Clinical Epidemiology & Evidence-Based Medicine Glossary:

Clinical Study Design and Methods Terminology

Updated November 02, 2010

Contents:


  1. Clinical Study Types: (In order from strongest to weakest empirical evidence inherent to the design when properly executed.)
    1. Experimental Studies: The hallmark of the experimental study is that the allocation or assignment of individuals is under control of investigator and thus can be randomized. The key is that the investigator controls the assignment of the exposure or of the treatment but otherwise symmetry of potential unknown confounders is maintained through randomization. Properly executed experimental studies provide the strongest empirical evidence. The randomization also provides a better foundation for statistical procedures than do observational studies.
      1. Randomized Controlled Clinical Trial (RCT): A prospective, analytical, experimental study using primary data generated in the clinical environment. Individuals similar at the beginning are randomly allocated to two or more treatment groups and the outcomes the groups are compared after sufficient follow-up time. Properly executed, the RCT is the strongest evidence of the clinical efficacy of preventive and therapeutic procedures in the clinical setting.
      2. Randomized Cross-Over Clinical Trial: A prospective, analytical, experimental study using primary data generated in the clinical environment. Individuals with a chronic condition are randomly allocated to one of two treatment groups, and, after a sufficient treatment period and often a washout period, are switched to the other treatment for the same period. This design is susceptible to bias if carry over effects from the first treatment occur. An important variant is the "N of One" clinical trial in which alternative treatments for a chronically affected individual are administered in a random sequence and the individual is observed in a double blind fashion to determine which treatment is the best.
      3. Randomized Controlled Laboratory Study: A prospective, analytical, experimental study using primary data generated in the laboratory environment. Laboratory studies are very powerful tools for doing basic research because all extraneous factors other than those of interest can be controlled or accounted for (e.g., age, gender, genetics, nutrition, environment, co-morbidity, strain of infectious agent). However, this control of other factors is also the weakness of this type of study. Animals in the clinical environment have a wide range of all these controlled factors as well as others that are unknown. If any interactions occur between these factors and the outcome of interest, which is usually the case, the laboratory results are not directly applicable to the clinical setting unless the impact of these interactions are also investigated.
    2. Observational Studies: The allocation or assignment of factors is not under control of investigator. In an observational study, the combinations are self-selected or are "experiments of nature". For those questions where it would be unethical to assign factors, investigators are limited to observational studies. Observational studies provide weaker empirical evidence than do experimental studies because of the potential for large confounding biases to be present when there is an unknown association between a factor and an outcome. The symmetry of unknown confounders cannot be maintained. The greatest value of these types of studies (e.g., case series, ecologic, case-control, cohort) is that they provide preliminary evidence that can be used as the basis for hypotheses in stronger experimental studies, such as randomized controlled trials.
      1. Cohort (Incidence, Longitudinal Study) Study: A prospective, analytical, observational study, based on data, usually primary, from a follow-up period of a group in which some have had, have or will have the exposure of interest, to determine the association between that exposure and an outcome. Cohort studies are susceptible to bias by differential loss to follow-up, the lack of control over risk assignment and thus confounder symmetry, and the potential for zero time bias when the cohort is assembled. Because of their prospective nature, cohort studies are stronger than case-control studies when well executed but they also are more expensive. Because of their observational nature, cohort studies do not provide empirical evidence that is as strong as that provided by properly executed randomized controlled clinical trials.
      2. Case-Control Study: A retrospective, analytical, observational study often based on secondary data in which the proportion of cases with a potential risk factor are compared to the proportion of controls (individuals without the disease) with the same risk factor. The common association measure for a case-control study is the odds ratio. These studies are commonly used for initial, inexpensive evaluation of risk factors and are particularly useful for rare conditions or for risk factors with long induction periods. Unfortunately, due to the potential for many forms of bias in this study type, case control studies provide relatively weak empirical evidence even when properly executed.
      3. Ecologic (Aggregate) Study: An observational analytical study based on aggregated secondary data. Aggregate data on risk factors and disease prevalence from different population groups is compared to identify associations. Because all data are aggregate at the group level, relationships at the individual level cannot be empirically determined but are rather inferred from the group level. Thus, because of the likelihood of an ecologic fallacy, this type of study provides weak empirical evidence.
      4. Cross-Sectional (Prevalence Study) Study: A descriptive study of the relationship between diseases and other factors at one point in time (usually) in a defined population. Cross sectional studies lack any information on timing of exposure and outcome relationships and include only prevalent cases.
      5. Case Series: A descriptive, observational study of a series of cases, typically describing the manifestations, clinical course, and prognosis of a condition. A case series provides weak empirical evidence because of the lack of comparability unless the findings are dramatically different from expectations. Case series are best used as a source of hypotheses for investigation by stronger study designs, leading some to suggest that the case series should be regarded as clinicians talking to researchers. Unfortunately, the case series is the most common study type in the clinical literature.
      6. Case Report: Anecdotal evidence. A description of a single case, typically describing the manifestations, clinical course, and prognosis of that case. Due to the wide range of natural biologic variability in these aspects, a single case report provides little empirical evidence to the clinician. They do describe how others diagnosed and treated the condition and what the clinical outcome was.

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  1. Validity vs. Bias:
    1. Validity: Truth
      1. External Validity (Generalizability): Truth beyond a study. A study is external valid if the study conclusions represent the truth for the population to which the results will be applied because both the study population and the reader’s population are similar enough in important characteristics. The important characteristics are those that would be expected to have an impact on a study’s results if they were different (e.g., age, previous disease history, disease severity, nutritional status, co-morbidity, ...). Whether or not the study is generalizable to the population of interest to the reader is a question only the reader can answer. External validity can occur only if the study is first internally valid.
      2. Internal Validity: Truth within a study. A study is internally valid if the study conclusions represent the truth for the individuals studied because the results were not likely due to the effects of chance, bias, or confounding because the study design, execution, and analysis were correct. The statistical assessment of the effects of chance is meaningless if sufficient bias has occurred to invalidate the study. All studies are flawed to some degree. The crucial question that the reader must answer is whether or not these problems were great enough that the study results are more likely due to the flaws than the hypothesis under investigation.
      3. Symmetry Principle: In a study, the principle of keeping all things between groups similar except for the treatment of interest. This means that the same instrument is used to measure each individual in each group, the observers know the same things about all individuals in all groups, randomization is used to obtain a similar allocation of individuals to each group, the groups are followed at the same time, ... .
    1. Confounding: Confounding is the distortion of the effect of one risk factor by the presence of another. Confounding occurs when another risk factor for a disease is also associated with the risk factor being studied but acts separately. Age, breed, gender and production levels are often confounding risk factors because animals with different values of these are often at different risk of disease. As a result of the association between the study and confounding risk factor, the confounder is not distributed randomly between the group with the study risk factor and the control group without the study factor. Confounding can be controlled by restriction, by matching on the confounding variable or by including it in the statistical analysis.
    1. Bias (Systematic Error): Any process or effect at any stage of a study from its design to its execution to the application of information from the study, that produces results or conclusions that differ systematically from the truth. Bias can be reduced only by proper study design and execution and not by increasing sample size (which only increases precision by reducing the opportunity for random chance deviation from the truth). Almost all studies have bias, but to varying degrees. The critical question is whether or not the results could be due in large part to bias, thus making the conclusions invalid. Observational study designs are inherently more susceptible to bias than are experimental study designs.
      1. Confounding Bias: Systematic error due to the failure to account for the effect of one or more variables that are related to both the causal factor being studied and the outcome and are not distributed the same between the groups being studied. The different distribution of these "lurking" variables between groups alters the apparent relationship between the factor of interest and the outcome. Confounding can be accounted for if the confounding variables are measured and are included in the statistical models of the cause-effect relationships.
      2. Ecological (Aggregation) Bias (Fallacy): Systematic error that occurs when an association observed between variables representing group averages is mistakenly taken to represent the actual association that exists between these variables for individuals. This bias occurs when the nature of the association at the individual level is different from the association observed at the group level. Data aggregated from individuals (e.g. census averages for a region) or proxy data from other sources (e.g., the amount of alcohol distributed in a region is a proxy for the amount of alcohol by individuals in that region) are often easier and less expensive to acquire than are data directly from individuals.
      3. Measurement Bias: Systematic error that occurs when, because of the lack of blinding or related reasons such as diagnostic suspicion, the measurement methods (instrument, or observer of instrument) are consistently different between groups in the study.
        1. Screening Bias: The bias that occurs when the presence of a disease is detected earlier during its latent period by screening tests but the course of the disease is not be changed by earlier intervention. Because the survival after screening detection is longer than survival after detection of clinical signs, ineffective interventions appear to be effective unless they are compared appropriately in clinical trials.
      4. Reader Bias: Systematic errors of interpretation made during inference by the user or reader of clinical information (papers, test results, ...). Such biases are due to clinical experience, tradition, credentials, prejudice and human nature. The human tendency is to accept information that supports pre-conceived opinions and to reject or trivialize that which does not support preconceived opinions or that which one does not understand. (JAMA 247:2533)
      5. Sampling (Selection) Biases: Systematic error that occurs when, because of design and execution errors in sampling, selection, or allocation methods, the study comparisons are between groups that differ with respect to the outcome of interest for reasons other than those under study.
      6. Zero Time Bias: The bias that occurs in a prospective study when individuals are found and enrolled in such a fashion that unintended systematic differences occur between groups at the beginning of the study (stage of disease, confounder distribution). Cohort studies are susceptible to zero-time bias if the cohort is not assembled properly.
    2. Bias Effect:
      1. Non-differential Bias: Opportunities for bias are equivalent in all study groups, which biases the outcome measure of the study toward the null of no difference between the groups.
      2. Differential Bias: Opportunities for bias are different in different study groups, which biases the outcome measure of the study in unknown ways. Case-control studies are highly susceptible to this form of bias between the case and control groups.

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  1. Study Objective, Direction and Timing:
    1. Analytic (Explanatory) Study: The objective of an analytic study is to make causal inferences about the nature of hypothesized relationships between risk factors and outcomes. Statistical procedures are used to determine if a relationship was likely to have occurred by chance alone. Analytic studies usually compare two or more groups, such as case-control studies, cohort studies, randomized controlled clinical trials, and laboratory studies.
    2. Descriptive Study: The objective of a descriptive study is to describe the distribution of variables in a group. Statistics serve only to describe the precision of those measurements or to make statistical inferences about the values in the population from which the sample was taken.
    3. Contemporary (Concurrent) Comparison: Comparison is between two groups experiencing the risk factor or the treatment at the same time. Contemporary comparison has the major advantages that symmetry of unknown risk factors for the condition that change over time is maintained and that measurement procedures can be performed as similarly as possible on both groups.
    4. Historical (Non-concurrent) Comparison: Comparison is of the same group or between groups at different times that are not experiencing the risk factor or the treatment at the same time. Historical comparison is often used to allow a group to serve as its own historical control or is done implicitly when a group is compared to expected standards of performance. This design provides weak evidence because symmetry isn’t assured. It is very susceptible to bias by changes over time in uncontrollable, confounding risk factors, such as differences in climate, management practices and nutrition. Bias due to differences in measuring procedures over time may also account for observed differences.
    5. Prospective Study (Data): Data collection and the events of interest occur after individuals are enrolled (e.g. clinical trials and cohort studies). This prospective collection enables the use of more solid, consistent criteria and avoids the potential biases of retrospective recall. Prospective studies are limited to those conditions that occur relatively frequently and to studies with relatively short follow-up periods so that sufficient numbers of eligible individuals can be enrolled and followed within a reasonable period.
    6. Retrospective Study (Data): All events of interest have already occurred and data are generated from historical records (secondary data) and from recall (which may result in the presence of significant recall bias). Retrospective data is relatively inexpensive compared to prospective studies because of the use of available information and is typically used in case-control studies. Retrospective studies of rare conditions are much more efficient than prospective studies because individuals experiencing the rare outcome can be found in patient records rather than following a large number of individuals to find a few cases.

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  1. Other Terms:
    1. Baseline: Health state (disease severity, confounding conditions) of individuals at beginning of a prospective study. A difference (asymmetry) in the distributions of baseline values between groups will bias the results.
    2. Blinding (Masking): Blinding is those methods to reduce bias by preventing observers and/or experimental subjects involved in an analytic study from knowing the hypothesis being investigated, the case-control classification, the assignment of individuals or groups, or the different treatments being provided. Blinding reduces bias by preserving symmetry in the observers’ measurements and assessments. This bias is usually not due to deliberate deception but is due to human nature and prior held beliefs about the area of study.
      1. Placebo: A placebo is the shame treatment used in a control group in place of the actual treatment. If a drug is being evaluated, the inactive vehicle or carrier is used alone so it is as similar as possible in appearance and in administration to the active drug. Placebos are used to blind observers and, for human trials, the patients to which group the patient is allocated.
    3. Case Definition: The set of history, clinical signs and laboratory findings that are used to classify an individual as a case or not for an epidemiological study. Case definitions are needed to exclude individuals with the other conditions that occur at an endemic background rate in a population or other characteristics that will confuse or reduce the precision of a clinical study.
    4. Cohort: A group of individuals identified on the basis of a common experience or characteristic that is usually monitored over time from the point of assembly.
    5. Experimental Unit, Unit of Concern (EU): In an experiment, the experimental unit are the units that are randomly selected or allocated to a treatment and the unit upon which the sample size calculations and subsequent data analysis must be based. Experimental units are often a pen of animals or a cage of mice rather than the individuals themselves. Analyzing data on an individual basis when groups (herds, pens) have been the basis of random allocation is a serious error because it over-estimates precision, possibly biasing the study toward a false-positive conclusion.

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  1. Sample Selection / Allocation Procedures:
    1. Matching: When confounding cannot be controlled by randomization, individual cases are matched with individual controls that have similar confounding factors, such as age, to reduce the effect of the confounding factors on the association being investigated in analytic studies. Most commonly seen in case-control studies.
    2. Restriction (Specification): Eligibility for entry into an analytic study is restricted to individuals within a certain range of values for a confounding factor, such as age, to reduce the effect of the confounding factor when it cannot be controlled by randomization. Restriction limits the external validity (generalizability) to those with the same confounder values.
    3. Census: A sample that includes every individual in a population or group (e.g., entire herd, all known cases). A census not feasible when group is large relative to the costs of obtaining information from individuals.
    4. Haphazard, Convenience, Volunteer, Judgmental Sampling: Any sampling not involving a truly random mechanism. A hallmark of this form of sampling is that the probability that a given individual will be in the sample is unknown before sampling;. The theoretical basis for statistical inference is lost and the result is inevitably biased in unknown ways. Despite their best intentions, humans cannot choose a sample in a random fashion without a formal randomizing mechanism.
    5. Consecutive (Quota) Sampling: Sampling individuals with a given characteristic as they are presented until enough with that characteristic are acquired. This method is okay for descriptive studies but unfortunately not much better than haphazard sampling for analytical observational studies.
    6. Random Sampling: Each individual in the group being sampled has a known probability of being included in the sample obtained from the group before the sampling occurs.
    7. Simple Random Sampling / Allocation: Sampling conducted such that each eligible individual in the population has the same chance of being selected or allocated to a group. This sampling procedure is the basis of the simpler statistical analysis procedures applied to sample data. Simple random sampling has the disadvantage of requiring a complete list of identified individuals making up the population (the list frame) before the sampling can be done.
    8. Stratified Random Sampling: The group from which the sample is to be taken is first stratified on the basis of a important characteristic related to the problem at hand (e.g., age, parity, weight) into subgroups such that each individual in a subgroup has the same probability of being included in the sample but the probabilities are different between the subgroups or strata. Stratified random sampling assures that the different categories of the characteristic that is the basis of the strata are sufficiently represented in the sample but the resulting data must be analyzed using more complicated statistical procedures (such as Mantel-Haenszel) in which the stratification is taken into account.
    9. Cluster Sampling: Staged sampling in which a random sample of natural groupings of individuals (houses, herds, kennels, households, stables) are selected and then sampling all the individuals within the cluster. Cluster sampling requires special statistical methods for proper analysis of the data and is not advantageous if the individuals are highly correlated within a group (a strong herd effect).
    10. Systematic Sampling: From a random start in first n individuals, sampling every nth animal as they are presented at the sampling site (clinic, chute, ...). Systematic sampling will not produce a random sample if a cyclical pattern is present in the important characteristics of the individuals as they are presented. Systematic sampling has the advantage of requiring only knowledge of the number of animals in the population to establish n and that anyone presenting the animals is blind to the sequence so they cannot bias it.

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