By: Chad Cook PT, PhD, FAPTA
Introduction: Imagine being able to say: “Based on your profile, you’re likely to respond better to graded activity than manual therapy.” Or: “Patients like you tend to improve more with individual physiotherapy than group-based exercise.” This should be the future of MSK care. It isn’t yet.
If you’ve spent any time treating patients with musculoskeletal (MSK) issues, you’ve likely seen something firsthand: two patients with the same diagnosis receive the same evidence based treatment, yet one improves dramatically while the other barely budges [1]. For decades, clinical research has focused on average treatment effects, the mean benefit of an intervention across a group. But averages hide the most clinically important truth: patients don’t respond the same way [2].
A growing body of research is shifting toward analyzing individualized treatment effects (ITEs), which are estimates of how much a specific person is likely to benefit from a given treatment. This shift in focus has enormous implications for MSK care, where heterogeneity is the rule, not the exception. Let’s unpack what ITEs are, why they matter, and how researchers are beginning to study them.
Average Treatment Effects: Useful, but Limited: Traditional randomized controlled trials (RCTs) are designed to answer one question: Does this treatment work better than the alternative on average? This is the foundation of evidence based practice, but it comes with a major limitation: averages smooth out individual differences. For example, a strengthening program for chronic low back pain may show a modest average improvement, but within that average, some patients improve a lot, some a little, and some not at all.
This variability is well documented in MSK research. Systematic reviews of exercise therapy, manual therapy, and physiotherapy programs consistently show wide ranges of individual responses, even when group means look similar. Average effects help guide population level recommendations, but they don’t tell us [3]:
1. Who will benefit most
2. Why they benefit
3. How to match treatments to individuals
If you are reading this, you may think “wait a minute, isn’t this the basis of ALL the arguments for or against a treatment on social media?” It generally is, and those people making arguments such as this are grossly misinformed. If you want to know who, why and how, you need to target individualized treatment effects and this requires looking beyond comparative results of an RCT.
Individualized Treatment Effects: An individualized treatment effect (ITE) estimates the expected benefit of a treatment for a specific patient, based on their characteristics, symptoms, and context. It does so with a more patient-centered lens [4]. ITE research asks questions such as: Which patients respond best to manual therapy? Who benefits more from group exercise vs. individual physiotherapy? Which baseline features predict a strong response to a specific intervention? This approach aligns with the movement toward precision rehabilitation, matching the right treatment to the right patient at the right time.
Recent methodological work highlights the importance of ITE estimation and the challenges of doing it well. For example, Bouvier et al. [5] emphasize that single RCTs are often underpowered to detect individual level differences, and that more sophisticated designs and analyses are needed.
How Do We Study Individualized Treatment Effects? Studying ITEs requires designs and analytic strategies that go beyond traditional RCTs. Here are the major approaches being used in MSK research today.
1. Individual Participant Data (IPD) Meta Analysis: Instead of pooling study level averages, IPD meta analysis aggregates raw data from multiple trials. This allows researchers to: 1) Examine treatment effect modifiers; 2) Identify subgroups with differential responses; and 3) Build predictive models of treatment benefit. IPD is considered one of the strongest methods for estimating ITEs because it increases statistical power and allows consistent modeling across datasets [5].
2. Stratified or Moderation Focused RCTs: These trials are designed to test whether certain baseline characteristics modify treatment response. Questions can be targeted toward individualized assessments such as: 1) Does high pain sensitivity predict better outcomes with manual therapy? Or 2) Do patients with high fear avoidance respond differently to graded activity? While many MSK trials explore moderators, most are underpowered to detect them reliably, a limitation highlighted across musculoskeletal return to work research [6].
3. N of 1 Trials: An N of 1 trial is a personalized crossover experiment where a single patient cycles through treatment and control conditions multiple times. This design is ideal when: 1) Treatment effects are rapid and reversible; 2) Patients vary widely in response; or 3) Individual decision making is the goal. Interpretation of an N‑of‑1 study includes understanding which approach leads to improved outcomes as the patient cycles through different treatment conditions (e.g., Treatment A vs. Treatment B) multiple times.
4. Machine Learning and AI Supported Predictive Modeling: Modern statistical learning methods can identify complex, nonlinear patterns that traditional models miss. These models can: 1) Predict individual responses; 2) Identify treatment effect heterogeneity; and 3) Support clinical decision tools. Emerging work in individualized treatment estimation using machine learning highlights the potential of these methods to handle composite outcomes and complex patient profiles.
Why This Matters for Musculoskeletal Care: MSK conditions are notoriously heterogeneous. Two patients may have entirely different pain mechanisms, psychosocial profiles, movement patterns, expectation and recovery trajectories. In fact, it is close-minded and naive to assume that one treatment will work with everyone and that people will response the same (at the same rate). ITE focused research helps clinicians move beyond one size fits all protocols and toward precision rehabilitation.
The Bottom Line: Average treatment effects tell us whether a treatment works in general. Individualized treatment effects tell us whether a treatment is likely to work for this patient sitting in front of us. As MSK research evolves, designs like IPD meta analysis, N of 1 trials, and machine learning based prediction models will help us understand, and ultimately harness, the rich variability in patient responses. But to optimize our possibilities, we need the following.
A. Full recognition of the limitations of a RCT. This includes the need to stop wasting time arguing over trials that find similar averages and wide dispersion in outcomes. Especially smaller trials that lack generalizability.
B. Funding to support alternative designs. As it stands, most funding agencies are more likely to fund an RCT or a basic science study. I’d like to give a shout out to PCORI who requires a heterogeneity of treatment effects analysis in their grants.
C. Musculoskeletal conditions vary widely from person to person. They rarely follow the predictable patterns seen in cardiac, neurological, or tumor related disorders. Instead, MSK presentations are shaped by a complex mix of biological, psychological, and social factors, which means symptoms, functional limitations, and treatment responses can differ dramatically between individuals. Appreciating this variability is essential for delivering care that is responsive, individualized, and grounded in the lived experience of each patient.
Portions of this blog were developed with assistance from AI‑based writing tools and were reviewed and edited by the author for accuracy and clarity.
References
1. Cook CE, George SZ, Keefe F. Different interventions, same outcomes? Here are four good reasons. Br J Sports Med. 2018 Aug;52(15):951-952.
2. Angus DC, Chang CH. Heterogeneity of Treatment Effect: Estimating How the Effects of Interventions Vary Across Individuals. JAMA. 2021;326(22):2312–2313.
3. McDevitt AW, O’Halloran B, Cook CE. Cracking the code: unveiling the specific and shared mechanisms behind musculoskeletal interventions. Arch Physiother. 2023 Jul 6;13(1):14.
4. Keter D, Hutting N, Vogsland R, Cook C. Integrating Person-Centered Concepts and Modern Manual Therapy. JOSPT Open. Published Online: December 29, 2023, Volume2, Issue1, pg 60-70.
5. Bouvier F, Chaimani A, Peyrot E, Gueyffier F, Grenet G, Porcher R. Estimating individualized treatment effects using an individual participant data meta-analysis. BMC Med Res Methodol. 2024 Mar 25;24(1):74. doi: 10.1186/s12874-024-02202-9.
6. Venter M, Grotle M, Øiestad BE, Aanesen F, Tingulstad A, Rysstad T, Ferraro MC, McAuley JH, Cashin AG. Treatment Effect Modifiers for Return-to-Work in Patients With Musculoskeletal Disorders. J Pain. 2024 Sep;25(9):104556.
