We understand bias as systematic errors that can lead to mistaken results or interpretation regarding the association under study, when the purpose of a study is assessing the association of certain factors toward supporting the causality of a health event or outcome [4]. Suppose we want to determine the effect of smoking on the frequency of laryngeal cancer in a population distribution that you see in the first table. This chapter highlights the types of biases, their origin, their effects on the validity of the study and ways to avoid or minimize them. We see the contingency table of our study with 1000 people, 80% men and 20% women in both groups exposed and unexposed. The general population consists of both healthy people and unhealthy people. When using modeling multivariate techniques, logistic regression or proportional hazard regression might be used, but researchers must be aware of how to interpret the results properly [2]. The expected attributable risk to both factors would be the addition of the TB incidence of (smoking + indoor contamination) = 34.9 + 0.4 = 35.3%. Let us try to find interaction in the same example. Given that there is a suspicion that cigarette smoking could modify the effect of indoor contamination on the risk of acquiring tuberculosis, smoking habits were considered. Subject Selection Bias. Plus, it’s the same risk as the global cohort, so it seems we have managed to close the back door. This is so because homogenizing according to the confounder we also do it according to other related factors, among other, the exposition itself. Then, when the difference between the calculated raw risk and the risk calculated by strata is over 15%, we could say that confusion is present. For this reason, pairing doesn’t guarantee closing the back door in case-control studies. We understand a confounder as a variable that is associated with the exposure as well as to the health event or outcome, but not being necessarily a cause of the event. Then, the expected incidence will be (31.1 + 35.3) = 66.4%. In a case control study, the major source of selection bias is the manner cases or controls or both are selected and the extent to which the presence (or absence) of exposure may influence such selection In cohort and experimental studies, the major source. Now, suppose that the evaluators applied two diagnostic tests to the exposed that resulted in an increased diagnosis of myocardial infarction among the exposed group. The results suggested that the prevalence estimates of exposures and the outcomes were biased due to self-selection in the Norwegian Mother and Child Cohort Study. Nevertheless, missing data could be present in retrospective cohort studies, where previously registered data are used. However, in past or present smokers, the risk of suffering from tuberculosis is 44% higher than in nonsmokers (from 2.57 to 2.12), when the indoor pollution was present, confirming that smoking habit acts like a confounder in the association between indoor smoke and tuberculosis incidence. In cohort studies, participants are sampled according to their exposures and followed over time for the incidence of outcomes. ... To summarise, the three source of erroneous conclusions are chance, confounding and bias. Confounding in cohort studies During the phase of analysis it is often used techniques as stratification or regression models to measure the association adjusted by the confounding variable. Stratification is a simple statistic technique that could be used during analysis, but that requires forethought concerning the possible confusion variables and registering them. This is what is known as pairing. 1 2 Learn how your comment data is processed. One way is to restrict the inclusion criteria in accordance with the confounding variable. Alike in randomized trials, the best way to avoid this bias is blinding the observers. How do I identify confounding factors in a cohort study measuring behaviour change? Finally, we have the selective survival bias. Antes de aceptar puedes ver. In the first place, it should be understood that both groups are representative of the general population from where they are taken, in order to facilitate the external validity of the study (basic condition to generalize the results in order to support causality). The researchers used Purdue Pegboard and MOART reaction time tests to measure the outcome. 14 Confounding can be a major problem with any observational (nonrandomized) study. The advice is to apply the same protocol and instruments to both groups of people, and in that way, bias introduced by the observer or the instruments is avoided. Several examples can be given in this matter: (1) In the aforementioned study about acetylsalicylic acid exposure and major bleeding, confounders considered were age, sex, previous hospitalization for alcoholism, non-bleeding ulcer disease, other non-bleeding conditions, and comorbidities [15]; then the researchers could adjust the risk ratios according to those variables. For example, one person-year could represent one person being followed for 1 year or two people being followed for 6 months. However, selection bias may be introduced when the completeness of follow-up or case ascertainment differs between exposure categories. The registered codes of the diagnoses may be mistaken and lead to misclassification of the outcomes. For its part, the risk in women is 1%, reaching 3% if they smoke. Therefore, the factor indoor exposure to wood smokefor food cooking turned out to be positively associated with the disease. Loss of five individuals during follow-up with the disease among the nonexposed: Incidence rate in nonexposed = 5/1000 = 0.005. It can happen that people, who know they are under observation, change their behavior. These confounding variables open a backdoor through which our data can slip, making those measures of association between exposure and effect that we estimate not to correspond to reality. Table 1 shows the correct classification. Prospective cohort studies are observational by design and have been described in a previous question.2 The participants were postmenopausal women originally recruited to the Women’s Health Initiative Observational Study, which had been designed to investigate causes of morbidity and mortality. In a case-control study, is information about exposure gathered in the same way for cases and controls? Instead of randomly taking 500 non-smokers controls, we include in the unexposed cohort one non-smoker man per each smoker man in the exposed cohort, and the same with women. If the evaluation of the exposure is misled in both groups due to the mistakes in the daily food register, this results in a non-differential misclassification. what is bias? Confounding bias is kept apart from biases in data analysis (according to the ideas of Steineck and Ahlbom 6 and Maclure and Schneeweiss 5). On the other hand, a prospective cohort design could be affected by the loss of follow-up. Selection bias and confounding are concerns in cohort studies where the reason for inclusion of subjects in the cohort may be related to the outcome of interest. Wholly or partially accounts for apparent effect of exposure on disease (either direction) ! Contents Bias and its types Confounding Bias in cohort study Bias in case control study Elimination of bias Control of confounding References 5/17/2017 2 3. We are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including Nobel Prize winners and some of the world’s most-cited researchers. In case-control studies, cases and Then, during the study, people who smoke can leave the consumption and/or people who do not smoke can start smoking. In cohort studies including randomised trials, differential loss-to-follow-up can lead to selection bias. When a large research team is involved, protocols must be in place for recruitment, evaluations, transporting and storage of samples and materials, laboratory procedures, recording data, backing up information and so on. The chapter also gives examples that allow better understanding of the concepts as well as practical advice when carrying out a cohort study. Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too. But in any case, this is a way of measuring incidence that is very useful in cohort studies because it avoids the issue of subjects shifting form one exposure group to the other. An increasingly common approach to the analysis of cohort studies of health care interventions is to use propensity score methods14 15—a technique that involves multivariate assessment of confounders (see bmj.com for a brief discussion and an example). The results obtained are presented in Table 4. Can we use the pairing? Unlike confounding bias, selection and information bias cannot be completely corrected after the completion of a study; thus we need to minimize their impact during the analysis phase. Matching normally is used in case control studies, but researchers could emphasize that the proportion of women and men would be 50% each or that a ratio of young/old people was similar in the exposure groups. As shown, biases can be present in any study, originating from multiple steps of the investigation. We know the number of expose and non-exposed from data we gave at the beginning of the general population, knowing the risk of cancer arising by gender and exposure to tobacco. Login to your personal dashboard for more detailed statistics on your publications. Confusion bias: their origin is in the relationship that other variables that are not the exposition are related to the outcome, and can modulate the effect(s) of the exposition, contributing to a spurious association. And the effect of the diagnosis of cardiovascular disease [ 17 ] groups ( exposed and nonexposed individuals into. 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