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Table 4 LD10, LD50, LD90 and LD99 values with their 95% CLs for the example data fitted to probit-log(dose) regression models using the ML procedure (Excel), Polo-Plus and SPSS

From: Comparing lethal dose ratios using probit regression with arbitrary slopes

Interested levels (π) Samples LDπ (95% CLs)
Excel Polo-Plus SPSS1a SPSS2a
10 Rotenone 2.405 (1.756, 3.295) 2.405 (1.889, 2.833) 2.405 (1.889, 2.833) 2.405 (1.889, 2.833)
Deguelin 3.229 (1.945, 5.360) 3.229 (0.606, 5.915) 3.229 (0.606, 5.915) 3.229 (0.606, 5.915)
Mixture 1.986 (1.209, 3.263) 1.986 (0.889, 3.059)b 1.986 (1.286, 2.672) 1.986 (1.286, 2.672)
Fairfax 1.329 (0.736, 2.400) 1.329 (0.392, 2.112) 1.329 (0.820, 1.782) 1.329 (0.820, 1.782)
Schaefer 1.321 (0.872, 2.001) 1.321 (0.207, 2.247) 1.321 (0.207, 2.247) 1.321 (0.207, 2.247)
Pixley c 6.307 (3.011, 13.210) 6.022 (0.393, 10.588) 6.011 (3.765, 7.969) 13.252 (5.512, 18.430)
BugRes 4.355 (1.721, 11.023) 3.194 (1.143, 5.583) 3.157 (1.373, 5.105) 4.174 (0.082, 11.078)
BugLab 6.246 (4.714, 8.275) 6.145 (2.450, 8.105) 5.488 (0.011, 8.109) 4.461 (0.927, 6.696)
50 Rotenone 4.845 (4.122, 5.696) 4.845(4.363, 5.354) 4.846 (4.363, 5.354) 4.846 (4.363, 5.354)
Deguelin 9.905 (7.658, 12.812) 9.905 (5.090, 14.626) 9.905 (5.090, 14.626) 9.905 (5.090, 14.626)
Mixture 6.366 (4.981, 8.135) 6.366 (4.564, 8.187) 6.366 (5.254, 7.484) 6.366 (5.254, 7.484)
Fairfax 4.139 (3.240, 5.288) 4.139 (2.926, 5.482) 4.139 (3.511, 4.800) 4.139 (3.511, 4.800)
Schaefer 3.773 (3.110, 4.579) 3.773 (2.198, 5.717) 3.773 (2.198, 5.717) 3.773 (2.198, 5.717)
Pixley c 16.967 (12.284, 23.436) 16.559 (8.096,24.636) 16.544 (13.963, 19.082) 24.208 (16.712, 29.114)
BugRes 23.981 (16.593, 34.658) 21.413 (11. 546, 28.362) 21.318 (16.502, 27.590) 23.612 (6.574, 35.519)
BugLab 10.638 (9.336, 12.121) 10.548 (7.912, 12.738) 9.971 (2.962, 14.238) 10.054 (6.699, 13.602)
90 Rotenone 9.761 (7.323, 13.011) 9.761(8.405, 12.134) 9.762 (8.405, 12.134) 9.762 (8.405, 12.134)
Deguelin 30.381 (22.388, 41.228) 30.381 (19.950, 77.517) 30.381 (19.950, 77.517) 30.381 (19.950, 77.517)
Mixture 20.407 (14.636, 28.454) 20.407 (15.015, 34.190) 20.407 (16.596, 27.120) 20.407 (16.596, 27.120)
Fairfax 12.892 (7.803, 21.299) 12.892 (8.611, 36.089) 12.892 (10.006, 19.424) 12.892 (10.006, 19.424)
Schaefer 10.777 (7.559, 15.365) 10.777 (6.747, 50.379) 10.777 (6.747, 50.379) 10.777 (6.747, 50.379)
Pixley c 45.645 (25.980, 80.196) 45.538 (28.964, 329.883) 45.533 (36.541, 64.751) 44.222 (36.854, 63.231)
BugRes 132.040 (52.601, 331.448) 143.532 (88.364, 344.840) 143.975 (88.678, 333.43) 133.577 (82.497, 723.399)
BugLab 18.118 (14.484, 22.665) 18.108 (14.508, 35.264) 18.118 (13.196, 1530.98) 22.662 (15.855, 84.406)
99 Rotenone 17.278 (10.761, 27.743) 17.278(13.588,24.958) 17.278 (13.588, 24.958) 9.762 (8.405, 12.134)
Deguelin 75.759 (44.790, 128.141) 75.759 (39.827, 460.545) 75.759 (39.827, 460.545) 75.759 (39.827, 460.545)
Mixture 52.753 (29.785, 93.433) 52.753 (32.074, 135.526) 52.753 (37.441, 87.710) 52.753 (37.441, 87.710)
Fairfax 32.548 (13.574, 78.046) 32.548 (16.589, 209.890) 32.548 (21.149, 67.448) 32.548 (21.149, 67.448)
Schaefer 25.356 (13.882, 46.314) 25.356 (12.119, 412.504) 25.356 (12.119, 412.504) 25.356 (12.119, 412.504)
Pixley c 102.28 (38.072, 274.763) 103.882 (49.732,4503.346) 103.939 (71.350,196.711) 72.273 (53.911,155.013)
BugRes 530.489 (109.45, 2571.23) 676.988 (295.27, 3261.06) 683.244 (302.10, 2931.66) 548.646 (209.66, 26,126.13)
BugLab 27.97 (19.047, 41.067) 28.133 (19.726, 97.529) 29.481 (17.762, 174,201.0) 43.958 (24.635, 485.621)
  1. aSPSS includes the natural responses proportion by inputting the value of C, and SPSS calculates the corrected p from the data
  2. bData in italic brackets indicated that he 95% CLs of LDπ calculated using Polo-Plus were not identical to those calculated using SPSS
  3. cBold items indicated the data sets included natural responses