Other datamethods threads discuss the pitfalls of “responder analysis” from a statistical standpoint. The purpose of this thread is to highlight the type of clinical evidence that’s needed to label a patient a “responder."
If we label a patient a “responder,” we are implying that his therapy has “caused” him to experience a certain effect of interest. In other words, this label requires demonstration of a causal effect of therapy at the level of an individual patient.
The type of evidence needed to call a patient a “responder” will depend on 1) the UNtreated natural history/trajectory of the disease in question; and 2) the expected impact of the therapy being tested.
The solid lines in the five clinical scenarios below show typical UNtreated disease trajectories for various diseases. We’ll imagine what might happen if we were to apply a therapy with intrinsic efficacy/biological activity. The arrow shows the time at which the therapy is applied. The highlighted dotted line after each arrow shows the potential effect of the therapy (with regard to sign/symptom severity/disease activity). The solid line extending past the arrow shows what the patient’s expected clinical trajectory would have looked like if the therapy hadn’t been applied. Examples of conditions that conform to each trajectory are provided, with therapeutic options noted in brackets.
Scenario 1:
“Waxing and waning” disease course- e.g., asthma (inhalers), chronic pain (analgesics), depression (antidepressants), mild to moderate autoimmune disease (immunosuppressants)
Type of evidence needed to show causality at the level of an individual patient: EITHER an RCT with multiple crossover periods OR an N-of-1 trial is needed. Observing a single period of therapy exposure is INSUFFICIENT to infer therapeutic efficacy for a particular patient.
Rationale; For diseases with a waxing/waning natural history, the causal effect of the intervention, in a specific patient, can only be disentangled from spontaneous improvement/natural fluctuation by observing REPLICATIONS of the effect via therapy dechallenge then rechallenge. Repeated demonstration of a close temporal relationship between therapy dechallenge/rechallenge and change in clinical status isolates the effect of the intervention from the effects of other (often unknown) factors that can contribute to fluctuation in disease course. The waxing/waning of these other factors would not be expected to occur in concert, co-incidentally and repeatedly, with pre-planned periods of therapy dechallenge/rechallenge.
Scenario 2:
Temporary “slowing” of disease progression, underlying disease is relentlessly progressive- e.g., Alzheimer’s disease (cholinesterase inhibitors)/other neurodegenerative diseases
Type of evidence needed to show causality at the level of an individual patient: Very challenging. Dechallenge/rechallenge is often never performed for fear of triggering more rapid deterioration and because of barriers to accurate assessment of clinical status over time.
Rationale: Therapies for some diseases work by slowing clinical deterioration (at least temporarily), for either all patients or some subset of patients, rather than by improving patients’ clinical state. Identifying “responders” in such scenarios can be very challenging, if not impossible. Causality assessment for individual patients hinges on valid and highly granular mapping of disease trajectory, comparing pre-treatment trajectory with the trajectory during treatment. However, clinical assessments needed to plot trajectory can be confounded by a sometimes “undulating” course of deterioration. For example, scores on cognitive/functional tests for some diseases can vary from day to day or hour to hour for unclear reasons.
Scenario 3:
Rapid improvement in signs/symptoms very soon after therapy is started, for a disease that has been highly symptomatic for a long period of time- e.g., steroid-dependent autoimmune diseases (asthma/RA/IBD/psoriasis) (biologics)
Type of evidence needed to show causality at the level of an individual patient: The causal effect of therapy is easy to identify clinically for individual patients. Most clinicians would reasonably infer causality even without demonstration of positive dechallenge/rechallenge.
Rationale: Abrupt improvement in the signs/symptoms of a disease that has been highly symptomatic for many years provides clinically compelling evidence that the therapy has caused the patient’s improvement. Therapies that cause such rapid/dramatic improvement are ones that tend to be highly efficacious.
Scenario 4:
Reduction in disease burden (e.g., tumour burden) soon after start of therapy, for a disease that is known, otherwise, to be relentlessly progressive- e.g., a tumour “melting away” on imaging after starting a new cancer therapy.
Type of evidence needed to show causality at the level of an individual patient: The causal effect of therapy might be easy to identify, clinically, for individual patients (“objective response”), provided techniques for measuring disease burden serially are sufficiently granular/reliable. Serial assessments can be tricky to interpret (e.g., lymph nodes can change slightly in size from scan to scan for reasons unrelated to tumour progression/regression). If scans show a clear trend to improvement in a given patient, dechallenge/rechallenge would not be needed to infer causality.
Rationale: Tumour burden is NOT expected to improve “spontaneously.” Therefore, reduction in tumour burden in a patient after application of a therapy indicates that the effect was caused by the therapy.
Scenario 5:
Rapid resolution of signs/symptoms soon after the start of a therapy for an acute, highly symptomatic medical condition- such therapies are considered “curative.” e.g., epinephrine for anaphylaxis, primary PCI for STEMI, naloxone for opioid overdose.
Type of evidence needed to show causality at the level of an individual patient: The causal effect of therapy is easy to identify clinically for individual patients. Dechallenge/rechallenge are not needed for the clinician to infer causality.
Rationale: The UNtreated clinical course for many acute conditions is often stereotypical/well-known. Rapid clinical reversal of such conditions is not expected to occur spontaneously. Therefore, abrupt reversal after applying the therapy provides clinically compelling evidence that the therapy caused the reversal/cure.
), but I would be a bit leery about mathematizing these ideas too much. Mathematizing them will tempt mathematically-focused but clinically inexperienced readers to retreat behind their numbers and to miss the main point: Gauging whether it’s possible to assess “response” (in the causal sense of the word) at the level of an individual patient requires knowledge of untreated disease trajectory and assessment of how that trajectory was impacted by treatment. In other words, these assessments require clinical, not simply mathematical, expertise.