<p>ADDITIONAL FILE 1</p><p>Alternative expression of etiologic and excess causal effects</p><p>In this paper, we have adapted the disease response type notation to frame the issue of redundancy. It is possible to express redundancy in a general way for target populations of any size with varying exposure distribution (assuming exposure is randomly distributed) and all forms of disease frequency measures. For example, we can expand the notation of Maldonado and Greenland (2002) “Estimating Causal Effects” [23] to alternatively express redundancy:</p><p>A11 A01 A1 A0</p><p>A10 A00</p><p>B1 B0</p><p>Target if exposure Target if exposure distribution 1 distribution 0</p><p> where Ai is the number of cases that would occur in the target under exposure distribution i, Ai1 is the number of etiologic cases under exposure distribution i, Ai0 is the number of non-causal cases under exposure distribution i, and Bi is the number of people in the target population under exposure distribution i. </p><p>Then,</p><p>(A1 / B1) Eq. A1 RRexcess =</p><p>(A0 / B0)</p><p>(A1 / B1) Eq. A2 RRetiology =</p><p>(A00 / B0)</p><p>Eq. A3 RRexcess / RRetiology = (A00) </p><p>(A0)</p><p>RRexcess is the causal relative risk of any form (i.e. incidence-proportion ratio, odds ratio, or person-time incidence rate ratio) that measures the excess causal effect of exposure; in Maldonado and Greenland’s terminology, this is RRcausal [23]. RRetiology is the causal relative risk of any form that measures the full causal effect of exposure.</p><p>The relationship between these causal contrasts can be expressed as the ratio of RRexcess to RRetiology, which reduces to Equation A3.</p><p>Note that the disease type notation presented in the main body of the paper assumes that we are interested in estimating the excess and etiologic causal effects in a population (of any size) of people actually exposed (i.e. the comparison is the counterfactual; the same population, all else equal, had they not been exposed). Thus, the disease type equations presented in the paper for RRetiology (Eq. 1), RRexcess (Eq. 2), and the ratio of RRexcess to RRetiology (Eq. 3) are only equivalent to equations A1-A3 if exposure distribution 0 is 0%. Then,</p><p>(p1n + p1r + p2n + p2r) Eq. A4 RR = excess (p1n + p1r + p2r)</p><p>= (A1 / B1) </p><p>(A0 / B0)</p><p>(p1n + p1r + p2n + p2r) Eq. A5 RRetiology = (p1n + p1r)</p><p>= (A1 / B1) </p><p>(A00 / B0)</p><p> n r p1 p1 + p1 Eq. A6 RR / RR = = excess etiology n r r (p1 + p2r) (p1 + p1 + p2 ) </p><p>= (A00) </p><p>(A0)</p><p>In terms of disease response types, Equation A6 shows that RRexcess underestimates RRetiology to the extent that there are individuals who are etiologic redundant (i.e., type 2r) in the population. Alternatively, Equation A6 shows that RRexcess underestimates RRetiology to the extent that there are causal cases in population A0. The relationship between these alternative notations when exposure distribution 0 is 0% is shown in Figure A1 below. Excess fraction = pkn Redundant fraction = pkr</p><p>Etiologic cases = Ai,1 Etiologic fraction = p2 Excess cases Etiologic [2n] redundant cases [2r] [4]</p><p>Non-causal Non-diseased Non-causal cases = Ai,0 fraction = p1 [1n] [1r]</p><p> p4</p><p>Number of people in target population under exposure distribution i = Bi</p><p>Figure A1. Relationship among etiologic, excess, etiologic redundant cases and non-causal cases. Where, ‘i’ represents the exposure distributions (1 or 0), ‘k’ represents etiologic or non- causal (2 or 1), ‘n’ equals non-redundant, ‘r’ equals redundant, ‘p’ means proportion Such that,</p><p>Bi is the number of people in the target population under exposure distribution i</p><p>Ai is the number of cases in the target population under exposure distribution i</p><p>Ai,1 is the number of etiologic cases in the target population under exposure distribution i</p><p>Ai,0 is the number of non-causal cases in the target population under exposure distribution i</p>