Comparing lethal dose ratios using probit regression with arbitrary slopes

Lei, Chengfeng; Sun, Xiulian

doi:10.1186/s40360-018-0250-1

BMC Pharmacology and Toxicology

Table 2 Slopes, intercepts and results of significance testing for the example data fitted to the 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

	Example	Estimates				Standard error (σ)				z ^b
	Example	Excel	Polo-Plus	SPSS^1a	SPSS^2a	Excel	Polo-Plus	SPSS^1a	SPSS^2a	Excel	Polo-Plus	SPSS^1a	SPSS^2a
β	Rotenone	4.213	4.213	4.213	4.213	0.481	0.478	0.478	0.478	8.767	8.809	8.809	8.809
	Deguelin	2.633	2.633	2.633	2.633	0.279	0.279	0.279	0.279	9.434	9.421	9.421	9.421
	Mixture	2.533	2.533	2.533	2.533	0.269	0.272	0.272	0.272	9.400	9.320	9.320	9.320
	Fairfax	2.598	2.598	2.598	2.598	0.352	0.353	0.353	0.353	7.370	7.369	7.369	7.369
	Schaefer	2.812	2.812	2.812	2.812	0.281	0.273	0.273	0.273	9.999	10.282	10.282	10.282
	Pixley ^c	2.982	2.917	2.915	4.897	0.401	0.402	0.401	1.200	9.999	7.248	7.264	4.080
	BugRes	1.730	1.551	1.545	1.703	0.270	0.252	0.229	0.532	6.402	6.148	6.736	3.202
	BugLab	5.541	5.461	4.941	3.631	0.960	1.062	0.948	0.716	5.771	5.142	5.215	5.071
α	Rotenone	−2.887	−2.887	−2.887	−2.887	0.351	0.350	0.350	0.350	−8.225	−8.247	−8.247	−8.247
	Deguelin	−2.622	−2.622	−2.622	−2.622	0.342	0.339	0.339	0.339	−7.670	−7.743	−7.743	−7.743
	Mixture	−2.036	− 2.036	− 2.036	− 2.036	0.271	0.272	0.272	0.272	−7.519	−7.491	−7.491	−7.491
	Fairfax	−1.603	−1.603	−1.603	−1.603	0.250	0.249	0.249	0.249	−6.413	−6.435	−6.435	−6.435
	Schaefer	−1.622	−1.622	−1.622	−1.622	0.190	0.186	0.186	0.186	−8.530	−8.728	−8.728	− 8.728
	Pixley ^c	−3.666	−3.556	−3.552	−6.778	0.531	0.529	0.527	1.832	−6.903	−6.719	−6.741	−3.699
	BugRes	−2.387	−2.064	−2.053	− 2.338	0.384	0.367	0.315	0.908	−6.218	−5.618	− 6.512	−2.575
	BugLab	−5.690	−5.587	−4.935	−3.640	1.028	1.141	0.997	0.754	−5.535	−4.897	−4.951	−4.826

^aSPSS includes the natural responses proportion (C) by two methods: 1, inputting the value of C; and 2, calculating the corrected p from the data. The d.f. = k − 2 in method 1, while it was k-3 in method 2
^bPolo-Plus used the t-ratio to test the significance of the linear regression. The significance criterion for the t-ratio (α = 0.05) was 1.96 (t-distribution with d.f. = ∞). This significance level was identical to that of the z test
^cBold items indicated the data sets included natural responses

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ISSN: 2050-6511

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