Observational study
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In fields such as epidemiology, social sciences, psychology and statistics, an observational study draws inferences from a sample to a population where the independent variable is not under the control of the researcher because of ethical concerns or logistical constraints. One common observational study is about the possible effect of a treatment on subjects, where the assignment of subjects into a treated group versus a control group is outside the control of the investigator.[1][2] This is in contrast with experiments, such as randomized controlled trials, where each subject is randomly assigned to a treated group or a control group. Observational studies, for lacking an assignment mechanism, naturally present difficulties for inferential analysis.
Motivation
[edit]The independent variable may be beyond the control of the investigator for a variety of reasons:
- A randomized experiment would violate ethical standards. Suppose one wanted to investigate the abortion – breast cancer hypothesis, which postulates a causal link between induced abortion and the incidence of breast cancer. In a hypothetical controlled experiment, one would start with a large subject pool of pregnant women and divide them randomly into a treatment group (receiving induced abortions) and a control group (not receiving abortions), and then conduct regular cancer screenings for women from both groups. Needless to say, such an experiment would run counter to common ethical principles. (It would also suffer from various confounds and sources of bias, e.g. it would be impossible to conduct it as a blind experiment.) The published studies investigating the abortion–breast cancer hypothesis generally start with a group of women who already have received abortions. Membership in this "treated" group is not controlled by the investigator: the group is formed after the "treatment" has been assigned.[citation needed]
- The investigator may simply lack the requisite influence. Suppose a scientist wants to study the public health effects of a community-wide ban on smoking in public indoor areas. In a controlled experiment, the investigator would randomly pick a set of communities to be in the treatment group. However, it is typically up to each community and/or its legislature to enact a smoking ban. The investigator can be expected to lack the political power to cause precisely those communities in the randomly selected treatment group to pass a smoking ban. In an observational study, the investigator would typically start with a treatment group consisting of those communities where a smoking ban is already in effect.[citation needed]
- A randomized experiment may be impractical. Suppose a researcher wants to study the suspected link between a certain medication and a very rare group of symptoms arising as a side effect. Setting aside any ethical considerations, a randomized experiment would be impractical because of the rarity of the effect. There may not be a subject pool large enough for the symptoms to be observed in at least one treated subject. An observational study would typically start with a group of symptomatic subjects and work backwards to find those who were given the medication and later developed the symptoms. Thus a subset of the treated group was determined based on the presence of symptoms, instead of by random assignment.[citation needed]
- Many randomized controlled trials are not broadly representative of real-world patients and this may limit their external validity. Patients who are eligible for inclusion in a randomized controlled trial are usually younger, more likely to be male, healthier and more likely to be treated according to recommendations from guidelines.[3] If and when the intervention is later added to routine-care, a large portion of the patients who will receive it may be old with many concomitant diseases and drug-therapies.
Types
[edit]- Case-control study: study originally developed in epidemiology, in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute.
- Cross-sectional study: involves data collection from a population, or a representative subset, at one specific point in time.
- Longitudinal study: correlational research study that involves repeated observations of the same variables over long periods of time. Cohort study and Panel study are particular forms of longitudinal study.
- Target trial emulation: an observational study that tries to emulate a randomized controlled trial.[4][5]
Degree of usefulness and reliability
[edit]"Although observational studies cannot be used to make definitive statements of fact about the "safety, efficacy, or effectiveness" of a practice, they can:[6]
- provide information on 'real world' use and practice;
- detect signals about the benefits and risks of...[the] use [of practices] in the general population;
- help formulate hypotheses to be tested in subsequent experiments;
- provide part of the community-level data needed to design more informative pragmatic clinical trials; and
- inform clinical practice."[6]
Bias and compensating methods
[edit] | This section needs additional citations for verification. (August 2021) |
In all of those cases, if a randomized experiment cannot be carried out, the alternative line of investigation suffers from the problem that the decision of which subjects receive the treatment is not entirely random and thus is a potential source of bias. A major challenge in conducting observational studies is to draw inferences that are acceptably free from influences by overt biases, as well as to assess the influence of potential hidden biases. The following are a non-exhaustive set of problems especially common in observational studies.
Matching techniques bias
[edit]In lieu of experimental control, multivariate statistical techniques allow the approximation of experimental control with statistical control by using matching methods. Matching methods account for the influences of observed factors that might influence a cause-and-effect relationship. In healthcare and the social sciences, investigators may use matching to compare units that nonrandomly received the treatment and control. One common approach is to use propensity score matching in order to reduce confounding,[7] although this has recently come under criticism for exacerbating the very problems it seeks to solve.[8]
Multiple comparison bias
[edit]Multiple comparison bias can occur when several hypotheses are tested at the same time. As the number of recorded factors increases, the likelihood increases that at least one of the recorded factors will be highly correlated with the data output simply by chance.[9]
Omitted variable bias
[edit]An observer of an uncontrolled experiment (or process) records potential factors and the data output: the goal is to determine the effects of the factors. Sometimes the recorded factors may not be directly causing the differences in the output. There may be more important factors which were not recorded but are, in fact, causal. Also, recorded or unrecorded factors may be correlated which may yield incorrect conclusions.[10]
Selection bias
[edit]Another difficulty with observational studies is that researchers may themselves be biased in their observational skills. This would allow for researchers to (either consciously or unconsciously) seek out the information they're looking for while conducting their research. For example, researchers may exaggerate the effect of one variable, or downplay the effect of another: researchers may even select in subjects that fit their conclusions. This selection bias can happen at any stage of the research process. This introduces bias into the data where certain variables are systematically incorrectly measured.[11]
Quality
[edit]A 2014 (updated in 2024) Cochrane review concluded that observational studies produce results similar to those conducted as randomized controlled trials.[12] The review reported little evidence for significant effect differences between observational studies and randomized controlled trials, regardless of design.[12] Differences need to be evaluated by looking at population, comparator, heterogeneity, and outcomes.[12]
See also
[edit]References
[edit]- ^ "Observational study". Archived from the original on 2016-04-27. Retrieved 2008-06-25.
- ^ Porta M, ed. (2008). A Dictionary of Epidemiology (5th ed.). New York: Oxford University Press. ISBN 9780195314496.
- ^ Kennedy-Martin T, Curtis S, Faries D, Robinson S, Johnston J (November 2015). "A literature review on the representativeness of randomized controlled trial samples and implications for the external validity of trial results". Trials. 16 (1) 495. doi:10.1186/s13063-015-1023-4. PMC 4632358. PMID 26530985.
- ^ Hernán, Miguel A.; Dahabreh, Issa J.; Dickerman, Barbra A.; Swanson, Sonja A. (2025). "The Target Trial Framework for Causal Inference From Observational Data: Why and When Is It Helpful?". Annals of Internal Medicine. 178 (3): 402–407. doi:10.7326/ANNALS-24-01871. ISSN 1539-3704. PMC 11936718. PMID 39961105.
- ^ Cashin, Aidan G.; Hansford, Harrison J.; Hernán, Miguel A.; Swanson, Sonja A.; Lee, Hopin; Jones, Matthew D.; Dahabreh, Issa J.; Dickerman, Barbra A.; Egger, Matthias; Garcia-Albeniz, Xabier; Golub, Robert M.; Islam, Nazrul; Lodi, Sara; Moreno-Betancur, Margarita; Pearson, Sallie-Anne; Schneeweiss, Sebastian; Sharp, Melissa K.; Sterne, Jonathan A. C.; Stuart, Elizabeth A.; McAuley, James H. (2025-09-03). "Transparent Reporting of Observational Studies Emulating a Target Trial-The TARGET Statement". JAMA. 334 (12): 1084–1093. doi:10.1001/jama.2025.13350. ISSN 1538-3598. PMID 40899949.
- ^ a b "Although observational studies cannot provide definitive evidence of safety, efficacy, or effectiveness, they can: 1) provide information on "real world" use and practice; 2) detect signals about the benefits and risks of complementary therapies use in the general population; 3) help formulate hypotheses to be tested in subsequent experiments; 4) provide part of the community-level data needed to design more informative pragmatic clinical trials; and 5) inform clinical practice." "Observational Studies and Secondary Data Analyses To Assess Outcomes in Complementary and Integrative Health Care." Richard Nahin, Ph.D., M.P.H., Senior Advisor for Scientific Coordination and Outreach, National Center for Complementary and Integrative Health, June 25, 2012
- ^ Rosenbaum, Paul R. 2009. Design of Observational Studies. New York: Springer.
- ^ King, Gary; Nielsen, Richard (2019-05-07). "Why Propensity Scores Should Not Be Used for Matching". Political Analysis. 27 (4): 435–454. doi:10.1017/pan.2019.11. hdl:1721.1/128459. ISSN 1047-1987. S2CID 53585283. | link to the full article (from the author's homepage
- ^ Benjamini, Yoav (2010). "Simultaneous and selective inference: Current successes and future challenges". Biometrical Journal. 52 (6): 708–721. doi:10.1002/bimj.200900299. PMID 21154895. S2CID 8806192.
- ^ "Introductory Econometrics Chapter 18: Omitted Variable Bias". www3.wabash.edu. Retrieved 2022-07-16.
- ^ Hammer, Gaël P; du Prel, Jean-Baptist; Blettner, Maria (2009-10-01). "Avoiding Bias in Observational Studies". Deutsches Ärzteblatt International. 106 (41): 664–668. doi:10.3238/arztebl.2009.0664. ISSN 1866-0452. PMC 2780010. PMID 19946431.
- ^ a b c Toews, Ingrid; Anglemyer, Andrew; Nyirenda, John Lz; Alsaid, Dima; Balduzzi, Sara; Grummich, Kathrin; Schwingshackl, Lukas; Bero, Lisa (2024-01-04). "Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials: a meta-epidemiological study". The Cochrane Database of Systematic Reviews. 1 (1): MR000034. doi:10.1002/14651858.MR000034.pub3. ISSN 1469-493X. PMC 10765475. PMID 38174786.
Further reading
[edit]- Rosenbaum PR (2002). Observational Studies (2nd ed.). New York: Springer-Verlag. ISBN 0387989676.
- "NIST/SEMATECH Handbook on Engineering Statistics" at NIST
Observational study
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Definition and Overview
Definition
An observational study is a research design in which investigators observe and measure variables of interest among participants without assigning or manipulating any interventions, treatments, or exposures.[9] Instead, researchers rely on naturally occurring conditions to assess associations between exposures and outcomes, such as disease incidence or health effects.[4] This approach contrasts with experimental designs by avoiding any deliberate influence on the study subjects' environments or behaviors.[3] Key characteristics of observational studies include the absence of randomization and the lack of control over independent variables, allowing exposures to occur as they would in real-world settings.[10] Data collection focuses on documenting existing differences between groups, such as those exposed versus unexposed to a risk factor, to infer potential causal relationships or patterns.[4] These studies are particularly valuable when ethical or practical constraints prevent experimental manipulation, enabling analysis of phenomena in their natural context.[9] The concept of observational studies has historical roots in early epidemiological work, notably John Snow's 1854 investigation of a cholera outbreak in London's Broad Street, which used spatial mapping of cases to identify a contaminated water source without intervening in the population.[11] The term and its formal distinction from experimental methods were established in epidemiology during the mid-20th century, as the field advanced with the rise of systematic data collection and statistical analysis.[3] Building on such foundational descriptive efforts, observational studies evolved into structured designs like cohort and case-control approaches.[4] In terms of basic structure, observational studies typically gather data on exposures and outcomes either prospectively—following participants forward in time—or retrospectively—examining past records—while maintaining no influence over how exposures are assigned to individuals.[4] This framework supports hypothesis generation and population-level insights without the ethical challenges of imposed conditions.[10]Comparison to Experimental Studies
Observational studies differ fundamentally from experimental studies in their design and approach to investigating relationships between exposures and outcomes. In observational studies, researchers do not intervene in the subjects' experiences; instead, they observe and measure exposures and outcomes as they occur naturally in real-world settings, without assigning treatments or manipulating variables.[3] This passive observation often leads to potential confounding factors, where associations between variables may be influenced by unmeasured or uncontrolled elements, making it challenging to establish causality.[12] In contrast, experimental studies, such as randomized controlled trials (RCTs), involve active intervention where the investigator deliberately assigns exposures or treatments to participants, typically through randomization, to directly test causal effects.[3] Experimental studies generally offer higher internal validity compared to observational studies due to features like randomization, control groups, and blinding, which minimize selection bias, confounding, and other systematic errors.[13] Randomization in RCTs helps ensure that groups are comparable at baseline, reducing the likelihood that differences in outcomes stem from factors other than the assigned treatment, thereby strengthening causal inferences.[14] Observational studies, lacking these controls, are more prone to biases that weaken the reliability of conclusions about cause and effect, though they can still provide valuable evidence of associations when experiments are not feasible.[13] Observational studies are often preferred over experimental ones when ethical constraints prohibit random assignment of exposures, such as in cases involving harmful factors like smoking or environmental toxins, where it would be unethical to deliberately expose participants.[6] They are also suitable for studying rare events or outcomes that occur infrequently, making it logistically impractical or prohibitively expensive to wait for them in a controlled experimental setting.[15] For instance, the Framingham Heart Study, a long-term observational cohort initiated in 1948, has tracked cardiovascular risk factors in a community population without interventions, revealing key associations like those between hypertension and heart disease that informed public health strategies.[16] In comparison, experimental clinical trials, such as RCTs evaluating drug efficacy for hypertension, actively assign treatments to participants to directly assess causal impacts on blood pressure and related outcomes.[17]Motivation and Applications
Reasons for Conducting Observational Studies
Observational studies are frequently employed when ethical constraints preclude the use of experimental designs, especially for exposures that are harmful, irreversible, or morally unacceptable to manipulate. For example, researchers cannot ethically assign participants to smoke cigarettes, endure radiation exposure, or engage in drug abuse to assess health outcomes, as such interventions would violate principles of non-maleficence and informed consent.00065-9/fulltext)[18][6] From a practical standpoint, observational studies offer significant advantages in terms of cost-effectiveness and logistical feasibility, particularly for large-scale or long-term investigations where intervention is unnecessary. They utilize routinely collected data or natural occurrences, reducing expenses associated with recruitment, randomization, and controlled follow-up compared to randomized controlled trials, while enabling the inclusion of diverse, real-world populations over extended periods.[19][20][21] Scientifically, these studies excel at capturing authentic behaviors, multifaceted interactions, and rare events in uncontrolled natural environments, yielding insights into ecological validity that experimental settings often compromise. They are indispensable for examining outcomes with prolonged latency or low prevalence, where artificial manipulation could distort genuine associations and generalizability.[22][23][24] A prominent historical illustration is the British Doctors Study (1951-2001), a prospective cohort investigation of over 34,000 male physicians that linked smoking to increased mortality, including lung cancer; it was designed observationally due to the ethical impossibility of randomizing participants to tobacco exposure.[25][26][27]Key Fields of Application
Observational studies are extensively applied in epidemiology to track disease patterns and identify risk factors without intervening in participants' lives. For instance, the Nurses' Health Study, launched in 1976, has followed over 280,000 female nurses to examine associations between lifestyle factors like diet, exercise, and smoking with outcomes such as cardiovascular disease and cancer incidence.[28] This prospective cohort design has generated evidence on how postmenopausal hormone use influences chronic disease risk, informing public health guidelines.[29] In the social sciences, observational studies facilitate the examination of behaviors, societal trends, and long-term outcomes through longitudinal surveys. The Panel Study of Income Dynamics (PSID), ongoing since 1968, tracks U.S. households to analyze intergenerational mobility, including how family socioeconomic status affects educational attainment and later life earnings.[30] Such studies reveal patterns in educational outcomes, such as the role of parental income in college completion rates, supporting policy analyses on inequality.[31] Economics relies on observational studies, particularly natural experiments, to evaluate policy impacts in labor markets without direct manipulation. The 1994 study by David Card and Alan Krueger used a quasi-experimental design comparing fast-food employment in New Jersey and Pennsylvania before and after a minimum wage increase, finding no significant job losses and challenging traditional economic models. This approach has been extended to assess monopsony power in nurse labor markets by exploiting exogenous wage changes at Veterans Affairs hospitals. In environmental science, observational studies monitor long-term exposure to pollutants and their health effects across populations. The Harvard Six Cities Study, initiated in the 1970s, followed over 8,000 adults in areas with varying air quality levels, establishing links between fine particulate matter (PM2.5) exposure and increased cardiopulmonary mortality rates.[32] Extended follow-ups through 2009 confirmed that chronic exposure elevates all-cause mortality risks by 14-37% per 10 μg/m³ increment in PM2.5. An emerging application involves big data and machine learning techniques applied to observational health records since the 2010s, enabling scalable analyses of vast datasets for pattern discovery. These methods process electronic health records (EHRs) to phenotype diseases and predict outcomes, as seen in studies using ML to identify at-risk populations for cognitive impairments from integrated claims and clinical data.[33] Such advancements have accelerated research in personalized medicine by handling heterogeneous big data while addressing biases in real-world evidence generation.[34]Types of Observational Studies
Cohort Studies
Cohort studies are a type of observational study design in which groups of individuals, known as cohorts, are selected based on their exposure status to a potential risk factor and followed over time to observe the occurrence of specific outcomes, such as disease development.[35] These studies can be prospective, where participants are enrolled at baseline and monitored forward in time as events unfold, or retrospective, where existing historical data are used to identify cohorts and reconstruct past exposures and outcomes.[35] This approach allows researchers to assess the natural progression from exposure to outcome without intervening in the subjects' lives.[36] The key steps in conducting a cohort study include defining the exposure and outcome variables clearly, selecting comparable exposed and unexposed cohorts from a source population free of the outcome at baseline, and ensuring systematic follow-up to measure incidence rates.[35] Outcomes are then compared between groups, often through calculation of the relative risk (RR), which quantifies the association between exposure and outcome as the ratio of the incidence in the exposed group to the incidence in the unexposed group:
where $ I_e $ is the incidence proportion (new cases divided by those at risk) in the exposed cohort and $ I_u $ is the corresponding incidence in the unexposed cohort.[37] A RR greater than 1 indicates an increased risk associated with the exposure.[38]
One primary strength of cohort studies is their ability to establish temporality, demonstrating that the exposure precedes the outcome, which supports causal inferences more robustly than designs that trace backward from outcomes, such as case-control studies.[35] They also permit the study of multiple outcomes from a single exposure and direct estimation of incidence and risk.[36]
A seminal example is the Framingham Heart Study, initiated in 1948, which enrolled an original cohort of 5,209 men and women aged 30 to 62 from Framingham, Massachusetts, and has followed participants prospectively to identify risk factors for cardiovascular disease.[39] This ongoing study has generated over 3,000 publications and established key modifiable risk factors, including high blood pressure, elevated cholesterol levels, smoking, and physical inactivity, which vary by sex and contribute to disease incidence.[35]
Case-Control Studies
A case-control study is a retrospective observational design that compares individuals with a specific outcome or disease, known as cases, to individuals without that outcome, known as controls, to evaluate prior exposure to potential risk factors.[40] In this approach, researchers select participants based on their outcome status and then look backward in time to assess differences in exposure history between the two groups.[41] This design is particularly suited for investigating associations where the outcome has already occurred, allowing for efficient hypothesis testing without waiting for events to unfold prospectively.[42] The process typically begins with the selection of cases, often drawn from hospital records or registries of individuals diagnosed with the condition of interest, ensuring clear diagnostic criteria to minimize misclassification.[40] Controls are then chosen from a comparable population without the outcome, ideally matched on factors like age, sex, or socioeconomic status to enhance validity, though unmatched designs are also common.[41] Exposure to risk factors—such as behaviors, environmental agents, or medical histories—is assessed retrospectively through interviews, medical records, or questionnaires for both groups.[42] The key measure of association is the odds ratio (OR), calculated as the odds of exposure among cases divided by the odds of exposure among controls:
For rare diseases, where the outcome prevalence is low (typically under 10%), the OR provides a close approximation to the relative risk (RR), enabling estimation of how much more likely the exposure is to lead to the outcome.[43]
One primary strength of case-control studies is their resource efficiency, making them ideal for studying rare outcomes that would require impractically large sample sizes in prospective designs like cohort studies.[40] They can be conducted relatively quickly and at lower cost, as they do not necessitate long-term follow-up, and they facilitate exploration of multiple exposures simultaneously for a single outcome.[41]
A seminal example is the 1950 study by Richard Doll and Austin Bradford Hill, which investigated the link between smoking and lung cancer by comparing 709 male lung cancer cases to 709 controls without lung cancer, primarily from hospital patients with other conditions. Through detailed interviews on smoking habits, they found that heavy smokers were far more likely to be cases, yielding an odds ratio of approximately 50 for heavy cigarette smokers compared to non-smokers, providing early evidence of a strong causal association.[44] This work highlighted the design's power in identifying risk factors for an emerging epidemic disease.