will prescribe the more effective treatment to the more severe patients and may prefer to start treatment of the milder patients with the better tolerated treatment. The simple comparison of outcomes of patients receiving the two treatments, which is the usual strategy in RCTs, can produce biased results since more severe patients may be prone to worse outcomes. This book will describe strategies to produce valid results taking into account the differences between treatment groups.
RCTs have other design features that improve internal validity, such as standardized treatment protocols; strict patient and investigator selection criteria; common data collection forms; and blinding of patients, treatment providers, and evaluators (Wells 1999, Rothwell 1995). However, these design features almost certainly compromise external validity or generalizability, posing important limitations on translating findings to common practice and informing clinical practice and policy decisions about treatments (Gilboby et al. 2002). Patients with co-morbidities, those who might be less compliant with treatments, and those who are difficult to treat are many times excluded from clinical trials. Accordingly, it is not clear if the findings from clinical trials can be generalized to the overall population of patients. Real world data by definition includes a more representative sample of patients, and therefore can produce more generalizable results.
The traditional view is that RWD, data from observational studies that is collected during usual clinical work, can complement the results of RCTs by assessing the outcomes of treatments in more representative samples of patients and in circumstances much nearer to the day-to-day clinical practice. However, real world data research is quickly expanding to a broader set of clinical questions for drug development and health policy as discussed in the next sections.
1.4 Types of Real World Studies
There are two large types of studies: descriptive and analytical. Descriptive studies simply describe a health situation such as a prevalence study that conducts a survey to determine the frequency or prevalence of a disorder or an incidence study in which we follow a group of individuals to determine the incidence of a given disease. In analytical studies, we analyze the influence of an intervention (exposure) on an outcome. Analytical studies can be divided, as we have seen above, into experimental and observational. In experimental studies, the investigator is able to select the interventions and then compare the outcomes (that is, cure from disease) of individuals exposed to the different interventions. The RCT is the typical example of a clinical experimental study. Conversely, in analytical observational studies, which are the ones that are conducted using RWD, the investigator only observes and records what happens, but does not modify the interventions the subjects receive. The rest of this section is a very brief and high-level look at the different types of analytical observational studies given in Table 1.1. For a thorough presentation of study designs, see the following references (Rothman et al. 2012, Fletcher et al. 2014).
Table 1.1: Types of Analytical Epidemiological and Clinical Studies
| Experimental | Observational |
| Randomized clinical trial | Cross-sectional |
| Randomized community intervention | Retrospective or case-control |
| Prospective or cohort |
1.4.1 Cross-sectional Studies
The classification of analytical observational studies is based on the time frame that we observe the subjects. In cross-sectional studies, we simultaneously study intervention/exposure and disease in a well-defined population at a given time. This simultaneous measurement does not allow us to know the temporal sequence of the events, and it is therefore not possible to determine whether the exposure preceded the disease or vice versa.
An example of a cross-sectional study is the assessment of individuals who are treated for a disease in a health care center. This information is very useful to assess the health status of a community and determine its needs, but cannot inform on the causes of a disorder or the outcomes of a treatment. Cross-sectional studies often serve as descriptive studies and help formulate etiological hypotheses.
1.4.2 Retrospective or Case-control Studies
Retrospective or case-control studies identify individuals who have already experienced the outcome of interest, for example, comparing individuals with a disease with an appropriate control group that does not have the disease. The relationship between one or several factors related to the disease are examined by comparing the frequency of exposure to risk or protective factors between cases and controls. These studies are named “retrospective” because they start from the effect and retrospectively evaluate the exposure of interest in the individuals who have and do not have the disease to ascertain the factors that may be related to that disease. If the frequency of exposure to the cause is greater in the group of cases of the disease than in the controls, we can say that there is an association between the exposure and the outcome.
1.4.3 Prospective or Cohort Studies
Finally, in cohort studies, individuals are identified based on the presence or absence of an intervention (for example, a treatment of interest). At this time, the participants have not experienced the outcome and are followed for a period of time to observe the frequency of the outcome of interests. At the end of the observation period, the outcomes from each of the cohorts (intervention groups) are compared. If the outcomes are different, we can conclude that there is a statistical association between the intervention and outcome. In this type of study, since the participants have not experienced the outcome at the start of the follow-up, the temporal sequence between exposure and disease can be established more clearly. In turn, this type of study allows the examination of multiple effects before a given intervention.
Cohort studies can be prospective and historical depending on the temporal relationship between the start of the study and the outcome of interest. In the retrospective, both the intervention and the outcome have already happened when the study was started. In the prospective, the exposure could have occurred or not, but the outcome has not been observed. Therefore, a follow-up period is required to determine the frequency of the outcome. Cohort studies are the observational studies most appropriate to analyze the effects of treatments and are the source for the data sets described in Chapter 3 that are used across the remainder of this book.
1.5 Questions Addressed by Real World Studies
Common objectives of health research include:
1. characterizing diseases and describing their natural course
2. assessing the frequency, impact and correlates of the diseases at the population level
3. finding the causes of diseases
4. discovering the best treatments
5. analyzing the best way to provide treatment
6. understanding the health systems and the costs associated with diseases
All these questions can be addressed with RWD and produce RWE. Real world research is actually the only way of addressing some of these questions, given feasibility and/or ethical challenges.
In drug development, there are a growing number of uses of RWE across the entire life cycle of a product. (See Figure 1.1.) Examples range from epidemiologic and treatment pattern studies to support early phase clinical development to comparative effectiveness, access and commercialization studies, and safety monitoring using claims and EMR data after launch. Recently, RWE has expanded to additional uses such as (1) forming control arms for single arm studies in rare or severe diseases for regulatory evaluation, and (2) used as the basis for evaluating value-based agreements between drug manufacturers and health care payers.
Figure 1.1: Use of RWE Across the Drug Development Life Cycle
1.6 The Issues: Bias and Confounding
Regardless of the type of design, any study should aim to produce results that are valid. Biases are the main threat to the validity of research studies. A bias is a systematic error in the design, implementation, or analysis of a study. While there are multiple classifications of the various types of biases, we follow the taxonomy used by Grimes et