To date, randomised controlled trials (RCTs) have been seen as the 'gold standard' in medical research, and whilst it is true that this kind of study is strictly required for any drug registration package or to provide substantial evidence of efficacy or safety of an intervention, they are not always feasible due to ethical issues or practical constraints.
Observational studies (OS) have long been a cornerstone of epidemiological research, but until relatively recently they had not made a significant breakthrough in the clinical development arena. Historically, results from observational studies were often discarded or only marginally considered because of their intrinsic susceptibility to bias.
Today, observational studies are increasingly recognised as a valuable complement to randomised trials rather than a competing source of evidence. When properly planned, conducted and analysed, they provide insight into how treatments perform in routine clinical practice, in broader and more heterogeneous populations, and over longer periods of time than is usually possible in a controlled trial setting.
Observational studies are non-interventional research designs in which outcomes are observed without the investigator actively assigning treatments or interventions. Instead, patients receive care according to routine clinical practice, and data are collected on exposures, outcomes, and relevant covariates as they occur naturally.
Unlike randomised trials, treatment decisions in observational studies are driven by clinical judgement rather than by a study protocol. As a result, these studies do not control treatment allocation, blinding or adherence in the same way as experimental designs. The defining feature of an observational study is therefore not the data source itself, but the absence of investigator-controlled intervention, which has important implications for study design, analysis and interpretation.
Observational data can originate from multiple sources, each with distinct strengths and limitations, these sources include:
The four main types of observational studies are cohort studies, case-control studies, cross-sectional studies, and case series or registries.
A cohort study is one type of observational study, so the terms are not alternatives: cohort studies are observational studies, but not all observational studies are cohort studies.
One of the main strengths of an RCT, its strictly controlled nature, is also one of its main limitations. Tight inclusion and exclusion criteria, intensive visit schedules and protocol-driven interventions can limit how well trial results generalise to routine clinical practice.
By contrast, observational studies often include patients treated in everyday care settings. These populations often have multiple comorbidities, variable adherence patterns and differing baseline risk profiles. As a result, observational studies can provide evidence that is more representative of the patients clinicians actually treat, improving external validity.
Observational studies are generally much cheaper to conduct than RCTs. In many cases, particularly retrospective or registry-based studies, data collection can be relatively rapid, allowing insights to be generated in a shorter time frame.
The cost‑to‑data‑availability ratio is therefore often far lower than even the most efficient RCT, freeing resources for exploratory analyses, modelling and sensitivity assessments.
Observational studies are often exploratory rather than confirmatory. They are well suited to hypothesis generation, evaluation of treatment patterns, and identification of signals that may warrant further investigation.
Formal control of family-wise error rates is not always the primary objective, and statistical tests often play an advisory rather than prescriptive role. This flexibility is a strength, provided results are interpreted with appropriate caution.
Despite their advantages, observational studies are vulnerable to several well-recognised sources of bias. Understanding these limitations is essential to designing credible analyses.
Selection bias arises when the patients included in a study are not a random sample of the target population. In observational studies, treatment allocation is typically influenced by clinical judgement rather than randomisation, which can result in systematic differences between treatment groups.
Closely related is confounding, where baseline characteristics are associated with both treatment assignment and outcome. A common example is channelling bias, where newer therapies are preferentially prescribed to patients with more severe disease. Without appropriate adjustment, uncorrected analyses can produce misleading conclusions.
Related to confounding is Simpson’s paradox, where the direction or magnitude of an association changes when data are stratified by a third variable. Aggregated results may suggest one conclusion, while subgroup analyses suggest the opposite. Which perspective is most relevant depends entirely on the scientific question being asked, highlighting the importance of careful study planning and analytic strategy.
Most RCTs are double-blinded, reducing the risk that patient or investigator expectations influence outcome assessment. In observational studies, blinding is rarely possible. Patients and clinicians are usually aware of the treatment being prescribed, which can influence subjective outcomes such as symptom scores or patient-reported measures.
This contributes to information bias, where recorded values differ systematically from the true underlying values.
Measurement error refers to random or systematic error in quantitative variables, such as laboratory values or physiological measurements. Even small amounts of error can lead to attenuation of associations, shrinking estimated effects towards the null.
This issue is particularly relevant in observational studies, where measurements may originate from multiple sources, instruments or clinical settings. Ignoring measurement error can materially bias treatment comparisons, particularly when baseline covariates differ.
This occurs when patients are incorrectly assigned to categories, whether for outcomes, exposures or covariates. Non-differential misclassification generally biases estimates towards the null, while differential misclassification can bias results in unpredictable directions.
In small samples or studies with modest true effects, misclassification can substantially reduce power and distort inference.
Missing data is ubiquitous in medical research. Mechanisms are commonly described as Missing Completely at Random (MCAR), Missing at Random (MAR) or Missing Not at Random (MNAR), with the latter two posing the greatest challenges.
There has been a substantial increase in the use of Multiple Imputation (MI), but MI is not a panacea. Its validity depends on correctly specifying the imputation model and transparently reporting assumptions and parameters. Poorly implemented MI can introduce bias rather than reduce it.
While these sources of bias cannot be eliminated entirely in observational studies, carefully chosen statistical analyses can be used to understand, mitigate and test the impact of differences between treatment groups.
The emphasis is therefore not on recreating the conditions of a randomised trial, but on applying analytical approaches that allow observed data to be interpreted in a transparent and scientifically credible way.
Before any formal modelling is undertaken, exploratory analysis plays a critical role in understanding how treatment groups differ at baseline. Because patients are not randomly allocated, differences in demographic characteristics, disease severity, comorbidities or prior treatment history are expected.
Baseline comparisons allow these differences to be identified and described, helping to diagnose where imbalances may influence outcome comparisons. The objective at this stage is not hypothesis testing, but characterisation, providing context for subsequent analytical choices and highlighting which variables may require adjustment.
One common analytical objective in observational studies is to improve comparability between treatment groups. This can be approached using matching or weighting techniques that aim to balance observed covariates without imposing experimental control.
Exact matching may be feasible when a limited number of key variables drive treatment selection, allowing patients with similar characteristics to be compared directly. More commonly, propensity score methods are used to summarise multiple covariates into a single measure representing the probability of receiving a particular treatment, often estimated using logistic regression. Patients can then be matched, stratified or weighted based on these scores.
These approaches do not remove unmeasured confounding, but they can substantially reduce systematic differences between groups when applied appropriately and supported by adequate overlap in baseline characteristics.
Even after matching or weighting, residual differences between groups often remain. Regression-based adjustment is therefore commonly used to account for remaining imbalance when estimating treatment effects.
Methods such as analysis of covariance or generalised linear models allow outcomes to be modelled while adjusting for relevant baseline covariates. In observational settings, careful interpretation is essential, as adjusted estimates reflect associations conditional on the variables included in the model rather than causal effects derived from randomisation.
The choice of covariates, model form and assumptions should be informed by clinical knowledge as well as exploratory analysis, recognising that over-adjustment or inappropriate model specification can introduce additional bias.
Observational data frequently contain measurement error arising from variability in clinical practice, data collection methods or recording systems. If ignored, this error can attenuate estimated associations and distort treatment comparisons.
Where appropriate, specialised methods such as error-in-variables models, Deming regression or simulation-extrapolation (SIMEX) can be used to account explicitly for measurement error. These approaches rely on assumptions about the structure and magnitude of error and should be applied with caution.
Sensitivity analyses provide an additional layer of assurance by testing how conclusions change under alternative assumptions, model specifications or analytical choices. Rather than seeking a single definitive estimate, this process helps to assess the robustness of findings and communicate uncertainty transparently.
When treatment groups are very different, it may be difficult to achieve adequate matching using either exact matching or propensity score methods. In some cases, there may be little or no overlap between groups.
As matching becomes more restrictive, the size of the analysable dataset can shrink, with patients at the extremes of the matching criteria excluded. This can reduce both sample size and variability, meaning the analysis no longer reflects typical patients in each treatment group.
In extreme cases, extensive matching can move the analysis closer to a controlled trial environment, undermining the original objective of studying real-world data. When this occurs, results must be interpreted with care, particularly if conclusions are presented as reflecting routine clinical practice.
Rare diseases provide a clear example of where observational studies are often the only viable option. Limited patient numbers, ethical considerations and long recruitment periods can make RCTs impractical.
Registries and real-world datasets allow investigators to study disease progression, treatment patterns and outcomes in these populations, generating evidence that would otherwise be unobtainable.
Unlike clinical trials, observational studies do not follow a single standardised framework. Study designs, data sources and analytic approaches vary widely.
Regulatory bodies, including the FDA, have increasingly accepted observational data, provided it is collected and analysed rigorously. The emphasis is less on the study label and more on the credibility and transparency of the methods used.
All of the above highlights the central truth that bias is an inherent risk in all observational studies. The goal is not to eliminate bias entirely, but to understand, diagnose, and mitigate it through thoughtful design and appropriate analysis.
Observational studies are indispensable for real‑world evidence. When carefully planned, transparently analysed and interpreted in context, they complement randomised trials and provide insights that trials alone cannot deliver.
Quanticate combines deep expertise in biostatistics, data science, and real-world evidence to help sponsors design and analyse robust observational studies that stand up to regulatory and scientific scrutiny. If you are planning an observational study or need support turning real-world data into decision-grade evidence, please request a consultation and a member of our team will be in touch.