations where confounding was present, the confidence intervals for matched studies were smaller than unmatched studies unless the odds ratio and the exposure of interest were large. However, the confidence intervals for the samples with randomly selected controls were always shorter when the number of controls was at least twice that of the cases. This is an important result, as efficiency is often touted as the benefit of an individually matched case-control study design. Simulations aside, Cochran (1953)is often cited as the theoretical paper that demonstrates the efficiency of matched designs. However, as noted by McKinlay (1977), Cochran’s result can be misleading. Comparisons between matched and unmatched study designs are often made with equalsample sizes and no other method of covariate adjustment (e.g. regression). In a matched design, controls may be discarded if they do not match a particular case on the variable or variables of interest. Multiple controls may be discarded per case, depending on the variables of interest (Freedman, 1950; Cochran and Chambers, 1965; McKinlay, 1977). In a typical randomly selected case-control study, these controls would be included. In many cases, if the discarded controls were available to be rejected in the matched study, they would be available for an unmatched design in the same investigation (Billewicz, 1965; McKinlay, 1977). Therefore, it may be more appropriate to compare the efficiencies of matched case-control studies of size nto randomly selected case-control studies of size n+number of discarded controls. Additionally, these randomly selected case-control studies should employ a method of analysis to reduce bias and variance. Therefore, the result from Kupper et al. (1981)is especially poignant, as all randomly selected case-control studies that had a size of at least 2nhad shorter confidence intervals than their matched counterparts of size n.
What is a case-control study?
In an individually matched case-control study, the population of interest is identified, and cases are randomly sampled or selected based on particular inclusion criteria. Although, as Rothman and Greenland (1998)note, the definition of a case may implicitly define the population of interest for cases and controls. Each of these cases is then matched to one or more controls based on a variable (or variables) believedto be a confounder. Much of the literature on individual matching in case-control studies, particularly earlier texts, describes these designs as a way to reduce confounding in the sampling design. Reference to this is made in: Miettinen (1970), Breslow et al. (1978), Breslow and Day (1980), Kupper et al. (1981), Schlesselman (1982), Collett (1991), and Costanza (1995), among others. However, several authors (Breslow and Day, 1980; Kupper et al., 1981; Schlesselman, 1982; Rothman and Greenland, 1998; Vandenbroucke et al., 2007) point out that the goal of matching is to increase the study’s efficiency by forcing the case and control samples to have similar distributions across confounding variables. Rothman and Greenland (1998)go on to say that while matching is intended to control confounding, it cannot do this in case-control study designs, and can, in fact, introduce bias. Costanza (1995)agreed, stating that matching on confounders in case-control studies does nothing to remove the confounding, but frequently introduces negative confounding.
What is the causal version of the effect parameters?
These causal versions of the effect parameters require the specification of the counterfactual outcomes Y0and Y1for binary Aand (W, A, Y = YA) as a time-ordered missing data structure on the full data structure (W , Y0, Y1). One must also make the randomization assumption: {A⊥ Y0, Y1| W}. Since these parameters are always well defined parameters of the distribution of the data, they can thereby be viewed as W-adjusted variable importance parameters. Then there is no need to make these assumptions. We refer to van der Laan (2006)for the details of this framework.
What is the purpose of matched case control studies?
Matched case-control study designs are commonly implemented in the field of public health. While matching is intended to eliminate confounding, the main potential benefit of matching in case-control studies is a gain in efficiency. Methods for analyzing matched case-control studies have focused on utilizing conditional logistic regression models that provide conditional and not causal estimates of the odds ratio. This article investigates the use of case-control weighted targeted maximum likelihood estimation to obtain marginal causal effects in matched case-control study designs. We compare the use of case-control weighted targeted maximum likelihood estimation in matched and unmatched designs in an effort to explore which design yields the most information about the marginal causal effect. The procedures require knowledge of certain prevalence probabilities and were previously described by van der Laan (2008). In many practical situations where a causal effect is the parameter of interest, researchers may be better served using an unmatched design.
What are two covariates used for?
for the risk difference. Two covariates are used for estimation of other parameters, such as the odds ratio:
What software is used for case control weighted maximum likelihood estimation?
Case-control weighted targeted maximum likelihood estimation for Case-Control Designs I and II can be implemented using existing software (including SAS, STATA, and R ). The implementation of case-control weighted targeted maximum likelihood for Case-Control Design II is also very similar to the implementation for Case-Control Design I. Key differences will be stressed here, but for more detail, we refer to Rose and van der Laan (2008).
What is independent case control sampling?
Independent case-control sampling is described as sampling nCcases from the conditional distribution of (W, A), given Y= 1, and sampling nCocontrols from (W, A), given Y= 0. The value of Jused to weight each control is then nCo/nC. We refer to independent case-control sampling as Case-Control Design I, and matched case-control sampling as Case-Control Design II.
