Osteoarthritis: rational approach to treating the individual

Osteoarthritis (OA) is the most common form of joint disease and the leading cause of pain and physical disability in older people. Risk factors for incidence and progression of osteoarthritis vary considerably according to the type of joint. Disease assessment is difficult and the relationship between the radiographic severity of joint damage and the incidence and severity of pain is only modest. Psychosocial and socio-economic factors play an important role. This chapter will discuss four main guiding principles to the management of OA: (1) to avoid overtreating people with mild symptoms; (2) to attempt to avoid doing more harm than good ('primum non nocere'); (3) to base patient management on the severity of pain, disability and distress, and not on the severity of joint damage or radiographic change; and (4) to start with advice about simple measures that patients can take to help themselves, and only progress to interventions that require supervision or specialist knowledge if simple measures fail. Effect sizes derived from meta-analyses of large randomized trials in OA are only small to moderate for most therapeutic interventions, but they are still valuable for patients and clinically relevant for physicians. Joint replacement may be the only option with a large effect size, but is only appropriate for the relatively small number of people with OA who have advanced disease and severe symptoms. The key to successful management involves patient and health professionals working together to develop optimal treatment strategies for the individual.

THE ROLE OF AN EXPLICIT CAUSAL FRAMEWORK IN AFFECTED SIB PAIR DESIGNS WITH COVARIATES

The affected sib/relative pair (ASP/ARP) design is often used with covariates to find genes that can cause a disease in pathways other than through those covariates. However, such "covariates" can themselves have genetic determinants, and the validity of existing methods has so far only been argued under implicit assumptions. We propose an explicit causal formulation of the problem using potential outcomes and principal stratification. The general role of this formulation is to identify and separate the meaning of the different assumptions that can provide valid causal inference in linkage analysis. This separation helps to (a) develop better methods under explicit assumptions, and (b) show the different ways in which these assumptions can fail, which is necessary for developing further specific designs to test these assumptions and confirm or improve the inference. Using this formulation in the specific problem above, we show that, when the "covariate" (e.g., addiction to smoking) also has genetic determinants, then existing methods, including those previously thought as valid, can declare linkage between the disease and marker loci even when no such linkage exists. We also introduce design strategies to address the problem.