access_time published 03.08.2017
Confused by clinical research methods? Get a clue!
Thomas Perneger
Research
Confused by clinical research methods? Get a clue!
03.08.2017
Clinical research used to be simple, a relaxing diversion after a long day at the surgery. A doctor reviewed the charts of all three patients in his or her practice who suffered from an exotic ailment, computed a proportion or two (“a third died”), took photographs of a Petri dish and of a face rash, added a black bar to cover the eyes of Exhibit 2 – done.
That was then. Today researchers are like wedding planners – they obsess about inclusion criteria, cover up adverse events before anyone notices, lose sleep over budget overruns, and miss out on all the fun. Readers of medical journals are not spared. Articles are full of jargon, and it’s not medical Latin like in the old days. Especially maddening are statistical concepts, disguised as single letters (p, r, kappa), cute metaphors (neural network, bootstrap), or outright lies (normality, significance, error). It’s no wonder doctors only read GetWithIt™ updates anymore! To remedy this situation, Swiss Medical Weekly proposes here an interactive learning tool that will turn you into an instant expert in current research methodology.
To proceed, consider each clue, close your eyes, and let free associations flow. Soon a statistical or epidemiological phrase will occur to you. Do not over-think it. For instance, if the clue is “Final ranking at a beauty pageant?” a valid answer is “Miss classification”. Get it? Misclassification! Don’t worry about sloppy spelling or awkward grammar. Click on “show answer” to verify that you got it right. If you are unfamiliar with the scientific concept, read the comment (if you don’t get the joke, you are on your own).
Let’s play!
Miss classification
(misclassification) Error in the measurement of a risk factor or a disease, which typically weakens the observed association (misclassification bias)
Case-control design
Type of study in which patients who have a disease are compared to controls who don't
Intention to treat
Analysis of a clinical trial that includes all participants as initially randomised (regardless of actual treatment received)
Relative risk
Ratio of risks in the exposed and the unexposed
Log transformation
Application of the logarithm function
Meta-analysis
Statistical synthesis of the results of several studies that address the same research question
Normal function
Bell-shaped probability density distribution; synonym of Gaussian distribution
Mean square error
In analysis of variance, the sum of squared differences between observed and predicted values divided by the degrees of freedom
Odds of exposure
Odds are a function of a proportion, namely the proportion p divided by its complement (1-p). Exposure is synonym of risk factor.
Psycho metric
(psychometric) Related to classic methods for the measurement of unobserved attributes (often psychological variables)
Forward selection
Method for selecting covariates for a multivariable model, which starts with the best univariable predictor, then adds the second best, etc.
Cost utility
Type of economic analysis where costs incurred are related to health utility gains
Two by two table
Common tool in epidemiology; e.g. tabulation of exposure by outcome
Uniform distribution
Distribution that assigns equal probability (or density) to all values on a given interval
Split-half validation
Validation method where a model is built on one half of the data and then applied to the other half
p-values
Probabilities of the observed result or more extreme results under the null hypothesis
Censored events
In time-to-event analysis, observations for which the event of interest has not occurred at the time of analysis
Composite endpoint
Occurrence of any of several events that are considered equally relevant and therefore combined in a clinical trial
Parsimonious model
Statistical model that includes as few covariates as possible while explaining as much as possible of the outcome
Single blind
A clinical trial in which the patients do not know which treatment they receive
Bar chart
Graph that represents by the height of a bar a characteristic of interest (mean, percentage, etc.) for several subgroups
Standard error
Level of uncertainty of an estimator (i.e., a value obtained from the data) due to the limited sample size
Number needed to treat
Measure of treatment effectiveness; the number of persons that must be treated in order to avoid one clinically relevant event
Logistic regression
Modelling method used for binary outcome variables
Monte Carlo simulation
Computer-based method that explores a phenomenon through the simulated behaviour of repeated samples
High outlier
Unusually high value of a variable
Within-group variance
In analysis of variance, variability that is not attributable to group membership
F-test
Statistical test used in analysis of variance
Sensitivity and specificity
Ability of a test to correctly identify the diseased (sensitivity) and the non-diseased (specificity)
Confidence limit
Upper or lower bound of a confidence interval, which is the range of values of a parameter that are compatible with the observed data
Discrete event modelling
Simulation technique of a complex process that can lead to one or several distinct events
Shrinkage parameter
In empirical Bayes estimation, the relative weight given to the overall mean when it is combined with the local estimate
p <0.001
Low p-value that typically leads to the rejection of the null hypothesis
Pyramid of evidence
Hierarchy of scientific evidence based on the trustworthiness of the methods used (meta-analysis of randomised trials at the top and expert opinion at the bottom)
Degrees of freedom
Number of independent bits of information that are used to compute a statistic or estimate a model
Safety monitoring
Procedures for detecting threats to patient safety in a clinical trial
Patient autonomy
Ethical principle that gives patients ultimate say in decisions that affect them, including participation in research
Recall bias
Systematic error that can affect case-control studies, because the cases may remember their exposure history differently from the controls
Representative sample
Sample drawn through a process that guarantees fair representation of the underlying population in the long run (e.g., at random)
Interaction test
Statistical procedure that examines the effect of a variable as a function of another; e.g. a test to compare the effectiveness of a treatment in men versus women
Adaptive design
Design of a clinical trial that incorporates future decisions based on accumulated data
Tripel blind
(triple blind) Triple blinding ensures that the patient, the researcher, and the data analyst are unaware of treatment allocation
Test of significance
Procedure for choosing between two hypotheses (typically “the treatment doesn’t work” vs “the treatment works”) based on observed data
Translational trials
PStudies that test whether basic science advances are applicable to patient care
Normal deviate
Normally distributed statistic or observation expressed in standard deviation units
Marginal conditioning
Treating marginal distributions as fixed when the association between two variables is examined; e.g. the row and column totals are considered fixed when a kappa statistic is computed
Non-informative prior
In Bayesian statistics, the a priori distribution of a parameter that reflects ignorance of its true value
Multiplicity problem
Proliferation of “significant” results due to type 1 errors when a large number of tests is performed
Law of large numbers
Principle that describes the average behaviour of a procedure when it is repeated many times
Forest plot
Graph used in meta-analysis representing the results of several studies, with their confidence intervals, plus the pooled effect
Direct standardisation
Method for removing confounding, where rates of disease observed in strata of the confounder (e.g., in age groups) are applied to a reference population
Research protocol
Document that describes in detail all procedures to be used in a study
Alpha and beta errors
Type 1 and type 2 errors. The former is the probability of rejecting the null hypothesis when it is true, the latter the probability of accepting the null hypothesis when the alternate is true
Null hypothesis
Hypothesis of no association or no effect that the researcher hopes to reject.
Non-inferiority test
Test of the hypothesis that a treatment is less effective than another by a pre-specified amount
Link function
Mathematical function applied to the expected value of the outcome variable in generalised linear models; e.g. logarithm function applied to the number of events in log-linear models
CONSORT statement
Guideline for the reporting of randomised controlled trials
N-of-1 trial
Trial where two treatments are alternated at random within a single patient
Goodness of fit
Measure of the adequacy of a model for a given dataset
Pairwise deletion
Method for dealing with missing data when correlations are obtained for >2 variables, where each correlation is computed on all available pairs
Disclosure of interests
Authors’ disclosures of financial or other links with private entities that may bias scientific conclusions
Two-sided test
Statistical test that leads to rejection of the null hypothesis if the data deviate from the null value in either direction
Second order uncertainty
In modelling, uncertainty about the values of parameters (whereas first order uncertainty concerns values of individual observations)
Discrimination
Ability of a diagnostic or predictive model to correctly categorise patients with and without a characteristic (e.g., a specific disease)
Imputation of missing data
Replacement of missing data by plausible values
Cross validation
(cross-validation) Method for verifying the properties of a predictive model
Citation count
The number of times an article is cited in the scientific literature
Peer review
Evaluation of research articles or protocols by experts in the field to aid the decision to publish the paper or to fund the study
Good clinical practice
(Good Clinical Practice) Regulations for clinical trials established by the International Conference on Harmonization
Residual plot
In regression modelling, differences between observed and predicted values, plotted against predicted values
Range of values
Lowest and highest values of a variable
Thank you for playing along!
Disclosure statement
No financial support and no other potential conflict of interest relevant to this article was reported.
Picture credit
Header image: Aprescindere / Dreamstime.com
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