By far, the prospective cohort design is the most powerful observational design. The design can yield a measure of incidence (number of new cases in a population), longitudinal effects (etiology and disease progression), and the potential for decreased observation bias (more control on study design and data collection).

Retrospective cohort designs can yield some measures of incidence in patient populations. However, researchers are limited to the variables that have been collected in an objective fashion within homogeneous populations. Incidence is a much more valid measure when generated using a prospective cohort design. Researchers choose in an a priori fashion exactly what variables will be collected in the measured.

Incidence is a much more precise measure of association versus prevalence. Prospective and experimental designs can yield measures of incidence and establish the relative risk of developing an outcome. Researchers and clinicians also have a better understanding of incidence and relative risk versus prevalence and odds ratios.

Longitudinal data is data collected over an extended period of time. Longitudinal data is necessary for understanding the etiology and progression of disease states. Survival and time-to-event analyses produce popular measures in medicine such as 1-year, 3-year, and 5-year survival and recurrence. The primary issue with collecting longitudinal data is attrition and loss to follow-up with the prospective sample. As participants fall out of the study or are lost, the validity of the data greatly decreases.

Again, it is important to state that prospective designs give more control to researchers in regards to what data is collected. Every variable that you find pertinent for establishing causal effects between predictor and outcome variables, when controlling for all important demographic, prognostic, clinical, and confounding variables, can be chosen and collected. Observational biases associated with retrospective research do not apply with these studies because you can collect all of the data on all the variables that you chose, given that there is a theoretical, conceptual, or physiological reason for doing so.

In my experience working as a biostatistician at a graduate school of medicine, I have learned that there are three precious commodities for busy clinicians and researchers: Time, competency, and accessibility to data.

Systematic reviews constitute the most prodigious contribution that scientific-practitioners can make to a given body of clinical knowledge. When conducted in a rigorous and objective fashion, the pooled treatment effects yielded from this design are considered the highest level of applied clinical evidence that exists. It is much more of an academic/empirical task versus applied experimental and observational designs. Yet, the time and experience needed to conduct a systematic review often impede these pursuits by researchers. (However, they are greatly needed and should be undertaken if at all possible!  I'm going to start my first one soon.) 

True experiments such as randomized controlled trials are not feasible for most researchers due to lack of funding, logistical support, and available resources. Also, researchers should first show observational evidence of a treatment effect before conducting a randomized controlled trial.  

Prospective cohort studies can generate important measures of incidence and relative risk, as well as longitudinal data. However, this type of design means you are moving forward in time and are dependent upon enough observations being generated from you methodology to have adequate statistical power. The logistics and time associated with this design also tend to hinder its application in busy clinical environments.

The next highest level of evidence is the retrospective cohort design and it is easily applied in a busy clinical environment. This is a retrospective design so the data already exists. One defines a cohort with inclusion and exclusion criteria. Then, members of the cohort are separated into independent groups according to some sort of exposure or non-exposure to a treatment, intervention, or risk. They are then followed up to a certain point in time to see if they did or did not have the outcome. There are obvious selection and observation biases associated with this design but it yields important measures of relative risk and many years of data can be mined for longitudinal or large-scale cohort analyses. Survival analyses are perfect for this type of design when establishing the 1-year, 3-year, or 5-year survival or "time-to-event" rates of an outcome.  They are also relatively inexpensive to conduct and time-friendly. This research design is much more preferable to case-control designs.