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.
Anthropological survey paper from 1961 by Juhan Aul (et) from University of Tartu who measured about 50 000 people
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, although these particular patient groups will not have been studied in the initial experimental trials. An observational study that examines the real-world patients in everyday routine care can complement the results from the randomized trial in order to be more generally applicable in the patient population.
Typesedit
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.
Degree of usefulness and reliabilityedit
"Although observational studies cannot be used to make definitive statements of fact about the "safety, efficacy, or effectiveness" of a practice, they can:[4]
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
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 biasedit
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,[5] although this has recently come under criticism for exacerbating the very problems it seeks to solve.[6]
Multiple comparison biasedit
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.[7]
Omitted variable biasedit
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.[8]
Selection biasedit
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.[9]
Qualityedit
A 2014 (updated in 2024) Cochrane review concluded that observational studies produce results similar to those conducted as randomized controlled trials.[10] The review reported little evidence for significant effect differences between observational studies and randomized controlled trials, regardless of design.[10] Differences need to be evaluated by looking at population, comparator, heterogeneity, and outcomes.[10]
^. 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. ISBN9780195314496.
^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. PMC4632358. PMID 26530985.
^ ab"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." 2019-09-29 at the Wayback Machine 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.
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February 15, 2024
observational, study, fields, such, epidemiology, social, sciences, psychology, statistics, observational, study, draws, inferences, from, sample, population, where, independent, variable, under, control, researcher, because, ethical, concerns, logistical, con. 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 Anthropological survey paper from 1961 by Juhan Aul et from University of Tartu who measured about 50 000 people Contents 1 Motivation 2 Types 3 Degree of usefulness and reliability 4 Bias and compensating methods 4 1 Matching techniques bias 4 2 Multiple comparison bias 4 3 Omitted variable bias 4 4 Selection bias 5 Quality 6 See also 7 References 8 Further readingMotivation editThe 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 although these particular patient groups will not have been studied in the initial experimental trials An observational study that examines the real world patients in everyday routine care can complement the results from the randomized trial in order to be more generally applicable in the patient population Types editCase 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 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 4 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 4 Bias and compensating methods editThis section needs additional citations for verification Please help improve this article by adding citations to reliable sources in this section Unsourced material may be challenged and removed Find sources Observational study news newspapers books scholar JSTOR August 2021 Learn how and when to remove this template message 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 5 although this has recently come under criticism for exacerbating the very problems it seeks to solve 6 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 7 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 8 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 9 Quality editA 2014 updated in 2024 Cochrane review concluded that observational studies produce results similar to those conducted as randomized controlled trials 10 The review reported little evidence for significant effect differences between observational studies and randomized controlled trials regardless of design 10 Differences need to be evaluated by looking at population comparator heterogeneity and outcomes 10 See also editCorrelation does not imply causation Observation Difference in differences Randomized controlled trial RCT Blinded experiment Scientific methodReferences 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 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 Archived 2019 09 29 at the Wayback Machine 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 Gael P du Prel Jean Baptist Blettner Maria 2009 10 01 Avoiding Bias in Observational Studies Deutsches Arzteblatt 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 editRosenbaum PR 2002 Observational Studies 2nd ed New York Springer Verlag ISBN 0387989676 NIST SEMATECH Handbook on Engineering Statistics at NIST Retrieved from https en wikipedia org w index php title Observational study amp oldid 1206736819, wikipedia, wiki, book, books, library,
