Is Biostatistics a Science of Managing Medical Uncertainties?
Abhaya Indrayan, PhD
Delhi University College of Medical Sciences
Delhi 110 095
From the epitome of crunching numbers, statistical science has traveled a long distance. It is time that it is realized as a management science. This is especially true for biostatistics. I seek to define biostatistics as the science of managing medical uncertanities1. This can provide a completely new orientation to the subject and integrate it fully well into medical disciplines. How is this new definition justifiable?
Essentially, management is a value addition process that tries to optimize the output by properly organizing the inputs. It involves elements such as goal setting; identifying quality and quantity of inputs such as men, machine, methods, material and money in a production line, and their adequate and timely provision; minimizing risk opportunities and maximizing conducive environment for optimal functioning of the inputs; gauging performance; and taking rectifying and promoting steps—thus starting the cycle all over again. Management is a flexible process instead of adhering to consistency and conformity. It is an art of accomplishing an assignment by translating complexity, specialization and talents into performace2.
Medical uncertainty is easy to appreciate when divided into two parts. At individual level, it is the potential fallibility of decisions regarding diagnosis, treatment and prognosis of health conditions. At the group or community level, medical uncertainty comprises lack of assurance regarding the role of primordial and proximal risk factors of various conditions of ill-health, and regarding the effect of various preventive and moderating interventions. In both setups, a prominent component is the uncertainty regarding the present state and future course of events.
Medical uncertainty is easy to handle when divided into aleatory and epistemic types. These terms may sound new to medicine but are commonly used in seismic science and economics. Aleatory uncertainty in medicine arises from endogenous factors such as inherent biological variation, environmental factors, socio-cultural and psychological factors, random variation due to observers, instruments and laboratories, etc. Epistemic uncertainty arises from lack of knowledge, conceptual errors, non-availability of tools, and biases of various types. These sources are exogenous in nature.
Value addition in the case of management of medical uncertainties is in terms of their control so that the impact of such uncertainties on decisions is minimal. Their description and assessment are integral part of the process. Performance is the key in this case also as is for management anywhere else. The inputs are the aleatory variations and epistemic bottlenecks. Study design is a tool that seeks to organize these inputs. A perfect design, when immaculately executed, would minimize the risk of reaching to an invalid or unreliable conclusion, and maximize the power of the study, for fixed inputs. Considerations such as definition of study units and variables, sample size, method of selection, confounders, potential sources of bias including reliability and validity of medical assessment, and the method of analysis of data, are the elements that provide definite help in reducing the risk of reaching to an invalid conclusion. With tools such as probability and its derivatives that include frequency distribution, sensitivity, specificity, relative risk and odds ratio; estimation methods in terms of confidence interval and meta-analysis; test of hypothesis for absence of medically important difference; and trend analysis that sieves clear signals from noise; biostatistics fits the bill quite admirably. By considering various options, it awards flexibility instead of consistency and conformity that could mar a management process. Decision analysis that allows infusion of value judgements regarding utility of various possible outcomes to the evidence-based risk assessments at the stage of diagnosis and treatment, is also an important function of biostatistical methods for managing medical uncertainty at individual level.
Aleatory uncertainties are the basic ingredient of statistical methods. These can be very adequately managed by these methods. The same can not be stated about epistemic uncertainties. Sensitivity analysis3 can be effectively used to delineate the impact of some epistemic uncertainties. However, they can be rarely minimized. There are epistemic bottlenecks for which apparently there is no solution except further research. If the underlying process of emergence and progression of a health condition is unclear, modeling will have to be based on conjectures. They may or may not stand the test of the time. No science is available that can adequately deal with the unknown except, to some extent, statistics that pools all these together under ‘error term’, and provides methods to examine them.
Considering all this, it seems very appropriate to define biostatistics as the science of managing medical uncertainties. No definition is perfect, nor one that is convincing to every one, but this definition seems to describe the subject in a very appropriate manner. Incidentally, this definition emphasizes that “bio” is an integral part of biostatistics and exemplifies fusion of medicine with statistics that Feinstein4 emphasized so much. Conventionally, biostatistics has come to be identified with medicine rather than other biological disciplines such as agriculture or fisheries—thus restricting it to the medical uncertainties looks appropriate.
1. Indrayan A, Sarmukaddam SB. Medical Biostatistics. New York: CRC Press, 2001:p2.
2. Magretta J. What Management Is. New York: The Free Press, 2002:pp1-4.
3. Saltellie A, Chan K, Scott EM, editors. Sensitivity Analysis. New York: John Wiley, 2000.
4. Feinstein AR. Clinical Biostatistics. Saint Louis: The CV Mosby Company, 1977:p4.