Biased effects of pre-diagnostic physical activity on breast cancer survival: Systematic review and meta-analysis

Pre-diagnostic physical activity is reported to improve survival for women with breast cancer. However, studies of pre-diagnostic exposures and cancer survival are susceptible to bias, made clear when applying a target trial framework. We investigated the impact of selection bias, immortal time bias, confounding and bias due to inappropriate adjustment for post-exposure variables in a systematic review and meta-analysis of pre-diagnostic physical activity and survival after breast cancer.

Medline, Embase and Emcare were searched from inception to November 2021 for studies examining pre-diagnostic physical activity and overall or breast cancer-specific survival for women with breast cancer. Random-effects meta-analysis was used to estimate pooled hazard ratios (HRs) and 95% confidence intervals (CIs) comparing highest versus lowest pre-diagnostic physical activity. Subgroup meta-analyses were used to compare HRs of studies with and without different biases. ROBINS-E was used to assess risk of bias.

We included 22 studies. Women with highest versus lowest pre-diagnostic physical activity had higher overall and breast cancer-specific survival across most analyses. The overall risk of bias was high. We observed marked differences in estimated HRs between studies that did and did not adjust for post-exposure variables or have immortal time bias. All studies were at risk of selection bias due to participants becoming eligible for study when they have survived to post-exposure events (e.g., breast cancer diagnosis). Insufficient studies were available to investigate confounding.

Biases can substantially change effect estimates. Due to misalignment of treatment assignment (before diagnosis), eligibility (survival to post-exposure events) and start of follow-up, bias is difficult to avoid. It is difficult to lend a causal interpretation to effect estimates from studies of pre-diagnostic physical activity and survival after cancer. Biased effect estimates that are difficult to interpret may be less useful for clinical or public health policy applications.