Clinical study design

Clinical study design is the formulation of trials and experiments, as well as observational studies in medical, clinical and other types of research (e.g., epidemiological) involving human beings.^[1] The goal of a clinical study is to assess the safety, efficacy, and / or the mechanism of action of an investigational medicinal product, or new drug or device that is in development, but potentially not yet approved by a health authority (e.g. FDA).

Some of the considerations here are shared under the more general topic of design of experiments but there can be others, in particular related to patient confidentiality and ethics.

Outline of types of designs for clinical studies 1
- Treatment studies 1.1
- Observational studies 1.2
Important considerations 2
- Seasonal studies 2.1
Other terms 3
See also 4
References 5
External links 6

Outline of types of designs for clinical studies

Treatment studies

Randomized controlled trial
- Blind trial
- Non-blind trial
Adaptive clinical trial
Nonrandomized trial (quasi-experiment)
- Interrupted time series design (measures on a sample or a series of samples from the same population are obtained several times before and after a manipulated event or a naturally occurring event) - considered a type of quasi-experiment

Observational studies

Cohort study
Case-control study
- Nested case-control study
Cross-sectional study
- Community survey (a type of cross-sectional study)
Ecological study

Important considerations

When choosing a study design, many factors must be taken into account. Different types of studies are subject to different types of bias. For example, recall bias is likely to occur in cross-sectional or case-control studies where subjects are asked to recall exposure to risk factors. Subjects with the relevant condition (e.g. breast cancer) may be more likely to recall the relevant exposures that they had undergone (e.g. hormone replacement therapy) than subjects who don't have the condition.

The ecological fallacy may occur when conclusions about individuals are drawn from analyses conducted on grouped data. The nature of this type of analysis tends to overestimate the degree of association between variables.

Seasonal studies

Conducting studies in seasonal indications (such as allergies, Seasonal Affective Disorder, influenza, and others) can complicate a trial as patients must be enrolled quickly. Additionally, seasonal variations and weather patterns can affect a seasonal study.^[2]^[3]

Other terms

The term retrospective study is sometimes used as another term for a case-control study. This use of the term "retrospective study" is misleading, however, and should be avoided because other research designs besides case-control studies are also retrospective in orientation.
Superiority trials are designed to demonstrate that one treatment is more effective than another. This type of study design is often used to test the effectiveness of a treatment compared to placebo.
Non-inferiority trials are designed to demonstrate that a treatment is at least not appreciably worse than another. This type of study design is often employed when comparing a new treatment to an established medical standard of care.
Equivalence trials are designed to demonstrate that one treatment is as effective as another.
When using "parallel groups", each patient receives one treatment; in a "crossover study", each patient receives several treatments.
A longitudinal study assesses research subjects over two or more points in time; by contrast, a cross-sectional study assesses research subjects at only one point in time (so case-control, cohort, and randomized studies are not cross-sectional).

References

^ Miquel Porta (2014) "A dictionary of epidemiology", 6th edn, New York: Oxford University Press. ISBN 9780199976737.
^ Yamin Khan and Sarah Tilly. "Flu, Season, Diseases Affect Trials".
^ Yamin Khan and Sarah Tilly. "Seasonality: The Clinical Trial Manager's Logistical Challenge" (PDF). published by: Pharm-Olam International (POI). Retrieved 26 April 2010.

External links

Epidemiologic.org Epidemiologic Inquiry online weblog for epidemiology researchers
Epidemiology Forum An epidemiology discussion and forum community to foster debates and collaborations in epidemiology
Some aspects of study design Tufts University web site
Comparison of strength Description of study designs from the National Cancer Institute
Political Science Research Design Handbook Truman State University website

Clinical research and experimental design

Overview	Clinical trial Trial protocols Adaptive clinical trial Academic clinical trials Clinical study design

Controlled study (EBM I to II-1; A to B)	Randomized controlled trial Scientific experiment Blind experiment Open-label trial

Observational study (EBM II-2 to II-3; B to C)	Cross-sectional study vs. Longitudinal study, Ecological study Cohort study Retrospective Prospective Case-control study (Nested case-control study) Case series Case study Case report

Epidemiology/ methods	occurrence: Incidence (Cumulative incidence) Prevalence Point Period association: absolute (Absolute risk reduction, Attributable risk, Attributable risk percent) relative (Relative risk, Odds ratio, Hazard ratio) other: Clinical endpoint Virulence Infectivity Mortality rate Morbidity Case fatality rate Specificity and sensitivity Likelihood-ratios Pre/post-test probability

Trial/test types	In vitro In vivo Animal testing Animal testing on non-human primates First-in-man study Multicenter trial Seeding trial Vaccine trial

Analysis of clinical trials	Risk–benefit ratio Systematic review Replication Meta-analysis

Interpretation of results	Selection bias Survivorship bias Correlation does not imply causation Null result

Category Glossary List of topics

Statistics

Outline
Index
Glossary

Descriptive statistics

Continuous data

Location	Mean arithmetic geometric harmonic Median Mode

Dispersion	Range Standard deviation Coefficient of variation Percentile Interquartile range

Shape	Variance Skewness Kurtosis Moments L-moments

Count data

Index of dispersion

Summary tables

Dependence

Statistical graphics

Data collection

Study design	Effect size Standard error Statistical power Sample size determination

Survey methodology	Sampling stratified cluster Opinion poll Questionnaire

Controlled experiments	Design control optimal Controlled trial Randomized Random assignment Replication Blocking Factorial experiment

Uncontrolled studies	Observational study Natural experiment Quasi-experiment

Statistical inference

Statistical theory

Frequentist inference

Confidence interval Testing hypotheses Power

Unbiased estimators	Mean unbiased minimum-variance Median unbiased

Biased estimators	Maximum likelihood Method of moments Minimum distance Density estimation

Parametric tests	Likelihood-ratio Wald Score

Specific tests

Z (normal) Student's t-test F Shapiro–Wilk Kolmogorov–Smirnov

Goodness of fit	Chi-squared G Sample source (Anderson–Darling) Sample normality (Shapiro–Wilk) Skewness / kurtosis normality (Jarque-Bera) Model comparison (Likelihood-ratio) Model quality (Akaike criterion)

Signed-rank	1-sample (Wilcoxon) 2-sample (Mann–Whitney U) 1-way anova (Kruskal–Wallis)

Bayesian inference

Correlation	Pearson product–moment Partial correlation Confounding variable Coefficient of determination

Regression analysis	Errors and residuals Regression model validation Mixed effects models Simultaneous equations models Multivariate adaptive regression splines (MARS)

Linear regression	Simple linear regression Ordinary least squares General linear model Bayesian regression

Non-standard predictors	Nonlinear regression Nonparametric Semiparametric Isotonic Robust Heteroscedasticity Homoscedasticity

Generalized linear model	Exponential families Logistic (Bernoulli) / Binomial / Poisson regressions

Partition of variance	Analysis of variance (ANOVA, anova) Analysis of covariance Multivariate ANOVA Degrees of freedom

Categorical / Multivariate / Time-series / Survival analysis

Categorical

Multivariate

Time-series

General	Decomposition Trend Stationarity Seasonal adjustment Exponential smoothing Cointegration Structural break Granger causality

Specific tests	Dickey–Fuller Johansen Q-statistic (Ljung–Box) Durbin–Watson Breusch–Godfrey

Time domain	Autocorrelation (ACF) partial (PACF) Cross-correlation (XCF) ARMA model ARIMA model (Box–Jenkins) Autoregressive conditional heteroskedasticity (ARCH) Vector autoregression (VAR)

Frequency domain	Spectral density estimation Fourier analysis Wavelet

Survival

Survival function	Kaplan–Meier estimator (product limit) Proportional hazards models Accelerated failure time (AFT) model First hitting time

Hazard function	Nelson–Aalen estimator

Test	Log-rank test

Applications

Biostatistics	Bioinformatics Clinical trials / studies Epidemiology Medical statistics

Engineering statistics	Chemometrics Methods engineering Probabilistic design Process / quality control Reliability System identification

Social statistics	Actuarial science Census Crime statistics Demography Econometrics National accounts Official statistics Population Psychometrics

Spatial statistics	Cartography Environmental statistics Geographic information system Geostatistics Kriging

Category
Portal

Clinical study design