Cohort study
A cohort study, sometimes called a panel study or an observational study, is a form of longitudinal study used in medicine and social science.
A cohort is a group of people who share a common characteristic or experience within a defined period (e.g., are born, leave school, lose their job, are exposed to a drug or a vaccine, etc.). Thus a group of people who were born on a day or in a particular period, say 1948, form a birth cohort. Some cohort studies track groups of children from their birth, and record a wide range of information (exposures) about them. The value of a cohort study depends on the researchers' capacity to stay in touch with all members of the cohort. Some of these studies have continued for decades. Subgroups within the cohort may be compared with each other.
Application
A cohort study is often undertaken to measure the association between a risk factor and a disease. Crucially, the cohort is identified before the appearance of the disease under investigation. The cohort is observed over time to determine the frequency of new incidence of the studied disease.
An example of an epidemiological question that can be answered by the use of a cohort study is: does exposure to X (for example, smoking) correlate with outcome Y (for example, lung cancer)? Such a study would recruit a cohort that contains both smokers and non-smokers. The investigators then follows the cohort for a set period of time and notes differences in the incidence of lung cancer between the smokers and non-smokers. The groups are matched statistically in terms of many other variables such as economic status and other health status so that the variable being assessed, the independent variable (in this case, smoking) can be isolated as the cause of the dependent variable (in this case, lung cancer).
Classification
Prospective cohort
An example of a cohort study that has been going on for more than 50 years is the Framingham Heart Study.
The largest cohort study in women is the Nurses' Health Study. Started in 1976, it is tracking over 120,000 nurses and has been analyzed for many different conditions and outcomes.
Retrospective cohort
A "prospective cohort" defines the groups before the study is done, while a "retrospective cohort" does the grouping after the data is collected. Thus a retrospective cohort study actually consists of two cohorts that are compared: the cohort with the exposure (independent variable) and the cohort without the exposure. Whereas prospective cohorts should be summarized with the relative risk, retrospective cohorts should be summarized with the odds ratio. Examples of a retrospective cohort are Long-Term Mortality after Gastric Bypass Surgery[1] and 'Alarm symptoms' in patients with dyspepsia: a three-year prospective study from general practice[2].
Nested case-control study
An example of a nested case-control study is Inflammatory markers and the risk of coronary heart disease in men and women which was a case control analysis extracted from the Framingham Heart Study cohort.[3]
Statistical analysis
Because the non-randomized allocation of subjects in a cohort study, several statistical approached have been developed to reduce confounding from selection bias.
A comparison of study in which three approaches (multiple regression, propensity score and grouped treatment variable) were compared in their ability to predict treatment outcomes in a cohort of patients who refused randomization in a chemotherapy trial.[4] The comparison study examined how well three statistical approaches were able to use the nonrandomized patients to replicate the results of the patients who consented to randomization. This comparison found that the propensity score did not add to traditional multiple regression while the grouped treatment variable was least successful.[4]
Multiple regression
Multiple regression with the Cox hazard ratio can be used to adjust for confounding variable. Multiple regression can only correct for confounding by independent variables that have been measured
Grouped treatment variable
Creating a grouped treatment variable attempts to correct for unmeasured confounding influences.[5] For example, in an observational study that included several hospitals, creating a variable for the proportion of patients exposed to the treatment may account for biases in each hospital in decided which patients get the treatment.[4]
Prior event rate ratio
The prior event rate ratio has been used to replicate with observational data from electronic health records the results of the Scandinavian Simvastatin Survival Study[6] and the HOPE and EUROPA trials. [7] Like the grouped treatment variable, the prior event ration attemmpts to correct for unmeasured confounding influences by using the "ratio of event rates between the Exposed and Unexposed cohorts prior to study start time to adjust the study hazard ratio".[7]
Principal components analysis
Principal components analysis was developed by Pearson in 1901.[8] The principal components analysis can only correct for confounding by independent variables that have been measured.
Propensity score matching
The propensity score was introduced by Rosenbaum in 1983.[9] The propensity score is the "conditional probability of receiving one of the treatments under comparison ... given the observed covariates."[4] The propensity score can only correct for confounding by independent variables that have been measured.
Alternative study designs
Rare outcomes, or those that slowly develop over long periods, are generally not studied with the use of a cohort study, but are rather studied with the use of a case-control study.
Randomized controlled trials (RCTs) are a superior methodology in the hierarchy of evidence, because they limit the potential for bias by randomly assigning one patient pool to an intervention and another patient pool to non-intervention (or placebo). This minimizes the chance that the incidence of confounding variables will differ between the two groups.
Nevertheless, it is sometimes not practical or ethical to perform RCTs to answer a clinical question. To take our example, if we already had reasonable evidence that smoking causes lung cancer then persuading a pool of non-smokers to take up smoking in order to test this hypothesis would generally be considered quite unethical.
References
- ↑ Adams TD, Gress RE, Smith SC, et al (2007). "Long-term mortality after gastric bypass surgery". N. Engl. J. Med. 357 (8): 753-61. DOI:10.1056/NEJMoa066603. PMID 17715409. Research Blogging.
- ↑ Meineche-Schmidt V, Jørgensen T (2002). "'Alarm symptoms' in patients with dyspepsia: a three-year prospective study from general practice". Scand. J. Gastroenterol. 37 (9): 999–1007. PMID 12374244. [e]
- ↑ Pai JK, Pischon T, Ma J, et al (2004). "Inflammatory markers and the risk of coronary heart disease in men and women". N. Engl. J. Med. 351 (25): 2599-610. DOI:10.1056/NEJMoa040967. PMID 15602020. Research Blogging.
- ↑ 4.0 4.1 4.2 4.3 Schmoor C, Caputo A, Schumacher M (May 2008). "Evidence from nonrandomized studies: a case study on the estimation of causal effects". Am. J. Epidemiol. 167 (9): 1120–9. DOI:10.1093/aje/kwn010. PMID 18334500. Research Blogging.
- ↑ Johnston SC, Henneman T, McCulloch CE, van der Laan M (October 2002). "Modeling treatment effects on binary outcomes with grouped-treatment variables and individual covariates". Am. J. Epidemiol. 156 (8): 753–60. PMID 12370164. [e]
- ↑ Weiner MG, Xie D, Tannen RL (March 2008). "Replication of the Scandinavian Simvastatin Survival Study using a primary care medical record database prompted exploration of a new method to address unmeasured confounding". Pharmacoepidemiol Drug Saf. DOI:10.1002/pds.1585. PMID 18327857. Research Blogging.
- ↑ 7.0 7.1 Tannen RL, Weiner MG, Xie D (March 2008). "Replicated studies of two randomized trials of angiotensin- converting enzyme inhibitors: further empiric validation of the 'prior event rate ratio' to adjust for unmeasured confounding by indication". Pharmacoepidemiol Drug Saf. DOI:10.1002/pds.1584. PMID 18327852. Research Blogging.
- ↑ Pearson, K (1901). "On lines and planes of closest fit to systems of points in space". Philosophical Magazine 2: 559–572. [e]
- ↑ Rosenbaum PR, Rubin DB (1983). "The central role of the propensity score in observational studies for causal effects". Biometrika 70 (1): 41. DOI:10.1093/biomet/70.1.41. Research Blogging.